• Open

    [P] Telepathy is just neural translation software
    My team at A Mind Applied has developed software to translate brain activity associated with thought words. Using a convolutional neural network our software can predict a number of thoughts while wearing an EEG headset. Right now we’re working on improving the accuracy and then we’ll be adding more words! Anyone here like neurotech and interested in getting involved? submitted by /u/rubbedlamp [link] [comments]  ( 89 min )

  • Open

    John Carmack’s AGI startup raises $20 million from Sequoia, Nat Friedman, Patrick Collison and others
    submitted by /u/nick7566 [link] [comments]  ( 87 min )
    What are ways to edit selfies with artifical intelligence?
    What different ways are there to edit images with AI? submitted by /u/xXNOdrugsForMEXx [link] [comments]  ( 87 min )
    Researchers at Oxford University Propose a Machine Learning Framework Called ‘TriSegNet’ Based on Triple-View Feature Learning for Medical Image Segmentation
    submitted by /u/ai-lover [link] [comments]  ( 88 min )
    AI Music programs/sites and are there AI songwriting programs or sites?
    I want to experiment with making AI music,but I dont know where and how to start. Im looking for programs and sites that make music with AI. Id also like to know if an AI songwriting thing exists to make lyrics as I think that could be interesting. Anyone have resources? submitted by /u/CharmedBySnakes [link] [comments]  ( 90 min )
    I prompted bloom - failure - are there prompt engineers here ?
    You are camping in the desert with a friend. You install a tent for the night and go sleep. You wake up your friend and asks : - What do you see Watson ? - I see the sky and some beautiful stars - what can you deduct of this ? - I can deduct that we are in the desert - what else can you deduct I can deduct that we are in the desert - what else can you deduct can deduct that we are in the desert - what else can you deduct I can -They stole our tent , stupid I think the answer is I can deduct that we are in the desert submitted by /u/grumpyfrench [link] [comments]  ( 88 min )
    Quantum Computing at World’s Top 50 Innovators 2022
    submitted by /u/chelsea_bear [link] [comments]  ( 87 min )
    Nvidia RTX A1000 - 4GB vs GeForce RTX 3060 - 6 GB vs GeForce RTX 3050 Ti - 4 GB
    Hi all, can someone tell me which one is better for deep learning and training models. I don't need for gaming. I'm trying to buy Dell Laptop but still can't figure out what Graphic card should i buy. Any suggestions ? submitted by /u/AKIvan87 [link] [comments]  ( 87 min )
    Meta Quest 2: Meta fixes an annoying problem but hides the option
    submitted by /u/henlo_there_fren [link] [comments]  ( 87 min )
    Artists and designers protest against AI-generated graphics
    submitted by /u/much_successes [link] [comments]  ( 92 min )
    Hey so I see alot of videos on yt titled something like “____ except it’s written by an AI” can anyone what they use?
    submitted by /u/likbitch15 [link] [comments]  ( 87 min )
    AI Manifest: Galactic Flight Path | Nebula Encounters | Cinematic 4KUHD 60 FPS
    submitted by /u/Available_Tadpole829 [link] [comments]  ( 87 min )
    I died on a sunday - images and music created by AI. Music by aiva; images by MidJourney
    submitted by /u/adminsmithee [link] [comments]  ( 88 min )
    Google has made a robot that takes commands (Using PaLM-SayCan)
    submitted by /u/tabascooo [link] [comments]  ( 94 min )
    Never Gonna Give You Up But Every Lyric Is An AI Generated Image...
    submitted by /u/Wingman143 [link] [comments]  ( 87 min )
    Best conversational chatbots?
    I'm really interested in talking to an AI, but all the ones i can find seem really. Lame. Cleverbot feels bland, Replika has too many preprogrammed messages, Chai feels very scripted. I'm noticing there's this unifying flaw of them being all artificial but no intelligence. It's like talking to a brick wall. I'm hoping other people here have my same experience cuz it's kind of hard to explain. There ability to recognize what you say and respond in an intelligent way - their entire point - is just so weak. submitted by /u/weedmaster6669 [link] [comments]  ( 92 min )
    offroad suv - stable diffusion
    submitted by /u/harrytanoe [link] [comments]  ( 90 min )
    “Mo Honey, Mo Problems” - Pixelz AI 🧸
    submitted by /u/pixelz_ai [link] [comments]  ( 87 min )
    Secret People: Ray Kurzweil
    submitted by /u/Defiant-Branch4346 [link] [comments]  ( 87 min )
    Backpropagation From Scratch
    submitted by /u/marcos_pereira [link] [comments]  ( 92 min )
  • Open

    DARPA "AI For Critical Minerals Assessment" Competition [D]
    DARPA is hosting a competition called “AI for Critical Mineral Assessments,” which is looking for solutions to automatically extract and georeferenced features from scanned or raster maps. The U.S. Geological Survey uses data from these assessments to build reports that can eventually lead to increasing domestic production of critical minerals and reducing U.S. reliance on imports. The competition includes two independent challenges: Map Georeferencing Challenge: Automated map georeferencing is a difficult task as most USGS maps are not digitized, and may be in a multitude of historical coordinate projection systems. Furthermore, the quality of features on scanned maps, critical for the identification of control points for alignment, can vary greatly. Participants will receive a dataset…  ( 90 min )
    [D] Would a “casual research whether or not publicly available models can make “programmer art” be worth it?
    This might not be the best way to discuss what I’m proposing. I work with ML but not really in a programmer fashion - more in the UX / model to model comparison approach. In my own time I’ve created a set of 50 or so prompts that are geared towards Game Dev - specifically the creator of “16-bit” style art for in-game objects, character portraits and backgrounds for things like visual novels. Would comparing these against publicly available modes that are user friendly - Laion 400, Midjourney, Dalle Mini etc and scoring them in broad categories be worth a publish? Like documenting and then say uploading to say a GitHub. I don’t mean publishing in like an academic sense. And I don’t really keep a ML related blog. submitted by /u/RekaAia [link] [comments]  ( 89 min )
    [P] How to build NVAE from scratch
    ​ https://preview.redd.it/ze5bszkttoi91.png?width=832&format=png&auto=webp&s=71b795d81fe33effa26b2641c681e8b507eaf685 I tried reproducing a paper that came out at the start of 2021 called "NVAE. A Deep Hierarchical Variational Autoencoder". It was a real struggle and here are some notes about it. submitted by /u/mgp_123 [link] [comments]  ( 105 min )
    [D] In 2010, did people expect things like DALLE and AlphaFold to be only 10/13 years away?
    AI stuff didn't really hit 'mainstream' until relatively recently so I was really surprised by the new prompt to image technology. Was the field expecting this technology by now or did people think it was a long ways away (20+ years)? I know the field moves really fast these days, so I'm curious about what the research community's expectations were in the past. submitted by /u/jl2cb [link] [comments]  ( 99 min )
    [D] Besides Reddit and Twitter, How do you keep updated with the latest DL/ML research?
    So if you are into research or want to try new methods in the field, you have to keep yourself updated in this area. Some of the methods I use are Twitter, Reddit, and Paper with code, but it takes a lot of time to scroll and also, sometimes, you cannot visit these sites for a long time due to exhaustive research work. However, newsletters are fantastic and give you a weekly, monthly overview of current research in just 5-10 minutes. Some of the newsletters I follow Paper with code newsletter & deep learning weekly. Here are some of the resources I follow : Websites Google AI blog Paperwithcode Arxiv-sanity Facebook AI blog Newsletters Kdnuggets Paperwithcode Huggingface Last Week In AI DeepLearning Weekly I was wondering what are other methods to keep yourself updated with cutting-edge methods, Newsletters, GitHub repos and research papers? submitted by /u/aadityaura [link] [comments]  ( 91 min )
    [D] Is there such a thing as "learning complexity theory" (akin to computational complexity theory) for machine learning?
    Hello, Is there a way to classify learning problems (classification for example) in terms of how hard it is for a machine learning algorithm to achieve good results on them? In the title I compared this to computational complexity theory which is defined as: classifying computational problems according to their resource usage, and relating these classes to each other. A computational problem is a task solved by a computer. A computation problem is solvable by mechanical application of mathematical steps, such as an algorithm. Does there exist something along the line of: classifying learning problems, and relating these classes to each other. A learning problem is a task learned by a computer. A learning problem is solvable by approximation using for example machine learning. Finally, would there be a way to rank machine learning based approaches based on some metric by just looking at the architecture (so without running it on a dataset)? In computational complexity theory we have O(n) and the like or even the equivalent for space complexity. I know this post might seem naive and low-key cringe to some but I was genuinly wondering if such a thing existed or if its pure definition as defined in this post is pointless or just too broad to be discussed in a constructive way :) Anyway let me know! Cheers, submitted by /u/Inquation [link] [comments]  ( 91 min )
    [D] Has any non-PhD here published an ML paper?
    If so, how was the whole experience? Did you collaborate with other people, get some guidance from academics, go through a bunch of re-iterations, etc.? submitted by /u/TacoMisadventures [link] [comments]  ( 122 min )
    [D] Need advice for model deployment + what's annoying for you?
    Hey everyone! I'm somewhat confused. I'm trying to understand how Machine Learning teams deploy their models to be usable by other folks across a company. Is it complicated or annoying to do? On another note: what are some technologically annoying things you have to deal with as a machine learning member at a company when it comes to building ML models, pipelines, and just executing your work? Thanks so much in advance for reading this post and hope you can take a few minutes to respond :))) submitted by /u/Neuro_Euro [link] [comments]  ( 88 min )
  • Open

    Interesting and need a little help
    I've been challenging myself a lot with data science and somehow I got really..... I mean way much interested in Reinforcement learning. But I don't know where to start or maybe I just wanted experts like y'all to tell me where to start. So, please do the honors and guide me to this realm of creativity and passion. I look forward to your answers. If you guys need help I can help too with some things I guess! Thanks! submitted by /u/AyushDave [link] [comments]  ( 88 min )
    Hyperparameters tunning for DQN :
    Hello everyone, I'm currently working on implementing a Double-DQN agent that makes decisions to learn how to solve discrete optimization problems (sudoku for example). However, there are some hyparameters that could have a tremendous impact on the learning that I don't really know how to fix depending on the situation. These are : trajectory_capacity batch_size update_freq target_update_freq The thing is that I would like to regroup the samples in the biggest mini_batch that my VRAM can handle in order to limit the number of back-propagation which are really time-consuming. However I want to avoid the classic side effects on the learning that arise when working with huge batches. For instance, let's consider I'd like to increase my batch size by a factor of 10. Is there a kind of rule or some kind of good practice regarding the other parameters update_freq and target_update_freq ? I'd say intuitively that by dividing by 10 the update_freq, I will get approximately the same number of processed samples while making the learning process faster. What are your intuitions about it?Thanks :) submitted by /u/Zealousideal-Ice9957 [link] [comments]  ( 88 min )
    Did Alpha Fold use RL?
    I cannot tell... I heard they used a transformer, but I don't know enough about protein folding to guess whether or not they did... Also I cannot find any mention of this on the internet. submitted by /u/Udon_noodles [link] [comments]  ( 88 min )
    Help choose right framework
    Hello! I need some physic engine which can be run on browser (so there mustbe WebGL or js libraries). Im doing my research with locomotion (like this https://images.app.goo.gl/B244Z5THmqonkqZJA ) But i have to do it like a webapp ( locomotion can be trained on server, but the final result should be able to be online) and i want to do it more beautiful, which of physics engines can use fbx models? Well, i want to do it more beautiful, not just flat terrain. What can u recommend me ? Online+imorted model from 3d software + RL? I worked with unity ml agents, which can build webgl games, but physx physics bad, its not realistic and not predictable. I i see there is 2 main Engines: mujoco and pybullet, but i dint find clear information that i can do a webapp and import models with them. submitted by /u/IndependenceCivil576 [link] [comments]  ( 88 min )
    D4RL, MuJoCo-py docker image
    Hey guys, I struggled quite a lot with installing MuJoCo-py and D4RL and would've wished a docker image was readily available. Even the Dockerfile on the MuJoCo-py github repo fails to build. Here's an image I created: https://hub.docker.com/r/chboe/rl-py I thought I'd leave it here for other people who are similarly struggling :) Let me know if there's something I should change, as I still don't have any experience with it and therefore haven't been able to test everything fully. submitted by /u/Dragonrooster [link] [comments]  ( 88 min )
    CartPole Swing RL task
    I want to implement the CartPole swing up game. Does anyone have references or articles that they can recommend. GitHub’s and articles are preferred. I seen a couple different ones that are “ok". Found this one on GitHub, https://github.com/0xangelo/gym-cartpole-swingup But I am not sure if this is the openai gym version or not. Feedback and suggestions greatly appreciated. I did manage to create my own custom gym environment, but the agent didn’t perform well. I am also assuming it didn’t perform well because I didn’t use a network of any sort. Just pure Q-Learning. If you have a DQN article I can read that would be nice. submitted by /u/Alternative-Price-27 [link] [comments]  ( 88 min )
  • Open

    Best practices for TensorFlow 1.x acceleration training on Amazon SageMaker
    Today, a lot of customers are using TensorFlow to train deep learning models for their clickthrough rate in advertising and personalization recommendations in ecommerce. As the behavior of their clients change, they can accumulate large amounts of new data every day. Model iteration is one of a data scientist’s daily jobs, but they face the […]  ( 13 min )
  • Open

    Help with Neural Neutwork for probability guessing
    Hi! I'm currently developing a NN for a wide range of probabilities. I'm using pytorch and this is the NN (simple): ​ https://preview.redd.it/lr7mc8998pi91.png?width=524&format=png&auto=webp&s=53ce7e0e3f48a3d44ee48edc8da4d93aeddab764 Also i'm applying to the data of the labels, since they are very low probabilities a multiplication factor of 10^8 and then appliging the logarith to the value. (Later on to test and use the tool i will just do the reverse). Nevertheless this Newrok learns a lot from the training set (about 10000) but then never fits the test set. I don't know what to change. It is suppose to be a 50/50 balanced dataset. I really don't know what to do to improve the non-training data accuracy. Any idea is wellcomed submitted by /u/Single_Vermicelli_33 [link] [comments]  ( 88 min )
    PyTorch tutorials on Information Retrieval, specifically Semantic Search
    I've created a PyTorch based repo which tries to cover the current progress in the world of information-retrieval using neural information retrievers / semantic search. Repo: https://github.com/kuutsav/information-retrieval . Most of the content follows the work of Nils Reimers (creator of the sentence_transformers library) and his research group. Topics covered Classic way of information retrieval Evaluation metrics Bi-Encoders Cross-Encoders Multilingual retrieval models Training techniques using no labeled data Domain adaptation - GPL, TSDAE, SimCSE Things to come Vector databases Approximate Nearest Neighbor techniques for quick retrieval submitted by /u/krumb0y [link] [comments]  ( 97 min )
    Backpropagation From Scratch
    submitted by /u/marcos_pereira [link] [comments]  ( 88 min )
  • Open

    NVIDIA to Share New Details on Grace CPU, Hopper GPU, NVLink Switch, Jetson Orin Module at Hot Chips
    In four talks over two days, senior NVIDIA engineers will describe innovations in accelerated computing for modern data centers and systems at the edge of the network. Speaking at a virtual Hot Chips event, an annual gathering of processor and system architects, they’ll disclose performance numbers and other technical details for NVIDIA’s first server CPU, Read article > The post NVIDIA to Share New Details on Grace CPU, Hopper GPU, NVLink Switch, Jetson Orin Module at Hot Chips appeared first on NVIDIA Blog.  ( 6 min )
    Meet the Omnivore: Startup in3D Turns Selfies Into Talking, Dancing Avatars With NVIDIA Omniverse
    Imagine taking a selfie and using it to get a moving, talking, customizable 3D avatar of yourself in just seconds.  The post Meet the Omnivore: Startup in3D Turns Selfies Into Talking, Dancing Avatars With NVIDIA Omniverse appeared first on NVIDIA Blog.  ( 6 min )
  • Open

    How AI & Big Data are Transforming the Healthcare Processes
    In the last 10 years, healthcare has been one of the fastest-growing sectors of the economy, i.e., the global economy as a whole. As…  ( 11 min )
  • Open

    Conflicting Interactions Among Protection Mechanisms for Machine Learning Models. (arXiv:2207.01991v2 [cs.LG] UPDATED)
    Nowadays, systems based on machine learning (ML) are widely used in different domains. Given their popularity, ML models have become targets for various attacks. As a result, research at the intersection of security/privacy and ML has flourished. Typically such work has focused on individual types of security/privacy concerns and mitigations thereof. However, in real-life deployments, an ML model will need to be protected against several concerns simultaneously. A protection mechanism optimal for one security or privacy concern may interact negatively with mechanisms intended to address other concerns. Despite its practical relevance, the potential for such conflicts has not been studied adequately. We first provide a framework for analyzing such "conflicting interactions". We then focus on systematically analyzing pairwise interactions between protection mechanisms for one concern, model and data ownership verification, with two other classes of ML protection mechanisms: differentially private training, and robustness against model evasion. We find that several pairwise interactions result in conflicts. We explore potential approaches for avoiding such conflicts. First, we study the effect of hyperparameter relaxations, finding that there is no sweet spot balancing the performance of both protection mechanisms. Second, we explore if modifying one type of protection mechanism (ownership verification) so as to decouple it from factors that may be impacted by a conflicting mechanism (differentially private training or robustness to model evasion) can avoid conflict. We show that this approach can avoid the conflict between ownership verification mechanisms when combined with differentially private training, but has no effect on robustness to model evasion. Finally, we identify the gaps in the landscape of studying interactions between other types of ML protection mechanisms.  ( 3 min )
    A Framework and Benchmark for Deep Batch Active Learning for Regression. (arXiv:2203.09410v2 [stat.ML] UPDATED)
    The acquisition of labels for supervised learning can be expensive. In order to improve the sample-efficiency of neural network regression, we study active learning methods that adaptively select batches of unlabeled data for labeling. We present a framework for constructing such methods out of (network-dependent) base kernels, kernel transformations and selection methods. Our framework encompasses many existing Bayesian methods based on Gaussian Process approximations of neural networks as well as non-Bayesian methods. Additionally, we propose to replace the commonly used last-layer features with sketched finite-width Neural Tangent Kernels, and to combine them with a novel clustering method. To evaluate different methods, we introduce an open-source benchmark consisting of 15 large tabular regression data sets. Our proposed method outperforms the state-of-the-art on our benchmark, scales to large data sets, and works out-of-the-box without adjusting the network architecture or training code. We provide open-source code that includes efficient implementations of all kernels, kernel transformations, and selection methods, and can be used for reproducing our results.  ( 3 min )
    What Makes the Story Forward? Inferring Commonsense Explanations as Prompts for Future Event Generation. (arXiv:2201.07099v2 [cs.CL] UPDATED)
    Prediction over event sequences is critical for many real-world applications in Information Retrieval and Natural Language Processing. Future Event Generation (FEG) is a challenging task in event sequence prediction because it requires not only fluent text generation but also commonsense reasoning to maintain the logical coherence of the entire event story. In this paper, we propose a novel explainable FEG framework, Coep. It highlights and integrates two types of event knowledge, sequential knowledge of direct event-event relations and inferential knowledge that reflects the intermediate character psychology between events, such as intents, causes, reactions, which intrinsically pushes the story forward. To alleviate the knowledge forgetting issue, we design two modules, Im and Gm, for each type of knowledge, which are combined via prompt tuning. First, Im focuses on understanding inferential knowledge to generate commonsense explanations and provide a soft prompt vector for Gm. We also design a contrastive discriminator for better generalization ability. Second, Gm generates future events by modeling direct sequential knowledge with the guidance of Im. Automatic and human evaluation demonstrate that our approach can generate more coherent, specific, and logical future events.  ( 3 min )
    Lyapunov-Net: A Deep Neural Network Architecture for Lyapunov Function Approximation. (arXiv:2109.13359v2 [cs.LG] UPDATED)
    We develop a versatile deep neural network architecture, called Lyapunov-Net, to approximate Lyapunov functions of dynamical systems in high dimensions. Lyapunov-Net guarantees positive definiteness, and thus it can be easily trained to satisfy the negative orbital derivative condition, which only renders a single term in the empirical risk function in practice. This significantly reduces the number of hyper-parameters compared to existing methods. We also provide theoretical justifications on the approximation power of Lyapunov-Net and its complexity bounds. We demonstrate the efficiency of the proposed method on nonlinear dynamical systems involving up to 30-dimensional state spaces, and show that the proposed approach significantly outperforms the state-of-the-art methods.  ( 2 min )
    Sequence Prediction Under Missing Data : An RNN Approach Without Imputation. (arXiv:2208.08933v1 [cs.LG])
    Missing data scenarios are very common in ML applications in general and time-series/sequence applications are no exceptions. This paper pertains to a novel Recurrent Neural Network (RNN) based solution for sequence prediction under missing data. Our method is distinct from all existing approaches. It tries to encode the missingness patterns in the data directly without trying to impute data either before or during model building. Our encoding is lossless and achieves compression. It can be employed for both sequence classification and forecasting. We focus on forecasting here in a general context of multi-step prediction in presence of possible exogenous inputs. In particular, we propose novel variants of Encoder-Decoder (Seq2Seq) RNNs for this. The encoder here adopts the above mentioned pattern encoding, while at the decoder which has a different structure, multiple variants are feasible. We demonstrate the utility of our proposed architecture via multiple experiments on both single and multiple sequence (real) data-sets. We consider both scenarios where (i)data is naturally missing and (ii)data is synthetically masked.  ( 2 min )
    Graph Coloring with Physics-Inspired Graph Neural Networks. (arXiv:2202.01606v2 [cs.LG] UPDATED)
    We show how graph neural networks can be used to solve the canonical graph coloring problem. We frame graph coloring as a multi-class node classification problem and utilize an unsupervised training strategy based on the statistical physics Potts model. Generalizations to other multi-class problems such as community detection, data clustering, and the minimum clique cover problem are straightforward. We provide numerical benchmark results and illustrate our approach with an end-to-end application for a real-world scheduling use case within a comprehensive encode-process-decode framework. Our optimization approach performs on par or outperforms existing solvers, with the ability to scale to problems with millions of variables.  ( 2 min )
    Few-Shot Forecasting of Time-Series with Heterogeneous Channels. (arXiv:2204.03456v2 [cs.LG] UPDATED)
    Learning complex time series forecasting models usually requires a large amount of data, as each model is trained from scratch for each task/data set. Leveraging learning experience with similar datasets is a well-established technique for classification problems called few-shot classification. However, existing approaches cannot be applied to time-series forecasting because i) multivariate time-series datasets have different channels and ii) forecasting is principally different from classification. In this paper we formalize the problem of few-shot forecasting of time-series with heterogeneous channels for the first time. Extending recent work on heterogeneous attributes in vector data, we develop a model composed of permutation-invariant deep set-blocks which incorporate a temporal embedding. We assemble the first meta-dataset of 40 multivariate time-series datasets and show through experiments that our model provides a good generalization, outperforming baselines carried over from simpler scenarios that either fail to learn across tasks or miss temporal information.  ( 2 min )
    UN-AVOIDS: Unsupervised and Nonparametric Approach for Visualizing Outliers and Invariant Detection Scoring. (arXiv:2111.10010v2 [cs.LG] UPDATED)
    The visualization and detection of anomalies (outliers) are of crucial importance to many fields, particularly cybersecurity. Several approaches have been proposed in these fields, yet to the best of our knowledge, none of them has fulfilled both objectives, simultaneously or cooperatively, in one coherent framework. The visualization methods of these approaches were introduced for explaining the output of a detection algorithm, not for data exploration that facilitates a standalone visual detection. This is our point of departure: UN-AVOIDS, an unsupervised and nonparametric approach for both visualization (a human process) and detection (an algorithmic process) of outliers, that assigns invariant anomalous scores (normalized to $[0,1]$), rather than hard binary-decision. The main aspect of novelty of UN-AVOIDS is that it transforms data into a new space, which is introduced in this paper as neighborhood cumulative density function (NCDF), in which both visualization and detection are carried out. In this space, outliers are remarkably visually distinguishable, and therefore the anomaly scores assigned by the detection algorithm achieved a high area under the ROC curve (AUC). We assessed UN-AVOIDS on both simulated and two recently published cybersecurity datasets, and compared it to three of the most successful anomaly detection methods: LOF, IF, and FABOD. In terms of AUC, UN-AVOIDS was almost an overall winner. The article concludes by providing a preview of new theoretical and practical avenues for UN-AVOIDS. Among them is designing a visualization aided anomaly detection (VAAD), a type of software that aids analysts by providing UN-AVOIDS' detection algorithm (running in a back engine), NCDF visualization space (rendered to plots), along with other conventional methods of visualization in the original feature space, all of which are linked in one interactive environment.  ( 3 min )
    A Framework for Understanding and Visualizing Strategies of RL Agents. (arXiv:2208.08552v1 [cs.AI])
    Recent years have seen significant advances in explainable AI as the need to understand deep learning models has gained importance with the increased emphasis on trust and ethics in AI. Comprehensible models for sequential decision tasks are a particular challenge as they require understanding not only individual predictions but a series of predictions that interact with environmental dynamics. We present a framework for learning comprehensible models of sequential decision tasks in which agent strategies are characterized using temporal logic formulas. Given a set of agent traces, we first cluster the traces using a novel embedding method that captures frequent action patterns. We then search for logical formulas that explain the agent strategies in the different clusters. We evaluate our framework on combat scenarios in StarCraft II (SC2), using traces from a handcrafted expert policy and a trained reinforcement learning agent. We implemented a feature extractor for SC2 environments that extracts traces as sequences of high-level features describing both the state of the environment and the agent's local behavior from agent replays. We further designed a visualization tool depicting the movement of units in the environment that helps understand how different task conditions lead to distinct agent behavior patterns in each trace cluster. Experimental results show that our framework is capable of separating agent traces into distinct groups of behaviors for which our approach to strategy inference produces consistent, meaningful, and easily understood strategy descriptions.  ( 3 min )
    Cooperate or Compete: A New Perspective on Training of Generative Networks. (arXiv:2207.02192v5 [cs.LG] UPDATED)
    GANs have two competing modules: the generator module is trained to generate new examples, and the discriminator module is trained to discriminate real examples from generated examples. The training procedure of GAN is modeled as a finitely repeated simultaneous game. Each module tries to increase its performance at every repetition of the base game (at every batch of training data) in a non-cooperative manner. We observed that each module can perform better and learn faster if training is modeled as an infinitely repeated simultaneous game. At every repetition of the base game (at every batch of training data) the stronger module (whose performance is increased or remains the same compared to the previous batch of training data) cooperates with the weaker module (whose performance is decreased compared to the previous batch of training data) and only the weaker module is allowed to increase its performance.  ( 2 min )
    Selective Classification Via Neural Network Training Dynamics. (arXiv:2205.13532v2 [cs.LG] UPDATED)
    Selective classification is the task of rejecting inputs a model would predict incorrectly on through a trade-off between input space coverage and model accuracy. Current methods for selective classification impose constraints on either the model architecture or the loss function; this inhibits their usage in practice. In contrast to prior work, we show that state-of-the-art selective classification performance can be attained solely from studying the (discretized) training dynamics of a model. We propose a general framework that, for a given test input, monitors metrics capturing the disagreement with the final predicted label over intermediate models obtained during training; we then reject data points exhibiting too much disagreement at late stages in training. In particular, we instantiate a method that tracks when the label predicted during training stops disagreeing with the final predicted label. Our experimental evaluation shows that our method achieves state-of-the-art accuracy/coverage trade-offs on typical selective classification benchmarks.  ( 2 min )
    Forgetting and Imbalance in Robot Lifelong Learning with Off-policy Data. (arXiv:2204.05893v2 [cs.RO] UPDATED)
    Robots will experience non-stationary environment dynamics throughout their lifetime: the robot dynamics can change due to wear and tear, or its surroundings may change over time. Eventually, the robots should perform well in all of the environment variations it has encountered. At the same time, it should still be able to learn fast in a new environment. We identify two challenges in Reinforcement Learning (RL) under such a lifelong learning setting with off-policy data: first, existing off-policy algorithms struggle with the trade-off between being conservative to maintain good performance in the old environment and learning efficiently in the new environment, despite keeping all the data in the replay buffer. We propose the Offline Distillation Pipeline to break this trade-off by separating the training procedure into an online interaction phase and an offline distillation phase.Second, we find that training with the imbalanced off-policy data from multiple environments across the lifetime creates a significant performance drop. We identify that this performance drop is caused by the combination of the imbalanced quality and size among the datasets which exacerbate the extrapolation error of the Q-function. During the distillation phase, we apply a simple fix to the issue by keeping the policy closer to the behavior policy that generated the data. In the experiments, we demonstrate these two challenges and the proposed solutions with a simulated bipedal robot walk-ing task across various environment changes. We show that the Offline Distillation Pipeline achieves better performance across all the encountered environments without affecting data collection. We also provide a comprehensive empirical study to support our hypothesis on the data imbalance issue.  ( 3 min )
    Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-implementation Guidelines. (arXiv:2206.03944v3 [cs.LG] UPDATED)
    Online reinforcement learning (RL) algorithms are increasingly used to personalize digital interventions in the fields of mobile health and online education. Common challenges in designing and testing an RL algorithm in these settings include ensuring the RL algorithm can learn and run stably under real-time constraints, and accounting for the complexity of the environment, e.g., a lack of accurate mechanistic models for the user dynamics. To guide how one can tackle these challenges, we extend the PCS (Predictability, Computability, Stability) framework, a data science framework that incorporates best practices from machine learning and statistics in supervised learning (Yu and Kumbier, 2020), to the design of RL algorithms for the digital interventions setting. Further, we provide guidelines on how to design simulation environments, a crucial tool for evaluating RL candidate algorithms using the PCS framework. We illustrate the use of the PCS framework for designing an RL algorithm for Oralytics, a mobile health study aiming to improve users' tooth-brushing behaviors through the personalized delivery of intervention messages. Oralytics will go into the field in late 2022.  ( 3 min )
    Momentum-Based Policy Gradient with Second-Order Information. (arXiv:2205.08253v2 [cs.LG] UPDATED)
    Variance-reduced gradient estimators for policy gradient methods have been one of the main focus of research in the reinforcement learning in recent years as they allow acceleration of the estimation process. We propose a variance-reduced policy-gradient method, called SHARP, which incorporates second-order information into stochastic gradient descent (SGD) using momentum with a time-varying learning rate. SHARP algorithm is parameter-free, achieving $\epsilon$-approximate first-order stationary point with $O(\epsilon^{-3})$ number of trajectories, while using a batch size of $O(1)$ at each iteration. Unlike most previous work, our proposed algorithm does not require importance sampling which can compromise the advantage of variance reduction process. Moreover, the variance of estimation error decays with the fast rate of $O(1/t^{2/3})$ where $t$ is the number of iterations. Our extensive experimental evaluations show the effectiveness of the proposed algorithm on various control tasks and its advantage over the state of the art in practice.  ( 2 min )
    Semi-self-supervised Automated ICD Coding. (arXiv:2205.10088v2 [cs.CL] UPDATED)
    Clinical Text Notes (CTNs) contain physicians' reasoning process, written in an unstructured free text format, as they examine and interview patients. In recent years, several studies have been published that provide evidence for the utility of machine learning for predicting doctors' diagnoses from CTNs, a task known as ICD coding. Data annotation is time consuming, particularly when a degree of specialization is needed, as is the case for medical data. This paper presents a method of augmenting a sparsely annotated dataset of Icelandic CTNs with a machine-learned imputation in a semi-self-supervised manner. We train a neural network on a small set of annotated CTNs and use it to extract clinical features from a set of un-annotated CTNs. These clinical features consist of answers to about a thousand potential questions that a physician might find the answers to during a consultation of a patient. The features are then used to train a classifier for the diagnosis of certain types of diseases. We report the results of an evaluation of this data augmentation method over three tiers of data availability to the physician. Our data augmentation method shows a significant positive effect which is diminished when clinical features from the examination of the patient and diagnostics are made available. We recommend our method for augmenting scarce datasets for systems that take decisions based on clinical features that do not include examinations or tests.  ( 3 min )
    Rethinking Spatial Invariance of Convolutional Networks for Object Counting. (arXiv:2206.05253v2 [cs.CV] UPDATED)
    Previous work generally believes that improving the spatial invariance of convolutional networks is the key to object counting. However, after verifying several mainstream counting networks, we surprisingly found too strict pixel-level spatial invariance would cause overfit noise in the density map generation. In this paper, we try to use locally connected Gaussian kernels to replace the original convolution filter to estimate the spatial position in the density map. The purpose of this is to allow the feature extraction process to potentially stimulate the density map generation process to overcome the annotation noise. Inspired by previous work, we propose a low-rank approximation accompanied with translation invariance to favorably implement the approximation of massive Gaussian convolution. Our work points a new direction for follow-up research, which should investigate how to properly relax the overly strict pixel-level spatial invariance for object counting. We evaluate our methods on 4 mainstream object counting networks (i.e., MCNN, CSRNet, SANet, and ResNet-50). Extensive experiments were conducted on 7 popular benchmarks for 3 applications (i.e., crowd, vehicle, and plant counting). Experimental results show that our methods significantly outperform other state-of-the-art methods and achieve promising learning of the spatial position of objects.  ( 3 min )
    Online Allocation Problem with Two-sided Resource Constraints: Fairness and Provable Guarantees. (arXiv:2112.13964v2 [cs.LG] UPDATED)
    In this paper, we investigate the online allocation problem of maximizing the overall revenue subject to both lower and upper bound constraints. Compared to the extensively studied online problems with only resource upper bounds, the two-sided constraints affect the prospects of resource consumption more severely. As a result, only limited violation of constraints or pessimistic competitive bounds could be guaranteed. To tackle the challenge, we define a measure of feasibility $\xi^*$ to evaluate the hardness of this problem, and estimate this measurement by an optimization routine with theoretical guarantees. We propose an online algorithm adopting a constructive framework, where we initialize a threshold price vector using the estimation, then dynamically update the price vector and use it for decision making at each step. It can be shown that the proposed algorithm is $\big(1-O(\frac{\varepsilon}{\xi^*-\varepsilon})\big)$ or $\big(1-O(\frac{\varepsilon}{\xi^*-\sqrt{\varepsilon}})\big)$ competitive with high probability for $\xi^*$ known or unknown respectively. To the best of our knowledge, this is the first result establishing a nearly optimal competitive algorithm for solving two-sided constrained online allocation problems with high probability of feasibility.  ( 3 min )
    Silicon Photonic Architecture for Training Deep Neural Networks with Direct Feedback Alignment. (arXiv:2111.06862v2 [cs.LG] UPDATED)
    There has been growing interest in using photonic processors for performing neural network inference operations; however, these networks are currently trained using standard digital electronics. Here, we propose on-chip training of neural networks enabled by a CMOS-compatible silicon photonic architecture to harness the potential for massively parallel, efficient, and fast data operations. Our scheme employs the direct feedback alignment training algorithm, which trains neural networks using error feedback rather than error backpropagation, and can operate at speeds of trillions of multiply-accumulate (MAC) operations per second while consuming less than one picojoule per MAC operation. The photonic architecture exploits parallelized matrix-vector multiplications using arrays of microring resonators for processing multi-channel analog signals along single waveguide buses to calculate the gradient vector for each neural network layer in situ. We also experimentally demonstrate training deep neural networks with the MNIST dataset using on-chip MAC operation results. Our novel approach for efficient, ultra-fast neural network training showcases photonics as a promising platform for executing AI applications.  ( 3 min )
    Data-driven End-to-end Learning of Pole Placement Control for Nonlinear Dynamics via Koopman Invariant Subspaces. (arXiv:2208.08883v1 [eess.SY])
    We propose a data-driven method for controlling the frequency and convergence rate of black-box nonlinear dynamical systems based on the Koopman operator theory. With the proposed method, a policy network is trained such that the eigenvalues of a Koopman operator of controlled dynamics are close to the target eigenvalues. The policy network consists of a neural network to find a Koopman invariant subspace, and a pole placement module to adjust the eigenvalues of the Koopman operator. Since the policy network is differentiable, we can train it in an end-to-end fashion using reinforcement learning. We demonstrate that the proposed method achieves better performance than model-free reinforcement learning and model-based control with system identification.  ( 2 min )
    NetKet 3: Machine Learning Toolbox for Many-Body Quantum Systems. (arXiv:2112.10526v2 [quant-ph] UPDATED)
    We introduce version 3 of NetKet, the machine learning toolbox for many-body quantum physics. NetKet is built around neural-network quantum states and provides efficient algorithms for their evaluation and optimization. This new version is built on top of JAX, a differentiable programming and accelerated linear algebra framework for the Python programming language. The most significant new feature is the possibility to define arbitrary neural network ans\"atze in pure Python code using the concise notation of machine-learning frameworks, which allows for just-in-time compilation as well as the implicit generation of gradients thanks to automatic differentiation. NetKet 3 also comes with support for GPU and TPU accelerators, advanced support for discrete symmetry groups, chunking to scale up to thousands of degrees of freedom, drivers for quantum dynamics applications, and improved modularity, allowing users to use only parts of the toolbox as a foundation for their own code.
    Causal Reasoning Meets Visual Representation Learning: A Prospective Study. (arXiv:2204.12037v7 [cs.CV] UPDATED)
    Visual representation learning is ubiquitous in various real-world applications, including visual comprehension, video understanding, multi-modal analysis, human-computer interaction, and urban computing. Due to the emergence of huge amounts of multi-modal heterogeneous spatial/temporal/spatial-temporal data in big data era, the lack of interpretability, robustness, and out-of-distribution generalization are becoming the challenges of the existing visual models. The majority of the existing methods tend to fit the original data/variable distributions and ignore the essential causal relations behind the multi-modal knowledge, which lacks unified guidance and analysis about why modern visual representation learning methods easily collapse into data bias and have limited generalization and cognitive abilities. Inspired by the strong inference ability of human-level agents, recent years have therefore witnessed great effort in developing causal reasoning paradigms to realize robust representation and model learning with good cognitive ability. In this paper, we conduct a comprehensive review of existing causal reasoning methods for visual representation learning, covering fundamental theories, models, and datasets. The limitations of current methods and datasets are also discussed. Moreover, we propose some prospective challenges, opportunities, and future research directions for benchmarking causal reasoning algorithms in visual representation learning. This paper aims to provide a comprehensive overview of this emerging field, attract attention, encourage discussions, bring to the forefront the urgency of developing novel causal reasoning methods, publicly available benchmarks, and consensus-building standards for reliable visual representation learning and related real-world applications more efficiently.
    Convergence Rates for Stochastic Approximation on a Boundary. (arXiv:2208.07243v2 [stat.ML] UPDATED)
    We analyze the behavior of projected stochastic gradient descent focusing on the case where the optimum is on the boundary of the constraint set and the gradient does not vanish at the optimum. Here iterates may in expectation make progress against the objective at each step. When this and an appropriate moment condition on noise holds, we prove that the convergence rate to the optimum of the constrained stochastic gradient descent will be different and typically be faster than the unconstrained stochastic gradient descent algorithm. Our results argue that the concentration around the optimum is exponentially distributed rather than normally distributed, which typically determines the limiting convergence in the unconstrained case. The methods that we develop rely on a geometric ergodicity proof. This extends a result on Markov chains by Hajek (1982) to the area of stochastic approximation algorithms. As examples, we show how the results apply to linear programming and tabular reinforcement learning.
    How many perturbations break this model? Evaluating robustness beyond adversarial accuracy. (arXiv:2207.04129v2 [cs.LG] UPDATED)
    Robustness to adversarial attack is typically evaluated with adversarial accuracy. This metric quantifies the number of points for which, given a threat model, successful adversarial perturbations cannot be found. While essential, this metric does not capture all aspects of robustness and in particular leaves out the question of how many perturbations can be found for each point. In this work we introduce an alternative approach, adversarial sparsity, which quantifies how difficult it is to find a successful perturbation given both an input point and a constraint on the direction of the perturbation. This constraint may be angular (L2 perturbations), or based on the number of pixels (Linf perturbations). We show that sparsity provides valuable insight on neural networks in multiple ways. analyzing the sparsity of existing robust models illustrates important differences between them that accuracy analysis does not, and suggests approaches for improving their robustness. When applying broken defenses effective against weak attacks but not strong ones, sparsity can discriminate between the totally ineffective and the partially effective defenses. Finally, with sparsity we can measure increases in robustness that do not affect accuracy: we show for example that data augmentation can by itself increase adversarial robustness, without using adversarial training.
    improving the Diversity of Bootstrapped DQN by Replacing Priors With Noise. (arXiv:2203.01004v2 [cs.LG] UPDATED)
    Q-learning is one of the most well-known Reinforcement Learning algorithms. There have been tremendous efforts to develop this algorithm using neural networks. Bootstrapped Deep Q-Learning Network is amongst them. It utilizes multiple neural network heads to introduce diversity into Q-learning. Diversity can sometimes be viewed as the amount of reasonable moves an agent can take at a given state, analogous to the definition of the exploration ratio in RL. Thus, the performance of Bootstrapped Deep Q-Learning Network is deeply connected with the level of diversity within the algorithm. In the original research, it was pointed out that a random prior could improve the performance of the model. In this article, we further explore the possibility of replacing priors with noise and sample the noise from a Gaussian distribution to introduce more diversity into this algorithm. We conduct our experiment on the Atari benchmark and compare our algorithm to both the original and other related algorithms. The results show that our modification of the Bootstrapped Deep Q-Learning algorithm achieves significantly higher evaluation scores across different types of Atari games. Thus, we conclude that replacing priors with noise can improve Bootstrapped Deep Q-Learning's performance by ensuring the integrity of diversities.
    One-Step Abductive Multi-Target Learning with Diverse Noisy Samples and Its Application to Tumour Segmentation for Breast Cancer. (arXiv:2110.10325v6 [cs.LG] UPDATED)
    Recent studies have demonstrated the effectiveness of the combination of machine learning and logical reasoning, including data-driven logical reasoning, knowledge driven machine learning and abductive learning, in inventing advanced artificial intelligence technologies. One-step abductive multi-target learning (OSAMTL), an approach inspired by abductive learning, via simply combining machine learning and logical reasoning in a one-step balanced way, has as well shown its effectiveness in handling complex noisy labels of a single noisy sample in medical histopathology whole slide image analysis (MHWSIA). However, OSAMTL is not suitable for the situation where diverse noisy samples (DiNS) are provided for a learning task. In this paper, giving definition of DiNS, we propose one-step abductive multi-target learning with DiNS (OSAMTL-DiNS) to expand the original OSAMTL to handle complex noisy labels of DiNS. Applying OSAMTL-DiNS to tumour segmentation for breast cancer in MHWSIA, we show that OSAMTL-DiNS is able to enable various state-of-the-art approaches for learning from noisy labels to achieve more rational predictions.
    A spatiotemporal machine learning approach to forecasting COVID-19 incidence at the county level in the USA. (arXiv:2109.12094v4 [stat.ML] UPDATED)
    With COVID-19 affecting every country globally and changing everyday life, the ability to forecast the spread of the disease is more important than any previous epidemic. The conventional methods of disease-spread modeling, compartmental models, are based on the assumption of spatiotemporal homogeneity of the spread of the virus, which may cause forecasting to underperform, especially at high spatial resolutions. In this paper we approach the forecasting task with an alternative technique - spatiotemporal machine learning. We present COVID-LSTM, a data-driven model based on a Long Short-term Memory deep learning architecture for forecasting COVID-19 incidence at the county-level in the US. We use the weekly number of new positive cases as temporal input, and hand-engineered spatial features from Facebook movement and connectedness datasets to capture the spread of the disease in time and space. COVID-LSTM outperforms the COVID-19 Forecast Hub's Ensemble model (COVIDhub-ensemble) on our 17-week evaluation period, making it the first model to be more accurate than the COVIDhub-ensemble over one or more forecast periods. Over the 4-week forecast horizon, our model is on average 50 cases per county more accurate than the COVIDhub-ensemble. We highlight that the underutilization of data-driven forecasting of disease spread prior to COVID-19 is likely due to the lack of sufficient data available for previous diseases, in addition to the recent advances in machine learning methods for spatiotemporal forecasting. We discuss the impediments to the wider uptake of data-driven forecasting, and whether it is likely that more deep learning-based models will be used in the future.
    t-METASET: Tailoring Property Bias of Large-Scale Metamaterial Datasets through Active Learning. (arXiv:2202.10565v2 [cs.CE] UPDATED)
    Inspired by the recent achievements of machine learning in diverse domains, data-driven metamaterials design has emerged as a compelling paradigm that can unlock the potential of multiscale architectures. The model-centric research trend, however, lacks principled frameworks dedicated to data acquisition, whose quality propagates into the downstream tasks. Often built by naive space-filling design in shape descriptor space, metamaterial datasets suffer from property distributions that are either highly imbalanced or at odds with design tasks of interest. To this end, we present t-METASET: an active-learning-based data acquisition framework aiming to guide both diverse and task-aware data generation. Distinctly, we seek a solution to a commonplace yet frequently overlooked scenario at early stages of data-driven design of metamaterials: when a massive (~O(10^4 )) shape-only library has been prepared with no properties evaluated. The key idea is to harness a data-driven shape descriptor learned from generative models, fit a sparse regressor as a start-up agent, and leverage metrics related to diversity to drive data acquisition to areas that help designers fulfill design goals. We validate the proposed framework in three deployment cases, which encompass general use, task-specific use, and tailorable use. Two large-scale mechanical metamaterial datasets are used to demonstrate the efficacy. Applicable to general image-based design representations, t-METASET could boost future advancements in data-driven design.
    High Dimensional Statistical Estimation under Uniformly Dithered One-bit Quantization. (arXiv:2202.13157v2 [stat.ML] UPDATED)
    In this paper, we propose a uniformly dithered one-bit quantization scheme for high-dimensional statistical estimation. The scheme contains truncation, dithering, and quantization as typical steps. As canonical examples, the quantization scheme is applied to three estimation problems: sparse covariance matrix estimation, sparse linear regression, and matrix completion. We study both sub-Gaussian and heavy-tailed regimes, with the underlying distribution of heavy-tailed data assumed to possess bounded second or fourth moment. For each model we propose new estimators based on one-bit quantized data. In sub-Gaussian regime, our estimators achieve optimal minimax rates up to logarithmic factors, which indicates that our quantization scheme nearly introduces no additional cost. In heavy-tailed regime, while the rates of our estimators become essentially slower, these results are either the first ones in such one-bit quantized and heavy-tailed setting, or exhibit significant improvements over existing comparable results. Moreover, we contribute considerably to the problems of one-bit compressed sensing and one-bit matrix completion. Specifically, we extend one-bit compressed sensing to sub-Gaussian or even heavy-tailed sensing vectors via convex programming. For one-bit matrix completion, our method is essentially different from the standard likelihood approach and can handle pre-quantization random noise with unknown distribution. Experimental results on synthetic data are presented to support our theoretical analysis.
    Motley: Benchmarking Heterogeneity and Personalization in Federated Learning. (arXiv:2206.09262v5 [cs.LG] UPDATED)
    Personalized federated learning considers learning models unique to each client in a heterogeneous network. The resulting client-specific models have been purported to improve metrics such as accuracy, fairness, and robustness in federated networks. However, despite a plethora of work in this area, it remains unclear: (1) which personalization techniques are most effective in various settings, and (2) how important personalization truly is for realistic federated applications. To better answer these questions, we propose Motley, a benchmark for personalized federated learning. Motley consists of a suite of cross-device and cross-silo federated datasets from varied problem domains, as well as thorough evaluation metrics for better understanding the possible impacts of personalization. We establish baselines on the benchmark by comparing a number of representative personalized federated learning methods. These initial results highlight strengths and weaknesses of existing approaches, and raise several open questions for the community. Motley aims to provide a reproducible means with which to advance developments in personalized and heterogeneity-aware federated learning, as well as the related areas of transfer learning, meta-learning, and multi-task learning.
    PADA: Pruning Assisted Domain Adaptation for Self-Supervised Speech Representations. (arXiv:2203.16965v2 [cs.CL] UPDATED)
    While self-supervised speech representation learning (SSL) models serve a variety of downstream tasks, these models have been observed to overfit to the domain from which the unlabelled data originates. To alleviate this issue, we propose PADA (Pruning Assisted Domain Adaptation) and zero out redundant weights from models pre-trained on large amounts of out-of-domain (OOD) data. Intuitively, this helps to make space for the target-domain ASR finetuning. The redundant weights can be identified through various pruning strategies which have been discussed in detail as a part of this work. Specifically, we investigate the effect of the recently discovered Task-Agnostic and Task-Aware pruning on PADA and propose a new pruning paradigm based on the latter, which we call Cross-Domain Task-Aware Pruning (CD-TAW). CD-TAW obtains the initial pruning mask from a well fine-tuned OOD model, which makes it starkly different from the rest of the pruning strategies discussed in the paper. Our proposed CD-TAW methodology achieves up to 20.6% relative WER improvement over our baseline when fine-tuned on a 2-hour subset of Switchboard data without language model (LM) decoding. Furthermore, we conduct a detailed analysis to highlight the key design choices of our proposed method.
    View-labels Are Indispensable: A Multifacet Complementarity Study of Multi-view Clustering. (arXiv:2205.02507v2 [cs.LG] UPDATED)
    Consistency and complementarity are two key ingredients for boosting multi-view clustering (MVC). Recently with the introduction of popular contrastive learning, the consistency learning of views has been further enhanced in MVC, leading to promising performance. However, by contrast, the complementarity has not received sufficient attention except just in the feature facet, where the Hilbert Schmidt Independence Criterion (HSIC) term or the independent encoder-decoder network is usually adopted to capture view-specific information. This motivates us to reconsider the complementarity learning of views comprehensively from multiple facets including the feature-, view-label- and contrast- facets, while maintaining the view consistency. We empirically find that all the facets contribute to the complementarity learning, especially the view-label facet, which is usually neglected by existing methods. Based on this, we develop a novel \underline{M}ultifacet \underline{C}omplementarity learning framework for \underline{M}ulti-\underline{V}iew \underline{C}lustering (MCMVC), which fuses multifacet complementarity information, especially explicitly embedding the view-label information. To our best knowledge, it is the first time to use view-labels explicitly to guide the complementarity learning of views. Compared with the SOTA baseline, MCMVC achieves remarkable improvements, e.g., by average margins over $5.00\%$ and $7.00\%$ respectively in complete and incomplete MVC settings on Caltech101-20 in terms of three evaluation metrics.
    Transformers and the representation of biomedical background knowledge. (arXiv:2202.02432v3 [cs.CL] UPDATED)
    Specialised transformers-based models (such as BioBERT and BioMegatron) are adapted for the biomedical domain based on publicly available biomedical corpora. As such, they have the potential to encode large-scale biological knowledge. We investigate the encoding and representation of biological knowledge in these models, and its potential utility to support inference in cancer precision medicine - namely, the interpretation of the clinical significance of genomic alterations. We compare the performance of different transformer baselines; we use probing to determine the consistency of encodings for distinct entities; and we use clustering methods to compare and contrast the internal properties of the embeddings for genes, variants, drugs and diseases. We show that these models do indeed encode biological knowledge, although some of this is lost in fine-tuning for specific tasks. Finally, we analyse how the models behave with regard to biases and imbalances in the dataset.
    Cross-Silo Heterogeneous Model Federated Multitask Learning. (arXiv:2202.08603v5 [cs.LG] UPDATED)
    Federated learning (FL) is a machine learning technique that enables participants to collaboratively train high-quality models without exchanging their private data. Participants utilizing cross-silo federated learning (CS-FL) settings are independent organizations with different task needs, and they are concerned not only with data privacy but also with independently training their unique models due to intellectual property considerations. Most existing FL methods are incapable of satisfying the above scenarios. In this study, we present a novel federated learning method CoFED based on unlabeled data pseudolabeling via a process known as cotraining. CoFED is a federated learning method that is compatible with heterogeneous models, tasks, and training processes. The experimental results suggest that the proposed method outperforms competing ones. This is especially true for non-independent and identically distributed settings and heterogeneous models, where the proposed method achieves a 35% performance improvement.
    Low Emission Building Control with Zero-Shot Reinforcement Learning. (arXiv:2206.14191v2 [eess.SY] UPDATED)
    Heating and cooling systems in buildings account for 31% of global energy use, much of which are regulated by Rule Based Controllers (RBCs) that neither maximise energy efficiency nor minimise emissions by interacting optimally with the grid. Control via Reinforcement Learning (RL) has been shown to significantly improve building energy efficiency, but existing solutions require access to building-specific simulators or data that cannot be expected for every building in the world. In response, we show it is possible to obtain emission-reducing policies without such knowledge a priori--a paradigm we call zero-shot building control. We combine ideas from system identification and model-based RL to create PEARL (Probabilistic Emission-Abating Reinforcement Learning) and show that a short period of active exploration is all that is required to build a performant model. In experiments across three varied building energy simulations, we show PEARL outperforms an existing RBC once, and popular RL baselines in all cases, reducing building emissions by as much as 31% whilst maintaining thermal comfort. Our source code is available online via https://enjeeneer.io/projects/pearl/
    Beyond the Hype: A Real-World Evaluation of the Impact and Cost of Machine Learning-Based Malware Detection. (arXiv:2012.09214v4 [cs.CR] UPDATED)
    In this paper, we present a scientific evaluation of four prominent malware detection tools to assist an organization with two primary questions: To what extent do ML-based tools accurately classify previously- and never-before-seen files? Is it worth purchasing a network-level malware detector? To identify weaknesses, we tested each tool against 3,536 total files (2,554 or 72\% malicious, 982 or 28\% benign) of a variety of file types, including hundreds of malicious zero-days, polyglots, and APT-style files, delivered on multiple protocols. We present statistical results on detection time and accuracy, consider complementary analysis (using multiple tools together), and provide two novel applications of the recent cost-benefit evaluation procedure of Iannacone \& Bridges. While the ML-based tools are more effective at detecting zero-day files and executables, the signature-based tool may still be an overall better option. Both network-based tools provide substantial (simulated) savings when paired with either host tool, yet both show poor detection rates on protocols other than HTTP or SMTP. Our results show that all four tools have near-perfect precision but alarmingly low recall, especially on file types other than executables and office files -- 37% of malware tested, including all polyglot files, were undetected. Priorities for researchers and takeaways for end users are given.
    Finding and Fixing Spurious Patterns with Explanations. (arXiv:2106.02112v3 [cs.LG] UPDATED)
    Image classifiers often use spurious patterns, such as "relying on the presence of a person to detect a tennis racket, which do not generalize. In this work, we present an end-to-end pipeline for identifying and mitigating spurious patterns for such models, under the assumption that we have access to pixel-wise object-annotations. We start by identifying patterns such as "the model's prediction for tennis racket changes 63% of the time if we hide the people." Then, if a pattern is spurious, we mitigate it via a novel form of data augmentation. We demonstrate that our method identifies a diverse set of spurious patterns and that it mitigates them by producing a model that is both more accurate on a distribution where the spurious pattern is not helpful and more robust to distribution shift.
    Ensemble learning using individual neonatal data for seizure detection. (arXiv:2204.07043v2 [eess.SP] UPDATED)
    Sharing medical data between institutions is difficult in practice due to data protection laws and official procedures within institutions. Therefore, most existing algorithms are trained on relatively small electroencephalogram (EEG) data sets which is likely to be detrimental to prediction accuracy. In this work, we simulate a case when the data can not be shared by splitting the publicly available data set into disjoint sets representing data in individual institutions. We propose to train a (local) detector in each institution and aggregate their individual predictions into one final prediction. Four aggregation schemes are compared, namely, the majority vote, the mean, the weighted mean and the Dawid-Skene method. The method was validated on an independent data set using only a subset of EEG channels. The ensemble reaches accuracy comparable to a single detector trained on all the data when sufficient amount of data is available in each institution. The weighted mean aggregation scheme showed best performance, it was only marginally outperformed by the Dawid--Skene method when local detectors approach performance of a single detector trained on all available data.
    Discovering Bugs in Vision Models using Off-the-shelf Image Generation and Captioning. (arXiv:2208.08831v1 [cs.CV])
    Automatically discovering failures in vision models under real-world settings remains an open challenge. This work demonstrates how off-the-shelf, large-scale, image-to-text and text-to-image models, trained on vast amounts of data, can be leveraged to automatically find such failures. In essence, a conditional text-to-image generative model is used to generate large amounts of synthetic, yet realistic, inputs given a ground-truth label. Misclassified inputs are clustered and a captioning model is used to describe each cluster. Each cluster's description is used in turn to generate more inputs and assess whether specific clusters induce more failures than expected. We use this pipeline to demonstrate that we can effectively interrogate classifiers trained on ImageNet to find specific failure cases and discover spurious correlations. We also show that we can scale the approach to generate adversarial datasets targeting specific classifier architectures. This work serves as a proof-of-concept demonstrating the utility of large-scale generative models to automatically discover bugs in vision models in an open-ended manner. We also describe a number of limitations and pitfalls related to this approach.
    ObfuNAS: A Neural Architecture Search-based DNN Obfuscation Approach. (arXiv:2208.08569v1 [cs.CR])
    Malicious architecture extraction has been emerging as a crucial concern for deep neural network (DNN) security. As a defense, architecture obfuscation is proposed to remap the victim DNN to a different architecture. Nonetheless, we observe that, with only extracting an obfuscated DNN architecture, the adversary can still retrain a substitute model with high performance (e.g., accuracy), rendering the obfuscation techniques ineffective. To mitigate this under-explored vulnerability, we propose ObfuNAS, which converts the DNN architecture obfuscation into a neural architecture search (NAS) problem. Using a combination of function-preserving obfuscation strategies, ObfuNAS ensures that the obfuscated DNN architecture can only achieve lower accuracy than the victim. We validate the performance of ObfuNAS with open-source architecture datasets like NAS-Bench-101 and NAS-Bench-301. The experimental results demonstrate that ObfuNAS can successfully find the optimal mask for a victim model within a given FLOPs constraint, leading up to 2.6% inference accuracy degradation for attackers with only 0.14x FLOPs overhead. The code is available at: https://github.com/Tongzhou0101/ObfuNAS.
    Global Convergence of Two-timescale Actor-Critic for Solving Linear Quadratic Regulator. (arXiv:2208.08744v1 [cs.LG])
    The actor-critic (AC) reinforcement learning algorithms have been the powerhouse behind many challenging applications. Nevertheless, its convergence is fragile in general. To study its instability, existing works mostly consider the uncommon double-loop variant or basic models with finite state and action space. We investigate the more practical single-sample two-timescale AC for solving the canonical linear quadratic regulator (LQR) problem, where the actor and the critic update only once with a single sample in each iteration on an unbounded continuous state and action space. Existing analysis cannot conclude the convergence for such a challenging case. We develop a new analysis framework that allows establishing the global convergence to an $\epsilon$-optimal solution with at most an $\tilde{\mathcal{O}}(\epsilon^{-2.5})$ sample complexity. To our knowledge, this is the first finite-time convergence analysis for the single sample two-timescale AC for solving LQR with global optimality. The sample complexity improves those of other variants by orders, which sheds light on the practical wisdom of single sample algorithms. We also further validate our theoretical findings via comprehensive simulation comparisons.
    Study of General Robust Subband Adaptive Filtering. (arXiv:2208.08856v1 [eess.SP])
    In this paper, we propose a general robust subband adaptive filtering (GR-SAF) scheme against impulsive noise by minimizing the mean square deviation under the random-walk model with individual weight uncertainty. Specifically, by choosing different scaling factors such as from the M-estimate and maximum correntropy robust criteria in the GR-SAF scheme, we can easily obtain different GR-SAF algorithms. Importantly, the proposed GR-SAF algorithm can be reduced to a variable regularization robust normalized SAF algorithm, thus having fast convergence rate and low steady-state error. Simulations in the contexts of system identification with impulsive noise and echo cancellation with double-talk have verified that the proposed GR-SAF algorithms outperforms its counterparts.
    Network inference via process motifs for lagged correlation in linear stochastic processes. (arXiv:2208.08871v1 [stat.ML])
    A major challenge for causal inference from time-series data is the trade-off between computational feasibility and accuracy. Motivated by process motifs for lagged covariance in an autoregressive model with slow mean-reversion, we propose to infer networks of causal relations via pairwise edge measure (PEMs) that one can easily compute from lagged correlation matrices. Motivated by contributions of process motifs to covariance and lagged variance, we formulate two PEMs that correct for confounding factors and for reverse causation. To demonstrate the performance of our PEMs, we consider network interference from simulations of linear stochastic processes, and we show that our proposed PEMs can infer networks accurately and efficiently. Specifically, for slightly autocorrelated time-series data, our approach achieves accuracies higher than or similar to Granger causality, transfer entropy, and convergent crossmapping -- but with much shorter computation time than possible with any of these methods. Our fast and accurate PEMs are easy-to-implement methods for network inference with a clear theoretical underpinning. They provide promising alternatives to current paradigms for the inference of linear models from time-series data, including Granger causality, vector-autoregression, and sparse inverse covariance estimation.
    Long-term dynamics of fairness: understanding the impact of data-driven targeted help on job seekers. (arXiv:2208.08881v1 [cs.LG])
    The use of data-driven decision support by public agencies is becoming more widespread and already influences the allocation of public resources. This raises ethical concerns, as it has adversely affected minorities and historically discriminated groups. In this paper, we use an approach that combines statistics and machine learning with dynamical modeling to assess long-term fairness effects of labor market interventions. Specifically, we develop and use a model to investigate the impact of decisions caused by a public employment authority that selectively supports job-seekers through targeted help. The selection of who receives what help is based on a data-driven intervention model that estimates an individual's chances of finding a job in a timely manner and is based on data that describes a population in which skills relevant to the labor market are unevenly distributed between two groups (e.g., males and females). The intervention model has incomplete access to the individual's actual skills and can augment this with knowledge of the individual's group affiliation, thus using a protected attribute to increase predictive accuracy. We assess this intervention model's dynamics -- especially fairness-related issues and trade-offs between different fairness goals -- over time and compare it to an intervention model that does not use group affiliation as a predictive feature. We conclude that in order to quantify the trade-off correctly and to assess the long-term fairness effects of such a system in the real-world, careful modeling of the surrounding labor market is indispensable.
    An intertwined neural network model for EEG classification in brain-computer interfaces. (arXiv:2208.08860v1 [eess.SP])
    The brain computer interface (BCI) is a nonstimulatory direct and occasionally bidirectional communication link between the brain and a computer or an external device. Classically, EEG-based BCI algorithms have relied on models such as support vector machines and linear discriminant analysis or multiclass common spatial patterns. During the last decade, however, more sophisticated machine learning architectures, such as convolutional neural networks, recurrent neural networks, long short-term memory networks and gated recurrent unit networks, have been extensively used to enhance discriminability in multiclass BCI tasks. Additionally, preprocessing and denoising of EEG signals has always been key in the successful decoding of brain activity, and the determination of an optimal and standardized EEG preprocessing activity is an active area of research. In this paper, we present a deep neural network architecture specifically engineered to a) provide state-of-the-art performance in multiclass motor imagery classification and b) remain robust to preprocessing to enable real-time processing of raw data as it streams from EEG and BCI equipment. It is based on the intertwined use of time-distributed fully connected (tdFC) and space-distributed 1D temporal convolutional layers (sdConv) and explicitly addresses the possibility that interaction of spatial and temporal features of the EEG signal occurs at all levels of complexity. Numerical experiments demonstrate that our architecture provides superior performance compared baselines based on a combination of 3D convolutions and recurrent neural networks in a six-class motor imagery network, with a subjectwise accuracy that reaches 99%. Importantly, these results remain unchanged when minimal or extensive preprocessing is applied, possibly paving the way for a more transversal and real-time use of deep learning architectures in EEG classification.
    Early heart disease prediction using hybrid quantum classification. (arXiv:2208.08882v1 [quant-ph])
    The rate of heart morbidity and heart mortality increases significantly which affect the global public health and world economy. Early prediction of heart disease is crucial for reducing heart morbidity and mortality. This paper proposes two quantum machine learning methods i.e. hybrid quantum neural network and hybrid random forest quantum neural network for early detection of heart disease. The methods are applied on the Cleveland and Statlog datasets. The results show that hybrid quantum neural network and hybrid random forest quantum neural network are suitable for high dimensional and low dimensional problems respectively. The hybrid quantum neural network is sensitive to outlier data while hybrid random forest is robust on outlier data. A comparison between different machine learning methods shows that the proposed quantum methods are more appropriate for early heart disease prediction where 96.43% and 97.78% area under curve are obtained for Cleveland and Statlog dataset respectively.
    Two-layer neural networks with values in a Banach space. (arXiv:2105.02095v3 [cs.LG] UPDATED)
    We study two-layer neural networks whose domain and range are Banach spaces with separable preduals. In addition, we assume that the image space is equipped with a partial order, i.e. it is a Riesz space. As the nonlinearity we choose the lattice operation of taking the positive part; in case of $\mathbb R^d$-valued neural networks this corresponds to the ReLU activation function. We prove inverse and direct approximation theorems with Monte-Carlo rates for a certain class of functions, extending existing results for the finite-dimensional case. In the second part of the paper, we study, from the regularisation theory viewpoint, the problem of finding optimal representations of such functions via signed measures on a latent space from a finite number of noisy observations. We discuss regularity conditions known as source conditions and obtain convergence rates in a Bregman distance for the representing measure in the regime when both the noise level goes to zero and the number of samples goes to infinity at appropriate rates.
    Learned Indexing in Proteins: Substituting Complex Distance Calculations with Embedding and Clustering Techniques. (arXiv:2208.08910v1 [cs.IR])
    Despite the constant evolution of similarity searching research, it continues to face the same challenges stemming from the complexity of the data, such as the curse of dimensionality and computationally expensive distance functions. Various machine learning techniques have proven capable of replacing elaborate mathematical models with combinations of simple linear functions, often gaining speed and simplicity at the cost of formal guarantees of accuracy and correctness of querying. The authors explore the potential of this research trend by presenting a lightweight solution for the complex problem of 3D protein structure search. The solution consists of three steps -- (i) transformation of 3D protein structural information into very compact vectors, (ii) use of a probabilistic model to group these vectors and respond to queries by returning a given number of similar objects, and (iii) a final filtering step which applies basic vector distance functions to refine the result.
    "Task-relevant autoencoding" enhances machine learning for human neuroscience. (arXiv:2208.08478v1 [q-bio.NC])
    In human neuroscience, machine learning can help reveal lower-dimensional neural representations relevant to subjects' behavior. However, state-of-the-art models typically require large datasets to train, so are prone to overfitting on human neuroimaging data that often possess few samples but many input dimensions. Here, we capitalized on the fact that the features we seek in human neuroscience are precisely those relevant to subjects' behavior. We thus developed a Task-Relevant Autoencoder via Classifier Enhancement (TRACE), and tested its ability to extract behaviorally-relevant, separable representations compared to a standard autoencoder for two severely truncated machine learning datasets. We then evaluated both models on fMRI data where subjects observed animals and objects. TRACE outperformed both the autoencoder and raw inputs nearly unilaterally, showing up to 30% increased classification accuracy and up to threefold improvement in discovering "cleaner", task-relevant representations. These results showcase TRACE's potential for a wide variety of data related to human behavior.
    Merchandise Recommendation for Retail Events with Word Embedding Weighted Tf-idf and Dynamic Query Expansion. (arXiv:2208.08581v1 [cs.IR])
    To recommend relevant merchandises for seasonal retail events, we rely on item retrieval from marketplace inventory. With feedback to expand query scope, we discuss keyword expansion candidate selection using word embedding similarity, and an enhanced tf-idf formula for expanded words in search ranking.
    Automatic Detection of Noisy Electrocardiogram Signals without Explicit Noise Labels. (arXiv:2208.08853v1 [eess.SP])
    Electrocardiogram (ECG) signals are beneficial in diagnosing cardiovascular diseases, which are one of the leading causes of death. However, they are often contaminated by noise artifacts and affect the automatic and manual diagnosis process. Automatic deep learning-based examination of ECG signals can lead to inaccurate diagnosis, and manual analysis involves rejection of noisy ECG samples by clinicians, which might cost extra time. To address this limitation, we present a two-stage deep learning-based framework to automatically detect the noisy ECG samples. Through extensive experiments and analysis on two different datasets, we observe that the deep learning-based framework can detect slightly and highly noisy ECG samples effectively. We also study the transfer of the model learned on one dataset to another dataset and observe that the framework effectively detects noisy ECG samples.
    Efficient Signed Graph Sampling via Balancing & Gershgorin Disc Perfect Alignment. (arXiv:2208.08726v1 [eess.SP])
    A basic premise in graph signal processing (GSP) is that a graph encoding pairwise (anti-)correlations of the targeted signal as edge weights is exploited for graph filtering. However, existing fast graph sampling schemes are designed and tested only for positive graphs describing positive correlations. In this paper, we show that for datasets with strong inherent anti-correlations, a suitable graph contains both positive and negative edge weights. In response, we propose a linear-time signed graph sampling method centered on the concept of balanced signed graphs. Specifically, given an empirical covariance data matrix $\bar{\bf{C}}$, we first learn a sparse inverse matrix (graph Laplacian) $\mathcal{L}$ corresponding to a signed graph $\mathcal{G}$. We define the eigenvectors of Laplacian $\mathcal{L}_B$ for a balanced signed graph $\mathcal{G}_B$ -- approximating $\mathcal{G}$ via edge weight augmentation -- as graph frequency components. Next, we choose samples to minimize the low-pass filter reconstruction error in two steps. We first align all Gershgorin disc left-ends of Laplacian $\mathcal{L}_B$ at smallest eigenvalue $\lambda_{\min}(\mathcal{L}_B)$ via similarity transform $\mathcal{L}_p = \S \mathcal{L}_B \S^{-1}$, leveraging a recent linear algebra theorem called Gershgorin disc perfect alignment (GDPA). We then perform sampling on $\mathcal{L}_p$ using a previous fast Gershgorin disc alignment sampling (GDAS) scheme. Experimental results show that our signed graph sampling method outperformed existing fast sampling schemes noticeably on various datasets.
    Never Worse, Mostly Better: Stable Policy Improvement in Deep Reinforcement Learning. (arXiv:1910.01062v3 [cs.LG] UPDATED)
    In recent years, there has been significant progress in applying deep reinforcement learning (RL) for solving challenging problems across a wide variety of domains. Nevertheless, convergence of various methods has been shown to suffer from inconsistencies, due to algorithmic instability and variance, as well as stochasticity in the benchmark environments. Particularly, despite the fact that the agent's performance may be improving on average, it may abruptly deteriorate at late stages of training. In this work, we study methods for enhancing the agent's learning process, by providing conservative updates with respect to either the obtained history or a reference benchmark policy. Our method, termed EVEREST, obtains high confidence improvements via confidence bounds of a reference policy. Through extensive empirical analysis we demonstrate the benefit of our approach in terms of both performance and stabilization, with significant improvements in continuous control and Atari benchmarks.
    Autism spectrum disorder classification based on interpersonal neural synchrony: Can classification be improved by dyadic neural biomarkers using unsupervised graph representation learning?. (arXiv:2208.08902v1 [cs.LG])
    Research in machine learning for autism spectrum disorder (ASD) classification bears the promise to improve clinical diagnoses. However, recent studies in clinical imaging have shown the limited generalization of biomarkers across and beyond benchmark datasets. Despite increasing model complexity and sample size in neuroimaging, the classification performance of ASD remains far away from clinical application. This raises the question of how we can overcome these barriers to develop early biomarkers for ASD. One approach might be to rethink how we operationalize the theoretical basis of this disease in machine learning models. Here we introduced unsupervised graph representations that explicitly map the neural mechanisms of a core aspect of ASD, deficits in dyadic social interaction, as assessed by dual brain recordings, termed hyperscanning, and evaluated their predictive performance. The proposed method differs from existing approaches in that it is more suitable to capture social interaction deficits on a neural level and is applicable to young children and infants. First results from functional-near infrared spectroscopy data indicate potential predictive capacities of a task-agnostic, interpretable graph representation. This first effort to leverage interaction-related deficits on neural level to classify ASD may stimulate new approaches and methods to enhance existing models to achieve developmental ASD biomarkers in the future.
    Projection-free Graph-based Classifier Learning using Gershgorin Disc Perfect Alignment. (arXiv:2106.01642v2 [cs.LG] UPDATED)
    In semi-supervised graph-based binary classifier learning, a subset of known labels $\hat{x}_i$ are used to infer unknown labels, assuming that the label signal $\mathbf{x}$ is smooth with respect to a similarity graph specified by a Laplacian matrix. When restricting labels $x_i$ to binary values, the problem is NP-hard. While a conventional semi-definite programming relaxation (SDR) can be solved in polynomial time using, for example, the alternating direction method of multipliers (ADMM), the complexity of projecting a candidate matrix $\mathbf{M}$ onto the positive semi-definite (PSD) cone ($\mathbf{M} \succeq 0$) per iteration remains high. In this paper, leveraging a recent linear algebraic theory called Gershgorin disc perfect alignment (GDPA), we propose a fast projection-free method by solving a sequence of linear programs (LP) instead. Specifically, we first recast the SDR to its dual, where a feasible solution $\mathbf{H} \succeq 0$ is interpreted as a Laplacian matrix corresponding to a balanced signed graph minus the last node. To achieve graph balance, we split the last node into two, each retains the original positive / negative edges, resulting in a new Laplacian $\bar{\mathbf{H}}$. We repose the SDR dual for solution $\bar{\mathbf{H}}$, then replace the PSD cone constraint $\bar{\mathbf{H} \succeq 0$ with linear constraints derived from GDPA -- sufficient conditions to ensure $\bar{\mathbf{H}}$ is PSD -- so that the optimization becomes an LP per iteration. Finally, we extract predicted labels from converged solution $\bar{\mathbf{H}}$. Experiments show that our algorithm enjoyed a $28\times$ speedup over the next fastest scheme while achieving comparable label prediction performance.
    Transformer Networks for Predictive Group Elevator Control. (arXiv:2208.08948v1 [physics.soc-ph])
    We propose a Predictive Group Elevator Scheduler by using predictive information of passengers arrivals from a Transformer based destination predictor and a linear regression model that predicts remaining time to destinations. Through extensive empirical evaluation, we find that the savings of Average Waiting Time (AWT) could be as high as above 50% for light arrival streams and around 15% for medium arrival streams in afternoon down-peak traffic regimes. Such results can be obtained after carefully setting the Predicted Probability of Going to Elevator (PPGE) threshold, thus avoiding a majority of false predictions for people heading to the elevator, while achieving as high as 80% of true predictive elevator landings as early as after having seen only 60% of the whole trajectory of a passenger.
    Towards Learning in Grey Spatiotemporal Systems: A Prophet to Non-consecutive Spatiotemporal Dynamics. (arXiv:2208.08878v1 [cs.LG])
    Spatiotemporal forecasting is an imperative topic in data science due to its diverse and critical applications in smart cities. Existing works mostly perform consecutive predictions of following steps with observations completely and continuously obtained, where nearest observations can be exploited as key knowledge for instantaneous status estimation. However, the practical issues of early activity planning and sensor failures elicit a brand-new task, i.e., non-consecutive forecasting. In this paper, we define spatiotemporal learning systems with missing observation as Grey Spatiotemporal Systems (G2S) and propose a Factor-Decoupled learning framework for G2S (FDG2S), where the core idea is to hierarchically decouple multi-level factors and enable both flexible aggregations and disentangled uncertainty estimations. Firstly, to compensate for missing observations, a generic semantic-neighboring sequence sampling is devised, which selects representative sequences to capture both periodical regularity and instantaneous variations. Secondly, we turn the predictions of non-consecutive statuses into inferring statuses under expected combined exogenous factors. In particular, a factor-decoupled aggregation scheme is proposed to decouple factor-induced predictive intensity and region-wise proximity by two energy functions of conditional random field. To infer region-wise proximity under flexible factor-wise combinations and enable dynamic neighborhood aggregations, we further disentangle compounded influences of exogenous factors on region-wise proximity and learn to aggregate them. Given the inherent incompleteness and critical applications of G2S, a DisEntangled Uncertainty Quantification is put forward, to identify two types of uncertainty for reliability guarantees and model interpretations.
    Learning-based estimation of in-situ wind speed from underwater acoustics. (arXiv:2208.08912v1 [cs.LG])
    Wind speed retrieval at sea surface is of primary importance for scientific and operational applications. Besides weather models, in-situ measurements and remote sensing technologies, especially satellite sensors, provide complementary means to monitor wind speed. As sea surface winds produce sounds that propagate underwater, underwater acoustics recordings can also deliver fine-grained wind-related information. Whereas model-driven schemes, especially data assimilation approaches, are the state-of-the-art schemes to address inverse problems in geoscience, machine learning techniques become more and more appealing to fully exploit the potential of observation datasets. Here, we introduce a deep learning approach for the retrieval of wind speed time series from underwater acoustics possibly complemented by other data sources such as weather model reanalyses. Our approach bridges data assimilation and learning-based frameworks to benefit both from prior physical knowledge and computational efficiency. Numerical experiments on real data demonstrate that we outperform the state-of-the-art data-driven methods with a relative gain up to 16% in terms of RMSE. Interestingly, these results support the relevance of the time dynamics of underwater acoustic data to better inform the time evolution of wind speed. They also show that multimodal data, here underwater acoustics data combined with ECMWF reanalysis data, may further improve the reconstruction performance, including the robustness with respect to missing underwater acoustics data.
    Estimating individual treatment effects under unobserved confounding using binary instruments. (arXiv:2208.08544v1 [stat.ME])
    Estimating individual treatment effects (ITEs) from observational data is relevant in many fields such as personalized medicine. However, in practice, the treatment assignment is usually confounded by unobserved variables and thus introduces bias. A remedy to remove the bias is the use of instrumental variables (IVs). Such settings are widespread in medicine (e.g., trials where compliance is used as binary IV). In this paper, we propose a novel, multiply robust machine learning framework, called MRIV, for estimating ITEs using binary IVs and thus yield an unbiased ITE estimator. Different from previous work for binary IVs, our framework estimates the ITE directly via a pseudo outcome regression. (1) We provide a theoretical analysis where we show that our framework yields multiply robust convergence rates: our ITE estimator achieves fast convergence even if several nuisance estimators converge slowly. (2) We further show that our framework asymptotically outperforms state-of-the-art plug-in IV methods for ITE estimation. (3) We build upon our theoretical results and propose a tailored deep neural network architecture called MRIV-Net for ITE estimation using binary IVs. Across various computational experiments, we demonstrate empirically that our MRIV-Net achieves state-of-the-art performance. To the best of our knowledge, our MRIV is the first machine learning framework for estimating ITEs in the binary IV setting shown to be multiply robust.
    Efficient data-driven gap filling of satellite image time series using deep neural networks with partial convolutions. (arXiv:2208.08781v1 [cs.LG])
    The abundance of gaps in satellite image time series often complicates the application of deep learning models such as convolutional neural networks for spatiotemporal modeling. Based on previous work in computer vision on image inpainting, this paper shows how three-dimensional spatiotemporal partial convolutions can be used as layers in neural networks to fill gaps in satellite image time series. To evaluate the approach, we apply a U-Net-like model on incomplete image time series of quasi-global carbon monoxide observations from the Sentinel-5P satellite. Prediction errors were comparable to two considered statistical approaches while computation times for predictions were up to three orders of magnitude faster, making the approach applicable to process large amounts of satellite data. Partial convolutions can be added as layers to other types of neural networks, making it relatively easy to integrate with existing deep learning models. However, the approach does not quantify prediction errors and further research is needed to understand and improve model transferability. The implementation of spatiotemporal partial convolutions and the U-Net-like model is available as open-source software.
    Evaluating Continual Test-Time Adaptation for Contextual and Semantic Domain Shifts. (arXiv:2208.08767v1 [cs.CV])
    In this paper, our goal is to adapt a pre-trained Convolutional Neural Network to domain shifts at test time. We do so continually with the incoming stream of test batches, without labels. Existing literature mostly operates on artificial shifts obtained via adversarial perturbations of a test image. Motivated by this, we evaluate the state of the art on two realistic and challenging sources of domain shifts, namely contextual and semantic shifts. Contextual shifts correspond to the environment types, for example a model pre-trained on indoor context has to adapt to the outdoor context on CORe-50 [7]. Semantic shifts correspond to the capture types, for example a model pre-trained on natural images has to adapt to cliparts, sketches and paintings on DomainNet [10]. We include in our analysis recent techniques such as Prediction-Time Batch Normalization (BN) [8], Test Entropy Minimization (TENT) [16] and Continual Test-Time Adaptation (CoTTA) [17]. Our findings are three-fold: i) Test-time adaptation methods perform better and forget less on contextual shifts compared to semantic shifts, ii) TENT outperforms other methods on short-term adaptation, whereas CoTTA outpeforms other methods on long-term adaptation, iii) BN is most reliable and robust.
    Truth-Table Net: A New Convolutional Architecture Encodable By Design Into SAT Formulas. (arXiv:2208.08609v1 [cs.AI])
    With the expanding role of neural networks, the need for complete and sound verification of their property has become critical. In the recent years, it was established that Binary Neural Networks (BNNs) have an equivalent representation in Boolean logic and can be formally analyzed using logical reasoning tools such as SAT solvers. However, to date, only BNNs can be transformed into a SAT formula. In this work, we introduce Truth Table Deep Convolutional Neural Networks (TTnets), a new family of SAT-encodable models featuring for the first time real-valued weights. Furthermore, it admits, by construction, some valuable conversion features including post-tuning and tractability in the robustness verification setting. The latter property leads to a more compact SAT symbolic encoding than BNNs. This enables the use of general SAT solvers, making property verification easier. We demonstrate the value of TTnets regarding the formal robustness property: TTnets outperform the verified accuracy of all BNNs with a comparable computation time. More generally, they represent a relevant trade-off between all known complete verification methods: TTnets achieve high verified accuracy with fast verification time, being complete with no timeouts. We are exploring here a proof of concept of TTnets for a very important application (complete verification of robustness) and we believe this novel real-valued network constitutes a practical response to the rising need for functional formal verification. We postulate that TTnets can apply to various CNN-based architectures and be extended to other properties such as fairness, fault attack and exact rule extraction.
    Physics-Informed Neural Network Method for Parabolic Differential Equations with Sharply Perturbed Initial Conditions. (arXiv:2208.08635v1 [math.NA])
    In this paper, we develop a physics-informed neural network (PINN) model for parabolic problems with a sharply perturbed initial condition. As an example of a parabolic problem, we consider the advection-dispersion equation (ADE) with a point (Gaussian) source initial condition. In the $d$-dimensional ADE, perturbations in the initial condition decay with time $t$ as $t^{-d/2}$, which can cause a large approximation error in the PINN solution. Localized large gradients in the ADE solution make the (common in PINN) Latin hypercube sampling of the equation's residual highly inefficient. Finally, the PINN solution of parabolic equations is sensitive to the choice of weights in the loss function. We propose a normalized form of ADE where the initial perturbation of the solution does not decrease in amplitude and demonstrate that this normalization significantly reduces the PINN approximation error. We propose criteria for weights in the loss function that produce a more accurate PINN solution than those obtained with the weights selected via other methods. Finally, we proposed an adaptive sampling scheme that significantly reduces the PINN solution error for the same number of the sampling (residual) points. We demonstrate the accuracy of the proposed PINN model for forward, inverse, and backward ADEs.
    Performance Evaluation of Selective Fixed-filter Active Noise Control based on Different Convolutional Neural Networks. (arXiv:2208.08440v1 [cs.LG])
    Due to its rapid response time and a high degree of robustness, the selective fixed-filter active noise control (SFANC) method appears to be a viable candidate for widespread use in a variety of practical active noise control (ANC) systems. In comparison to conventional fixed-filter ANC methods, SFANC can select the pre-trained control filters for different types of noise. Deep learning technologies, thus, can be used in SFANC methods to enable a more flexible selection of the most appropriate control filters for attenuating various noises. Furthermore, with the assistance of a deep neural network, the selecting strategy can be learned automatically from noise data rather than through trial and error, which significantly simplifies and improves the practicability of ANC design. Therefore, this paper investigates the performance of SFANC based on different one-dimensional and two-dimensional convolutional neural networks. Additionally, we conducted comparative analyses of several network training strategies and discovered that fine-tuning could improve selection performance.
    Communication-Efficient Decentralized Online Continuous DR-Submodular Maximization. (arXiv:2208.08681v1 [cs.LG])
    Maximizing a monotone submodular function is a fundamental task in machine learning, economics, and statistics. In this paper, we present two communication-efficient decentralized online algorithms for the monotone continuous DR-submodular maximization problem, both of which reduce the number of per-function gradient evaluations and per-round communication complexity from $T^{3/2}$ to $1$. The first one, One-shot Decentralized Meta-Frank-Wolfe (Mono-DMFW), achieves a $(1-1/e)$-regret bound of $O(T^{4/5})$. As far as we know, this is the first one-shot and projection-free decentralized online algorithm for monotone continuous DR-submodular maximization. Next, inspired by the non-oblivious boosting function \citep{zhang2022boosting}, we propose the Decentralized Online Boosting Gradient Ascent (DOBGA) algorithm, which attains a $(1-1/e)$-regret of $O(\sqrt{T})$. To the best of our knowledge, this is the first result to obtain the optimal $O(\sqrt{T})$ against a $(1-1/e)$-approximation with only one gradient inquiry for each local objective function per step. Finally, various experimental results confirm the effectiveness of the proposed methods.
    Meta Sparse Principle Component Analysis. (arXiv:2208.08938v1 [stat.ML])
    We study the meta-learning for support (i.e. the set of non-zero entries) recovery in high-dimensional Principal Component Analysis. We reduce the sufficient sample complexity in a novel task with the information that is learned from auxiliary tasks. We assume each task to be a different random Principal Component (PC) matrix with a possibly different support and that the support union of the PC matrices is small. We then pool the data from all the tasks to execute an improper estimation of a single PC matrix by maximising the $l_1$-regularised predictive covariance to establish that with high probability the true support union can be recovered provided a sufficient number of tasks $m$ and a sufficient number of samples $ O\left(\frac{\log(p)}{m}\right)$ for each task, for $p$-dimensional vectors. Then, for a novel task, we prove that the maximisation of the $l_1$-regularised predictive covariance with the additional constraint that the support is a subset of the estimated support union could reduce the sufficient sample complexity of successful support recovery to $O(\log |J|)$, where $J$ is the support union recovered from the auxiliary tasks. Typically, $|J|$ would be much less than $p$ for sparse matrices. Finally, we demonstrate the validity of our experiments through numerical simulations.
    Memory and Capacity of Graph Embedding Methods. (arXiv:2208.08769v1 [stat.ML])
    We introduce a method for embedding graphs as vectors in a structure-preserving manner. In this paper, we showcase its rich representational capacity and give some theoretical properties of our method. In particular, our procedure falls under the bind-and-sum approach, and we show that our binding operation - the tensor product - is the most general binding operation that respects the principle of superposition. Similarly, we show that the spherical code achieves optimal compression. We then establish some precise results characterizing the performance our method as well as some experimental results showcasing how it can accurately perform various graph operations even when the number of edges is quite large. Finally, we conclude with establishing a link to adjacency matrices, showing that our method is, in some sense, a generalization of adjacency matrices with applications towards large sparse graphs.
    Outlier Detection using Self-Organizing Maps for Automated Blood Cell Analysis. (arXiv:2208.08834v1 [eess.IV])
    The quality of datasets plays a crucial role in the successful training and deployment of deep learning models. Especially in the medical field, where system performance may impact the health of patients, clean datasets are a safety requirement for reliable predictions. Therefore, outlier detection is an essential process when building autonomous clinical decision systems. In this work, we assess the suitability of Self-Organizing Maps for outlier detection specifically on a medical dataset containing quantitative phase images of white blood cells. We detect and evaluate outliers based on quantization errors and distance maps. Our findings confirm the suitability of Self-Organizing Maps for unsupervised Out-Of-Distribution detection on the dataset at hand. Self-Organizing Maps perform on par with a manually specified filter based on expert domain knowledge. Additionally, they show promise as a tool in the exploration and cleaning of medical datasets. As a direction for future research, we suggest a combination of Self-Organizing Maps and feature extraction based on deep learning.
    Deep Neural Network Approximation of Invariant Functions through Dynamical Systems. (arXiv:2208.08707v1 [cs.LG])
    We study the approximation of functions which are invariant with respect to certain permutations of the input indices using flow maps of dynamical systems. Such invariant functions includes the much studied translation-invariant ones involving image tasks, but also encompasses many permutation-invariant functions that finds emerging applications in science and engineering. We prove sufficient conditions for universal approximation of these functions by a controlled equivariant dynamical system, which can be viewed as a general abstraction of deep residual networks with symmetry constraints. These results not only imply the universal approximation for a variety of commonly employed neural network architectures for symmetric function approximation, but also guide the design of architectures with approximation guarantees for applications involving new symmetry requirements.
    Deep Recursive Embedding for High-Dimensional Data. (arXiv:2111.00622v2 [cs.LG] UPDATED)
    Embedding high-dimensional data onto a low-dimensional manifold is of both theoretical and practical value. In this paper, we propose to combine deep neural networks (DNN) with mathematics-guided embedding rules for high-dimensional data embedding. We introduce a generic deep embedding network (DEN) framework, which is able to learn a parametric mapping from high-dimensional space to low-dimensional space, guided by well-established objectives such as Kullback-Leibler (KL) divergence minimization. We further propose a recursive strategy, called deep recursive embedding (DRE), to make use of the latent data representations for boosted embedding performance. We exemplify the flexibility of DRE by different architectures and loss functions, and benchmarked our method against the two most popular embedding methods, namely, t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP). The proposed DRE method can map out-of-sample data and scale to extremely large datasets. Experiments on a range of public datasets demonstrated improved embedding performance in terms of local and global structure preservation, compared with other state-of-the-art embedding methods.
    Understanding Scaling Laws for Recommendation Models. (arXiv:2208.08489v1 [cs.IR])
    Scale has been a major driving force in improving machine learning performance, and understanding scaling laws is essential for strategic planning for a sustainable model quality performance growth, long-term resource planning and developing efficient system infrastructures to support large-scale models. In this paper, we study empirical scaling laws for DLRM style recommendation models, in particular Click-Through Rate (CTR). We observe that model quality scales with power law plus constant in model size, data size and amount of compute used for training. We characterize scaling efficiency along three different resource dimensions, namely data, parameters and compute by comparing the different scaling schemes along these axes. We show that parameter scaling is out of steam for the model architecture under study, and until a higher-performing model architecture emerges, data scaling is the path forward. The key research questions addressed by this study include: Does a recommendation model scale sustainably as predicted by the scaling laws? Or are we far off from the scaling law predictions? What are the limits of scaling? What are the implications of the scaling laws on long-term hardware/system development?
    DIET: Conditional independence testing with marginal dependence measures of residual information. (arXiv:2208.08579v1 [stat.ME])
    Conditional randomization tests (CRTs) assess whether a variable $x$ is predictive of another variable $y$, having observed covariates $z$. CRTs require fitting a large number of predictive models, which is often computationally intractable. Existing solutions to reduce the cost of CRTs typically split the dataset into a train and test portion, or rely on heuristics for interactions, both of which lead to a loss in power. We propose the decoupled independence test (DIET), an algorithm that avoids both of these issues by leveraging marginal independence statistics to test conditional independence relationships. DIET tests the marginal independence of two random variables: $F(x \mid z)$ and $F(y \mid z)$ where $F(\cdot \mid z)$ is a conditional cumulative distribution function (CDF). These variables are termed "information residuals." We give sufficient conditions for DIET to achieve finite sample type-1 error control and power greater than the type-1 error rate. We then prove that when using the mutual information between the information residuals as a test statistic, DIET yields the most powerful conditionally valid test. Finally, we show DIET achieves higher power than other tractable CRTs on several synthetic and real benchmarks.
    SensorSCAN: Self-Supervised Learning and Deep Clustering for Fault Diagnosis in Chemical Processes. (arXiv:2208.08879v1 [cs.LG])
    Modern industrial facilities generate large volumes of raw sensor data during production process. This data is used to monitor and control the processes and can be analyzed to detect and predict process abnormalities. Typically, the data has to be annotated by experts to be further used in predictive modeling. Most of today's research is focusing on either unsupervised anomaly detection algorithms or supervised methods, that require manually annotated data. The studies are often done using process simulator generated data for a narrow class of events and proposed algorithms are rarely verified on publicly available datasets. In this paper, we propose a novel method SensorSCAN for unsupervised fault detection and diagnosis designed for industrial chemical sensor data. We demonstrate our model performance on two publicly available datasets based on the Tennessee Eastman Process with various fault types. Results show that our method significantly outperforms existing approaches (+0.2-0.3 TPR for a fixed FPR) and detects most of the process faults without the use of expert annotation. In addition, we performed experiments to show that our method is suitable for real-world applications where the number of fault types is not known in advance.
    Analyzing Robustness of End-to-End Neural Models for Automatic Speech Recognition. (arXiv:2208.08509v1 [cs.CL])
    We investigate robustness properties of pre-trained neural models for automatic speech recognition. Real life data in machine learning is usually very noisy and almost never clean, which can be attributed to various factors depending on the domain, e.g. outliers, random noise and adversarial noise. Therefore, the models we develop for various tasks should be robust to such kinds of noisy data, which led to the thriving field of robust machine learning. We consider this important issue in the setting of automatic speech recognition. With the increasing popularity of pre-trained models, it's an important question to analyze and understand the robustness of such models to noise. In this work, we perform a robustness analysis of the pre-trained neural models wav2vec2, HuBERT and DistilHuBERT on the LibriSpeech and TIMIT datasets. We use different kinds of noising mechanisms and measure the model performances as quantified by the inference time and the standard Word Error Rate metric. We also do an in-depth layer-wise analysis of the wav2vec2 model when injecting noise in between layers, enabling us to predict at a high level what each layer learns. Finally for this model, we visualize the propagation of errors across the layers and compare how it behaves on clean versus noisy data. Our experiments conform the predictions of Pasad et al. [2021] and also raise interesting directions for future work.
    Quality issues in Machine Learning Software Systems. (arXiv:2208.08982v1 [cs.SE])
    Context: An increasing demand is observed in various domains to employ Machine Learning (ML) for solving complex problems. ML models are implemented as software components and deployed in Machine Learning Software Systems (MLSSs). Problem: There is a strong need for ensuring the serving quality of MLSSs. False or poor decisions of such systems can lead to malfunction of other systems, significant financial losses, or even threat to human life. The quality assurance of MLSSs is considered as a challenging task and currently is a hot research topic. Moreover, it is important to cover all various aspects of the quality in MLSSs. Objective: This paper aims to investigate the characteristics of real quality issues in MLSSs from the viewpoint of practitioners. This empirical study aims to identify a catalog of bad-practices related to poor quality in MLSSs. Method: We plan to conduct a set of interviews with practitioners/experts, believing that interviews are the best method to retrieve their experience and practices when dealing with quality issues. We expect that the catalog of issues developed at this step will also help us later to identify the severity, root causes, and possible remedy for quality issues of MLSSs, allowing us to develop efficient quality assurance tools for ML models and MLSSs.
    Quantifying the Knowledge in a DNN to Explain Knowledge Distillation for Classification. (arXiv:2208.08741v1 [cs.LG])
    Compared to traditional learning from scratch, knowledge distillation sometimes makes the DNN achieve superior performance. This paper provides a new perspective to explain the success of knowledge distillation, i.e., quantifying knowledge points encoded in intermediate layers of a DNN for classification, based on the information theory. To this end, we consider the signal processing in a DNN as the layer-wise information discarding. A knowledge point is referred to as an input unit, whose information is much less discarded than other input units. Thus, we propose three hypotheses for knowledge distillation based on the quantification of knowledge points. 1. The DNN learning from knowledge distillation encodes more knowledge points than the DNN learning from scratch. 2. Knowledge distillation makes the DNN more likely to learn different knowledge points simultaneously. In comparison, the DNN learning from scratch tends to encode various knowledge points sequentially. 3. The DNN learning from knowledge distillation is often optimized more stably than the DNN learning from scratch. In order to verify the above hypotheses, we design three types of metrics with annotations of foreground objects to analyze feature representations of the DNN, \textit{i.e.} the quantity and the quality of knowledge points, the learning speed of different knowledge points, and the stability of optimization directions. In experiments, we diagnosed various DNNs for different classification tasks, i.e., image classification, 3D point cloud classification, binary sentiment classification, and question answering, which verified above hypotheses.
    On an Application of Generative Adversarial Networks on Remaining Lifetime Estimation. (arXiv:2208.08666v1 [cs.LG])
    A major problem of structural health monitoring (SHM) has been the prognosis of damage and the definition of the remaining useful life of a structure. Both tasks depend on many parameters, many of which are often uncertain. Many models have been developed for the aforementioned tasks but they have been either deterministic or stochastic with the ability to take into account only a restricted amount of past states of the structure. In the current work, a generative model is proposed in order to make predictions about the damage evolution of structures. The model is able to perform in a population-based SHM (PBSHM) framework, to take into account many past states of the damaged structure, to incorporate uncertainties in the modelling process and to generate potential damage evolution outcomes according to data acquired from a structure. The algorithm is tested on a simulated damage evolution example and the results reveal that it is able to provide quite confident predictions about the remaining useful life of structures within a population.
    Enhancing Targeted Attack Transferability via Diversified Weight Pruning. (arXiv:2208.08677v1 [cs.CV])
    Malicious attackers can generate targeted adversarial examples by imposing human-imperceptible noise on images, forcing neural network models to produce specific incorrect outputs. With cross-model transferable adversarial examples, the vulnerability of neural networks remains even if the model information is kept secret from the attacker. Recent studies have shown the effectiveness of ensemble-based methods in generating transferable adversarial examples. However, existing methods fall short under the more challenging scenario of creating targeted attacks transferable among distinct models. In this work, we propose Diversified Weight Pruning (DWP) to further enhance the ensemble-based methods by leveraging the weight pruning method commonly used in model compression. Specifically, we obtain multiple diverse models by a random weight pruning method. These models preserve similar accuracies and can serve as additional models for ensemble-based methods, yielding stronger transferable targeted attacks. Experiments on ImageNet-Compatible Dataset under the more challenging scenarios are provided: transferring to distinct architectures and to adversarially trained models. The results show that our proposed DWP improves the targeted attack success rates with up to 4.1% and 8.0% on the combination of state-of-the-art methods, respectively
    Pandemic Control, Game Theory and Machine Learning. (arXiv:2208.08646v1 [math.OC])
    Game theory has been an effective tool in the control of disease spread and in suggesting optimal policies at both individual and area levels. In this AMS Notices article, we focus on the decision-making development for the intervention of COVID-19, aiming to provide mathematical models and efficient machine learning methods, and justifications for related policies that have been implemented in the past and explain how the authorities' decisions affect their neighboring regions from a game theory viewpoint.
    Bayesian Optimization Augmented with Actively Elicited Expert Knowledge. (arXiv:2208.08742v1 [cs.LG])
    Bayesian optimization (BO) is a well-established method to optimize black-box functions whose direct evaluations are costly. In this paper, we tackle the problem of incorporating expert knowledge into BO, with the goal of further accelerating the optimization, which has received very little attention so far. We design a multi-task learning architecture for this task, with the goal of jointly eliciting the expert knowledge and minimizing the objective function. In particular, this allows for the expert knowledge to be transferred into the BO task. We introduce a specific architecture based on Siamese neural networks to handle the knowledge elicitation from pairwise queries. Experiments on various benchmark functions with both simulated and actual human experts show that the proposed method significantly speeds up BO even when the expert knowledge is biased compared to the objective function.
    Psychophysiological Arousal in Young Children Who Stutter: An Interpretable AI Approach. (arXiv:2208.08859v1 [eess.SP])
    The presented first-of-its-kind study effectively identifies and visualizes the second-by-second pattern differences in the physiological arousal of preschool-age children who do stutter (CWS) and who do not stutter (CWNS) while speaking perceptually fluently in two challenging conditions i.e speaking in stressful situations and narration. The first condition may affect children's speech due to high arousal; the latter introduces linguistic, cognitive, and communicative demands on speakers. We collected physiological parameters data from 70 children in the two target conditions. First, we adopt a novel modality-wise multiple-instance-learning (MI-MIL) approach to classify CWS vs. CWNS in different conditions effectively. The evaluation of this classifier addresses four critical research questions that align with state-of-the-art speech science studies' interests. Later, we leverage SHAP classifier interpretations to visualize the salient, fine-grain, and temporal physiological parameters unique to CWS at the population/group-level and personalized-level. While group-level identification of distinct patterns would enhance our understanding of stuttering etiology and development, the personalized-level identification would enable remote, continuous, and real-time assessment of stuttering children's physiological arousal, which may lead to personalized, just-in-time interventions, resulting in an improvement in speech fluency. The presented MI-MIL approach is novel, generalizable to different domains, and real-time executable. Finally, comprehensive evaluations are done on multiple datasets, presented framework, and several baselines that identified notable insights on CWSs' physiological arousal during speech production.
    EEG-BBNet: a Hybrid Framework for Brain Biometric using Graph Connectivity. (arXiv:2208.08901v1 [eess.SP])
    Brain biometrics based on electroencephalography (EEG) have been used increasingly for personal identification. Traditional machine learning techniques as well as modern day deep learning methods have been applied with promising results. In this paper we present EEG-BBNet, a hybrid network which integrates convolutional neural networks (CNN) with graph convolutional neural networks (GCNN). The benefit of the CNN in automatic feature extraction and the capability of GCNN in learning connectivity between EEG electrodes through graph representation are jointly exploited. We examine various connectivity measures, namely the Euclidean distance, Pearson's correlation coefficient, phase-locked value, phase-lag index, and Rho index. The performance of the proposed method is assessed on a benchmark dataset consisting of various brain-computer interface (BCI) tasks and compared to other state-of-the-art approaches. We found that our models outperform all baselines in the event-related potential (ERP) task with an average correct recognition rates up to 99.26% using intra-session data. EEG-BBNet with Pearson's correlation and RHO index provide the best classification results. In addition, our model demonstrates greater adaptability using inter-session and inter-task data. We also investigate the practicality of our proposed model with smaller number of electrodes. Electrode placements over the frontal lobe region appears to be most appropriate with minimal lost in performance.
    Siamese Prototypical Contrastive Learning. (arXiv:2208.08819v1 [cs.CV])
    Contrastive Self-supervised Learning (CSL) is a practical solution that learns meaningful visual representations from massive data in an unsupervised approach. The ordinary CSL embeds the features extracted from neural networks onto specific topological structures. During the training progress, the contrastive loss draws the different views of the same input together while pushing the embeddings from different inputs apart. One of the drawbacks of CSL is that the loss term requires a large number of negative samples to provide better mutual information bound ideally. However, increasing the number of negative samples by larger running batch size also enhances the effects of false negatives: semantically similar samples are pushed apart from the anchor, hence downgrading downstream performance. In this paper, we tackle this problem by introducing a simple but effective contrastive learning framework. The key insight is to employ siamese-style metric loss to match intra-prototype features, while increasing the distance between inter-prototype features. We conduct extensive experiments on various benchmarks where the results demonstrate the effectiveness of our method on improving the quality of visual representations. Specifically, our unsupervised pre-trained ResNet-50 with a linear probe, out-performs the fully-supervised trained version on the ImageNet-1K dataset.
    RC-Struct: A Structure-based Neural Network Approach for MIMO-OFDM Detection. (arXiv:2110.02219v2 [cs.IT] UPDATED)
    In this paper, we introduce a structure-based neural network architecture, namely RC-Struct, for MIMO-OFDM symbol detection. The RC-Struct exploits the temporal structure of the MIMO-OFDM signals through reservoir computing (RC). A binary classifier leverages the repetitive constellation structure in the system to perform multi-class detection. The incorporation of RC allows the RC-Struct to be learned in a purely online fashion with extremely limited pilot symbols in each OFDM subframe. The binary classifier enables the efficient utilization of the precious online training symbols and allows an easy extension to high-order modulations without a substantial increase in complexity. Experiments show that the introduced RC-Struct outperforms both the conventional model-based symbol detection approaches and the state-of-the-art learning-based strategies in terms of bit error rate (BER). The advantages of RC-Struct over existing methods become more significant when rank and link adaptation are adopted. The introduced RC-Struct sheds light on combining communication domain knowledge and learning-based receive processing for 5G/5G-Advanced and Beyond.
    Private, Efficient, and Accurate: Protecting Models Trained by Multi-party Learning with Differential Privacy. (arXiv:2208.08662v1 [cs.CR])
    Secure multi-party computation-based machine learning, referred to as MPL, has become an important technology to utilize data from multiple parties with privacy preservation. While MPL provides rigorous security guarantees for the computation process, the models trained by MPL are still vulnerable to attacks that solely depend on access to the models. Differential privacy could help to defend against such attacks. However, the accuracy loss brought by differential privacy and the huge communication overhead of secure multi-party computation protocols make it highly challenging to balance the 3-way trade-off between privacy, efficiency, and accuracy. In this paper, we are motivated to resolve the above issue by proposing a solution, referred to as PEA (Private, Efficient, Accurate), which consists of a secure DPSGD protocol and two optimization methods. First, we propose a secure DPSGD protocol to enforce DPSGD in secret sharing-based MPL frameworks. Second, to reduce the accuracy loss led by differential privacy noise and the huge communication overhead of MPL, we propose two optimization methods for the training process of MPL: (1) the data-independent feature extraction method, which aims to simplify the trained model structure; (2) the local data-based global model initialization method, which aims to speed up the convergence of the model training. We implement PEA in two open-source MPL frameworks: TF-Encrypted and Queqiao. The experimental results on various datasets demonstrate the efficiency and effectiveness of PEA. E.g. when ${\epsilon}$ = 2, we can train a differentially private classification model with an accuracy of 88% for CIFAR-10 within 7 minutes under the LAN setting. This result significantly outperforms the one from CryptGPU, one SOTA MPL framework: it costs more than 16 hours to train a non-private deep neural network model on CIFAR-10 with the same accuracy.
    POCS-based Clustering Algorithm. (arXiv:2208.08888v1 [cs.LG])
    A novel clustering technique based on the projection onto convex set (POCS) method, called POCS-based clustering algorithm, is proposed in this paper. The proposed POCS-based clustering algorithm exploits a parallel projection method of POCS to find appropriate cluster prototypes in the feature space. The algorithm considers each data point as a convex set and projects the cluster prototypes parallelly to the member data points. The projections are convexly combined to minimize the objective function for data clustering purpose. The performance of the proposed POCS-based clustering algorithm is verified through experiments on various synthetic datasets. The experimental results show that the proposed POCS-based clustering algorithm is competitive and efficient in terms of clustering error and execution speed when compared with other conventional clustering methods including Fuzzy C-Means (FCM) and K-means clustering algorithms.  ( 2 min )
    Frequency propagation: Multi-mechanism learning in nonlinear physical networks. (arXiv:2208.08862v1 [cond-mat.dis-nn])
    We introduce frequency propagation, a learning algorithm for nonlinear physical networks. In a resistive electrical circuit with variable resistors, an activation current is applied at a set of input nodes at one frequency, and an error current is applied at a set of output nodes at another frequency. The voltage response of the circuit to these boundary currents is the superposition of an `activation signal' and an `error signal' whose coefficients can be read in different frequencies of the frequency domain. Each conductance is updated proportionally to the product of the two coefficients. The learning rule is local and proved to perform gradient descent on a loss function. We argue that frequency propagation is an instance of a multi-mechanism learning strategy for physical networks, be it resistive, elastic, or flow networks. Multi-mechanism learning strategies incorporate at least two physical quantities, potentially governed by independent physical mechanisms, to act as activation and error signals in the training process. Locally available information about these two signals is then used to update the trainable parameters to perform gradient descent. We demonstrate how earlier work implementing learning via chemical signaling in flow networks also falls under the rubric of multi-mechanism learning.
    Learning to Generate Image Source-Agnostic Universal Adversarial Perturbations. (arXiv:2009.13714v4 [cs.LG] UPDATED)
    Adversarial perturbations are critical for certifying the robustness of deep learning models. A universal adversarial perturbation (UAP) can simultaneously attack multiple images, and thus offers a more unified threat model, obviating an image-wise attack algorithm. However, the existing UAP generator is underdeveloped when images are drawn from different image sources (e.g., with different image resolutions). Towards an authentic universality across image sources, we take a novel view of UAP generation as a customized instance of few-shot learning, which leverages bilevel optimization and learning-to-optimize (L2O) techniques for UAP generation with improved attack success rate (ASR). We begin by considering the popular model agnostic meta-learning (MAML) framework to meta-learn a UAP generator. However, we see that the MAML framework does not directly offer the universal attack across image sources, requiring us to integrate it with another meta-learning framework of L2O. The resulting scheme for meta-learning a UAP generator (i) has better performance (50% higher ASR) than baselines such as Projected Gradient Descent, (ii) has better performance (37% faster) than the vanilla L2O and MAML frameworks (when applicable), and (iii) is able to simultaneously handle UAP generation for different victim models and image data sources.
    X-GOAL: Multiplex Heterogeneous Graph Prototypical Contrastive Learning. (arXiv:2109.03560v3 [cs.LG] UPDATED)
    Graphs are powerful representations for relations among objects, which have attracted plenty of attention. A fundamental challenge for graph learning is how to train an effective Graph Neural Network (GNN) encoder without labels, which are expensive and time consuming to obtain. Contrastive Learning (CL) is one of the most popular paradigms to address this challenge, which trains GNNs by discriminating positive and negative node pairs. Despite the success of recent CL methods, there are still two under-explored problems. First, how to reduce the semantic error introduced by random topology based data augmentations. Traditional CL defines positive and negative node pairs via the node-level topological proximity, which is solely based on the graph topology regardless of the semantic information of node attributes, and thus some semantically similar nodes could be wrongly treated as negative pairs. Second, how to effectively model the multiplexity of the real-world graphs, where nodes are connected by various relations and each relation could form a homogeneous graph layer. To solve these problems, we propose a novel multiplex heterogeneous graph prototypical contrastive leaning (X-GOAL) framework to extract node embeddings. X-GOAL is comprised of two components: the GOAL framework, which learns node embeddings for each homogeneous graph layer, and an alignment regularization, which jointly models different layers by aligning layer-specific node embeddings. Specifically, the GOAL framework captures the node-level information by a succinct graph transformation technique, and captures the cluster-level information by pulling nodes within the same semantic cluster closer in the embedding space. The alignment regularization aligns embeddings across layers at both node and cluster levels. We evaluate X-GOAL on various real-world datasets and downstream tasks to demonstrate its effectiveness.
    Learning to Infer Structures of Network Games. (arXiv:2206.08119v2 [cs.LG] UPDATED)
    Strategic interactions between a group of individuals or organisations can be modelled as games played on networks, where a player's payoff depends not only on their actions but also on those of their neighbours. Inferring the network structure from observed game outcomes (equilibrium actions) is an important problem with numerous potential applications in economics and social sciences. Existing methods mostly require the knowledge of the utility function associated with the game, which is often unrealistic to obtain in real-world scenarios. We adopt a transformer-like architecture which correctly accounts for the symmetries of the problem and learns a mapping from the equilibrium actions to the network structure of the game without explicit knowledge of the utility function. We test our method on three different types of network games using both synthetic and real-world data, and demonstrate its effectiveness in network structure inference and superior performance over existing methods.
    Continual Learning in Deep Networks: an Analysis of the Last Layer. (arXiv:2106.01834v3 [cs.LG] UPDATED)
    We study how different output layer parameterizations of a deep neural network affects learning and forgetting in continual learning settings. The following three effects can cause catastrophic forgetting in the output layer: (1) weights modifications, (2) interference, and (3) projection drift. In this paper, our goal is to provide more insights into how changing the output layer parameterization may address (1) and (2). Some potential solutions to those issues are proposed and evaluated here in several continual learning scenarios. We show that the best-performing type of output layer depends on the data distribution drifts and/or the amount of data available. In particular, in some cases where a standard linear layer would fail, changing parameterization is sufficient to achieve a significantly better performance, without introducing any continual-learning algorithm but instead by using standard SGD to train a model. Our analysis and results shed light on the dynamics of the output layer in continual learning scenarios and suggest a way of selecting the best type of output layer for a given scenario.
    Intention estimation from gaze and motion features for human-robot shared-control object manipulation. (arXiv:2208.08688v1 [cs.RO])
    Shared control can help in teleoperated object manipulation by assisting with the execution of the user's intention. To this end, robust and prompt intention estimation is needed, which relies on behavioral observations. Here, an intention estimation framework is presented, which uses natural gaze and motion features to predict the current action and the target object. The system is trained and tested in a simulated environment with pick and place sequences produced in a relatively cluttered scene and with both hands, with possible hand-over to the other hand. Validation is conducted across different users and hands, achieving good accuracy and earliness of prediction. An analysis of the predictive power of single features shows the predominance of the grasping trigger and the gaze features in the early identification of the current action. In the current framework, the same probabilistic model can be used for the two hands working in parallel and independently, while a rule-based model is proposed to identify the resulting bimanual action. Finally, limitations and perspectives of this approach to more complex, full-bimanual manipulations are discussed.
    RRWaveNet: A Compact End-to-End Multi-Scale Residual CNN for Robust PPG Respiratory Rate Estimation. (arXiv:2208.08672v1 [eess.SP])
    Respiratory rate (RR) is an important biomarker as RR changes can reflect severe medical events such as heart disease, lung disease, and sleep disorders. Unfortunately, however, standard manual RR counting is prone to human error and cannot be performed continuously. This study proposes a method for continuously estimating RR, RRWaveNet. The method is a compact end-to-end deep learning model which does not require feature engineering and can use low-cost raw photoplethysmography (PPG) as input signal. RRWaveNet was tested subject-independently and compared to baseline in three datasets (BIDMC, CapnoBase, and WESAD) and using three window sizes (16, 32, and 64 seconds). RRWaveNet outperformed current state-of-the-art methods with mean absolute errors at optimal window size of 1.66 \pm 1.01, 1.59 \pm 1.08, and 1.92 \pm 0.96 breaths per minute for each dataset. In remote monitoring settings, such as in the WESAD dataset, we apply transfer learning to two other ICU datasets, reducing the MAE to 1.52 \pm 0.50 breaths per minute, showing this model allows accurate and practical estimation of RR on affordable and wearable devices. Our study shows feasibility of remote RR monitoring in the context of telemedicine and at home.
    A Hybrid Self-Supervised Learning Framework for Vertical Federated Learning. (arXiv:2208.08934v1 [cs.LG])
    Federated learning (FL) enables independent parties to collaboratively build machine learning (ML) models while protecting data privacy. Vertical federated learning (VFL), a variant of FL, has recently drawn increasing attention as the VFL matches the enterprises' demands of leveraging more valuable features to achieve better model performance without jeopardizing data privacy. However, conventional VFL may run into data deficiency as it is only able to exploit aligned samples (belonging to different parties) with labels, leaving often the majority of unaligned and unlabeled samples unused. The data deficiency hampers the effort of the federation. In this work, we propose a Federated Hybrid Self-Supervised Learning framework, coined FedHSSL, to utilize all available data (including unaligned and unlabeled samples) of participants to train the joint VFL model. The core idea of FedHSSL is to utilize cross-party views (i.e., dispersed features) of samples aligned among parties and local views (i.e., augmentations) of samples within each party to improve the representation learning capability of the joint VFL model through SSL (e.g., SimSiam). FedHSSL further exploits generic features shared among parties to boost the performance of the joint model through partial model aggregation. We empirically demonstrate that our FedHSSL achieves significant performance gains compared with baseline methods, especially when the number of labeled samples is small. We provide an in-depth analysis of FedHSSL regarding privacy leakage, which is rarely discussed in existing self-supervised VFL works. We investigate the protection mechanism for FedHSSL. The results show our protection can thwart the state-of-the-art label inference attack.  ( 3 min )
    Conviformers: Convolutionally guided Vision Transformer. (arXiv:2208.08900v1 [cs.CV])
    Vision transformers are nowadays the de-facto preference for image classification tasks. There are two broad categories of classification tasks, fine-grained and coarse-grained. In fine-grained classification, the necessity is to discover subtle differences due to the high level of similarity between sub-classes. Such distinctions are often lost as we downscale the image to save the memory and computational cost associated with vision transformers (ViT). In this work, we present an in-depth analysis and describe the critical components for developing a system for the fine-grained categorization of plants from herbarium sheets. Our extensive experimental analysis indicated the need for a better augmentation technique and the ability of modern-day neural networks to handle higher dimensional images. We also introduce a convolutional transformer architecture called Conviformer which, unlike the popular Vision Transformer (ConViT), can handle higher resolution images without exploding memory and computational cost. We also introduce a novel, improved pre-processing technique called PreSizer to resize images better while preserving their original aspect ratios, which proved essential for classifying natural plants. With our simple yet effective approach, we achieved SoTA on Herbarium 202x and iNaturalist 2019 dataset.
    Learning Generative Models for Active Inference using Tensor Networks. (arXiv:2208.08713v1 [cs.LG])
    Active inference provides a general framework for behavior and learning in autonomous agents. It states that an agent will attempt to minimize its variational free energy, defined in terms of beliefs over observations, internal states and policies. Traditionally, every aspect of a discrete active inference model must be specified by hand, i.e.\ by manually defining the hidden state space structure, as well as the required distributions such as likelihood and transition probabilities. Recently, efforts have been made to learn state space representations automatically from observations using deep neural networks. However, these models are typically overparameterized, with the risk of overfitting the data at hand. In this paper, we present a novel approach of learning state spaces using quantum physics-inspired tensor networks. The ability of tensor networks to represent the probabilistic nature of quantum states as well as to reduce large state spaces makes tensor networks a natural candidate for active inference. We show how tensor networks can be used as a generative model for sequential data. Furthermore, we show how one can obtain beliefs from such a generative model and how an active inference agent can use these to compute the expected free energy. Finally, we demonstrate our method on the classic T-maze environment.
    Neural Payoff Machines: Predicting Fair and Stable Payoff Allocations Among Team Members. (arXiv:2208.08798v1 [cs.LG])
    In many multi-agent settings, participants can form teams to achieve collective outcomes that may far surpass their individual capabilities. Measuring the relative contributions of agents and allocating them shares of the reward that promote long-lasting cooperation are difficult tasks. Cooperative game theory offers solution concepts identifying distribution schemes, such as the Shapley value, that fairly reflect the contribution of individuals to the performance of the team or the Core, which reduces the incentive of agents to abandon their team. Applications of such methods include identifying influential features and sharing the costs of joint ventures or team formation. Unfortunately, using these solutions requires tackling a computational barrier as they are hard to compute, even in restricted settings. In this work, we show how cooperative game-theoretic solutions can be distilled into a learned model by training neural networks to propose fair and stable payoff allocations. We show that our approach creates models that can generalize to games far from the training distribution and can predict solutions for more players than observed during training. An important application of our framework is Explainable AI: our approach can be used to speed-up Shapley value computations on many instances.  ( 3 min )
    SWP-LeafNET: A novel multistage approach for plant leaf identification based on deep CNN. (arXiv:2009.05139v2 [cs.CV] UPDATED)
    Modern scientific and technological advances allow botanists to use computer vision-based approaches for plant identification tasks. These approaches have their own challenges. Leaf classification is a computer-vision task performed for the automated identification of plant species, a serious challenge due to variations in leaf morphology, including its size, texture, shape, and venation. Researchers have recently become more inclined toward deep learning-based methods rather than conventional feature-based methods due to the popularity and successful implementation of deep learning methods in image analysis, object recognition, and speech recognition. In this paper, to have an interpretable and reliable system, a botanist's behavior is modeled in leaf identification by proposing a highly-efficient method of maximum behavioral resemblance developed through three deep learning-based models. Different layers of the three models are visualized to ensure that the botanist's behavior is modeled accurately. The first and second models are designed from scratch. Regarding the third model, the pre-trained architecture MobileNetV2 is employed along with the transfer-learning technique. The proposed method is evaluated on two well-known datasets: Flavia and MalayaKew. According to a comparative analysis, the suggested approach is more accurate than hand-crafted feature extraction methods and other deep learning techniques in terms of 99.67% and 99.81% accuracy. Unlike conventional techniques that have their own specific complexities and depend on datasets, the proposed method requires no hand-crafted feature extraction. Also, it increases accuracy as compared with other deep learning techniques. Moreover, SWP-LeafNET is distributable and considerably faster than other methods because of using shallower models with fewer parameters asynchronously.
    On the Universality of the Double Descent Peak in Ridgeless Regression. (arXiv:2010.01851v7 [stat.ML] UPDATED)
    We prove a non-asymptotic distribution-independent lower bound for the expected mean squared generalization error caused by label noise in ridgeless linear regression. Our lower bound generalizes a similar known result to the overparameterized (interpolating) regime. In contrast to most previous works, our analysis applies to a broad class of input distributions with almost surely full-rank feature matrices, which allows us to cover various types of deterministic or random feature maps. Our lower bound is asymptotically sharp and implies that in the presence of label noise, ridgeless linear regression does not perform well around the interpolation threshold for any of these feature maps. We analyze the imposed assumptions in detail and provide a theory for analytic (random) feature maps. Using this theory, we can show that our assumptions are satisfied for input distributions with a (Lebesgue) density and feature maps given by random deep neural networks with analytic activation functions like sigmoid, tanh, softplus or GELU. As further examples, we show that feature maps from random Fourier features and polynomial kernels also satisfy our assumptions. We complement our theory with further experimental and analytic results.
    AoI-based Temporal Attention Graph Neural Network for Popularity Prediction and Content Caching. (arXiv:2208.08606v1 [cs.LG])
    Along with the fast development of network technology and the rapid growth of network equipment, the data throughput is sharply increasing. To handle the problem of backhaul bottleneck in cellular network and satisfy people's requirements about latency, the network architecture like information-centric network (ICN) intends to proactively keep limited popular content at the edge of network based on predicted results. Meanwhile, the interactions between the content (e.g., deep neural network models, Wikipedia-alike knowledge base) and users could be regarded as a dynamic bipartite graph. In this paper, to maximize the cache hit rate, we leverage an effective dynamic graph neural network (DGNN) to jointly learn the structural and temporal patterns embedded in the bipartite graph. Furthermore, in order to have deeper insights into the dynamics within the evolving graph, we propose an age of information (AoI) based attention mechanism to extract valuable historical information while avoiding the problem of message staleness. Combining this aforementioned prediction model, we also develop a cache selection algorithm to make caching decisions in accordance with the prediction results. Extensive results demonstrate that our model can obtain a higher prediction accuracy than other state-of-the-art schemes in two real-world datasets. The results of hit rate further verify the superiority of the caching policy based on our proposed model over other traditional ways.
    Robust Causal Graph Representation Learning against Confounding Effects. (arXiv:2208.08584v1 [cs.LG])
    The prevailing graph neural network models have achieved significant progress in graph representation learning. However, in this paper, we uncover an ever-overlooked phenomenon: the pre-trained graph representation learning model tested with full graphs underperforms the model tested with well-pruned graphs. This observation reveals that there exist confounders in graphs, which may interfere with the model learning semantic information, and current graph representation learning methods have not eliminated their influence. To tackle this issue, we propose Robust Causal Graph Representation Learning (RCGRL) to learn robust graph representations against confounding effects. RCGRL introduces an active approach to generate instrumental variables under unconditional moment restrictions, which empowers the graph representation learning model to eliminate confounders, thereby capturing discriminative information that is causally related to downstream predictions. We offer theorems and proofs to guarantee the theoretical effectiveness of the proposed approach. Empirically, we conduct extensive experiments on a synthetic dataset and multiple benchmark datasets. The results demonstrate that compared with state-of-the-art methods, RCGRL achieves better prediction performance and generalization ability.
    KDD CUP 2022 Wind Power Forecasting Team 88VIP Solution. (arXiv:2208.08952v1 [cs.LG])
    KDD CUP 2022 proposes a time-series forecasting task on spatial dynamic wind power dataset, in which the participants are required to predict the future generation given the historical context factors. The evaluation metrics contain RMSE and MAE. This paper describes the solution of Team 88VIP, which mainly comprises two types of models: a gradient boosting decision tree to memorize the basic data patterns and a recurrent neural network to capture the deep and latent probabilistic transitions. Ensembling these models contributes to tackle the fluctuation of wind power, and training submodels targets on the distinguished properties in heterogeneous timescales of forecasting, from minutes to days. In addition, feature engineering, imputation techniques and the design of offline evaluation are also described in details. The proposed solution achieves an overall online score of -45.213 in Phase 3.
    Profiler: Profile-Based Model to Detect Phishing Emails. (arXiv:2208.08745v1 [cs.CR])
    Email phishing has become more prevalent and grows more sophisticated over time. To combat this rise, many machine learning (ML) algorithms for detecting phishing emails have been developed. However, due to the limited email data sets on which these algorithms train, they are not adept at recognising varied attacks and, thus, suffer from concept drift; attackers can introduce small changes in the statistical characteristics of their emails or websites to successfully bypass detection. Over time, a gap develops between the reported accuracy from literature and the algorithm's actual effectiveness in the real world. This realises itself in frequent false positive and false negative classifications. To this end, we propose a multidimensional risk assessment of emails to reduce the feasibility of an attacker adapting their email and avoiding detection. This horizontal approach to email phishing detection profiles an incoming email on its main features. We develop a risk assessment framework that includes three models which analyse an email's (1) threat level, (2) cognitive manipulation, and (3) email type, which we combine to return the final risk assessment score. The Profiler does not require large data sets to train on to be effective and its analysis of varied email features reduces the impact of concept drift. Our Profiler can be used in conjunction with ML approaches, to reduce their misclassifications or as a labeller for large email data sets in the training stage. We evaluate the efficacy of the Profiler against a machine learning ensemble using state-of-the-art ML algorithms on a data set of 9000 legitimate and 900 phishing emails from a large Australian research organisation. Our results indicate that the Profiler's mitigates the impact of concept drift, and delivers 30% less false positive and 25% less false negative email classifications over the ML ensemble's approach.  ( 3 min )
    Choquet regularization for reinforcement learning. (arXiv:2208.08497v1 [stat.ML])
    We propose \emph{Choquet regularizers} to measure and manage the level of exploration for reinforcement learning (RL), and reformulate the continuous-time entropy-regularized RL problem of Wang et al. (2020, JMLR, 21(198)) in which we replace the differential entropy used for regularization with a Choquet regularizer. We derive the Hamilton--Jacobi--Bellman equation of the problem, and solve it explicitly in the linear--quadratic (LQ) case via maximizing statically a mean--variance constrained Choquet regularizer. Under the LQ setting, we derive explicit optimal distributions for several specific Choquet regularizers, and conversely identify the Choquet regularizers that generate a number of broadly used exploratory samplers such as $\epsilon$-greedy, exponential, uniform and Gaussian.  ( 2 min )
    Geometric Scattering on Measure Spaces. (arXiv:2208.08561v1 [stat.ML])
    The scattering transform is a multilayered, wavelet-based transform initially introduced as a model of convolutional neural networks (CNNs) that has played a foundational role in our understanding of these networks' stability and invariance properties. Subsequently, there has been widespread interest in extending the success of CNNs to data sets with non-Euclidean structure, such as graphs and manifolds, leading to the emerging field of geometric deep learning. In order to improve our understanding of the architectures used in this new field, several papers have proposed generalizations of the scattering transform for non-Euclidean data structures such as undirected graphs and compact Riemannian manifolds without boundary. In this paper, we introduce a general, unified model for geometric scattering on measure spaces. Our proposed framework includes previous work on geometric scattering as special cases but also applies to more general settings such as directed graphs, signed graphs, and manifolds with boundary. We propose a new criterion that identifies to which groups a useful representation should be invariant and show that this criterion is sufficient to guarantee that the scattering transform has desirable stability and invariance properties. Additionally, we consider finite measure spaces that are obtained from randomly sampling an unknown manifold. We propose two methods for constructing a data-driven graph on which the associated graph scattering transform approximates the scattering transform on the underlying manifold. Moreover, we use a diffusion-maps based approach to prove quantitative estimates on the rate of convergence of one of these approximations as the number of sample points tends to infinity. Lastly, we showcase the utility of our method on spherical images, directed graphs, and on high-dimensional single-cell data.  ( 3 min )
    Domain-Specific Risk Minimization. (arXiv:2208.08661v1 [cs.LG])
    Learning a domain-invariant representation has become one of the most popular approaches for domain adaptation/generalization. In this paper, we show that the invariant representation may not be sufficient to guarantee a good generalization, where the labeling function shift should be taken into consideration. Inspired by this, we first derive a new generalization upper bound on the empirical risk that explicitly considers the labeling function shift. We then propose Domain-specific Risk Minimization (DRM), which can model the distribution shifts of different domains separately and select the most appropriate one for the target domain. Extensive experiments on four popular domain generalization datasets, CMNIST, PACS, VLCS, and DomainNet, demonstrate the effectiveness of the proposed DRM for domain generalization with the following advantages: 1) it significantly outperforms competitive baselines; 2) it enables either comparable or superior accuracies on all training domains comparing to vanilla empirical risk minimization (ERM); 3) it remains very simple and efficient during training, and 4) it is complementary to invariant learning approaches.  ( 2 min )
    Lifted Bregman Training of Neural Networks. (arXiv:2208.08772v1 [math.OC])
    We introduce a novel mathematical formulation for the training of feed-forward neural networks with (potentially non-smooth) proximal maps as activation functions. This formulation is based on Bregman distances and a key advantage is that its partial derivatives with respect to the network's parameters do not require the computation of derivatives of the network's activation functions. Instead of estimating the parameters with a combination of first-order optimisation method and back-propagation (as is the state-of-the-art), we propose the use of non-smooth first-order optimisation methods that exploit the specific structure of the novel formulation. We present several numerical results that demonstrate that these training approaches can be equally well or even better suited for the training of neural network-based classifiers and (denoising) autoencoders with sparse coding compared to more conventional training frameworks.  ( 2 min )
    NET-FLEET: Achieving Linear Convergence Speedup for Fully Decentralized Federated Learning with Heterogeneous Data. (arXiv:2208.08490v1 [cs.LG])
    Federated learning (FL) has received a surge of interest in recent years thanks to its benefits in data privacy protection, efficient communication, and parallel data processing. Also, with appropriate algorithmic designs, one could achieve the desirable linear speedup for convergence effect in FL. However, most existing works on FL are limited to systems with i.i.d. data and centralized parameter servers and results on decentralized FL with heterogeneous datasets remains limited. Moreover, whether or not the linear speedup for convergence is achievable under fully decentralized FL with data heterogeneity remains an open question. In this paper, we address these challenges by proposing a new algorithm, called NET-FLEET, for fully decentralized FL systems with data heterogeneity. The key idea of our algorithm is to enhance the local update scheme in FL (originally intended for communication efficiency) by incorporating a recursive gradient correction technique to handle heterogeneous datasets. We show that, under appropriate parameter settings, the proposed NET-FLEET algorithm achieves a linear speedup for convergence. We further conduct extensive numerical experiments to evaluate the performance of the proposed NET-FLEET algorithm and verify our theoretical findings.  ( 2 min )
    CTRL: Clustering Training Losses for Label Error Detection. (arXiv:2208.08464v1 [cs.LG])
    In supervised machine learning, use of correct labels is extremely important to ensure high accuracy. Unfortunately, most datasets contain corrupted labels. Machine learning models trained on such datasets do not generalize well. Thus, detecting their label errors can significantly increase their efficacy. We propose a novel framework, called CTRL (Clustering TRaining Losses for label error detection), to detect label errors in multi-class datasets. It detects label errors in two steps based on the observation that models learn clean and noisy labels in different ways. First, we train a neural network using the noisy training dataset and obtain the loss curve for each sample. Then, we apply clustering algorithms to the training losses to group samples into two categories: cleanly-labeled and noisily-labeled. After label error detection, we remove samples with noisy labels and retrain the model. Our experimental results demonstrate state-of-the-art error detection accuracy on both image (CIFAR-10 and CIFAR-100) and tabular datasets under simulated noise. We also use a theoretical analysis to provide insights into why CTRL performs so well.  ( 2 min )
    Generating Synthetic Clinical Data that Capture Class Imbalanced Distributions with Generative Adversarial Networks: Example using Antiretroviral Therapy for HIV. (arXiv:2208.08655v1 [cs.LG])
    Clinical data usually cannot be freely distributed due to their highly confidential nature and this hampers the development of machine learning in the healthcare domain. One way to mitigate this problem is by generating realistic synthetic datasets using generative adversarial networks (GANs). However, GANs are known to suffer from mode collapse and thus creating outputs of low diveristy. In this paper, we extend the classic GAN setup with an external memory to replay features from real samples. Using antiretroviral therapy for human immunodeficiency virus (ART for HIV) as a case study, we show that our extended setup increases convergence and more importantly, it is effective in capturing the severe class imbalanced distributions common to real world clinical data.  ( 2 min )
    Challenges and opportunities in applying Neural Temporal Point Processes to large scale industry data. (arXiv:2208.08623v1 [cs.LG])
    In this work, we identify open research opportunities in applying Neural Temporal Point Process (NTPP) models to industry scale customer behavior data by carefully reproducing NTPP models published up to date on known literature benchmarks as well as applying NTPP models to a novel, real world consumer behavior dataset that is twice as large as the largest publicly available NTPP benchmark. We identify the following challenges. First, NTPP models, albeit their generative nature, remain vulnerable to dataset imbalances and cannot forecast rare events. Second, NTPP models based on stochastic differential equations, despite their theoretical appeal and leading performance on literature benchmarks, do not scale easily to large industry-scale data. The former is in light of previously made observations on deep generative models. Additionally, to combat a cold-start problem, we explore a novel addition to NTPP models - a parametrization based on static user features.  ( 2 min )
    Speech Representation Disentanglement with Adversarial Mutual Information Learning for One-shot Voice Conversion. (arXiv:2208.08757v1 [eess.AS])
    One-shot voice conversion (VC) with only a single target speaker's speech for reference has become a hot research topic. Existing works generally disentangle timbre, while information about pitch, rhythm and content is still mixed together. To perform one-shot VC effectively with further disentangling these speech components, we employ random resampling for pitch and content encoder and use the variational contrastive log-ratio upper bound of mutual information and gradient reversal layer based adversarial mutual information learning to ensure the different parts of the latent space containing only the desired disentangled representation during training. Experiments on the VCTK dataset show the model achieves state-of-the-art performance for one-shot VC in terms of naturalness and intellgibility. In addition, we can transfer characteristics of one-shot VC on timbre, pitch and rhythm separately by speech representation disentanglement. Our code, pre-trained models and demo are available at https://im1eon.github.io/IS2022-SRDVC/.  ( 2 min )
    Learning with Local Gradients at the Edge. (arXiv:2208.08503v1 [cs.LG])
    To enable learning on edge devices with fast convergence and low memory, we present a novel backpropagation-free optimization algorithm dubbed Target Projection Stochastic Gradient Descent (tpSGD). tpSGD generalizes direct random target projection to work with arbitrary loss functions and extends target projection for training recurrent neural networks (RNNs) in addition to feedforward networks. tpSGD uses layer-wise stochastic gradient descent (SGD) and local targets generated via random projections of the labels to train the network layer-by-layer with only forward passes. tpSGD doesn't require retaining gradients during optimization, greatly reducing memory allocation compared to SGD backpropagation (BP) methods that require multiple instances of the entire neural network weights, input/output, and intermediate results. Our method performs comparably to BP gradient-descent within 5% accuracy on relatively shallow networks of fully connected layers, convolutional layers, and recurrent layers. tpSGD also outperforms other state-of-the-art gradient-free algorithms in shallow models consisting of multi-layer perceptrons, convolutional neural networks (CNNs), and RNNs with competitive accuracy and less memory and time. We evaluate the performance of tpSGD in training deep neural networks (e.g. VGG) and extend the approach to multi-layer RNNs. These experiments highlight new research directions related to optimized layer-based adaptor training for domain-shift using tpSGD at the edge.  ( 2 min )
    Nearly Optimal Latent State Decoding in Block MDPs. (arXiv:2208.08480v1 [cs.LG])
    We investigate the problems of model estimation and reward-free learning in episodic Block MDPs. In these MDPs, the decision maker has access to rich observations or contexts generated from a small number of latent states. We are first interested in estimating the latent state decoding function (the mapping from the observations to latent states) based on data generated under a fixed behavior policy. We derive an information-theoretical lower bound on the error rate for estimating this function and present an algorithm approaching this fundamental limit. In turn, our algorithm also provides estimates of all the components of the MDP. We then study the problem of learning near-optimal policies in the reward-free framework. Based on our efficient model estimation algorithm, we show that we can infer a policy converging (as the number of collected samples grows large) to the optimal policy at the best possible rate. Interestingly, our analysis provides necessary and sufficient conditions under which exploiting the block structure yields improvements in the sample complexity for identifying near-optimal policies. When these conditions are met, the sample complexity in the minimax reward-free setting is improved by a multiplicative factor $n$, where $n$ is the number of possible contexts.  ( 2 min )
    CP-PINNs: Changepoints Detection in PDEs using Physics Informed Neural Networks with Total-Variation Penalty. (arXiv:2208.08626v1 [stat.ML])
    We consider the inverse problem for the Partial Differential Equations (PDEs) such that the parameters of the dependency structure can exhibit random changepoints over time. This can arise, for example, when the physical system is either under malicious attack (e.g., hacker attacks on power grids and internet networks) or subject to extreme external conditions (e.g., weather conditions impacting electricity grids or large market movements impacting valuations of derivative contracts). For that purpose, we employ Physics Informed Neural Networks (PINNs) -- universal approximators that can incorporate prior information from any physical law described by a system of PDEs. This prior knowledge acts in the training of the neural network as a regularization that limits the space of admissible solutions and increases the correctness of the function approximation. We show that when the true data generating process exhibits changepoints in the PDE dynamics, this regularization can lead to a complete miss-calibration and a failure of the model. Therefore, we propose an extension of PINNs using a Total-Variation penalty which accommodates (multiple) changepoints in the PDE dynamics. These changepoints can occur at random locations over time, and they are estimated together with the solutions. We propose an additional refinement algorithm that combines changepoints detection with a reduced dynamic programming method that is feasible for the computationally intensive PINNs methods, and we demonstrate the benefits of the proposed model empirically using examples of different equations with changes in the parameters. In case of no changepoints in the data, the proposed model reduces to the original PINNs model. In the presence of changepoints, it leads to improvements in parameter estimation, better model fitting, and a lower training error compared to the original PINNs model.  ( 3 min )
    Resisting Adversarial Attacks in Deep Neural Networks using Diverse Decision Boundaries. (arXiv:2208.08697v1 [cs.LG])
    The security of deep learning (DL) systems is an extremely important field of study as they are being deployed in several applications due to their ever-improving performance to solve challenging tasks. Despite overwhelming promises, the deep learning systems are vulnerable to crafted adversarial examples, which may be imperceptible to the human eye, but can lead the model to misclassify. Protections against adversarial perturbations on ensemble-based techniques have either been shown to be vulnerable to stronger adversaries or shown to lack an end-to-end evaluation. In this paper, we attempt to develop a new ensemble-based solution that constructs defender models with diverse decision boundaries with respect to the original model. The ensemble of classifiers constructed by (1) transformation of the input by a method called Split-and-Shuffle, and (2) restricting the significant features by a method called Contrast-Significant-Features are shown to result in diverse gradients with respect to adversarial attacks, which reduces the chance of transferring adversarial examples from the original to the defender model targeting the same class. We present extensive experimentations using standard image classification datasets, namely MNIST, CIFAR-10 and CIFAR-100 against state-of-the-art adversarial attacks to demonstrate the robustness of the proposed ensemble-based defense. We also evaluate the robustness in the presence of a stronger adversary targeting all the models within the ensemble simultaneously. Results for the overall false positives and false negatives have been furnished to estimate the overall performance of the proposed methodology.  ( 3 min )
    A Tree-structured Transformer for Program Representation Learning. (arXiv:2208.08643v1 [cs.SE])
    When using deep learning techniques to model program languages, neural networks with tree or graph structures are widely adopted to capture the rich structural information within program abstract syntax trees (AST). However, long-term/global dependencies widely exist in programs, and most of these neural architectures fail to capture these dependencies. In this paper, we propose Tree-Transformer, a novel recursive tree-structured neural network which aims to overcome the above limitations. Tree-Transformer leverages two multi-head attention units to model the dependency between siblings and parent-children node pairs. Moreover, we propose a bi-directional propagation strategy to allow node information passing in two directions: bottom-up and top-down along trees. By combining bottom-up and top-down propagation, Tree-Transformer can learn both global contexts and meaningful node features. The extensive experimental results show that our Tree-Transformer outperforms existing tree-based or graph-based neural networks in program-related tasks with tree-level and node-level prediction tasks, indicating that Tree-Transformer performs well on learning both tree-level and node-level representations.  ( 2 min )
    Musika! Fast Infinite Waveform Music Generation. (arXiv:2208.08706v1 [cs.SD])
    Fast and user-controllable music generation could enable novel ways of composing or performing music. However, state-of-the-art music generation systems require large amounts of data and computational resources for training, and are slow at inference. This makes them impractical for real-time interactive use. In this work, we introduce Musika, a music generation system that can be trained on hundreds of hours of music using a single consumer GPU, and that allows for much faster than real-time generation of music of arbitrary length on a consumer CPU. We achieve this by first learning a compact invertible representation of spectrogram magnitudes and phases with adversarial autoencoders, then training a Generative Adversarial Network (GAN) on this representation for a particular music domain. A latent coordinate system enables generating arbitrarily long sequences of excerpts in parallel, while a global context vector allows the music to remain stylistically coherent through time. We perform quantitative evaluations to assess the quality of the generated samples and showcase options for user control in piano and techno music generation. We release the source code and pretrained autoencoder weights at github.com/marcoppasini/musika, such that a GAN can be trained on a new music domain with a single GPU in a matter of hours.  ( 2 min )
    Complex-Value Spatio-temporal Graph Convolutional Neural Networks and its Applications to Electric Power Systems AI. (arXiv:2208.08485v1 [cs.LG])
    The effective representation, precessing, analysis, and visualization of large-scale structured data over graphs are gaining a lot of attention. So far most of the literature has focused on real-valued signals. However, signals are often sparse in the Fourier domain, and more informative and compact representations for them can be obtained using the complex envelope of their spectral components, as opposed to the original real-valued signals. Motivated by this fact, in this work we generalize graph convolutional neural networks (GCN) to the complex domain, deriving the theory that allows to incorporate a complex-valued graph shift operators (GSO) in the definition of graph filters (GF) and process complex-valued graph signals (GS). The theory developed can handle spatio-temporal complex network processes. We prove that complex-valued GCNs are stable with respect to perturbations of the underlying graph support, the bound of the transfer error and the bound of error propagation through multiply layers. Then we apply complex GCN to power grid state forecasting, power grid cyber-attack detection and localization.  ( 2 min )
    Enhancing Diffusion-Based Image Synthesis with Robust Classifier Guidance. (arXiv:2208.08664v1 [cs.CV])
    Denoising diffusion probabilistic models (DDPMs) are a recent family of generative models that achieve state-of-the-art results. In order to obtain class-conditional generation, it was suggested to guide the diffusion process by gradients from a time-dependent classifier. While the idea is theoretically sound, deep learning-based classifiers are infamously susceptible to gradient-based adversarial attacks. Therefore, while traditional classifiers may achieve good accuracy scores, their gradients are possibly unreliable and might hinder the improvement of the generation results. Recent work discovered that adversarially robust classifiers exhibit gradients that are aligned with human perception, and these could better guide a generative process towards semantically meaningful images. We utilize this observation by defining and training a time-dependent adversarially robust classifier and use it as guidance for a generative diffusion model. In experiments on the highly challenging and diverse ImageNet dataset, our scheme introduces significantly more intelligible intermediate gradients, better alignment with theoretical findings, as well as improved generation results under several evaluation metrics. Furthermore, we conduct an opinion survey whose findings indicate that human raters prefer our method's results.  ( 2 min )
    Intelligent problem-solving as integrated hierarchical reinforcement learning. (arXiv:2208.08731v1 [cs.AI])
    According to cognitive psychology and related disciplines, the development of complex problem-solving behaviour in biological agents depends on hierarchical cognitive mechanisms. Hierarchical reinforcement learning is a promising computational approach that may eventually yield comparable problem-solving behaviour in artificial agents and robots. However, to date the problem-solving abilities of many human and non-human animals are clearly superior to those of artificial systems. Here, we propose steps to integrate biologically inspired hierarchical mechanisms to enable advanced problem-solving skills in artificial agents. Therefore, we first review the literature in cognitive psychology to highlight the importance of compositional abstraction and predictive processing. Then we relate the gained insights with contemporary hierarchical reinforcement learning methods. Interestingly, our results suggest that all identified cognitive mechanisms have been implemented individually in isolated computational architectures, raising the question of why there exists no single unifying architecture that integrates them. As our final contribution, we address this question by providing an integrative perspective on the computational challenges to develop such a unifying architecture. We expect our results to guide the development of more sophisticated cognitively inspired hierarchical machine learning architectures.  ( 3 min )
    Tree species classification from hyperspectral data using graph-regularized neural networks. (arXiv:2208.08675v1 [cs.CV])
    Manual labeling of tree species remains a challenging task, especially in tropical regions, owing to inaccessibility and labor-intensive ground-based surveys. Hyperspectral images (HSIs), through their narrow and contiguous bands, can assist in distinguishing tree species based on their spectral properties. Therefore, automated classification algorithms on HSI images can help augment the limited labeled information and generate a real-time classification map for various tree species. Achieving high classification accuracy with a limited amount of labeled information in an image is one of the key challenges that researchers have started addressing in recent years. We propose a novel graph-regularized neural network (GRNN) algorithm that encompasses the superpixel-based segmentation for graph construction, a pixel-wise neural network classifier, and the label propagation technique to generate an accurate classification map. GRNN outperforms several state-of-the-art techniques not only for the standard Indian Pines HSI but also achieves a high classification accuracy (approx. 92%) on a new HSI data set collected over the forests of French Guiana (FG) even when less than 1% of the pixels are labeled. We show that GRNN is not only competitive with the state-of-the-art semi-supervised methods, but also exhibits lower variance in accuracy for different number of training samples and over different independent random sampling of the labeled pixels for training.  ( 3 min )
  • Open

    Geometric Scattering on Measure Spaces. (arXiv:2208.08561v1 [stat.ML])
    The scattering transform is a multilayered, wavelet-based transform initially introduced as a model of convolutional neural networks (CNNs) that has played a foundational role in our understanding of these networks' stability and invariance properties. Subsequently, there has been widespread interest in extending the success of CNNs to data sets with non-Euclidean structure, such as graphs and manifolds, leading to the emerging field of geometric deep learning. In order to improve our understanding of the architectures used in this new field, several papers have proposed generalizations of the scattering transform for non-Euclidean data structures such as undirected graphs and compact Riemannian manifolds without boundary. In this paper, we introduce a general, unified model for geometric scattering on measure spaces. Our proposed framework includes previous work on geometric scattering as special cases but also applies to more general settings such as directed graphs, signed graphs, and manifolds with boundary. We propose a new criterion that identifies to which groups a useful representation should be invariant and show that this criterion is sufficient to guarantee that the scattering transform has desirable stability and invariance properties. Additionally, we consider finite measure spaces that are obtained from randomly sampling an unknown manifold. We propose two methods for constructing a data-driven graph on which the associated graph scattering transform approximates the scattering transform on the underlying manifold. Moreover, we use a diffusion-maps based approach to prove quantitative estimates on the rate of convergence of one of these approximations as the number of sample points tends to infinity. Lastly, we showcase the utility of our method on spherical images, directed graphs, and on high-dimensional single-cell data.
    High Probability Bounds for Stochastic Subgradient Schemes with Heavy Tailed Noise. (arXiv:2208.08567v1 [math.OC])
    In this work we study high probability bounds for stochastic subgradient methods under heavy tailed noise. In this case the noise is only assumed to have finite variance as opposed to a sub-Gaussian distribution for which it is known that standard subgradient methods enjoys high probability bounds. We analyzed a clipped version of the projected stochastic subgradient method, where subgradient estimates are truncated whenever they have large norms. We show that this clipping strategy leads both to near optimal any-time and finite horizon bounds for many classical averaging schemes. Preliminary experiments are shown to support the validity of the method.
    Acquisition of Chess Knowledge in AlphaZero. (arXiv:2111.09259v3 [cs.AI] UPDATED)
    What is learned by sophisticated neural network agents such as AlphaZero? This question is of both scientific and practical interest. If the representations of strong neural networks bear no resemblance to human concepts, our ability to understand faithful explanations of their decisions will be restricted, ultimately limiting what we can achieve with neural network interpretability. In this work we provide evidence that human knowledge is acquired by the AlphaZero neural network as it trains on the game of chess. By probing for a broad range of human chess concepts we show when and where these concepts are represented in the AlphaZero network. We also provide a behavioural analysis focusing on opening play, including qualitative analysis from chess Grandmaster Vladimir Kramnik. Finally, we carry out a preliminary investigation looking at the low-level details of AlphaZero's representations, and make the resulting behavioural and representational analyses available online.
    A Framework and Benchmark for Deep Batch Active Learning for Regression. (arXiv:2203.09410v2 [stat.ML] UPDATED)
    The acquisition of labels for supervised learning can be expensive. In order to improve the sample-efficiency of neural network regression, we study active learning methods that adaptively select batches of unlabeled data for labeling. We present a framework for constructing such methods out of (network-dependent) base kernels, kernel transformations and selection methods. Our framework encompasses many existing Bayesian methods based on Gaussian Process approximations of neural networks as well as non-Bayesian methods. Additionally, we propose to replace the commonly used last-layer features with sketched finite-width Neural Tangent Kernels, and to combine them with a novel clustering method. To evaluate different methods, we introduce an open-source benchmark consisting of 15 large tabular regression data sets. Our proposed method outperforms the state-of-the-art on our benchmark, scales to large data sets, and works out-of-the-box without adjusting the network architecture or training code. We provide open-source code that includes efficient implementations of all kernels, kernel transformations, and selection methods, and can be used for reproducing our results.
    How many perturbations break this model? Evaluating robustness beyond adversarial accuracy. (arXiv:2207.04129v2 [cs.LG] UPDATED)
    Robustness to adversarial attack is typically evaluated with adversarial accuracy. This metric quantifies the number of points for which, given a threat model, successful adversarial perturbations cannot be found. While essential, this metric does not capture all aspects of robustness and in particular leaves out the question of how many perturbations can be found for each point. In this work we introduce an alternative approach, adversarial sparsity, which quantifies how difficult it is to find a successful perturbation given both an input point and a constraint on the direction of the perturbation. This constraint may be angular (L2 perturbations), or based on the number of pixels (Linf perturbations). We show that sparsity provides valuable insight on neural networks in multiple ways. analyzing the sparsity of existing robust models illustrates important differences between them that accuracy analysis does not, and suggests approaches for improving their robustness. When applying broken defenses effective against weak attacks but not strong ones, sparsity can discriminate between the totally ineffective and the partially effective defenses. Finally, with sparsity we can measure increases in robustness that do not affect accuracy: we show for example that data augmentation can by itself increase adversarial robustness, without using adversarial training.
    High Dimensional Statistical Estimation under Uniformly Dithered One-bit Quantization. (arXiv:2202.13157v2 [stat.ML] UPDATED)
    In this paper, we propose a uniformly dithered one-bit quantization scheme for high-dimensional statistical estimation. The scheme contains truncation, dithering, and quantization as typical steps. As canonical examples, the quantization scheme is applied to three estimation problems: sparse covariance matrix estimation, sparse linear regression, and matrix completion. We study both sub-Gaussian and heavy-tailed regimes, with the underlying distribution of heavy-tailed data assumed to possess bounded second or fourth moment. For each model we propose new estimators based on one-bit quantized data. In sub-Gaussian regime, our estimators achieve optimal minimax rates up to logarithmic factors, which indicates that our quantization scheme nearly introduces no additional cost. In heavy-tailed regime, while the rates of our estimators become essentially slower, these results are either the first ones in such one-bit quantized and heavy-tailed setting, or exhibit significant improvements over existing comparable results. Moreover, we contribute considerably to the problems of one-bit compressed sensing and one-bit matrix completion. Specifically, we extend one-bit compressed sensing to sub-Gaussian or even heavy-tailed sensing vectors via convex programming. For one-bit matrix completion, our method is essentially different from the standard likelihood approach and can handle pre-quantization random noise with unknown distribution. Experimental results on synthetic data are presented to support our theoretical analysis.
    Estimating individual treatment effects under unobserved confounding using binary instruments. (arXiv:2208.08544v1 [stat.ME])
    Estimating individual treatment effects (ITEs) from observational data is relevant in many fields such as personalized medicine. However, in practice, the treatment assignment is usually confounded by unobserved variables and thus introduces bias. A remedy to remove the bias is the use of instrumental variables (IVs). Such settings are widespread in medicine (e.g., trials where compliance is used as binary IV). In this paper, we propose a novel, multiply robust machine learning framework, called MRIV, for estimating ITEs using binary IVs and thus yield an unbiased ITE estimator. Different from previous work for binary IVs, our framework estimates the ITE directly via a pseudo outcome regression. (1) We provide a theoretical analysis where we show that our framework yields multiply robust convergence rates: our ITE estimator achieves fast convergence even if several nuisance estimators converge slowly. (2) We further show that our framework asymptotically outperforms state-of-the-art plug-in IV methods for ITE estimation. (3) We build upon our theoretical results and propose a tailored deep neural network architecture called MRIV-Net for ITE estimation using binary IVs. Across various computational experiments, we demonstrate empirically that our MRIV-Net achieves state-of-the-art performance. To the best of our knowledge, our MRIV is the first machine learning framework for estimating ITEs in the binary IV setting shown to be multiply robust.
    Meta Sparse Principle Component Analysis. (arXiv:2208.08938v1 [stat.ML])
    We study the meta-learning for support (i.e. the set of non-zero entries) recovery in high-dimensional Principal Component Analysis. We reduce the sufficient sample complexity in a novel task with the information that is learned from auxiliary tasks. We assume each task to be a different random Principal Component (PC) matrix with a possibly different support and that the support union of the PC matrices is small. We then pool the data from all the tasks to execute an improper estimation of a single PC matrix by maximising the $l_1$-regularised predictive covariance to establish that with high probability the true support union can be recovered provided a sufficient number of tasks $m$ and a sufficient number of samples $ O\left(\frac{\log(p)}{m}\right)$ for each task, for $p$-dimensional vectors. Then, for a novel task, we prove that the maximisation of the $l_1$-regularised predictive covariance with the additional constraint that the support is a subset of the estimated support union could reduce the sufficient sample complexity of successful support recovery to $O(\log |J|)$, where $J$ is the support union recovered from the auxiliary tasks. Typically, $|J|$ would be much less than $p$ for sparse matrices. Finally, we demonstrate the validity of our experiments through numerical simulations.
    DIET: Conditional independence testing with marginal dependence measures of residual information. (arXiv:2208.08579v1 [stat.ME])
    Conditional randomization tests (CRTs) assess whether a variable $x$ is predictive of another variable $y$, having observed covariates $z$. CRTs require fitting a large number of predictive models, which is often computationally intractable. Existing solutions to reduce the cost of CRTs typically split the dataset into a train and test portion, or rely on heuristics for interactions, both of which lead to a loss in power. We propose the decoupled independence test (DIET), an algorithm that avoids both of these issues by leveraging marginal independence statistics to test conditional independence relationships. DIET tests the marginal independence of two random variables: $F(x \mid z)$ and $F(y \mid z)$ where $F(\cdot \mid z)$ is a conditional cumulative distribution function (CDF). These variables are termed "information residuals." We give sufficient conditions for DIET to achieve finite sample type-1 error control and power greater than the type-1 error rate. We then prove that when using the mutual information between the information residuals as a test statistic, DIET yields the most powerful conditionally valid test. Finally, we show DIET achieves higher power than other tractable CRTs on several synthetic and real benchmarks.
    Memory and Capacity of Graph Embedding Methods. (arXiv:2208.08769v1 [stat.ML])
    We introduce a method for embedding graphs as vectors in a structure-preserving manner. In this paper, we showcase its rich representational capacity and give some theoretical properties of our method. In particular, our procedure falls under the bind-and-sum approach, and we show that our binding operation - the tensor product - is the most general binding operation that respects the principle of superposition. Similarly, we show that the spherical code achieves optimal compression. We then establish some precise results characterizing the performance our method as well as some experimental results showcasing how it can accurately perform various graph operations even when the number of edges is quite large. Finally, we conclude with establishing a link to adjacency matrices, showing that our method is, in some sense, a generalization of adjacency matrices with applications towards large sparse graphs.
    Network inference via process motifs for lagged correlation in linear stochastic processes. (arXiv:2208.08871v1 [stat.ML])
    A major challenge for causal inference from time-series data is the trade-off between computational feasibility and accuracy. Motivated by process motifs for lagged covariance in an autoregressive model with slow mean-reversion, we propose to infer networks of causal relations via pairwise edge measure (PEMs) that one can easily compute from lagged correlation matrices. Motivated by contributions of process motifs to covariance and lagged variance, we formulate two PEMs that correct for confounding factors and for reverse causation. To demonstrate the performance of our PEMs, we consider network interference from simulations of linear stochastic processes, and we show that our proposed PEMs can infer networks accurately and efficiently. Specifically, for slightly autocorrelated time-series data, our approach achieves accuracies higher than or similar to Granger causality, transfer entropy, and convergent crossmapping -- but with much shorter computation time than possible with any of these methods. Our fast and accurate PEMs are easy-to-implement methods for network inference with a clear theoretical underpinning. They provide promising alternatives to current paradigms for the inference of linear models from time-series data, including Granger causality, vector-autoregression, and sparse inverse covariance estimation.
    CP-PINNs: Changepoints Detection in PDEs using Physics Informed Neural Networks with Total-Variation Penalty. (arXiv:2208.08626v1 [stat.ML])
    We consider the inverse problem for the Partial Differential Equations (PDEs) such that the parameters of the dependency structure can exhibit random changepoints over time. This can arise, for example, when the physical system is either under malicious attack (e.g., hacker attacks on power grids and internet networks) or subject to extreme external conditions (e.g., weather conditions impacting electricity grids or large market movements impacting valuations of derivative contracts). For that purpose, we employ Physics Informed Neural Networks (PINNs) -- universal approximators that can incorporate prior information from any physical law described by a system of PDEs. This prior knowledge acts in the training of the neural network as a regularization that limits the space of admissible solutions and increases the correctness of the function approximation. We show that when the true data generating process exhibits changepoints in the PDE dynamics, this regularization can lead to a complete miss-calibration and a failure of the model. Therefore, we propose an extension of PINNs using a Total-Variation penalty which accommodates (multiple) changepoints in the PDE dynamics. These changepoints can occur at random locations over time, and they are estimated together with the solutions. We propose an additional refinement algorithm that combines changepoints detection with a reduced dynamic programming method that is feasible for the computationally intensive PINNs methods, and we demonstrate the benefits of the proposed model empirically using examples of different equations with changes in the parameters. In case of no changepoints in the data, the proposed model reduces to the original PINNs model. In the presence of changepoints, it leads to improvements in parameter estimation, better model fitting, and a lower training error compared to the original PINNs model.
    ManiFlow: Implicitly Representing Manifolds with Normalizing Flows. (arXiv:2208.08932v1 [cs.CV])
    Normalizing Flows (NFs) are flexible explicit generative models that have been shown to accurately model complex real-world data distributions. However, their invertibility constraint imposes limitations on data distributions that reside on lower dimensional manifolds embedded in higher dimensional space. Practically, this shortcoming is often bypassed by adding noise to the data which impacts the quality of the generated samples. In contrast to prior work, we approach this problem by generating samples from the original data distribution given full knowledge about the perturbed distribution and the noise model. To this end, we establish that NFs trained on perturbed data implicitly represent the manifold in regions of maximum likelihood. Then, we propose an optimization objective that recovers the most likely point on the manifold given a sample from the perturbed distribution. Finally, we focus on 3D point clouds for which we utilize the explicit nature of NFs, i.e. surface normals extracted from the gradient of the log-likelihood and the log-likelihood itself, to apply Poisson surface reconstruction to refine generated point sets.
    Choquet regularization for reinforcement learning. (arXiv:2208.08497v1 [stat.ML])
    We propose \emph{Choquet regularizers} to measure and manage the level of exploration for reinforcement learning (RL), and reformulate the continuous-time entropy-regularized RL problem of Wang et al. (2020, JMLR, 21(198)) in which we replace the differential entropy used for regularization with a Choquet regularizer. We derive the Hamilton--Jacobi--Bellman equation of the problem, and solve it explicitly in the linear--quadratic (LQ) case via maximizing statically a mean--variance constrained Choquet regularizer. Under the LQ setting, we derive explicit optimal distributions for several specific Choquet regularizers, and conversely identify the Choquet regularizers that generate a number of broadly used exploratory samplers such as $\epsilon$-greedy, exponential, uniform and Gaussian.
    On an Application of Generative Adversarial Networks on Remaining Lifetime Estimation. (arXiv:2208.08666v1 [cs.LG])
    A major problem of structural health monitoring (SHM) has been the prognosis of damage and the definition of the remaining useful life of a structure. Both tasks depend on many parameters, many of which are often uncertain. Many models have been developed for the aforementioned tasks but they have been either deterministic or stochastic with the ability to take into account only a restricted amount of past states of the structure. In the current work, a generative model is proposed in order to make predictions about the damage evolution of structures. The model is able to perform in a population-based SHM (PBSHM) framework, to take into account many past states of the damaged structure, to incorporate uncertainties in the modelling process and to generate potential damage evolution outcomes according to data acquired from a structure. The algorithm is tested on a simulated damage evolution example and the results reveal that it is able to provide quite confident predictions about the remaining useful life of structures within a population.
    Discovering Bugs in Vision Models using Off-the-shelf Image Generation and Captioning. (arXiv:2208.08831v1 [cs.CV])
    Automatically discovering failures in vision models under real-world settings remains an open challenge. This work demonstrates how off-the-shelf, large-scale, image-to-text and text-to-image models, trained on vast amounts of data, can be leveraged to automatically find such failures. In essence, a conditional text-to-image generative model is used to generate large amounts of synthetic, yet realistic, inputs given a ground-truth label. Misclassified inputs are clustered and a captioning model is used to describe each cluster. Each cluster's description is used in turn to generate more inputs and assess whether specific clusters induce more failures than expected. We use this pipeline to demonstrate that we can effectively interrogate classifiers trained on ImageNet to find specific failure cases and discover spurious correlations. We also show that we can scale the approach to generate adversarial datasets targeting specific classifier architectures. This work serves as a proof-of-concept demonstrating the utility of large-scale generative models to automatically discover bugs in vision models in an open-ended manner. We also describe a number of limitations and pitfalls related to this approach.
    Nearly Optimal Latent State Decoding in Block MDPs. (arXiv:2208.08480v1 [cs.LG])
    We investigate the problems of model estimation and reward-free learning in episodic Block MDPs. In these MDPs, the decision maker has access to rich observations or contexts generated from a small number of latent states. We are first interested in estimating the latent state decoding function (the mapping from the observations to latent states) based on data generated under a fixed behavior policy. We derive an information-theoretical lower bound on the error rate for estimating this function and present an algorithm approaching this fundamental limit. In turn, our algorithm also provides estimates of all the components of the MDP. We then study the problem of learning near-optimal policies in the reward-free framework. Based on our efficient model estimation algorithm, we show that we can infer a policy converging (as the number of collected samples grows large) to the optimal policy at the best possible rate. Interestingly, our analysis provides necessary and sufficient conditions under which exploiting the block structure yields improvements in the sample complexity for identifying near-optimal policies. When these conditions are met, the sample complexity in the minimax reward-free setting is improved by a multiplicative factor $n$, where $n$ is the number of possible contexts.
    Data-driven emergence of convolutional structure in neural networks. (arXiv:2202.00565v2 [cond-mat.dis-nn] UPDATED)
    Exploiting data invariances is crucial for efficient learning in both artificial and biological neural circuits. Understanding how neural networks can discover appropriate representations capable of harnessing the underlying symmetries of their inputs is thus crucial in machine learning and neuroscience. Convolutional neural networks, for example, were designed to exploit translation symmetry and their capabilities triggered the first wave of deep learning successes. However, learning convolutions directly from translation-invariant data with a fully-connected network has so far proven elusive. Here, we show how initially fully-connected neural networks solving a discrimination task can learn a convolutional structure directly from their inputs, resulting in localised, space-tiling receptive fields. These receptive fields match the filters of a convolutional network trained on the same task. By carefully designing data models for the visual scene, we show that the emergence of this pattern is triggered by the non-Gaussian, higher-order local structure of the inputs, which has long been recognised as the hallmark of natural images. We provide an analytical and numerical characterisation of the pattern-formation mechanism responsible for this phenomenon in a simple model and find an unexpected link between receptive field formation and tensor decomposition of higher-order input correlations. These results provide a new perspective on the development of low-level feature detectors in various sensory modalities, and pave the way for studying the impact of higher-order statistics on learning in neural networks.
    A spatiotemporal machine learning approach to forecasting COVID-19 incidence at the county level in the USA. (arXiv:2109.12094v4 [stat.ML] UPDATED)
    With COVID-19 affecting every country globally and changing everyday life, the ability to forecast the spread of the disease is more important than any previous epidemic. The conventional methods of disease-spread modeling, compartmental models, are based on the assumption of spatiotemporal homogeneity of the spread of the virus, which may cause forecasting to underperform, especially at high spatial resolutions. In this paper we approach the forecasting task with an alternative technique - spatiotemporal machine learning. We present COVID-LSTM, a data-driven model based on a Long Short-term Memory deep learning architecture for forecasting COVID-19 incidence at the county-level in the US. We use the weekly number of new positive cases as temporal input, and hand-engineered spatial features from Facebook movement and connectedness datasets to capture the spread of the disease in time and space. COVID-LSTM outperforms the COVID-19 Forecast Hub's Ensemble model (COVIDhub-ensemble) on our 17-week evaluation period, making it the first model to be more accurate than the COVIDhub-ensemble over one or more forecast periods. Over the 4-week forecast horizon, our model is on average 50 cases per county more accurate than the COVIDhub-ensemble. We highlight that the underutilization of data-driven forecasting of disease spread prior to COVID-19 is likely due to the lack of sufficient data available for previous diseases, in addition to the recent advances in machine learning methods for spatiotemporal forecasting. We discuss the impediments to the wider uptake of data-driven forecasting, and whether it is likely that more deep learning-based models will be used in the future.
    Never Worse, Mostly Better: Stable Policy Improvement in Deep Reinforcement Learning. (arXiv:1910.01062v3 [cs.LG] UPDATED)
    In recent years, there has been significant progress in applying deep reinforcement learning (RL) for solving challenging problems across a wide variety of domains. Nevertheless, convergence of various methods has been shown to suffer from inconsistencies, due to algorithmic instability and variance, as well as stochasticity in the benchmark environments. Particularly, despite the fact that the agent's performance may be improving on average, it may abruptly deteriorate at late stages of training. In this work, we study methods for enhancing the agent's learning process, by providing conservative updates with respect to either the obtained history or a reference benchmark policy. Our method, termed EVEREST, obtains high confidence improvements via confidence bounds of a reference policy. Through extensive empirical analysis we demonstrate the benefit of our approach in terms of both performance and stabilization, with significant improvements in continuous control and Atari benchmarks.
    Restructurable Activation Networks. (arXiv:2208.08562v1 [cs.CV])
    Is it possible to restructure the non-linear activation functions in a deep network to create hardware-efficient models? To address this question, we propose a new paradigm called Restructurable Activation Networks (RANs) that manipulate the amount of non-linearity in models to improve their hardware-awareness and efficiency. First, we propose RAN-explicit (RAN-e) -- a new hardware-aware search space and a semi-automatic search algorithm -- to replace inefficient blocks with hardware-aware blocks. Next, we propose a training-free model scaling method called RAN-implicit (RAN-i) where we theoretically prove the link between network topology and its expressivity in terms of number of non-linear units. We demonstrate that our networks achieve state-of-the-art results on ImageNet at different scales and for several types of hardware. For example, compared to EfficientNet-Lite-B0, RAN-e achieves a similar accuracy while improving Frames-Per-Second (FPS) by 1.5x on Arm micro-NPUs. On the other hand, RAN-i demonstrates up to 2x reduction in #MACs over ConvNexts with a similar or better accuracy. We also show that RAN-i achieves nearly 40% higher FPS than ConvNext on Arm-based datacenter CPUs. Finally, RAN-i based object detection networks achieve a similar or higher mAP and up to 33% higher FPS on datacenter CPUs compared to ConvNext based models.
    Lost in the Shuffle: Testing Power in the Presence of Errorful Network Vertex Labels. (arXiv:2208.08638v1 [stat.ME])
    Many two-sample network hypothesis testing methodologies operate under the implicit assumption that the vertex correspondence across networks is a priori known. In this paper, we consider the degradation of power in two-sample graph hypothesis testing when there are misaligned/label-shuffled vertices across networks. In the context of stochastic block model networks, we theoretically explore the power loss due to shuffling for a pair of hypothesis tests based on Frobenius norm differences between estimated edge probability matrices or between adjacency matrices. The loss in testing power is further reinforced by numerous simulations and experiments, both in the stochastic block model and in the random dot product graph model, where we compare the power loss across multiple recently proposed tests in the literature. Lastly, we demonstrate the impact that shuffling can have in real-data testing in a pair of examples from neuroscience and from social network analysis.
    Learning to Generate Image Source-Agnostic Universal Adversarial Perturbations. (arXiv:2009.13714v4 [cs.LG] UPDATED)
    Adversarial perturbations are critical for certifying the robustness of deep learning models. A universal adversarial perturbation (UAP) can simultaneously attack multiple images, and thus offers a more unified threat model, obviating an image-wise attack algorithm. However, the existing UAP generator is underdeveloped when images are drawn from different image sources (e.g., with different image resolutions). Towards an authentic universality across image sources, we take a novel view of UAP generation as a customized instance of few-shot learning, which leverages bilevel optimization and learning-to-optimize (L2O) techniques for UAP generation with improved attack success rate (ASR). We begin by considering the popular model agnostic meta-learning (MAML) framework to meta-learn a UAP generator. However, we see that the MAML framework does not directly offer the universal attack across image sources, requiring us to integrate it with another meta-learning framework of L2O. The resulting scheme for meta-learning a UAP generator (i) has better performance (50% higher ASR) than baselines such as Projected Gradient Descent, (ii) has better performance (37% faster) than the vanilla L2O and MAML frameworks (when applicable), and (iii) is able to simultaneously handle UAP generation for different victim models and image data sources.
    UN-AVOIDS: Unsupervised and Nonparametric Approach for Visualizing Outliers and Invariant Detection Scoring. (arXiv:2111.10010v2 [cs.LG] UPDATED)
    The visualization and detection of anomalies (outliers) are of crucial importance to many fields, particularly cybersecurity. Several approaches have been proposed in these fields, yet to the best of our knowledge, none of them has fulfilled both objectives, simultaneously or cooperatively, in one coherent framework. The visualization methods of these approaches were introduced for explaining the output of a detection algorithm, not for data exploration that facilitates a standalone visual detection. This is our point of departure: UN-AVOIDS, an unsupervised and nonparametric approach for both visualization (a human process) and detection (an algorithmic process) of outliers, that assigns invariant anomalous scores (normalized to $[0,1]$), rather than hard binary-decision. The main aspect of novelty of UN-AVOIDS is that it transforms data into a new space, which is introduced in this paper as neighborhood cumulative density function (NCDF), in which both visualization and detection are carried out. In this space, outliers are remarkably visually distinguishable, and therefore the anomaly scores assigned by the detection algorithm achieved a high area under the ROC curve (AUC). We assessed UN-AVOIDS on both simulated and two recently published cybersecurity datasets, and compared it to three of the most successful anomaly detection methods: LOF, IF, and FABOD. In terms of AUC, UN-AVOIDS was almost an overall winner. The article concludes by providing a preview of new theoretical and practical avenues for UN-AVOIDS. Among them is designing a visualization aided anomaly detection (VAAD), a type of software that aids analysts by providing UN-AVOIDS' detection algorithm (running in a back engine), NCDF visualization space (rendered to plots), along with other conventional methods of visualization in the original feature space, all of which are linked in one interactive environment.
    On the Universality of the Double Descent Peak in Ridgeless Regression. (arXiv:2010.01851v7 [stat.ML] UPDATED)
    We prove a non-asymptotic distribution-independent lower bound for the expected mean squared generalization error caused by label noise in ridgeless linear regression. Our lower bound generalizes a similar known result to the overparameterized (interpolating) regime. In contrast to most previous works, our analysis applies to a broad class of input distributions with almost surely full-rank feature matrices, which allows us to cover various types of deterministic or random feature maps. Our lower bound is asymptotically sharp and implies that in the presence of label noise, ridgeless linear regression does not perform well around the interpolation threshold for any of these feature maps. We analyze the imposed assumptions in detail and provide a theory for analytic (random) feature maps. Using this theory, we can show that our assumptions are satisfied for input distributions with a (Lebesgue) density and feature maps given by random deep neural networks with analytic activation functions like sigmoid, tanh, softplus or GELU. As further examples, we show that feature maps from random Fourier features and polynomial kernels also satisfy our assumptions. We complement our theory with further experimental and analytic results.
    Semi-self-supervised Automated ICD Coding. (arXiv:2205.10088v2 [cs.CL] UPDATED)
    Clinical Text Notes (CTNs) contain physicians' reasoning process, written in an unstructured free text format, as they examine and interview patients. In recent years, several studies have been published that provide evidence for the utility of machine learning for predicting doctors' diagnoses from CTNs, a task known as ICD coding. Data annotation is time consuming, particularly when a degree of specialization is needed, as is the case for medical data. This paper presents a method of augmenting a sparsely annotated dataset of Icelandic CTNs with a machine-learned imputation in a semi-self-supervised manner. We train a neural network on a small set of annotated CTNs and use it to extract clinical features from a set of un-annotated CTNs. These clinical features consist of answers to about a thousand potential questions that a physician might find the answers to during a consultation of a patient. The features are then used to train a classifier for the diagnosis of certain types of diseases. We report the results of an evaluation of this data augmentation method over three tiers of data availability to the physician. Our data augmentation method shows a significant positive effect which is diminished when clinical features from the examination of the patient and diagnostics are made available. We recommend our method for augmenting scarce datasets for systems that take decisions based on clinical features that do not include examinations or tests.
    Selective Classification Via Neural Network Training Dynamics. (arXiv:2205.13532v2 [cs.LG] UPDATED)
    Selective classification is the task of rejecting inputs a model would predict incorrectly on through a trade-off between input space coverage and model accuracy. Current methods for selective classification impose constraints on either the model architecture or the loss function; this inhibits their usage in practice. In contrast to prior work, we show that state-of-the-art selective classification performance can be attained solely from studying the (discretized) training dynamics of a model. We propose a general framework that, for a given test input, monitors metrics capturing the disagreement with the final predicted label over intermediate models obtained during training; we then reject data points exhibiting too much disagreement at late stages in training. In particular, we instantiate a method that tracks when the label predicted during training stops disagreeing with the final predicted label. Our experimental evaluation shows that our method achieves state-of-the-art accuracy/coverage trade-offs on typical selective classification benchmarks.

  • Open

    This is what DeepAI art generator came up with for "typical Reddit user". These things are getting good!
    submitted by /u/dingdongschlonglong [link] [comments]  ( 87 min )
    Building a App for Stable Diffusion: Text to Image generation in Python
    submitted by /u/Illustrious_Row_9971 [link] [comments]  ( 87 min )
    My favorite creation from midjourney
    submitted by /u/ExtensionVirtual471 [link] [comments]  ( 87 min )
    Amazing AI Clip!
    submitted by /u/nalr00n [link] [comments]  ( 87 min )
    Eteria AI - API for BLOOM 176B Language Model
    submitted by /u/brthornbury [link] [comments]  ( 87 min )
    Hey, this is pretty cool, man!
    submitted by /u/NebelLicht [link] [comments]  ( 87 min )
    Come join me for the very first global online Cohere + lablab.ai hackathon. 1000$ in Cohere credits and really cute swag are waiting for the winners. We start on Friday 12:oo pm EDT. Bring ur friends. Come check how fun large language models are.
    submitted by /u/techn0_cratic [link] [comments]  ( 87 min )
    Researchers at Stanford and Meta AI have Developed a Dataset Pruning Technique for Scaling Artificial Intelligence AI Training
    submitted by /u/ai-lover [link] [comments]  ( 89 min )
    Has anyone done anything with machine learning and the The On-Line Encyclopedia of Integer Sequences (OEIS)?
    I am not trained in machine learning, but I am curious about machine learning and integer sequences. Website: https://oeis.org/ I was thinking in terms of transformers like GPT-3 or other similar algorithms. For those who are unfamiliar here is the Wikipedia page intro: The On-Line Encyclopedia of Integer Sequences (OEIS) is an online database of integer sequences. It was created and maintained by Neil Sloane while researching at AT&T Labs. He transferred the intellectual property and hosting of the OEIS to the OEIS Foundation in 2009.[4] Sloane is chairman of the OEIS Foundation. OEIS records information on integer sequences of interest to both professional and amateur mathematicians, and is widely cited. As of January 2022, it contains over 350,000 sequences, making it the largest database of its kind. Each entry contains the leading terms of the sequence, keywords, mathematical motivations, literature links, and more, including the option to generate a graph or play a musical representation of the sequence. The database is searchable by keyword, by subsequence, or by any of 16 fields. submitted by /u/arisbe__ [link] [comments]  ( 88 min )
    Drift Detection: Automated Monitoring for Production ML Models
    One of the elements of an MLOps pipeline is drift detection, which helps you monitor model performance over time and identify when it's time for retraining. Once you've successfully deployed models and are running live inferences in production, you'll encounter yet another obstacle: monitoring model performance over time. We monitor several model performance indicators, including overall scoring, inference speed, latency, accuracy, and finally data drift and model drift. This tech talk covers the algorithms we've developed to automate detection of data drift and model drift, or input and output drift. https://youtu.be/EHjO4k7SE44 submitted by /u/modzykirsten [link] [comments]  ( 87 min )
    As It Was by Harry Styles, but visualized by AI!
    submitted by /u/Wingman143 [link] [comments]  ( 87 min )
    General solo undergraduate paper expectation, do I need to implement part of my paper or can I just describe the architecture and theory?
    I’m working on a undergrad paper, not a top tier school that needs to be completed within a term. I see deepmind papers always supported by rough implementations and results of what they’ve theorised. I understand that is the gold standard. What is the general consensus on undergrad papers do they expect you to write code and implement some of what you’re theorising? In my research I’ve came across papers like this which seem only theorised, without implementation: http://www.paulmckevitt.com/pubs/artsit16scenemaker.pdf submitted by /u/abittooambitious [link] [comments]  ( 103 min )
    What Is More Beneficial for Data Science Career: Projects or Certifications?
    submitted by /u/saik2363 [link] [comments]  ( 87 min )
    Open source language models for non English language?
    Hello, What are some good opensource model for non Latin alphabet. I think it's possible to retrain GPT-2 layers but are there other option that are specifically built up for non english language. Like arabic or japanese for example. submitted by /u/Mister-Khalifa [link] [comments]  ( 87 min )
    Create Apps without any coding using AI !
    submitted by /u/SamuelSmith1416 [link] [comments]  ( 87 min )
    Chatbots for SEO, benefits and tips
    Hi there, I'm from chatbot development company and we build custom chatbots. We recently published an article on how chatbots can help in seo campaigns, reduce bounce rate, boost returning visitors rate, increase time spent on page, and if you've already have a chatbot, then tips on optimazing your chatbot. Link here. submitted by /u/Avandegraund [link] [comments]  ( 91 min )
    Wanted: Coder to develop a poker training AI
    Apologies if this is post isn't appropriate for the subreddit. I'm looking for a coder to develop a poker AI for me that uses deep learning, specifications will be given upon request. contact me on discord for more info: cherrymx#5304 submitted by /u/silvercoinz12 [link] [comments]  ( 87 min )
    I make video with ai
    submitted by /u/Due-Ad9795 [link] [comments]  ( 87 min )
    Three step-by-step tutorials using Cohere AI
    Hello everybody! This weekend starting 19/08/2022 lablab.ai is hosting AI hackathon in partnership with Cohere. It is the last chance to get involved in by enrolling for FREE here. You can create your own team or look for an already existing one. During the event our participants will be using Cohere's one of the world’s most powerful NLP engines to build applications based on large language models. If you are not familiar with its functionalities we have prepared some tutorials for you! Find out how to create AI powered Google Chrome extensions for your medium.com posts Or how to create your own Q&A chatbot Or your own product description generator API with Cohere. ​ https://preview.redd.it/y5jdoh68ufi91.png?width=1200&format=png&auto=webp&s=1dc7210577e4e56680085428e8eca87f2d0b8e85 submitted by /u/Viseden [link] [comments]  ( 94 min )
    Created by Kein Künstler
    submitted by /u/widgia [link] [comments]  ( 88 min )
    Am I the only one scared from AI replacing his job?
    Hello everyone , I'm 23 , and I'm thinking about going to learn AI/ML even tho coding isn't really my dream job , I don't really know if I like it or hate it since I never worked with people , I was doing it alone but never tried working with it so idk . the problem is I'm a video editor , I like commercials stuff and marketing , I want to create a business BUT I'm really scared from AI . I read alot of posts some are saying that it will not replaces us and some tend to say it will . to be honest I don't see why not in the next 10-20years , yes now that's still in development and its not as good as a human , but those stuff are upgrading over the years and gets better and better . I'm so confused , I don't really knows what to choose for my main career in the future , I'm currently working as video editor and my goal was to make a business out of it , but after watching some posts it made me think twice .. in the other hand , I tend to believe it will help us the editors instead of taking our jobs . What do you guys think? submitted by /u/furytayx [link] [comments]  ( 92 min )
    Midjourney + GPT-3 = Amazing results?
    submitted by /u/kbf_ [link] [comments]  ( 87 min )
  • Open

    More planning steps in Dyna = less episodes but more computation/training time?
    Hi, In Sutton's book, it says that as the # of planning steps increases in dyna, the # of episodes needed for the optimal policy decreases substantially. However, I was wondering about the pure training time between N=0 and N=~50. In pure direct RL, more episodes may be needed, but in general, does it converge at a similar time to N=50? (assuming that interaction with the environment is not time-costly) Thanks! submitted by /u/JonathanMonathan62 [link] [comments]  ( 87 min )
    Training Atari using RAM
    I'm having some issues training Atari with RAM observation and i'm wondering if its necessary to use state frame stacking to train the newtork. As stated in the paper if you are training from pixels you have to stack last 4 observations but i dont know how to in RAM. In such case which whould be the input for the network?. A 128x4 array? Im using PyTorch and any helped is thanked. submitted by /u/aarribas12 [link] [comments]  ( 88 min )
    Please confirm my reasoning about Off-Policy SARSA / Monte Carlo
    Hi, I would greatly appreciate it if anyone could confirm if my understanding about One-step Q-learning Off-policy Monte-Carlo Off-policy, n-step SARSA are correct, specifically in the area of importance sampling. (Bolded regions are where I'm uncertain if I'm correct!) Off-policy monte-carlo: After reaching the end of the episode, the value back propagates through the trajectory, and if the behavior policy's action (B-Action) != target policy's greedy (deterministic) action (T-Action), the rest of the back propagation of the value is canceled out due to importance sampling. However, Q values and the target policy behavior are updated for a B-action BEFORE T-action = argmax(actions) and it's evaluated on whether the B-action == T-action. Thus, the algorithm is still able to explore because the tails become bigger and bigger until the T-policy is improving over time. One-step Q-learning: There is no importance sampling because the algorithm must explore, thus it shouldn't implement importance sampling in the first action because the Q(S,A) needs to be updated for every state-action pair in order to explore Off-policy, n-step SARSA Importance sampling is NOT implemented for the first time step because the algorithm must explore? In other words, Q(S,A) needs to be updated for every state-action pair, so it shouldn't cancel out the first action if T-action != B-action. However, importance sampling is implemented in subsequent steps because potentially incorrect actions shouldn't be used to optimize potentially correct actions. ​ Thanks so much! submitted by /u/JonathanMonathan62 [link] [comments]  ( 88 min )
    Training DDPG Agent
    I'm trying to train a DDPG agent but I keep running into the same problem. The agent explores well at the beginning and starts to accumulate positive reward, but just before hitting the reward threshold it just seems to not know that it's doing well and just plunge back down into the negative. I'm not sure what I need to change to have more consistent results. Edit: I wrote this post in a rush and realized later I have barely included any details. I am using a Simulink RL Agent and my environment is rendered through Simulink. The agent has to learn to apply force (through some forward kinematics) to track a random sinusoidal target. The agent is able to track the signal fairly well before it crashes and either stops applying any force or saturates the force input. I am using a buffer size of 1e6, a discount factor of 0.6, and a minibatch size of 125. submitted by /u/aok76 [link] [comments]  ( 89 min )
    How can Heuristic search and RL be related?
    HI guys, What is the relationship between Heuristic search methods and RL ? submitted by /u/souhaielbensalem [link] [comments]  ( 102 min )
  • Open

    Run PyTorch Lightning and native PyTorch DDP on Amazon SageMaker Training, featuring Amazon Search
    So much data, so little time. Machine learning (ML) experts, data scientists, engineers and enthusiasts have encountered this problem the world over. From natural language processing to computer vision, tabular to time series, and everything in-between, the age-old problem of optimizing for speed when running data against as many GPUs as you can get has […]  ( 9 min )
    Visualize your Amazon Lookout for Metrics anomaly results with Amazon QuickSight
    One of the challenges encountered by teams using Amazon Lookout for Metrics is quickly and efficiently connecting it to data visualization. The anomalies are presented individually on the Lookout for Metrics console, each with their own graph, making it difficult to view the set as a whole. An automated, integrated solution is needed for deeper […]  ( 14 min )
  • Open

    [News] SCALE Transform X conference
    Hello everyone! SCALE AI is hosting a set of conferences from OCT 19- 21ST with the world's top experts on AI & Machine Learning. Here's the link if someone wants to register to the event! ​ https://preview.redd.it/omxo8oqi6ji91.png?width=843&format=png&auto=webp&s=33954cd578a4c4209aec4a10bd0cf86209924fbf The event is both online or in person for those in the Bay Area and has no cost at all. Thought it might be of your interest as there's gonna be discussions about AI & ML for all sort of industries. Here's the link if someone wants to register to the event! submitted by /u/Financial_Astronaut_ [link] [comments]  ( 88 min )
    [N] Driving SMARTS Competition @ NeurIPS 2022
    Driving SMARTS Competition at NeurIPS 2022 This competition seeks to advance autonomous driving by developing agents that can drive as quickly and safely as possible from the start to destination amid background traffic. Data for the competition consists of large-scale naturalistic driving data replayed within SMARTS simulation environment. The following typical driving scenarios are tested: cruising, overtaking, merging, left turns at unsignalized intersections and being cut off by another vehicle. These scenarios are mined from the naturalistic data, manipulated and replayed in SMARTS. For some scenarios, interactive background vehicles are added in SMARTS. Agents will be ranked according to metrics on safety and comfort (smoothness and safe driving), task completion (% of completed scenarios), traffic rule violation, and completion time. Competition tracks There are two tracks in the competition. Track 1: The participants may use any method and training data to develop their solutions. Track 2: The participants are only allowed to train their methods on the offline datasets. Competition timeline • Aug. 1, 2022: competition opens. • Nov. 1, 2022: competition closes at 11:59pm Pacific Time. • Nov. 5, 2022: finalists will be announced and will be asked to submit their code and models for evaluation for track 2. • Nov. 15, 2022: winning teams announced. Prizes Top participants in each track will receive the following prizes: • Gold US$6000 • Silver US$4000 • Bronze US$2000 Additional prizes: • US$1000 for the most innovative approach out of top-6 entries in both tracks • US$1000 given to one of the valid submissions not in top-3 positions in either track For more information regarding the rules and how to participate please refer to the competition website: https://smarts-project.github.io/ submitted by /u/driving_science [link] [comments]  ( 90 min )
    [R] LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale - Facebook AI 2022 - Inference in LLMs with up to 175B parameters without performance degradation and making it possible to use these models on a single server with consumer GPUs!
    Paper: https://arxiv.org/abs/2208.07339 Github: https://github.com/timdettmers/bitsandbytes Software Blogpost: https://huggingface.co/blog/hf-bitsandbytes-integration Emergent Features Blogpost: https://timdettmers.com/2022/08/17/llm-int8-and-emergent-features/ Abstract: Large language models have been widely adopted but require significant GPU memory for inference. We develop a procedure for Int8 matrix multiplication for feed-forward and attention projection layers in transformers, which cut the memory needed for inference by half while retaining full precision performance. With our method, a 175B parameter 16/32-bit checkpoint can be loaded, converted to Int8, and used immediately without performance degradation. This is made possible by understanding and working around propertie…  ( 109 min )
    [D] Drift Detection: Automated Monitoring for Production ML
    One of the elements of an MLOps pipeline is drift detection, which helps you monitor model performance over time and identify when it's time for retraining. Once you've successfully deployed models and are running live inferences in production, you'll encounter yet another obstacle: monitoring model performance over time. We monitor several model performance indicators, including overall scoring, inference speed, latency, accuracy, and finally data drift and model drift. This tech talk covers the algorithms we've developed to automate detection of data drift and model drift, or input and output drift. https://youtu.be/EHjO4k7SE44 submitted by /u/modzykirsten [link] [comments]  ( 89 min )
    [D] How to use Ai to mix images together ?
    Bonjour everybody, I’m a professional artist from France, and I’m really interested by Art assisted by Ai to boost my workflow or give me more inspiration, and creating a final oil painting inspired by these Ai references. But problem : I’m pretty newb to Ai world and there is so many different to use. Usually, with my regular painting process, I just use few references from photographies I took myself, I pin them on a wall, and I create oil painting sketches and color studies after mixing all the element in my head. After that, I will do my final sketch and start painting on a bigger canvas size. Sometimes, I will do the exact same thing, but will do all my sketches on photoshop first and then reproduce the final painting on canvas. But with Ai, everything is changing, and that’s wh…  ( 91 min )
    [D] How do you decide which ML algorithm will work on which data ?
    So, ok I wanna know can you get intuition from data that this ML algorithm will work best for this data or just run all and compare each ones result ? Or there is some thumb rule ? submitted by /u/kanda_bhaji_pav [link] [comments]  ( 91 min )
    [D] any good resources to learn/advance in ML and AI?
    youtube, websites, ect. submitted by /u/sonderind [link] [comments]  ( 88 min )
    NLP Project [P] for Master's Thesis
    I am doing an Online master's program [P] and need recommendations on the topic for a master's thesis. Background: I am data scientist with 10 years of experience but have done only 1-2 projects in NLP/Vision. I've been trying to learn transformers and others deep learning techniques recently. Thought it will be a good idea to take up a topic that covers breadth of nlp.For example - comparing the performance of fine tuning a model against various nlp tasks. What do you think? Suggest me something that could boost my overall profile. submitted by /u/aalwiz099 [link] [comments]  ( 89 min )
    [D] Temporal generalisation in Transformer networks
    Hi, I've been trying to understand temporal generalisation/extrapolation in transformers and i have had conflicting information regarding the capacity of transformers to do the same from various papers. I guess lstms and transformers can do linear extrapolation but nothing otherwise. Is there a way to do parametric extrapolation in transformers ? Any references are welcome. submitted by /u/Cool_Abbreviations_9 [link] [comments]  ( 89 min )
    [D] No Shortcuts To Knowledge: Why AI Needs To Ease Up On Scaling And Learn How To Code
    https://deoxyribose.github.io/No-Shortcuts-to-Knowledge/ submitted by /u/EducationalCicada [link] [comments]  ( 93 min )
    [D] Approaches to new code: create a map of the code structure, does it make sense?
    Most of the time, when I try to implement (and understand) a new code on GitHub I feel overwhelmed by the complexity of it. In my opinion, one of the hardest part is trying to understand its structure and how it works. When you are facing this issue, what is your modus operandi? Does it make sense to produce a map of the code structure? Does it make sense to spend a bit of time to create it while digging into it? If you are already doing it, is there any tool that produces an automatic map of the code? An example can be found here: https://github.com/dvlab-research/ECCV22-P3AFormer-Tracking-Objects-as-Pixel-wise-Distributions/raw/main/figs/model_mind_flow.png submitted by /u/madeInSwamp [link] [comments]  ( 89 min )
    [D] Conferences in the Winter Break
    Hi, currently there are less conference deadlines over the fall and winter period. Which conferences do you recommend submitting computer vision papers to? I have read about SCIA 2023 and ICMVA 2023, has anybody an idea of these conferences or an idea of another CV conferences which have a deadline this year? submitted by /u/SeucheAchat9115 [link] [comments]  ( 89 min )
    [P] Win $150K Prize from PSG Challenge
    Hi Community, we are currently hosting a challenge called “Panoptic Scene Graph Generation (PSG) Challenge”, which asks the participants to solve the PSG task: given a complex scene image as the input, the model should interpret the image with several “subject-verb-object” triplets, which should comprehensively cover the relations in the image. The subject/object should be grounded by a pixel-accurate segmentation mask at the same time. The task is based on our ECCV’22 work: Panoptic Scene Graph Generation. The PSG Challenge is jointly sponsored by International Algorithm Case Competition (hosted by Pazhou Lab) and ECCV’22 SenseHuman Workshop (hosted by MMLab@NTU), with an astonishing $150,000 Prize Pool for the winners (fully sponsored by Pazhou Lab). Champion: 1 Team - 600,000 RMB (~US$90,000) Second Prize: 2 Teams - 100,000 RMB each (~US$15,000 each) Third Prize: 5 Teams - 40,000 RMB each (~US$6,000 each) The preliminary round of the challenge will be ended on Oct. 6th. The top 15 teams will be able to join the final round, which will end around Nov. 25th, (after the CVPR deadline). Official Info of PSG Challenge: The main page of the PSG challenge is https://www.cvmart.net/race/10349/base. We provide an English version in CodaLab to be accessed here. However, the purpose of CodaLab is only to provide information for non-Chinese participants. To download the dataset or evaluate the model for ranking and prize-winning, the participants should only use the links from the official competition website. To Download PSG Dataset: https://www.cvmart.net/race/10349/dataset For Submission and Ranking: https://www.cvmart.net/race/10349/my-submission If you have any questions regarding the registration or the competition itself, please join our slack space and ask us! ​ Hugging Face Demo for the PSG model. The PSG Challenge asks participants to develop a better PSG model. submitted by /u/Sad-Barber-60 [link] [comments]  ( 90 min )
    [P] Compiling ML models' hyperparameter and accuracy metadata. Any previous review papers or projects that can help me along?
    Just wanted to know if there was any previously collected data that could speed things along. Couldn't find any such thing but let me know if there are any past papers that do this. submitted by /u/Typical-Ad-7443 [link] [comments]  ( 104 min )
    [D] How many NN optimization techniques are actually incorporated?
    Hey guys, I’m an undergraduate student just getting into this field so forgive me if this question is dumb. I noticed that a lot of papers at NIPS/ICML/ICLR are concerned with NN optimization; for example, this paper https://arxiv.org/pdf/2010.14501.pdf from ICLR 2021 claims to reduce memory requirement by 3x for various PyTorch models, which ostensibly would be massive. However, it was also “only” cited 8 times, which indicates that it isn’t being used that often. I know that a lot of these NN optimization things are overrated in the sense that they tend to only work in specific circumstances, and aren’t actually broadly applicable. I’d also imagine that this fact means that most DL researchers don’t have time to comb through swaths of NN optimization papers accepted into top conferences to find out which ones are actually broadly useful and reliable. So with that being said: how many NN optimization techniques are actually applied on a large scale? Are the NN libraries typically used like Tensorflow and PyTorch being continuously updated with the latest research? submitted by /u/sjames898 [link] [comments]  ( 93 min )
  • Open

    OptFormer: Towards Universal Hyperparameter Optimization with Transformers
    Posted by Yutian Chen, Staff Research Scientist, DeepMind, and Xingyou (Richard) Song, Research Scientist, Google Research, Brain Team One of the most important aspects in machine learning is hyperparameter optimization, as finding the right hyperparameters for a machine learning task can make or break a model’s performance. Internally, we regularly use Google Vizier as the default platform for hyperparameter optimization. Throughout its deployment over the last 5 years, Google Vizier has been used more than 10 million times, over a vast class of applications, including machine learning applications from vision, reinforcement learning, and language but also scientific applications such as protein discovery and hardware acceleration. As Google Vizier is able to keep track of use patterns i…  ( 22 min )
  • Open

    Startup Digs Into Public Filings With GPU-Driven Machine Learning to Serve Up Alternative Financial Data Services
    When Rachel Carpenter and Joseph French founded Intrinio a decade ago, the fintech revolution had only just begun. But they saw an opportunity to apply machine learning to vast amounts of financial filings to create an alternative data provider among the giants. The startup, based in St. Petersburg, Fla., delivers financial data to hedge funds, Read article > The post Startup Digs Into Public Filings With GPU-Driven Machine Learning to Serve Up Alternative Financial Data Services appeared first on NVIDIA Blog.  ( 6 min )
    Boldly Go: Discover New Frontiers in AI-Powered Transportation at GTC
    AI and the metaverse are revolutionizing every aspect of the way we live, work and play — including how we move. Leaders in the automotive and technology industries will come together at NVIDIA GTC to discuss the newest breakthroughs driving intelligent vehicles, whether in the real world or in simulation. The virtual conference, which runs Read article > The post Boldly Go: Discover New Frontiers in AI-Powered Transportation at GTC appeared first on NVIDIA Blog.  ( 5 min )
    Startup’s Vision AI Software Trains Itself — in One Hour — to Detect Manufacturing Defects in Real Time
    Cameras have been deployed in factories for over a decade — so why, Franz Tschimben wondered, hasn’t automated visual inspection yet become the worldwide standard? This question motivated Tschimben and his colleagues to found Covision Quality, an AI-based visual-inspection software startup that uses NVIDIA technology to transform end-of-line defect detection for the manufacturing industry. “The Read article > The post Startup’s Vision AI Software Trains Itself — in One Hour — to Detect Manufacturing Defects in Real Time appeared first on NVIDIA Blog.  ( 6 min )
    Easy A: GeForce NOW Brings Higher Resolution and Frame Rates for Browser Streaming on PC
    Class is in session this GFN Thursday as GeForce NOW makes the up-grade with support for higher resolutions and frame rates in Chrome browser on PC. It’s the easiest way to spice up a boring study session. When the lecture is over, dive into the six games joining the GeForce NOW library this week, where Read article > The post Easy A: GeForce NOW Brings Higher Resolution and Frame Rates for Browser Streaming on PC appeared first on NVIDIA Blog.  ( 5 min )
  • Open

    How AI and ML are Transforming the FinTech Industry
    By 2022, the AI in FinTech market will be worth $7.25 billion. Artificial Intelligence (AI) is driving a new wave in FinTech. From banks to…  ( 12 min )
  • Open

    Help with CNN LSTM Image Captioning
    A project I have been working on, and I am completely a beginner in these topics (familiar with Image Processing). I'm literally struggling to get it started, got multiple problems, but mostly with some array size issues. Can somebody please help me figure it out? submitted by /u/Maleficient_Entity [link] [comments]  ( 96 min )
  • Open

    Learning to Operate an Electric Vehicle Charging Station Considering Vehicle-grid Integration. (arXiv:2111.01294v2 [cs.LG] UPDATED)
    The rapid adoption of electric vehicles (EVs) calls for the widespread installation of EV charging stations. To maximize the profitability of charging stations, intelligent controllers that provide both charging and electric grid services are in great need. However, it is challenging to determine the optimal charging schedule due to the uncertain arrival time and charging demands of EVs. In this paper, we propose a novel centralized allocation and decentralized execution (CADE) reinforcement learning (RL) framework to maximize the charging station's profit. In the centralized allocation process, EVs are allocated to either the waiting or charging spots. In the decentralized execution process, each charger makes its own charging/discharging decision while learning the action-value functions from a shared replay memory. This CADE framework significantly improves the scalability and sample efficiency of the RL algorithm. Numerical results show that the proposed CADE framework is both computationally efficient and scalable, and significantly outperforms the baseline model predictive control (MPC). We also provide an in-depth analysis of the learned action-value function to explain the inner working of the reinforcement learning agent.  ( 3 min )
    Model-Aware Contrastive Learning: Towards Escaping Uniformity-Tolerance Dilemma in Training. (arXiv:2207.07874v2 [cs.LG] UPDATED)
    Contrastive learning (CL) has achieved remarkable success in learning transferable representations. It has been identified that the temperature $ \tau $ of CL loss plays an essential role in automatically concentrating on hard negative samples. However, recent work also indicates a uniformity-tolerance dilemma (UTD) connected to $ \tau $, which will lead to unexpected performance degradation. We argue that it is the fixity of temperature that is inextricably linked to UTD and suboptimal embedding space. To tackle the challenge of UTD, we enrich the CL loss family by presenting a Model-Aware Contrastive Learning (MACL) strategy. In MACL, the temperature parameter is adaptive to the magnitude of alignment that reflects the basic confidence of the instance discrimination task. Lower alignment implies poor discrimination for the undertrained phase, then there is less possibility that the high similarity region contains latent positive samples (LPs). Thus, a small $ \tau $ can impose larger penalties on hard negative samples to learn uniformly informative embeddings. Instead, a larger $ \tau $ in the well-trained phase facilitates the exploration of semantic structures due to its increased tolerance for LPs. Besides, theoretically, we uncover why contrastive learning requires a large number of negative samples from a unified gradient reduction perspective. Based on MACL and these analyses, a new CL loss is proposed. Experimental results validate the effectiveness of our approach to escape UTD, which can achieve state-of-the-art performance and training with fewer negative samples.  ( 3 min )
    Classifications of Skull Fractures using CT Scan Images via CNN with Lazy Learning Approach. (arXiv:2203.10786v1 [eess.IV] CROSS LISTED)
    Classification of skull fracture is a challenging task for both radiologists and researchers. Skull fractures result in broken pieces of bone, which can cut into the brain and cause bleeding and other injury types. So it is vital to detect and classify the fracture very early. In real world, often fractures occur at multiple sites. This makes it harder to detect the fracture type where many fracture types might summarize a skull fracture. Unfortunately, manual detection of skull fracture and the classification process is time-consuming, threatening a patient's life. Because of the emergence of deep learning, this process could be automated. Convolutional Neural Networks (CNNs) are the most widely used deep learning models for image categorization because they deliver high accuracy and outstanding outcomes compared to other models. We propose a new model called SkullNetV1 comprising a novel CNN by taking advantage of CNN for feature extraction and lazy learning approach which acts as a classifier for classification of skull fractures from brain CT images to classify five fracture types. Our suggested model achieved a subset accuracy of 88%, an F1 score of 93%, the Area Under the Curve (AUC) of 0.89 to 0.98, a Hamming score of 92% and a Hamming loss of 0.04 for this seven-class multi-labeled classification.  ( 3 min )
    Deep Representations for Time-varying Brain Datasets. (arXiv:2205.11648v3 [cs.LG] UPDATED)
    Finding an appropriate representation of dynamic activities in the brain is crucial for many downstream applications. Due to its highly dynamic nature, temporally averaged fMRI (functional magnetic resonance imaging) can only provide a narrow view of underlying brain activities. Previous works lack the ability to learn and interpret the latent dynamics in brain architectures. This paper builds an efficient graph neural network model that incorporates both region-mapped fMRI sequences and structural connectivities obtained from DWI (diffusion-weighted imaging) as inputs. We find good representations of the latent brain dynamics through learning sample-level adaptive adjacency matrices and performing a novel multi-resolution inner cluster smoothing. We also attribute inputs with integrated gradients, which enables us to infer (1) highly involved brain connections and subnetworks for each task, (2) temporal keyframes of imaging sequences that characterize tasks, and (3) subnetworks that discriminate between individual subjects. This ability to identify critical subnetworks that characterize signal states across heterogeneous tasks and individuals is of great importance to neuroscience and other scientific domains. Extensive experiments and ablation studies demonstrate our proposed method's superiority and efficiency in spatial-temporal graph signal modeling with insightful interpretations of brain dynamics.  ( 3 min )
    Supervised PCA: A Multiobjective Approach. (arXiv:2011.05309v4 [stat.ML] UPDATED)
    Methods for supervised principal component analysis (SPCA) aim to incorporate label information into principal component analysis (PCA), so that the extracted features are more useful for a prediction task of interest. Prior work on SPCA has focused primarily on optimizing prediction error, and has neglected the value of maximizing variance explained by the extracted features. We propose a new method for SPCA that addresses both of these objectives jointly, and demonstrate empirically that our approach dominates existing approaches, i.e., outperforms them with respect to both prediction error and variation explained. Our approach accommodates arbitrary supervised learning losses and, through a statistical reformulation, provides a novel low-rank extension of generalized linear models.  ( 2 min )
    Wave simulation in non-smooth media by PINN with quadratic neural network and PML condition. (arXiv:2208.08276v1 [physics.geo-ph])
    Frequency-domain simulation of seismic waves plays an important role in seismic inversion, but it remains challenging in large models. The recently proposed physics-informed neural network (PINN), as an effective deep learning method, has achieved successful applications in solving a wide range of partial differential equations (PDEs), and there is still room for improvement on this front. For example, PINN can lead to inaccurate solutions when PDE coefficients are non-smooth and describe structurally-complex media. In this paper, we solve the acoustic and visco-acoustic scattered-field wave equation in the frequency domain with PINN instead of the wave equation to remove source singularity. We first illustrate that non-smooth velocity models lead to inaccurate wavefields when no boundary conditions are implemented in the loss function. Then, we add the perfectly matched layer (PML) conditions in the loss function of PINN and design a quadratic neural network to overcome the detrimental effects of non-smooth models in PINN. We show that PML and quadratic neurons improve the results as well as attenuation and discuss the reason for this improvement. We also illustrate that a network trained during a wavefield simulation can be used to pre-train the neural network of another wavefield simulation after PDE-coefficient alteration and improve the convergence speed accordingly. This pre-training strategy should find application in iterative full waveform inversion (FWI) and time-lag target-oriented imaging when the model perturbation between two consecutive iterations or two consecutive experiments can be small.
    Learning Generative Factors of EEG Data with Variational auto-encoders. (arXiv:2206.01939v3 [cs.LG] UPDATED)
    Electroencephalography produces high-dimensional, stochastic data from which it might be challenging to extract high-level knowledge about the phenomena of interest. We address this challenge by applying the framework of variational auto-encoders to 1) classify multiple pathologies and 2) recover the neurological mechanisms of those pathologies in a data-driven manner. Our framework learns generative factors of data related to pathologies. We provide an algorithm to decode those factors further and discover how different pathologies affect observed data. We illustrate the applicability of the proposed approach to identifying schizophrenia, either followed or not by auditory verbal hallucinations. We further demonstrate the ability of the framework to learn disease-related mechanisms consistent with current domain knowledge. We also compare the proposed framework with several benchmark approaches and indicate its classification performance and interpretability advantages.  ( 2 min )
    Discovering Agents. (arXiv:2208.08345v1 [cs.AI])
    Causal models of agents have been used to analyse the safety aspects of machine learning systems. But identifying agents is non-trivial -- often the causal model is just assumed by the modeler without much justification -- and modelling failures can lead to mistakes in the safety analysis. This paper proposes the first formal causal definition of agents -- roughly that agents are systems that would adapt their policy if their actions influenced the world in a different way. From this we derive the first causal discovery algorithm for discovering agents from empirical data, and give algorithms for translating between causal models and game-theoretic influence diagrams. We demonstrate our approach by resolving some previous confusions caused by incorrect causal modelling of agents.
    HypoSVI: Hypocenter inversion with Stein variational inference and Physics Informed Neural Networks. (arXiv:2101.03271v3 [physics.geo-ph] UPDATED)
    We introduce a scheme for probabilistic hypocenter inversion with Stein variational inference. Our approach uses a differentiable forward model in the form of a physics informed neural network, which we train to solve the Eikonal equation. This allows for rapid approximation of the posterior by iteratively optimizing a collection of particles against a kernelized Stein discrepancy. We show that the method is well-equipped to handle highly multimodal posterior distributions, which are common in hypocentral inverse problems. A suite of experiments is performed to examine the influence of the various hyperparameters. Once trained, the method is valid for any seismic network geometry within the study area without the need to build travel time tables. We show that the computational demands scale efficiently with the number of differential times, making it ideal for large-N sensing technologies like Distributed Acoustic Sensing. The techniques outlined in this manuscript have considerable implications beyond just ray-tracing procedures, with the work flow applicable to other fields with computationally expensive inversion procedures such as full waveform inversion.
    Predicting Corporate Risk by Jointly Modeling Company Networks and Dialogues in Earnings Conference Calls. (arXiv:2206.06174v3 [cs.CL] UPDATED)
    Earnings conference calls are significant information events for volatility forecasting, which is essential for financial risk management and asset pricing. Although some recent volatility forecasting models have utilized the textual content of conference calls, the dialogue structures of conference calls and company relationships are almost ignored in extant literature. To bridge this gap, we propose a new model called Temporal Virtual Graph Neural Network (TVGNN) for volatility forecasting by jointly modeling conference call dialogues and company networks. Our model differs from existing models in several important ways. First, we propose to exploit more dialogue structures by encoding position, utterance, speaker role, and Q\&A segments. Second, we propose to encode the market states for volatility forecasting by extending the Gated Recurrent Units (GRU). Third, we propose a new method for constructing temporal company networks in which the messages can only flow from temporally preceding to successive nodes, and extend the Graph Attention Networks (GAT) for modeling company relationships. We collect conference call transcripts of S\&P500 companies from 2008 to 2019, and construct a dataset of conference call dialogues with additional information on dialogue structures and company networks. Empirical results on our dataset demonstrate the superiority of our model over competitive baselines for volatility forecasting. We also conduct supplementary analyses to examine the effectiveness of our model's key components and interpretability.
    From Shapley back to Pearson: Hypothesis Testing via the Shapley Value. (arXiv:2207.07038v2 [cs.LG] UPDATED)
    Machine learning models, in particular artificial neural networks, are increasingly used to inform decision making in high-stakes scenarios across a variety of fields -- from financial services, to public safety, and healthcare. While neural networks have achieved remarkable performance in many settings, their complex nature raises concerns on their reliability, trustworthiness, and fairness in real-world scenarios. As a result, several a-posteriori explanation methods have been proposed to highlight the features that influence a model's prediction. Notably, the Shapley value -- a game theoretic quantity that satisfies several desirable properties -- has gained popularity in the machine learning explainability literature. More traditionally, however, feature importance in statistical learning has been formalized by conditional independence, and a standard way to test for it is via Conditional Randomization Tests (CRTs). So far, these two perspectives on interpretability and feature importance have been considered distinct and separate. In this work, we show that Shapley-based explanation methods and conditional independence testing for feature importance are closely related. More precisely, we prove that for binary classification problems, evaluating a Shapley coefficient amounts to performing a specific set of conditional independence tests, as implemented by a procedure similar to the CRT but for a different null hypothesis. Furthermore, the obtained game-theoretic values upper bound the $p$-values of such tests. As a result, we grant large Shapley coefficients with a precise statistical sense of importance with controlled type I error.
    Feature Structure Distillation with Centered Kernel Alignment in BERT Transferring. (arXiv:2204.08922v2 [cs.CL] UPDATED)
    Knowledge distillation is an approach to transfer information on representations from a teacher to a student by reducing their difference. A challenge of this approach is to reduce the flexibility of the student's representations inducing inaccurate learning of the teacher's knowledge. To resolve it in BERT transferring, we investigate distillation of structures of representations specified to three types: intra-feature, local inter-feature, global inter-feature structures. To transfer them, we introduce \textit{feature structure distillation} methods based on the Centered Kernel Alignment, which assigns a consistent value to similar features structures and reveals more informative relations. In particular, a memory-augmented transfer method with clustering is implemented for the global structures. In the experiments on the nine tasks for language understanding of the GLUE dataset, the proposed methods effectively transfer the three types of structures and improve performance compared to state-of-the-art distillation methods. Indeed, the code for the methods is available in https://github.com/maroo-sky/FSD  ( 2 min )
    Learning Neural Set Functions Under the Optimal Subset Oracle. (arXiv:2203.01693v2 [cs.LG] UPDATED)
    Learning neural set functions becomes increasingly more important in many applications like product recommendation and compound selection in AI-aided drug discovery. The majority of existing works study methodologies of set function learning under the function value oracle, which, however, requires expensive supervision signals. This renders it impractical for applications with only weak supervisions under the Optimal Subset (OS) oracle, the study of which is surprisingly overlooked. In this work, we present a principled yet practical maximum likelihood learning framework, termed as EquiVSet, that simultaneously meets the following desiderata of learning set functions under the OS oracle: i) permutation invariance of the set mass function being modeled; ii) permission of varying ground set; iii) minimum prior; and iv) scalability. The main components of our framework involve: an energy-based treatment of the set mass function, DeepSet-style architectures to handle permutation invariance, mean-field variational inference, and its amortized variants. Thanks to the elegant combination of these advanced architectures, empirical studies on three real-world applications (including Amazon product recommendation, set anomaly detection, and compound selection for virtual screening) demonstrate that EquiVSet outperforms the baselines by a large margin.  ( 2 min )
    Analysis of Digitalized ECG Signals Based on Artificial Intelligence and Spectral Analysis Methods Specialized in ARVC. (arXiv:2203.00504v2 [eess.SP] UPDATED)
    Arrhythmogenic right ventricular cardiomyopathy (ARVC) is an inherited heart muscle disease that appears between the second and forth decade of a patient's life, being responsible for 20% of sudden cardiac deaths before the age of 35. The effective and punctual diagnosis of this disease based on Electrocardiograms (ECGs) could have a vital role in reducing premature cardiovascular mortality. In our analysis, we firstly outline the digitalization process of paper - based ECG signals enhanced by a spatial filter aiming to eliminate dark regions in the dataset's images that do not correspond to ECG waveform, producing undesirable noise. Next, we propose the utilization of a low - complexity convolutional neural network for the detection of an arrhythmogenic heart disease, that has not been studied through the usage of deep learning methodology to date, achieving high classification accuracy, namely 99.98% training and 98.6% testing accuracy, on a disease the major identification criterion of which are infinitesimal millivolt variations in the ECG's morphology, in contrast with other arrhythmogenic abnormalities. Finally, by performing spectral analysis we investigate significant differentiations in the field of frequencies between normal ECGs and ECGs corresponding to patients suffering from ARVC. In 16 out of the 18 frequencies where we encounter statistically significant differentiations, the normal ECGs are characterized by greater normalized amplitudes compared to the abnormal ones. The overall research carried out in this article highlights the importance of integrating mathematical methods into the examination and effective diagnosis of various diseases, aiming to a substantial contribution to their successful treatment.  ( 3 min )
    E2FL: Equal and Equitable Federated Learning. (arXiv:2205.10454v2 [cs.LG] UPDATED)
    Federated Learning (FL) enables data owners to train a shared global model without sharing their private data. Unfortunately, FL is susceptible to an intrinsic fairness issue: due to heterogeneity in clients' data distributions, the final trained model can give disproportionate advantages across the participating clients. In this work, we present Equal and Equitable Federated Learning (E2FL) to produce fair federated learning models by preserving two main fairness properties, equity and equality, concurrently. We validate the efficiency and fairness of E2FL in different real-world FL applications, and show that E2FL outperforms existing baselines in terms of the resulting efficiency, fairness of different groups, and fairness among all individual clients.  ( 2 min )
    FRL: Federated Rank Learning. (arXiv:2110.04350v3 [cs.LG] UPDATED)
    Federated learning (FL) allows mutually untrusted clients to collaboratively train a common machine learning model without sharing their private/proprietary training data among each other. FL is unfortunately susceptible to poisoning by malicious clients who aim to hamper the accuracy of the commonly trained model through sending malicious model updates during FL's training process. We argue that the key factor to the success of poisoning attacks against existing FL systems is the large space of model updates available to the clients, allowing malicious clients to search for the most poisonous model updates, e.g., by solving an optimization problem. To address this, we propose Federated Rank Learning (FRL). FRL reduces the space of client updates from model parameter updates (a continuous space of float numbers) in standard FL to the space of parameter rankings (a discrete space of integer values). To be able to train the global model using parameter ranks (instead of parameter weights), FRL leverage ideas from recent supermasks training mechanisms. Specifically, FRL clients rank the parameters of a randomly initialized neural network (provided by the server) based on their local training data. The FRL server uses a voting mechanism to aggregate the parameter rankings submitted by clients in each training epoch to generate the global ranking of the next training epoch. Intuitively, our voting-based aggregation mechanism prevents poisoning clients from making significant adversarial modifications to the global model, as each client will have a single vote! We demonstrate the robustness of FRL to poisoning through analytical proofs and experimentation. We also show FRL's high communication efficiency. Our experiments demonstrate the superiority of FRL in real-world FL settings.  ( 3 min )
    Are Transformers Effective for Time Series Forecasting?. (arXiv:2205.13504v3 [cs.AI] UPDATED)
    Recently, there has been a surge of Transformer-based solutions for the long-term time series forecasting (LTSF) task. Despite the growing performance over the past few years, we question the validity of this line of research in this work. Specifically, Transformers is arguably the most successful solution to extract the semantic correlations among the elements in a long sequence. However, in time series modeling, we are to extract the temporal relations in an ordered set of continuous points. While employing positional encoding and using tokens to embed sub-series in Transformers facilitate preserving some ordering information, the nature of the \emph{permutation-invariant} self-attention mechanism inevitably results in temporal information loss. To validate our claim, we introduce a set of embarrassingly simple one-layer linear models named LTSF-Linear for comparison. Experimental results on nine real-life datasets show that LTSF-Linear surprisingly outperforms existing sophisticated Transformer-based LTSF models in all cases, and often by a large margin. Moreover, we conduct comprehensive empirical studies to explore the impacts of various design elements of LTSF models on their temporal relation extraction capability. We hope this surprising finding opens up new research directions for the LTSF task. We also advocate revisiting the validity of Transformer-based solutions for other time series analysis tasks (e.g., anomaly detection) in the future. Code is available at: \url{https://github.com/cure-lab/LTSF-Linear}.
    Automated Learning for Deformable Medical Image Registration by Jointly Optimizing Network Architectures and Objective Functions. (arXiv:2203.06810v2 [cs.CV] UPDATED)
    Deformable image registration plays a critical role in various tasks of medical image analysis. A successful registration algorithm, either derived from conventional energy optimization or deep networks requires tremendous efforts from computer experts to well design registration energy or to carefully tune network architectures for the specific type of medical data. To tackle the aforementioned problems, this paper proposes an automated learning registration algorithm (AutoReg) that cooperatively optimizes both architectures and their corresponding training objectives, enabling non-computer experts, e.g., medical/clinical users, to conveniently find off-the-shelf registration algorithms for diverse scenarios. Specifically, we establish a triple-level framework to deduce registration network architectures and objectives with an auto-searching mechanism and cooperating optimization. We conduct image registration experiments on multi-site volume datasets and various registration tasks. Extensive results demonstrate that our AutoReg may automatically learn an optimal deep registration network for given volumes and achieve state-of-the-art performance, also significantly improving computation efficiency than the mainstream UNet architectures (from 0.558 to 0.270 seconds for a 3D image pair on the same configuration).  ( 2 min )
    Disentangled Modeling of Domain and Relevance for Adaptable Dense Retrieval. (arXiv:2208.05753v1 [cs.IR] CROSS LISTED)
    Recent advance in Dense Retrieval (DR) techniques has significantly improved the effectiveness of first-stage retrieval. Trained with large-scale supervised data, DR models can encode queries and documents into a low-dimensional dense space and conduct effective semantic matching. However, previous studies have shown that the effectiveness of DR models would drop by a large margin when the trained DR models are adopted in a target domain that is different from the domain of the labeled data. One of the possible reasons is that the DR model has never seen the target corpus and thus might be incapable of mitigating the difference between the training and target domains. In practice, unfortunately, training a DR model for each target domain to avoid domain shift is often a difficult task as it requires additional time, storage, and domain-specific data labeling, which are not always available. To address this problem, in this paper, we propose a novel DR framework named Disentangled Dense Retrieval (DDR) to support effective and flexible domain adaptation for DR models. DDR consists of a Relevance Estimation Module (REM) for modeling domain-invariant matching patterns and several Domain Adaption Modules (DAMs) for modeling domain-specific features of multiple target corpora. By making the REM and DAMs disentangled, DDR enables a flexible training paradigm in which REM is trained with supervision once and DAMs are trained with unsupervised data. Comprehensive experiments in different domains and languages show that DDR significantly improves ranking performance compared to strong DR baselines and substantially outperforms traditional retrieval methods in most scenarios.  ( 3 min )
    Score-Based Generative Models Detect Manifolds. (arXiv:2206.01018v2 [stat.ML] UPDATED)
    Score-based generative models (SGMs) need to approximate the scores $\nabla \log p_t$ of the intermediate distributions as well as the final distribution $p_T$ of the forward process. The theoretical underpinnings of the effects of these approximations are still lacking. We find precise conditions under which SGMs are able to produce samples from an underlying (low-dimensional) data manifold $\mathcal{M}$. This assures us that SGMs are able to generate the "right kind of samples". For example, taking $\mathcal{M}$ to be the subset of images of faces, we find conditions under which the SGM robustly produces an image of a face, even though the relative frequencies of these images might not accurately represent the true data generating distribution. Moreover, this analysis is a first step towards understanding the generalization properties of SGMs: Taking $\mathcal{M}$ to be the set of all training samples, our results provide a precise description of when the SGM memorizes its training data.  ( 2 min )
    Label Flipping Data Poisoning Attack Against Wearable Human Activity Recognition System. (arXiv:2208.08433v1 [cs.CR])
    Human Activity Recognition (HAR) is a problem of interpreting sensor data to human movement using an efficient machine learning (ML) approach. The HAR systems rely on data from untrusted users, making them susceptible to data poisoning attacks. In a poisoning attack, attackers manipulate the sensor readings to contaminate the training set, misleading the HAR to produce erroneous outcomes. This paper presents the design of a label flipping data poisoning attack for a HAR system, where the label of a sensor reading is maliciously changed in the data collection phase. Due to high noise and uncertainty in the sensing environment, such an attack poses a severe threat to the recognition system. Besides, vulnerability to label flipping attacks is dangerous when activity recognition models are deployed in safety-critical applications. This paper shades light on how to carry out the attack in practice through smartphone-based sensor data collection applications. This is an earlier research work, to our knowledge, that explores attacking the HAR models via label flipping poisoning. We implement the proposed attack and test it on activity recognition models based on the following machine learning algorithms: multi-layer perceptron, decision tree, random forest, and XGBoost. Finally, we evaluate the effectiveness of K-nearest neighbors (KNN)-based defense mechanism against the proposed attack.  ( 3 min )
    DSFormer: A Dual-domain Self-supervised Transformer for Accelerated Multi-contrast MRI Reconstruction. (arXiv:2201.10776v2 [eess.IV] UPDATED)
    Multi-contrast MRI (MC-MRI) captures multiple complementary imaging modalities to aid in radiological decision-making. Given the need for lowering the time cost of multiple acquisitions, current deep accelerated MRI reconstruction networks focus on exploiting the redundancy between multiple contrasts. However, existing works are largely supervised with paired data and/or prohibitively expensive fully-sampled MRI sequences. Further, reconstruction networks typically rely on convolutional architectures which are limited in their capacity to model long-range interactions and may lead to suboptimal recovery of fine anatomical detail. To these ends, we present a dual-domain self-supervised transformer (DSFormer) for accelerated MC-MRI reconstruction. DSFormer develops a deep conditional cascade transformer (DCCT) consisting of several cascaded Swin transformer reconstruction networks (SwinRN) trained under two deep conditioning strategies to enable MC-MRI information sharing. We further present a dual-domain (image and k-space) self-supervised learning strategy for DCCT to alleviate the costs of acquiring fully sampled training data. DSFormer generates high-fidelity reconstructions which experimentally outperform current fully-supervised baselines. Moreover, we find that DSFormer achieves nearly the same performance when trained either with full supervision or with our proposed dual-domain self-supervision.
    Conformal Inference for Online Prediction with Arbitrary Distribution Shifts. (arXiv:2208.08401v1 [stat.ME])
    Conformal inference is a flexible methodology for transforming the predictions made by any black-box model (e.g. neural nets, random forests) into valid prediction sets. The only necessary assumption is that the training and test data be exchangeable (e.g. i.i.d.). Unfortunately, this assumption is usually unrealistic in online environments in which the processing generating the data may vary in time and consecutive data-points are often temporally correlated. In this article, we develop an online algorithm for producing prediction intervals that are robust to these deviations. Our methods build upon conformal inference and thus can be combined with any black-box predictor. We show that the coverage error of our algorithm is controlled by the size of the underlying change in the environment and thus directly connect the size of the distribution shift with the difficulty of the prediction problem. Finally, we apply our procedure in two real-world settings and find that our method produces robust prediction intervals under real-world dynamics.
    Motion Inbetweening via Deep $\Delta$-Interpolator. (arXiv:2201.06701v4 [cs.LG] UPDATED)
    We show that the task of synthesizing human motion conditioned on a set of key frames can be solved more accurately and effectively if a deep learning based interpolator operates in the delta mode using the spherical linear interpolator as a baseline. We empirically demonstrate the strength of our approach on publicly available datasets achieving state-of-the-art performance. We further generalize these results by showing that the $\Delta$-regime is viable with respect to the reference of the last known frame (also known as the zero-velocity model). This supports the more general conclusion that operating in the reference frame local to input frames is more accurate and robust than in the global (world) reference frame advocated in previous work. Our code is publicly available at https://github.com/boreshkinai/delta-interpolator.  ( 2 min )
    Physics-Guided Discovery of Highly Nonlinear Parametric Partial Differential Equations. (arXiv:2106.01078v2 [cs.LG] UPDATED)
    Partial differential equations (PDEs) fitting scientific data can represent physical laws with explainable mechanisms for various mathematically-oriented subjects. The data-driven discovery of PDEs from scientific data thrives as a new attempt to model complex phenomena in nature, but the effectiveness of current practice is typically limited by the scarcity of data and the complexity of phenomena. Especially, the discovery of PDEs with highly nonlinear coefficients from low-quality data remains largely under-addressed. To deal with this challenge, we propose a novel physics-guided learning method, which can not only encode observation knowledge such as initial and boundary conditions but also incorporate the basic physical principles and laws to guide the model optimization. We empirically demonstrate that the proposed method is more robust against data noise and sparsity, and can reduce the estimation error by a large margin; moreover, for the first time we are able to discover PDEs with highly nonlinear coefficients. With the promising performance, the proposed method pushes forward the boundary of the PDEs that can be found by machine learning models for scientific discovery.  ( 2 min )
    Frequency-Severity Experience Rating based on Latent Markovian Risk Profiles. (arXiv:2109.01413v2 [stat.AP] UPDATED)
    Bonus-Malus Systems traditionally consider a customer's number of claims irrespective of their sizes, even though these components are dependent in practice. We propose a novel joint experience rating approach based on latent Markovian risk profiles to allow for a positive or negative individual frequency-severity dependence. The latent profiles evolve over time in a Hidden Markov Model to capture updates in a customer's claims experience, making claim counts and sizes conditionally independent. We show that the resulting risk premia lead to a dynamic, claims experience-weighted mixture of standard credibility premia. The proposed approach is applied to a Dutch automobile insurance portfolio and identifies customer risk profiles with distinctive claiming behavior. These profiles, in turn, enable us to better distinguish between customer risks.  ( 2 min )
    A Hybrid SFANC-FxNLMS Algorithm for Active Noise Control based on Deep Learning. (arXiv:2208.08082v1 [eess.SY])
    The selective fixed-filter active noise control (SFANC) method selecting the best pre-trained control filters for various types of noise can achieve a fast response time. However, it may lead to large steady-state errors due to inaccurate filter selection and the lack of adaptability. In comparison, the filtered-X normalized least-mean-square (FxNLMS) algorithm can obtain lower steady-state errors through adaptive optimization. Nonetheless, its slow convergence has a detrimental effect on dynamic noise attenuation. Therefore, this paper proposes a hybrid SFANC-FxNLMS approach to overcome the adaptive algorithm's slow convergence and provide a better noise reduction level than the SFANC method. A lightweight one-dimensional convolutional neural network (1D CNN) is designed to automatically select the most suitable pre-trained control filter for each frame of the primary noise. Meanwhile, the FxNLMS algorithm continues to update the coefficients of the chosen pre-trained control filter at the sampling rate. Owing to the effective combination of the two algorithms, experimental results show that the hybrid SFANC-FxNLMS algorithm can achieve a rapid response time, a low noise reduction error, and a high degree of robustness.
    Generative Thermal Design Through Boundary Representation and Multi-Agent Cooperative Environment. (arXiv:2208.07952v1 [cs.LG])
    Generative design has been growing across the design community as a viable method for design space exploration. Thermal design is more complex than mechanical or aerodynamic design because of the additional convection-diffusion equation and its pertinent boundary interaction. We present a generative thermal design using cooperative multi-agent deep reinforcement learning and continuous geometric representation of the fluid and solid domain. The proposed framework consists of a pre-trained neural network surrogate model as an environment to predict heat transfer and pressure drop of the generated geometries. The design space is parameterized by composite Bezier curve to solve multiple fin shape optimization. We show that our multi-agent framework can learn the policy for design strategy using multi-objective reward without the need for shape derivation or differentiable objective function.
    CoSimGNN: Towards Large-scale Graph Similarity Computation. (arXiv:2005.07115v7 [cs.LG] UPDATED)
    The ability to compute similarity scores between graphs based on metrics such as Graph Edit Distance (GED) is important in many real-world applications. Computing exact GED values is typically an NP-hard problem and traditional algorithms usually achieve an unsatisfactory trade-off between accuracy and efficiency. Recently, Graph Neural Networks (GNNs) provide a data-driven solution for this task, which is more efficient while maintaining prediction accuracy in small graph (around 10 nodes per graph) similarity computation. Existing GNN-based methods, which either respectively embeds two graphs (lack of low-level cross-graph interactions) or deploy cross-graph interactions for whole graph pairs (redundant and time-consuming), are still not able to achieve competitive results when the number of nodes in graphs increases. In this paper, we focus on similarity computation for large-scale graphs and propose the "embedding-coarsening-matching" framework CoSimGNN, which first embeds and coarsens large graphs with adaptive pooling operation and then deploys fine-grained interactions on the coarsened graphs for final similarity scores. Furthermore, we create several synthetic datasets which provide new benchmarks for graph similarity computation. Detailed experiments on both synthetic and real-world datasets have been conducted and CoSimGNN achieves the best performance while the inference time is at most 1/3 of that of previous state-of-the-art.  ( 3 min )
    Commander's Intent: A Dataset and Modeling Approach for Human-AI Task Specification in Strategic Play. (arXiv:2208.08374v1 [cs.AI])
    Effective Human-AI teaming requires the ability to communicate the goals of the team and constraints under which you need the agent to operate. Providing the ability to specify the shared intent or operation criteria of the team can enable an AI agent to perform its primary function while still being able to cater to the specific desires of the current team. While significant work has been conducted to instruct an agent to perform a task, via language or demonstrations, prior work lacks a focus on building agents which can operate within the parameters specified by a team. Worse yet, there is a dearth of research pertaining to enabling humans to provide their specifications through unstructured, naturalist language. In this paper, we propose the use of goals and constraints as a scaffold to modulate and evaluate autonomous agents. We contribute to this field by presenting a novel dataset, and an associated data collection protocol, which maps language descriptions to goals and constraints corresponding to specific strategies developed by human participants for the board game Risk. Leveraging state-of-the-art language models and augmentation procedures, we develop a machine learning framework which can be used to identify goals and constraints from unstructured strategy descriptions. To empirically validate our approach we conduct a human-subjects study to establish a human-baseline for our dataset. Our results show that our machine learning architecture is better able to interpret unstructured language descriptions into strategy specifications than human raters tasked with performing the same machine translation task (F(1,272.53) = 17.025, p < 0.001).  ( 3 min )
    Superior generalization of smaller models in the presence of significant label noise. (arXiv:2208.08003v1 [cs.LG])
    The benefits of over-parameterization in achieving superior generalization performance have been shown in several recent studies, justifying the trend of using larger models in practice. In the context of robust learning however, the effect of neural network size has not been well studied. In this work, we find that in the presence of a substantial fraction of mislabeled examples, increasing the network size beyond some point can be harmful. In particular, the originally monotonic or `double descent' test loss curve (w.r.t. network width) turns into a U-shaped or a double U-shaped curve when label noise increases, suggesting that the best generalization is achieved by some model with intermediate size. We observe that when network size is controlled by density through random pruning, similar test loss behaviour is observed. We also take a closer look into both phenomenon through bias-variance decomposition and theoretically characterize how label noise shapes the variance term. Similar behavior of the test loss can be observed even when state-of-the-art robust methods are applied, indicating that limiting the network size could further boost existing methods. Finally, we empirically examine the effect of network size on the smoothness of learned functions, and find that the originally negative correlation between size and smoothness is flipped by label noise.
    An Adjoint-Free Algorithm for CNOP via Sampling. (arXiv:2208.00956v2 [math.OC] UPDATED)
    In this paper, we propose a sampling algorithm based on statistical machine learning to obtain conditional nonlinear optimal perturbation (CNOP), which is essentially different from the traditional deterministic optimization methods. The new approach does not only reduce the extremely expensive gradient (first-order) information directly by the objective value (zeroth-order) information, but also avoid the use of adjoint technique that gives rise to the huge storage problem and the instability from linearization. Meanwhile, an intuitive anlysis and a rigorous concentration inequality for the approximate gradient by sampling are shown. The numerical experiments to obtain the CNOPs by the performance of standard spatial sturctures for a theoretical model, Burgers equation with small viscosity, demonstrate that at the cost of losing accuracy, fewer samples spend time relatively shorter than the adjoint-based method and directly from definition. Finally, we reveal that the nonlinear time evolution of the CNOPs obtained by all the algorithms are almost consistent with the quantity of norm square of perturbations, their difference and relative difference on the basis of the definition method.
    Which Factors Drive Open Access Publishing? A Springer Nature Case Study. (arXiv:2208.08221v1 [cs.DL])
    Open Access (OA) facilitates access to articles. But, authors or funders often must pay the publishing costs preventing authors who do not receive financial support from participating in OA publishing and citation advantage for OA articles. OA may exacerbate existing inequalities in the publication system rather than overcome them. To investigate this, we studied 522,664 articles published by Springer Nature. Employing statistical methods, we describe the relationship between authors affiliated with countries from different income levels, their choice of publishing (OA or closed access), and the citation impact of their papers. A machine learning classification method helped us to explore the association between OA-publishing and attributes of the author, especially eligibility for APC-waivers or discounts, journal, country, and paper. The results indicate that authors eligible for the APC-waivers publish more in gold-OA-journals than other authors. In contrast, authors eligible for an APC discount have the lowest ratio of OA publications, leading to the assumption that this discount insufficiently motivates authors to publish in a gold-OA-journal. The rank of journals is a significant driver for publishing in a gold-OA-journal, whereas the OA option is mostly avoided in hybrid journals. Seniority, experience with OA publications, and the scientific field are the most decisive factors in OA-publishing.
    Transformer Vs. MLP-Mixer Exponential Expressive Gap For NLP Problems. (arXiv:2208.08191v1 [cs.CL])
    Vision-Transformers are widely used in various vision tasks. Meanwhile, there is another line of works starting with the MLP-mixer trying to achieve similar performance using mlp-based architectures. Interestingly, until now none reported using them for NLP tasks, additionally until now non of those mlp-based architectures claimed to achieve state-of-the-art in vision tasks. In this paper, we analyze the expressive power of mlp-based architectures in modeling dependencies between multiple different inputs simultaneously, and show an exponential gap between the attention and the mlp-based mechanisms. Our results suggest a theoretical explanation for the mlp inability to compete with attention-based mechanisms in NLP problems, they also suggest that the performance gap in vision tasks may be due to the mlp relative weakness in modeling dependencies between multiple different locations, and that combining smart input permutations to the mlp architectures may not suffice alone to close the performance gap.
    GraPE: fast and scalable Graph Processing and Embedding. (arXiv:2110.06196v2 [cs.LG] UPDATED)
    Graph Representation Learning methods opened new possibilities for addressing complex, real-world problems represented by graphs. However, many graphs used in these applications comprise millions of nodes and billions of edges and are beyond the capabilities of current methods and software implementations. We present GRAPE, a software resource for graph processing and representation learning that is able to scale with big graphs by using specialized and smart data structures, algorithms, and a fast parallel implementation. When compared with state of the art software resources, GRAPE shows an improvement of orders of magnitude in empirical space and time complexity, as well as a substantial and statistically significant improvement in edge prediction and node label prediction performance. Furthermore, GRAPE provides over 80, 000 graphs from the literature and other sources, standardized interfaces allowing a straightforward integration of third-party libraries, 61 node embedding methods, 25 inference models, and 3 modular pipelines to allow a FAIR and reproducible comparison of methods and libraries for graph processing and embedding.  ( 3 min )
    Adversarial Inverse Reinforcement Learning for Mean Field Games. (arXiv:2104.14654v3 [cs.LG] UPDATED)
    Mean field games (MFGs) provide a mathematically tractable framework for modelling large-scale multi-agent systems by leveraging mean field theory to simplify interactions among agents. It enables applying inverse reinforcement learning (IRL) to predict behaviours of large populations by recovering reward signals from demonstrated behaviours. However, existing IRL methods for MFGs are powerless to reason about uncertainties in demonstrated behaviours of individual agents. This paper proposes a novel framework, Mean-Field Adversarial IRL (MF-AIRL), which is capable of tackling uncertainties in demonstrations. We build MF-AIRL upon maximum entropy IRL and a new equilibrium concept. We evaluate our approach on simulated tasks with imperfect demonstrations. Experimental results demonstrate the superiority of MF-AIRL over existing methods in reward recovery.  ( 2 min )
    A Framework for Machine Learning of Model Error in Dynamical Systems. (arXiv:2107.06658v3 [math.DS] UPDATED)
    The development of data-informed predictive models for dynamical systems is of widespread interest in many disciplines. We present a unifying framework for blending mechanistic and machine-learning approaches to identify dynamical systems from noisily and partially observed data. We compare pure data-driven learning with hybrid models which incorporate imperfect domain knowledge. Our formulation is agnostic to the chosen machine learning model, is presented in both continuous- and discrete-time settings, and is compatible both with model errors that exhibit substantial memory and errors that are memoryless. First, we study memoryless linear (w.r.t. parametric-dependence) model error from a learning theory perspective, defining excess risk and generalization error. For ergodic continuous-time systems, we prove that both excess risk and generalization error are bounded above by terms that diminish with the square-root of T, the time-interval over which training data is specified. Secondly, we study scenarios that benefit from modeling with memory, proving universal approximation theorems for two classes of continuous-time recurrent neural networks (RNNs): both can learn memory-dependent model error. In addition, we connect one class of RNNs to reservoir computing, thereby relating learning of memory-dependent error to recent work on supervised learning between Banach spaces using random features. Numerical results are presented (Lorenz '63, Lorenz '96 Multiscale systems) to compare purely data-driven and hybrid approaches, finding hybrid methods less data-hungry and more parametrically efficient. Finally, we demonstrate numerically how data assimilation can be leveraged to learn hidden dynamics from noisy, partially-observed data, and illustrate challenges in representing memory by this approach, and in the training of such models.  ( 3 min )
    A Low-Cost Neural ODE with Depthwise Separable Convolution for Edge Domain Adaptation on FPGAs. (arXiv:2107.12824v3 [cs.LG] UPDATED)
    High-performance deep neural network (DNN)-based systems are in high demand in edge environments. Due to its high computational complexity, it is challenging to deploy DNNs on edge devices with strict limitations on computational resources. In this paper, we derive a compact while highly-accurate DNN model, termed dsODENet, by combining recently-proposed parameter reduction techniques: Neural ODE (Ordinary Differential Equation) and DSC (Depthwise Separable Convolution). Neural ODE exploits a similarity between ResNet and ODE, and shares most of weight parameters among multiple layers, which greatly reduces the memory consumption. We apply dsODENet to a domain adaptation as a practical use case with image classification datasets. We also propose a resource-efficient FPGA-based design for dsODENet, where all the parameters and feature maps except for pre- and post-processing layers can be mapped onto on-chip memories. It is implemented on Xilinx ZCU104 board and evaluated in terms of domain adaptation accuracy, inference speed, FPGA resource utilization, and speedup rate compared to a software counterpart. The results demonstrate that dsODENet achieves comparable or slightly better domain adaptation accuracy compared to our baseline Neural ODE implementation, while the total parameter size without pre- and post-processing layers is reduced by 54.2% to 79.8%. Our FPGA implementation accelerates the inference speed by 23.8 times.
    Localized Debiased Machine Learning: Efficient Inference on Quantile Treatment Effects and Beyond. (arXiv:1912.12945v5 [stat.ML] UPDATED)
    We consider estimating a low-dimensional parameter in an estimating equation involving high-dimensional nuisances that depend on the parameter. A central example is the efficient estimating equation for the (local) quantile treatment effect ((L)QTE) in causal inference, which involves as a nuisance the covariate-conditional cumulative distribution function evaluated at the quantile to be estimated. Debiased machine learning (DML) is a data-splitting approach to estimating high-dimensional nuisances using flexible machine learning methods, but applying it to problems with parameter-dependent nuisances is impractical. For (L)QTE, DML requires we learn the whole covariate-conditional cumulative distribution function. We instead propose localized debiased machine learning (LDML), which avoids this burdensome step and needs only estimate nuisances at a single initial rough guess for the parameter. For (L)QTE, LDML involves learning just two regression functions, a standard task for machine learning methods. We prove that under lax rate conditions our estimator has the same favorable asymptotic behavior as the infeasible estimator that uses the unknown true nuisances. Thus, LDML notably enables practically-feasible and theoretically-grounded efficient estimation of important quantities in causal inference such as (L)QTEs when we must control for many covariates and/or flexible relationships, as we demonstrate in empirical studies.
    Learning low-rank latent mesoscale structures in networks. (arXiv:2102.06984v3 [cs.SI] UPDATED)
    It is common to use networks to encode the architecture of interactions between entities in complex systems in applications in the physical, biological, social, and information sciences. To study the large-scale behavior of complex systems, it is useful to study mesoscale structures in networks as building blocks that influence such behavior. We present a new approach for describing low-rank mesoscale structure in networks, and we illustrate our approach using several synthetic network models and empirical friendship, collaboration, and protein--protein interaction (PPI) networks. We find that these networks possess a relatively small number of `latent motifs' that together can successfully approximate most subgraphs of a network at a fixed mesoscale. We use an algorithm that we call `network dictionary learning' (NDL), which combines a network-sampling method and nonnegative matrix factorization, to learn the latent motifs of a given network. The ability to encode a network using a set of latent motifs has a wide variety of applications to network-analysis tasks, such as comparison, denoising, and edge inference. Additionally, using our new network denoising and reconstruction (NDR) algorithm, we demonstrate how to denoise a corrupted network by using only the latent motifs that one learns directly from the corrupted networks.  ( 3 min )
    Adversarial Image Color Transformations in Explicit Color Filter Space. (arXiv:2011.06690v2 [cs.CV] UPDATED)
    Deep Neural Networks have been shown to be vulnerable to adversarial images. Conventional attacks strive for indistinguishable adversarial images with strictly restricted perturbations. Recently, researchers have moved to explore distinguishable yet non-suspicious adversarial images and demonstrated that color transformation attacks are effective. In this work, we propose Adversarial Color Filter (AdvCF), a novel color transformation attack that is optimized with gradient information in the parameter space of a simple color filter. In particular, our color filter space is explicitly specified so that we are able to provide a systematic analysis of model robustness against adversarial color transformations, from both the attack and defense perspectives. In contrast, existing color transformation attacks do not offer the opportunity for systematic analysis due to the lack of such an explicit space. We further conduct extensive comparisons between different color transformation attacks on both the success rate and image acceptability, through a user study. Additional results provide interesting new insights into model robustness against AdvCF in another three visual tasks. We also highlight the human-interpretability of AdvCF, which is promising in practical use scenarios, and show its superiority over the state-of-the-art human-interpretable color transformation attack on both the image acceptability and efficiency.
    FCN-Transformer Feature Fusion for Polyp Segmentation. (arXiv:2208.08352v1 [eess.IV])
    Colonoscopy is widely recognised as the gold standard procedure for the early detection of colorectal cancer (CRC). Segmentation is valuable for two significant clinical applications, namely lesion detection and classification, providing means to improve accuracy and robustness. The manual segmentation of polyps in colonoscopy images is time-consuming. As a result, the use of deep learning (DL) for automation of polyp segmentation has become important. However, DL-based solutions can be vulnerable to overfitting and the resulting inability to generalise to images captured by different colonoscopes. Recent transformer-based architectures for semantic segmentation both achieve higher performance and generalise better than alternatives, however typically predict a segmentation map of $\frac{h}{4}\times\frac{w}{4}$ spatial dimensions for a $h\times w$ input image. To this end, we propose a new architecture for full-size segmentation which leverages the strengths of a transformer in extracting the most important features for segmentation in a primary branch, while compensating for its limitations in full-size prediction with a secondary fully convolutional branch. The resulting features from both branches are then fused for final prediction of a $h\times w$ segmentation map. We demonstrate our method's state-of-the-art performance with respect to the mDice, mIoU, mPrecision, and mRecall metrics, on both the Kvasir-SEG and CVC-ClinicDB dataset benchmarks. Additionally, we train the model on each of these datasets and evaluate on the other to demonstrate its superior generalisation performance.
    Quadratic Multiform Separation: A New Classification Model in Machine Learning. (arXiv:2110.04925v2 [stat.ML] UPDATED)
    In this paper we present a new classification model in machine learning. Our result is threefold: 1) The model produces comparable predictive accuracy to that of most common classification models. 2) It runs significantly faster than most common classification models. 3) It has the ability to identify a portion of unseen samples for which class labels can be found with much higher predictive accuracy. Currently there are several patents pending on the proposed model.  ( 2 min )
    RegMix: Data Mixing Augmentation for Regression. (arXiv:2106.03374v4 [cs.LG] UPDATED)
    Data augmentation is becoming essential for improving regression performance in critical applications including manufacturing, climate prediction, and finance. Existing techniques for data augmentation largely focus on classification tasks and do not readily apply to regression tasks. In particular, the recent Mixup techniques for classification have succeeded in improving the model performance, which is reasonable due to the characteristics of the classification task, but has limitations in regression. We show that mixing examples that have large data distances using linear interpolations may have increasingly-negative effects on model performance. Our key idea is thus to limit the distances between examples that are mixed. We propose RegMix, a data augmentation framework for regression that learns for each example how many nearest neighbors it should be mixed with for the best model performance using a validation set. Our experiments conducted both on synthetic and real datasets show that RegMix outperforms state-of-the-art data augmentation baselines applicable to regression.  ( 2 min )
    Novel Deep Learning Approach to Derive Cytokeratin Expression and Epithelium Segmentation from DAPI. (arXiv:2208.08284v1 [eess.IV])
    Generative Adversarial Networks (GANs) are state of the art for image synthesis. Here, we present dapi2ck, a novel GAN-based approach to synthesize cytokeratin (CK) staining from immunofluorescent (IF) DAPI staining of nuclei in non-small cell lung cancer (NSCLC) images. We use the synthetic CK to segment epithelial regions, which, compared to expert annotations, yield equally good results as segmentation on stained CK. Considering the limited number of markers in a multiplexed IF (mIF) panel, our approach allows to replace CK by another marker addressing the complexity of the tumor micro-environment (TME) to facilitate patient selection for immunotherapies. In contrast to stained CK, dapi2ck does not suffer from issues like unspecific CK staining or loss of tumoral CK expression.  ( 2 min )
    On the Privacy Effect of Data Enhancement via the Lens of Memorization. (arXiv:2208.08270v1 [cs.LG])
    Machine learning poses severe privacy concerns as it is shown that the learned models can reveal sensitive information about their training data. Many works have investigated the effect of widely-adopted data augmentation (DA) and adversarial training (AT) techniques, termed data enhancement in the paper, on the privacy leakage of machine learning models. Such privacy effects are often measured by membership inference attacks (MIAs), which aim to identify whether a particular example belongs to the training set or not. We propose to investigate privacy from a new perspective called memorization. Through the lens of memorization, we find that previously deployed MIAs produce misleading results as they are less likely to identify samples with higher privacy risks as members compared to samples with low privacy risks. To solve this problem, we deploy a recent attack that can capture the memorization degrees of individual samples for evaluation. Through extensive experiments, we unveil non-trivial findings about the connections between three important properties of machine learning models, including privacy, generalization gap, and adversarial robustness. We demonstrate that, unlike existing results, the generalization gap is shown not highly correlated with privacy leakage. Moreover, stronger adversarial robustness does not necessarily imply that the model is more susceptible to privacy attacks.  ( 3 min )
    Semantic Communications with Discrete-time Analog Transmission: A PAPR Perspective. (arXiv:2208.08342v1 [cs.IT])
    Recent progress in deep learning (DL)-based joint source-channel coding (DeepJSCC) has led to a new paradigm of semantic communications. Two salient features of DeepJSCC-based semantic communications are the exploitation of semantic-aware features directly from the source signal, and the discrete-time analog transmission (DTAT) of these features. Compared with traditional digital communications, semantic communications with DeepJSCC provide superior reconstruction performance at the receiver and graceful degradation with diminishing channel quality, but also exhibit a large peak-to-average power ratio (PAPR) in the transmitted signal. An open question has been whether the gains of DeepJSCC come from the additional freedom brought by the high-PAPR continuous-amplitude signal. In this paper, we address this question by exploring three PAPR reduction techniques in the application of image transmission. We confirm that the superior image reconstruction performance of DeepJSCC-based semantic communications can be retained while the transmitted PAPR is suppressed to an acceptable level. This observation is an important step towards the implementation of DeepJSCC in practical semantic communication systems.  ( 2 min )
    Deep Gaussian Process Emulation using Stochastic Imputation. (arXiv:2107.01590v2 [stat.ML] UPDATED)
    Deep Gaussian processes (DGPs) provide a rich class of models that can better represent functions with varying regimes or sharp changes, compared to conventional GPs. In this work, we propose a novel inference method for DGPs for computer model emulation. By stochastically imputing the latent layers, our approach transforms a DGP into a linked GP: a novel emulator developed for systems of linked computer models. This transformation permits an efficient DGP training procedure that only involves optimizations of conventional GPs. In addition, predictions from DGP emulators can be made in a fast and analytically tractable manner by naturally utilizing the closed form predictive means and variances of linked GP emulators. We demonstrate the method in a series of synthetic examples and empirical applications, and show that it is a competitive candidate for DGP surrogate inference, combining efficiency that is comparable to doubly stochastic variational inference and uncertainty quantification that is comparable to the fully-Bayesian approach. A $\texttt{Python}$ package $\texttt{dgpsi}$ implementing the method is also produced and available at https://github.com/mingdeyu/DGP.  ( 2 min )
    Arachne: Search Based Repair of Deep Neural Networks. (arXiv:1912.12463v2 [cs.LG] UPDATED)
    The rapid and widespread adoption of Deep Neural Networks (DNNs) has called for ways to test their behaviour, and many testing approaches have successfully revealed misbehaviour of DNNs. However, it is relatively unclear what one can do to correct such behaviour after revelation, as retraining involves costly data collection and does not guarantee to fix the underlying issue. This paper introduces Arachne, a novel program repair technique for DNNs, which directly repairs DNNs using their input-output pairs as a specification. Arachne localises neural weights on which it can generate effective patches and uses Differential Evolution to optimise the localised weights and correct the misbehaviour. An empirical study using different benchmarks shows that Arachne can fix specific misclassifications of a DNN without reducing general accuracy significantly. On average, patches generated by Arachne generalise to 61.3% of unseen misbehaviour, whereas those by a state-of-the-art DNN repair technique generalise only to 10.2% and sometimes to none while taking tens of times more than Arachne. We also show that Arachne can address fairness issues by debiasing a gender classification model. Finally, we successfully apply Arachne to a text sentiment model to show that it generalises beyond Convolutional Neural Networks.  ( 3 min )
    Sparse Nonnegative Tucker Decomposition and Completion under Noisy Observations. (arXiv:2208.08287v1 [cs.LG])
    Tensor decomposition is a powerful tool for extracting physically meaningful latent factors from multi-dimensional nonnegative data, and has been an increasing interest in a variety of fields such as image processing, machine learning, and computer vision. In this paper, we propose a sparse nonnegative Tucker decomposition and completion method for the recovery of underlying nonnegative data under noisy observations. Here the underlying nonnegative data tensor is decomposed into a core tensor and several factor matrices with all entries being nonnegative and the factor matrices being sparse. The loss function is derived by the maximum likelihood estimation of the noisy observations, and the $\ell_0$ norm is employed to enhance the sparsity of the factor matrices. We establish the error bound of the estimator of the proposed model under generic noise scenarios, which is then specified to the observations with additive Gaussian noise, additive Laplace noise, and Poisson observations, respectively. Our theoretical results are better than those by existing tensor-based or matrix-based methods. Moreover, the minimax lower bounds are shown to be matched with the derived upper bounds up to logarithmic factors. Numerical examples on both synthetic and real-world data sets demonstrate the superiority of the proposed method for nonnegative tensor data completion.  ( 2 min )
    Deep Contrastive Multiview Network Embedding. (arXiv:2108.08296v2 [cs.LG] UPDATED)
    Multiview network embedding aims at projecting nodes in the network to low-dimensional vectors, while preserving their multiple relations and attribute information. Contrastive learning approaches have shown promising performance in this task. However, they neglect the semantic consistency between fused and view representations and have difficulty in modeling complementary information between different views. To deal with these deficiencies, this work presents a novel Contrastive leaRning framEwork for Multiview network Embedding (CREME). In our work, different views can be obtained based on the various relations among nodes. Then, we generate view embeddings via proper view encoders and utilize an attentive multiview aggregator to fuse these representations. Particularly, we design two collaborative contrastive objectives, view fusion InfoMax and inter-view InfoMin, to train the model in a self-supervised manner. The former objective distills information from embeddings generated from different views, while the latter captures complementary information among views to promote distinctive view embeddings. We also show that the two objectives can be unified into one objective for model training. Extensive experiments on three real-world datasets demonstrate that our proposed CREME is able to consistently outperform state-of-the-art methods.  ( 3 min )
    SYNTHESIS: A Semi-Asynchronous Path-Integrated Stochastic Gradient Method for Distributed Learning in Computing Clusters. (arXiv:2208.08425v1 [cs.LG])
    To increase the training speed of distributed learning, recent years have witnessed a significant amount of interest in developing both synchronous and asynchronous distributed stochastic variance-reduced optimization methods. However, all existing synchronous and asynchronous distributed training algorithms suffer from various limitations in either convergence speed or implementation complexity. This motivates us to propose an algorithm called \algname (\ul{s}emi-as\ul{yn}chronous pa\ul{th}-int\ul{e}grated \ul{s}tochastic grad\ul{i}ent \ul{s}earch), which leverages the special structure of the variance-reduction framework to overcome the limitations of both synchronous and asynchronous distributed learning algorithms, while retaining their salient features. We consider two implementations of \algname under distributed and shared memory architectures. We show that our \algname algorithms have \(O(\sqrt{N}\epsilon^{-2}(\Delta+1)+N)\) and \(O(\sqrt{N}\epsilon^{-2}(\Delta+1) d+N)\) computational complexities for achieving an \(\epsilon\)-stationary point in non-convex learning under distributed and shared memory architectures, respectively, where \(N\) denotes the total number of training samples and \(\Delta\) represents the maximum delay of the workers. Moreover, we investigate the generalization performance of \algname by establishing algorithmic stability bounds for quadratic strongly convex and non-convex optimization. We further conduct extensive numerical experiments to verify our theoretical findings  ( 2 min )
    Minimum Cost Adaptive Submodular Cover. (arXiv:2208.08351v1 [cs.DS])
    We consider the problem of minimum cost cover of adaptive-submodular functions, and provide a 4(ln Q+1)-approximation algorithm, where Q is the goal value. This bound is nearly the best possible as the problem does not admit any approximation ratio better than ln Q (unless P=NP). Our result is the first O(ln Q)-approximation algorithm for this problem. Previously, O(ln Q) approximation algorithms were only known assuming either independent items or unit-cost items. Furthermore, our result easily extends to the setting where one wants to simultaneously cover multiple adaptive-submodular functions: we obtain the first approximation algorithm for this generalization.  ( 2 min )
    LAMA-Net: Unsupervised Domain Adaptation via Latent Alignment and Manifold Learning for RUL Prediction. (arXiv:2208.08388v1 [cs.LG])
    Prognostics and Health Management (PHM) is an emerging field which has received much attention from the manufacturing industry because of the benefits and efficiencies it brings to the table. And Remaining Useful Life (RUL) prediction is at the heart of any PHM system. Most recent data-driven research demand substantial volumes of labelled training data before a performant model can be trained under the supervised learning paradigm. This is where Transfer Learning (TL) and Domain Adaptation (DA) methods step in and make it possible for us to generalize a supervised model to other domains with different data distributions with no labelled data. In this paper, we propose \textit{LAMA-Net}, an encoder-decoder based model (Transformer) with an induced bottleneck, Latent Alignment using Maximum Mean Discrepancy (MMD) and manifold learning is proposed to tackle the problem of Unsupervised Homogeneous Domain Adaptation for RUL prediction. \textit{LAMA-Net} is validated using the C-MAPSS Turbofan Engine dataset by NASA and compared against other state-of-the-art techniques for DA. The results suggest that the proposed method offers a promising approach to perform domain adaptation in RUL prediction. Code will be made available once the paper comes out of review.  ( 2 min )
    The Counterfactual-Shapley Value: Attributing Change in System Metrics. (arXiv:2208.08399v1 [cs.LG])
    Given an unexpected change in the output metric of a large-scale system, it is important to answer why the change occurred: which inputs caused the change in metric? A key component of such an attribution question is estimating the counterfactual: the (hypothetical) change in the system metric due to a specified change in a single input. However, due to inherent stochasticity and complex interactions between parts of the system, it is difficult to model an output metric directly. We utilize the computational structure of a system to break up the modelling task into sub-parts, such that each sub-part corresponds to a more stable mechanism that can be modelled accurately over time. Using the system's structure also helps to view the metric as a computation over a structural causal model (SCM), thus providing a principled way to estimate counterfactuals. Specifically, we propose a method to estimate counterfactuals using time-series predictive models and construct an attribution score, CF-Shapley, that is consistent with desirable axioms for attributing an observed change in the output metric. Unlike past work on causal shapley values, our proposed method can attribute a single observed change in output (rather than a population-level effect) and thus provides more accurate attribution scores when evaluated on simulated datasets. As a real-world application, we analyze a query-ad matching system with the goal of attributing observed change in a metric for ad matching density. Attribution scores explain how query volume and ad demand from different query categories affect the ad matching density, leading to actionable insights and uncovering the role of external events (e.g., "Cheetah Day") in driving the matching density.  ( 3 min )
    Open Long-Tailed Recognition in a Dynamic World. (arXiv:2208.08349v1 [cs.CV])
    Real world data often exhibits a long-tailed and open-ended (with unseen classes) distribution. A practical recognition system must balance between majority (head) and minority (tail) classes, generalize across the distribution, and acknowledge novelty upon the instances of unseen classes (open classes). We define Open Long-Tailed Recognition++ (OLTR++) as learning from such naturally distributed data and optimizing for the classification accuracy over a balanced test set which includes both known and open classes. OLTR++ handles imbalanced classification, few-shot learning, open-set recognition, and active learning in one integrated algorithm, whereas existing classification approaches often focus only on one or two aspects and deliver poorly over the entire spectrum. The key challenges are: 1) how to share visual knowledge between head and tail classes, 2) how to reduce confusion between tail and open classes, and 3) how to actively explore open classes with learned knowledge. Our algorithm, OLTR++, maps images to a feature space such that visual concepts can relate to each other through a memory association mechanism and a learned metric (dynamic meta-embedding) that both respects the closed world classification of seen classes and acknowledges the novelty of open classes. Additionally, we propose an active learning scheme based on visual memory, which learns to recognize open classes in a data-efficient manner for future expansions. On three large-scale open long-tailed datasets we curated from ImageNet (object-centric), Places (scene-centric), and MS1M (face-centric) data, as well as three standard benchmarks (CIFAR-10-LT, CIFAR-100-LT, and iNaturalist-18), our approach, as a unified framework, consistently demonstrates competitive performance. Notably, our approach also shows strong potential for the active exploration of open classes and the fairness analysis of minority groups.  ( 3 min )
    Ask Question First for Enhancing Lifelong Language Learning. (arXiv:2208.08367v1 [cs.CL])
    Lifelong language learning aims to stream learning NLP tasks while retaining knowledge of previous tasks. Previous works based on the language model and following data-free constraint approaches have explored formatting all data as "begin token (\textit{B}) + context (\textit{C}) + question (\textit{Q}) + answer (\textit{A})" for different tasks. However, they still suffer from catastrophic forgetting and are exacerbated when the previous task's pseudo data is insufficient for the following reasons: (1) The model has difficulty generating task-corresponding pseudo data, and (2) \textit{A} is prone to error when \textit{A} and \textit{C} are separated by \textit{Q} because the information of the \textit{C} is diminished before generating \textit{A}. Therefore, we propose the Ask Question First and Replay Question (AQF-RQ), including a novel data format "\textit{BQCA}" and a new training task to train pseudo questions of previous tasks. Experimental results demonstrate that AQF-RQ makes it easier for the model to generate more pseudo data that match corresponding tasks, and is more robust to both sufficient and insufficient pseudo-data when the task boundary is both clear and unclear. AQF-RQ can achieve only 0.36\% lower performance than multi-task learning.  ( 2 min )
    Extract fundamental frequency based on CNN combined with PYIN. (arXiv:2208.08354v1 [cs.SD])
    This paper refers to the extraction of multiple fundamental frequencies (multiple F0) based on PYIN, an algorithm for extracting the fundamental frequency (F0) of monophonic music, and a trained convolutional neural networks (CNN) model, where a pitch salience function of the input signal is produced to estimate the multiple F0. The implementation of these two algorithms and their corresponding advantages and disadvantages are discussed in this article. Analysing the different performance of these two methods, PYIN is applied to supplement the F0 extracted from the trained CNN model to combine the advantages of these two algorithms. For evaluation, four pieces played by two violins are used, and the performance of the models are evaluated accoring to the flatness of the F0 curve extracted. The result shows the combined model outperforms the original algorithms when extracting F0 from monophonic music and polyphonic music.  ( 2 min )
    Deep Generative Views to Mitigate Gender Classification Bias Across Gender-Race Groups. (arXiv:2208.08382v1 [cs.CV])
    Published studies have suggested the bias of automated face-based gender classification algorithms across gender-race groups. Specifically, unequal accuracy rates were obtained for women and dark-skinned people. To mitigate the bias of gender classifiers, the vision community has developed several strategies. However, the efficacy of these mitigation strategies is demonstrated for a limited number of races mostly, Caucasian and African-American. Further, these strategies often offer a trade-off between bias and classification accuracy. To further advance the state-of-the-art, we leverage the power of generative views, structured learning, and evidential learning towards mitigating gender classification bias. We demonstrate the superiority of our bias mitigation strategy in improving classification accuracy and reducing bias across gender-racial groups through extensive experimental validation, resulting in state-of-the-art performance in intra- and cross dataset evaluations.  ( 2 min )
    Leukocyte Classification using Multimodal Architecture Enhanced by Knowledge Distillation. (arXiv:2208.08331v1 [eess.IV])
    Recently, a lot of automated white blood cells (WBC) or leukocyte classification techniques have been developed. However, all of these methods only utilize a single modality microscopic image i.e. either blood smear or fluorescence based, thus missing the potential of a better learning from multimodal images. In this work, we develop an efficient multimodal architecture based on a first of its kind multimodal WBC dataset for the task of WBC classification. Specifically, our proposed idea is developed in two steps - 1) First, we learn modality specific independent subnetworks inside a single network only; 2) We further enhance the learning capability of the independent subnetworks by distilling knowledge from high complexity independent teacher networks. With this, our proposed framework can achieve a high performance while maintaining low complexity for a multimodal dataset. Our unique contribution is two-fold - 1) We present a first of its kind multimodal WBC dataset for WBC classification; 2) We develop a high performing multimodal architecture which is also efficient and low in complexity at the same time.  ( 2 min )
    Transformer-Based Deep Learning Model for Stock Price Prediction: A Case Study on Bangladesh Stock Market. (arXiv:2208.08300v1 [q-fin.ST])
    In modern capital market the price of a stock is often considered to be highly volatile and unpredictable because of various social, financial, political and other dynamic factors. With calculated and thoughtful investment, stock market can ensure a handsome profit with minimal capital investment, while incorrect prediction can easily bring catastrophic financial loss to the investors. This paper introduces the application of a recently introduced machine learning model - the Transformer model, to predict the future price of stocks of Dhaka Stock Exchange (DSE), the leading stock exchange in Bangladesh. The transformer model has been widely leveraged for natural language processing and computer vision tasks, but, to the best of our knowledge, has never been used for stock price prediction task at DSE. Recently the introduction of time2vec encoding to represent the time series features has made it possible to employ the transformer model for the stock price prediction. This paper concentrates on the application of transformer-based model to predict the price movement of eight specific stocks listed in DSE based on their historical daily and weekly data. Our experiments demonstrate promising results and acceptable root mean squared error on most of the stocks.  ( 3 min )
    Error Parity Fairness: Testing for Group Fairness in Regression Tasks. (arXiv:2208.08279v1 [cs.LG])
    The applications of Artificial Intelligence (AI) surround decisions on increasingly many aspects of human lives. Society responds by imposing legal and social expectations for the accountability of such automated decision systems (ADSs). Fairness, a fundamental constituent of AI accountability, is concerned with just treatment of individuals and sensitive groups (e.g., based on sex, race). While many studies focus on fair learning and fairness testing for the classification tasks, the literature is rather limited on how to examine fairness in regression tasks. This work presents error parity as a regression fairness notion and introduces a testing methodology to assess group fairness based on a statistical hypothesis testing procedure. The error parity test checks whether prediction errors are distributed similarly across sensitive groups to determine if an ADS is fair. It is followed by a suitable permutation test to compare groups on several statistics to explore disparities and identify impacted groups. The usefulness and applicability of the proposed methodology are demonstrated via a case study on COVID-19 projections in the US at the county level, which revealed race-based differences in forecast errors. Overall, the proposed regression fairness testing methodology fills a gap in the fair machine learning literature and may serve as a part of larger accountability assessments and algorithm audits.  ( 3 min )
    Position-aware Structure Learning for Graph Topology-imbalance by Relieving Under-reaching and Over-squashing. (arXiv:2208.08302v1 [cs.LG])
    Topology-imbalance is a graph-specific imbalance problem caused by the uneven topology positions of labeled nodes, which significantly damages the performance of GNNs. What topology-imbalance means and how to measure its impact on graph learning remain under-explored. In this paper, we provide a new understanding of topology-imbalance from a global view of the supervision information distribution in terms of under-reaching and over-squashing, which motivates two quantitative metrics as measurements. In light of our analysis, we propose a novel position-aware graph structure learning framework named PASTEL, which directly optimizes the information propagation path and solves the topology-imbalance issue in essence. Our key insight is to enhance the connectivity of nodes within the same class for more supervision information, thereby relieving the under-reaching and over-squashing phenomena. Specifically, we design an anchor-based position encoding mechanism, which better incorporates relative topology position and enhances the intra-class inductive bias by maximizing the label influence. We further propose a class-wise conflict measure as the edge weights, which benefits the separation of different node classes. Extensive experiments demonstrate the superior potential and adaptability of PASTEL in enhancing GNNs' power in different data annotation scenarios.  ( 3 min )
    Quantum Machine Learning for Material Synthesis and Hardware Security. (arXiv:2208.08273v1 [quant-ph])
    Using quantum computing, this paper addresses two scientifically pressing and day-to-day relevant problems, namely, chemical retrosynthesis which is an important step in drug/material discovery and security of the semiconductor supply chain. We show that Quantum Long Short-Term Memory (QLSTM) is a viable tool for retrosynthesis. We achieve 65% training accuracy with QLSTM, whereas classical LSTM can achieve 100%. However, in testing, we achieve 80% accuracy with the QLSTM while classical LSTM peaks at only 70% accuracy! We also demonstrate an application of Quantum Neural Network (QNN) in the hardware security domain, specifically in Hardware Trojan (HT) detection using a set of power and area Trojan features. The QNN model achieves detection accuracy as high as 97.27%.  ( 2 min )
    Domain Knowledge in A*-Based Causal Discovery. (arXiv:2208.08247v1 [stat.ML])
    Causal discovery has become a vital tool for scientists and practitioners wanting to discover causal relationships from observational data. While most previous approaches to causal discovery have implicitly assumed that no expert domain knowledge is available, practitioners can often provide such domain knowledge from prior experience. Recent work has incorporated domain knowledge into constraint-based causal discovery. The majority of such constraint-based methods, however, assume causal faithfulness, which has been shown to be frequently violated in practice. Consequently, there has been renewed attention towards exact-search score-based causal discovery methods, which do not assume causal faithfulness, such as A*-based methods. However, there has been no consideration of these methods in the context of domain knowledge. In this work, we focus on efficiently integrating several types of domain knowledge into A*-based causal discovery. In doing so, we discuss and explain how domain knowledge can reduce the graph search space and then provide an analysis of the potential computational gains. We support these findings with experiments on synthetic and real data, showing that even small amounts of domain knowledge can dramatically speed up A*-based causal discovery and improve its performance and practicality.  ( 2 min )
    SMPL-IK: Learned Morphology-Aware Inverse Kinematics for AI Driven Artistic Workflows. (arXiv:2208.08274v1 [cs.GR])
    Inverse Kinematics (IK) systems are often rigid with respect to their input character, thus requiring user intervention to be adapted to new skeletons. In this paper we aim at creating a flexible, learned IK solver applicable to a wide variety of human morphologies. We extend a state-of-the-art machine learning IK solver to operate on the well known Skinned Multi-Person Linear model (SMPL). We call our model SMPL-IK, and show that when integrated into real-time 3D software, this extended system opens up opportunities for defining novel AI-assisted animation workflows. For example, pose authoring can be made more flexible with SMPL-IK by allowing users to modify gender and body shape while posing a character. Additionally, when chained with existing pose estimation algorithms, SMPL-IK accelerates posing by allowing users to bootstrap 3D scenes from 2D images while allowing for further editing. Finally, we propose a novel SMPL Shape Inversion mechanism (SMPL-SI) to map arbitrary humanoid characters to the SMPL space, allowing artists to leverage SMPL-IK on custom characters. In addition to qualitative demos showing proposed tools, we present quantitative SMPL-IK baselines on the H36M and AMASS datasets.  ( 2 min )
    DICE: Data-Efficient Clinical Event Extraction with Generative Models. (arXiv:2208.07989v1 [cs.CL])
    Event extraction in the clinical domain is an under-explored research area. The lack of training data in addition to the high volume of domain-specific jargon that includes long entities with vague boundaries make the task especially challenging. In this paper, we introduce DICE, a robust and data-efficient generative model for clinical event extraction. DICE frames event extraction as a conditional generation problem and utilizes descriptions provided by domain experts to boost the performance under low-resource settings. Furthermore, DICE learns to locate and bound biomedical mentions with an auxiliary mention identification task trained jointly with event extraction tasks to leverage inter-task dependencies and further incorporates the identified mentions as trigger and argument candidates for their respective tasks. We also introduce MACCROBAT-EE, the first clinical event extraction dataset with event argument annotation. Our experiments demonstrate the robustness of DICE under low data settings for the clinical domain and the benefits of incorporating flexible joint training and mention markers into generative approaches.  ( 2 min )
    Sampling Through the Lens of Sequential Decision Making. (arXiv:2208.08056v1 [cs.LG])
    Sampling is ubiquitous in machine learning methodologies. Due to the growth of large datasets and model complexity, we want to learn and adapt the sampling process while training a representation. Towards achieving this grand goal, a variety of sampling techniques have been proposed. However, most of them either use a fixed sampling scheme or adjust the sampling scheme based on simple heuristics. They cannot choose the best sample for model training in different stages. Inspired by "Think, Fast and Slow" (System 1 and System 2) in cognitive science, we propose a reward-guided sampling strategy called Adaptive Sample with Reward (ASR) to tackle this challenge. To the best of our knowledge, this is the first work utilizing reinforcement learning (RL) to address the sampling problem in representation learning. Our approach optimally adjusts the sampling process to achieve optimal performance. We explore geographical relationships among samples by distance-based sampling to maximize overall cumulative reward. We apply ASR to the long-standing sampling problems in similarity-based loss functions. Empirical results in information retrieval and clustering demonstrate ASR's superb performance across different datasets. We also discuss an engrossing phenomenon which we name as "ASR gravity well" in experiments.  ( 2 min )
    Maximising the Utility of Validation Sets for Imbalanced Noisy-label Meta-learning. (arXiv:2208.08132v1 [cs.LG])
    Meta-learning is an effective method to handle imbalanced and noisy-label learning, but it depends on a validation set containing randomly selected, manually labelled and balanced distributed samples. The random selection and manual labelling and balancing of this validation set is not only sub-optimal for meta-learning, but it also scales poorly with the number of classes. Hence, recent meta-learning papers have proposed ad-hoc heuristics to automatically build and label this validation set, but these heuristics are still sub-optimal for meta-learning. In this paper, we analyse the meta-learning algorithm and propose new criteria to characterise the utility of the validation set, based on: 1) the informativeness of the validation set; 2) the class distribution balance of the set; and 3) the correctness of the labels of the set. Furthermore, we propose a new imbalanced noisy-label meta-learning (INOLML) algorithm that automatically builds a validation set by maximising its utility using the criteria above. Our method shows significant improvements over previous meta-learning approaches and sets the new state-of-the-art on several benchmarks.  ( 2 min )
    HELP ME THINK: A Simple Prompting Strategy for Non-experts to Create Customized Content with Models. (arXiv:2208.08232v1 [cs.CL])
    Controlling the text generated by language models and customizing the content has been a long-standing challenge. Existing prompting techniques proposed in pursuit of providing control are task-specific and lack generality; this provides overwhelming choices for non-expert users to find a suitable method for their task. The effort associated with those techniques, such as in writing examples, explanations, instructions, etc. further limits their adoption among non-expert users. In this paper, we propose a simple prompting strategy HELP ME THINK where we encourage GPT3 to help non-expert users by asking a set of relevant questions and leveraging user answers to execute the task. We demonstrate the efficacy of our technique HELP ME THINK on a variety of tasks. Specifically, we focus on tasks that are hard for average humans and require significant thinking to perform. We hope our work will encourage the development of unconventional ways to harness the power of large language models.  ( 2 min )
    Prediction of Oral Food Challenges via Machine Learning. (arXiv:2208.08268v1 [cs.LG])
    Oral Food Challenges (OFCs) are essential to accurately diagnosing food allergy in patients. However, patients are hesitant to undergo OFCs, and for those that do, there is limited access to allergists in rural/community healthcare settings. The prediction of OFC outcomes through machine learning methods can facilitate the de-labeling of food allergens at home, improve patient and physician comfort during OFCs, and economize medical resources by minimizing the number of OFCs performed. Clinical data was gathered from 1,112 patients who collectively underwent a total of 1,284 OFCs, and consisted of clinical factors including serum specific IgE, total IgE, skin prick tests (SPTs), symptoms, sex, and age. Using these clinical features, machine learning models were constructed to predict outcomes for peanut, egg, and milk challenge. The best performing model for each allergen was created using the Learning Using Concave and Convex Kernels (LUCCK) method, which achieved an Area under the Curve (AUC) for peanut, egg, and milk OFC prediction of 0.76, 0.68, and 0.70, respectively. Model interpretation via SHapley Additive exPlanations (SHAP) indicate that specific IgE, along with wheal and flare values from SPTs, are highly predictive of OFC outcomes. The results of this analysis suggest that machine learning has the potential to predict OFC outcomes and reveal relevant clinical factors for further study.  ( 3 min )
    Towards an Error-free Deep Occupancy Detector for Smart Camera Parking System. (arXiv:2208.08220v1 [cs.CV])
    Although the smart camera parking system concept has existed for decades, a few approaches have fully addressed the system's scalability and reliability. As the cornerstone of a smart parking system is the ability to detect occupancy, traditional methods use the classification backbone to predict spots from a manual labeled grid. This is time-consuming and loses the system's scalability. Additionally, most of the approaches use deep learning models, making them not error-free and not reliable at scale. Thus, we propose an end-to-end smart camera parking system where we provide an autonomous detecting occupancy by an object detector called OcpDet. Our detector also provides meaningful information from contrastive modules: training and spatial knowledge, which avert false detections during inference. We benchmark OcpDet on the existing PKLot dataset and reach competitive results compared to traditional classification solutions. We also introduce an additional SNU-SPS dataset, in which we estimate the system performance from various views and conduct system evaluation in parking assignment tasks. The result from our dataset shows that our system is promising for real-world applications.  ( 2 min )
    Deep Autoencoder Model Construction Based on Pytorch. (arXiv:2208.08231v1 [cs.LG])
    This paper proposes a deep autoencoder model based on Pytorch. This algorithm introduces the idea of Pytorch into the auto-encoder, and randomly clears the input weights connected to the hidden layer neurons with a certain probability, so as to achieve the effect of sparse network, which is similar to the starting point of the sparse auto-encoder. The new algorithm effectively solves the problem of possible overfitting of the model and improves the accuracy of image classification. Finally, the experiment is carried out, and the experimental results are compared with ELM, RELM, AE, SAE, DAE.  ( 2 min )
    Dynamical softassign and adaptive parameter tuning for graph matching. (arXiv:2208.08233v1 [math.CO])
    This paper studies a framework, projected fixed-point method, for graph matching. The framework contains a class of popular graph matching algorithms, including graduated assignment (GA), integer projected fixed-point method (IPFP) and doubly stochastic projected fixed-point method (DSPFP). We propose an adaptive strategy to tune the step size parameter in this framework. Such a strategy improves these algorithms in efficiency and accuracy. Further, it guarantees the convergence of the underlying algorithms. Some preliminary analysis based on distance geometry seems to support that the optimal step size parameter has a high probability of 1 when graphs are fully connected. Secondly, it is observed that a popular projection method, softassign, is sensitive to graphs' cardinality(size). We proposed a dynamical softassign algorithm that is robust to graphs' cardinality. Combining the adaptive step size and the dynamical softassign, we propose a novel graph matching algorithm: the adaptive projected fixed-point method with dynamical softassign. Various experiments demonstrate that the proposed algorithm is significantly faster than several other state-of-art algorithms with no loss of accuracy.  ( 2 min )
    Semi-Supervised Anomaly Detection Based on Quadratic Multiform Separation. (arXiv:2208.08265v1 [stat.ML])
    In this paper we propose a novel method for semi-supervised anomaly detection (SSAD). Our classifier is named QMS22 as its inception was dated 2022 upon the framework of quadratic multiform separation (QMS), a recently introduced classification model. QMS22 tackles SSAD by solving a multi-class classification problem involving both the training set and the test set of the original problem. The classification problem intentionally includes classes with overlapping samples. One of the classes contains mixture of normal samples and outliers, and all other classes contain only normal samples. An outlier score is then calculated for every sample in the test set using the outcome of the classification problem. We also include performance evaluation of QMS22 against top performing classifiers using ninety-five benchmark imbalanced datasets from the KEEL repository. These classifiers are BRM (Bagging-Random Miner), OCKRA (One-Class K-means with Randomly-projected features Algorithm), ISOF (Isolation Forest), and ocSVM (One-Class Support Vector Machine). It is shown by using the area under the curve of the receiver operating characteristic curve as the performance measure, QMS22 significantly outperforms ISOF and ocSVM. Moreover, the Wilcoxon signed-rank tests reveal that there is no statistically significant difference when testing QMS22 against BRM nor QMS22 against OCKRA.  ( 2 min )
    Deep Learning-Based Discrete Calibrated Survival Prediction. (arXiv:2208.08182v1 [cs.LG])
    Deep neural networks for survival prediction outper-form classical approaches in discrimination, which is the ordering of patients according to their time-of-event. Conversely, classical approaches like the Cox Proportional Hazards model display much better calibration, the correct temporal prediction of events of the underlying distribution. Especially in the medical domain, where it is critical to predict the survival of a single patient, both discrimination and calibration are important performance metrics. Here we present Discrete Calibrated Survival (DCS), a novel deep neural network for discriminated and calibrated survival prediction that outperforms competing survival models in discrimination on three medical datasets, while achieving best calibration among all discrete time models. The enhanced performance of DCS can be attributed to two novel features, the variable temporal output node spacing and the novel loss term that optimizes the use of uncensored and censored patient data. We believe that DCS is an important step towards clinical application of deep-learning-based survival prediction with state-of-the-art discrimination and good calibration.  ( 2 min )
    Constrained Few-Shot Learning: Human-Like Low Sample Complexity Learning and Non-Episodic Text Classification. (arXiv:2208.08089v1 [cs.LG])
    Few-shot learning (FSL) is an emergent paradigm of learning that attempts to learn with low sample complexity to mimic the way humans can learn, generalise and extrapolate based on only a few examples. While FSL attempts to mimic these human characteristics, fundamentally, the task of FSL as conventionally described and modelled using meta-learning with episodic-based training does not fully align with how humans acquire and reason with knowledge. FSL with episodic training, while only using $K$ instances of each test class, still requires a large number of labelled instances from disjoint training classes. In this paper, we introduce the novel task of constrained few-shot learning (CFSL), a special case of FSL where the number of training instances of each class is constrained to be less than some value $M$ thus applying a similar restriction during training and test. We propose a method for CFSL leveraging Cat2Vec using a novel categorical contrastive loss inspired by cognitive theories such as fuzzy trace theory and prototype theory.  ( 2 min )
    Shallow neural network representation of polynomials. (arXiv:2208.08138v1 [stat.ML])
    We show that $d$-variate polynomials of degree $R$ can be represented on $[0,1]^d$ as shallow neural networks of width $d+1+\sum_{r=2}^R\binom{r+d-1}{d-1}[\binom{r+d-1}{d-1}+1]$. Also, by SNN representation of localized Taylor polynomials of univariate $C^\beta$-smooth functions, we derive for shallow networks the minimax optimal rate of convergence, up to a logarithmic factor, to unknown univariate regression function.  ( 2 min )
    DPA-1: Pretraining of Attention-based Deep Potential Model for Molecular Simulation. (arXiv:2208.08236v1 [physics.chem-ph])
    Machine learning assisted modeling of the inter-atomic potential energy surface (PES) is revolutionizing the field of molecular simulation. With the accumulation of high-quality electronic structure data, a model that can be pretrained on all available data and finetuned on downstream tasks with a small additional effort would bring the field to a new stage. Here we propose DPA-1, a Deep Potential model with a novel attention mechanism, which is highly effective for representing the conformation and chemical spaces of atomic systems and learning the PES. We tested DPA-1 on a number of systems and observed superior performance compared with existing benchmarks. When pretrained on large-scale datasets containing 56 elements, DPA-1 can be successfully applied to various downstream tasks with a great improvement of sample efficiency. Surprisingly, for different elements, the learned type embedding parameters form a $spiral$ in the latent space and have a natural correspondence with their positions on the periodic table, showing interesting interpretability of the pretrained DPA-1 model.  ( 2 min )
    A Scalable and Extensible Approach to Benchmarking NL2Code for 18 Programming Languages. (arXiv:2208.08227v1 [cs.LG])
    Large language models have demonstrated the ability to condition on and generate both natural language and programming language text. Such models open up the possibility of multi-language code generation: could code generation models generalize knowledge from one language to another? Although contemporary code generation models can generate semantically correct Python code, little is known about their abilities with other languages. We facilitate the exploration of this topic by proposing MultiPL-E, the first multi-language parallel benchmark for natural-language-to-code-generation. MultiPL-E extends the HumanEval benchmark (Chen et al, 2021) to support 18 more programming languages, encompassing a range of programming paradigms and popularity. We evaluate two state-of-the-art code generation models on MultiPL-E: Codex and InCoder. We find that on several languages, Codex matches and even exceeds its performance on Python. The range of programming languages represented in MultiPL-E allow us to explore the impact of language frequency and language features on model performance. Finally, the MultiPL-E approach of compiling code generation benchmarks to new programming languages is both scalable and extensible. We describe a general approach for easily adding support for new benchmarks and languages to MultiPL-E.  ( 2 min )
    Two-Stage Robust and Sparse Distributed Statistical Inference for Large-Scale Data. (arXiv:2208.08230v1 [stat.ML])
    In this paper, we address the problem of conducting statistical inference in settings involving large-scale data that may be high-dimensional and contaminated by outliers. The high volume and dimensionality of the data require distributed processing and storage solutions. We propose a two-stage distributed and robust statistical inference procedures coping with high-dimensional models by promoting sparsity. In the first stage, known as model selection, relevant predictors are locally selected by applying robust Lasso estimators to the distinct subsets of data. The variable selections from each computation node are then fused by a voting scheme to find the sparse basis for the complete data set. It identifies the relevant variables in a robust manner. In the second stage, the developed statistically robust and computationally efficient bootstrap methods are employed. The actual inference constructs confidence intervals, finds parameter estimates and quantifies standard deviation. Similar to stage 1, the results of local inference are communicated to the fusion center and combined there. By using analytical methods, we establish the favorable statistical properties of the robust and computationally efficient bootstrap methods including consistency for a fixed number of predictors, and robustness. The proposed two-stage robust and distributed inference procedures demonstrate reliable performance and robustness in variable selection, finding confidence intervals and bootstrap approximations of standard deviations even when data is high-dimensional and contaminated by outliers.  ( 3 min )
    Multimodal Lecture Presentations Dataset: Understanding Multimodality in Educational Slides. (arXiv:2208.08080v1 [cs.AI])
    Lecture slide presentations, a sequence of pages that contain text and figures accompanied by speech, are constructed and presented carefully in order to optimally transfer knowledge to students. Previous studies in multimedia and psychology attribute the effectiveness of lecture presentations to their multimodal nature. As a step toward developing AI to aid in student learning as intelligent teacher assistants, we introduce the Multimodal Lecture Presentations dataset as a large-scale benchmark testing the capabilities of machine learning models in multimodal understanding of educational content. Our dataset contains aligned slides and spoken language, for 180+ hours of video and 9000+ slides, with 10 lecturers from various subjects (e.g., computer science, dentistry, biology). We introduce two research tasks which are designed as stepping stones towards AI agents that can explain (automatically captioning a lecture presentation) and illustrate (synthesizing visual figures to accompany spoken explanations) educational content. We provide manual annotations to help implement these two research tasks and evaluate state-of-the-art models on them. Comparing baselines and human student performances, we find that current models struggle in (1) weak crossmodal alignment between slides and spoken text, (2) learning novel visual mediums, (3) technical language, and (4) long-range sequences. Towards addressing this issue, we also introduce PolyViLT, a multimodal transformer trained with a multi-instance learning loss that is more effective than current approaches. We conclude by shedding light on the challenges and opportunities in multimodal understanding of educational presentations.  ( 3 min )
    FedPerm: Private and Robust Federated Learning by Parameter Permutation. (arXiv:2208.07922v1 [cs.LG])
    Federated Learning (FL) is a distributed learning paradigm that enables mutually untrusting clients to collaboratively train a common machine learning model. Client data privacy is paramount in FL. At the same time, the model must be protected from poisoning attacks from adversarial clients. Existing solutions address these two problems in isolation. We present FedPerm, a new FL algorithm that addresses both these problems by combining a novel intra-model parameter shuffling technique that amplifies data privacy, with Private Information Retrieval (PIR) based techniques that permit cryptographic aggregation of clients' model updates. The combination of these techniques further helps the federation server constrain parameter updates from clients so as to curtail effects of model poisoning attacks by adversarial clients. We further present FedPerm's unique hyperparameters that can be used effectively to trade off computation overheads with model utility. Our empirical evaluation on the MNIST dataset demonstrates FedPerm's effectiveness over existing Differential Privacy (DP) enforcement solutions in FL.  ( 2 min )
    DLCFT: Deep Linear Continual Fine-Tuning for General Incremental Learning. (arXiv:2208.08112v1 [cs.LG])
    Pre-trained representation is one of the key elements in the success of modern deep learning. However, existing works on continual learning methods have mostly focused on learning models incrementally from scratch. In this paper, we explore an alternative framework to incremental learning where we continually fine-tune the model from a pre-trained representation. Our method takes advantage of linearization technique of a pre-trained neural network for simple and effective continual learning. We show that this allows us to design a linear model where quadratic parameter regularization method is placed as the optimal continual learning policy, and at the same time enjoying the high performance of neural networks. We also show that the proposed algorithm enables parameter regularization methods to be applied to class-incremental problems. Additionally, we provide a theoretical reason why the existing parameter-space regularization algorithms such as EWC underperform on neural networks trained with cross-entropy loss. We show that the proposed method can prevent forgetting while achieving high continual fine-tuning performance on image classification tasks. To show that our method can be applied to general continual learning settings, we evaluate our method in data-incremental, task-incremental, and class-incremental learning problems.  ( 2 min )
    Metric Residual Networks for Sample Efficient Goal-conditioned Reinforcement Learning. (arXiv:2208.08133v1 [cs.LG])
    Goal-conditioned reinforcement learning (GCRL) has a wide range of potential real-world applications, including manipulation and navigation problems in robotics. Especially in such robotics task, sample efficiency is of the utmost importance for GCRL since, by default, the agent is only rewarded when it reaches its goal. While several methods have been proposed to improve the sample efficiency of GCRL, one relatively under-studied approach is the design of neural architectures to support sample efficiency. In this work, we introduce a novel neural architecture for GCRL that achieves significantly better sample efficiency than the commonly-used monolithic network architecture. They key insight is that the optimal action value function Q^*(s, a, g) must satisfy the triangle inequality in a specific sense. Furthermore, we introduce the metric residual network (MRN) that deliberately decomposes the action-value function Q(s,a,g) into the negated summation of a metric plus a residual asymmetric component. MRN provably approximates any optimal action-value function Q^*(s,a,g), thus making it a fitting neural architecture for GCRL. We conduct comprehensive experiments across 12 standard benchmark environments in GCRL. The empirical results demonstrate that MRN uniformly outperforms other state-of-the-art GCRL neural architectures in terms of sample efficiency.  ( 2 min )
    ILLUME: Rationalizing Vision-Language Models by Interacting with their Jabber. (arXiv:2208.08241v1 [cs.LG])
    Bootstrapping from pre-trained language models has been proven to be an efficient approach for building foundation vision-language models (VLM) for tasks such as image captioning or visual question answering. However, it is difficult-if not impossible-to utilize it to make the model conform with user's rationales for specific answers. To elicit and reinforce commonsense reasons, we propose an iterative sampling and tuning paradigm, called ILLUME, that executes the following loop: Given an image-question-answer prompt, the VLM samples multiple candidate rationales, and a human critic provides minimal feedback via preference selection, used for fine-tuning. This loop increases the training data and gradually carves out the VLM's rationalization capabilities. Our exhaustive experiments demonstrate that ILLUME is competitive with standard supervised fine-tuning while using significantly fewer training data and only requiring minimal feedback.  ( 2 min )
    Assurance Cases as Foundation Stone for Auditing AI-enabled and Autonomous Systems: Workshop Results and Political Recommendations for Action from the ExamAI Project. (arXiv:2208.08198v1 [cs.SE])
    The European Machinery Directive and related harmonized standards do consider that software is used to generate safety-relevant behavior of the machinery but do not consider all kinds of software. In particular, software based on machine learning (ML) are not considered for the realization of safety-relevant behavior. This limits the introduction of suitable safety concepts for autonomous mobile robots and other autonomous machinery, which commonly depend on ML-based functions. We investigated this issue and the way safety standards define safety measures to be implemented against software faults. Functional safety standards use Safety Integrity Levels (SILs) to define which safety measures shall be implemented. They provide rules for determining the SIL and rules for selecting safety measures depending on the SIL. In this paper, we argue that this approach can hardly be adopted with respect to ML and other kinds of Artificial Intelligence (AI). Instead of simple rules for determining an SIL and applying related measures against faults, we propose the use of assurance cases to argue that the individually selected and applied measures are sufficient in the given case. To get a first rating regarding the feasibility and usefulness of our proposal, we presented and discussed it in a workshop with experts from industry, German statutory accident insurance companies, work safety and standardization commissions, and representatives from various national, European, and international working groups dealing with safety and AI. In this paper, we summarize the proposal and the workshop discussion. Moreover, we check to which extent our proposal is in line with the European AI Act proposal and current safety standardization initiatives addressing AI and Autonomous Systems  ( 3 min )
    Efficient Detection and Filtering Systems for Distributed Training. (arXiv:2208.08085v1 [cs.LG])
    A plethora of modern machine learning tasks require the utilization of large-scale distributed clusters as a critical component of the training pipeline. However, abnormal Byzantine behavior of the worker nodes can derail the training and compromise the quality of the inference. Such behavior can be attributed to unintentional system malfunctions or orchestrated attacks; as a result, some nodes may return arbitrary results to the parameter server (PS) that coordinates the training. Recent work considers a wide range of attack models and has explored robust aggregation and/or computational redundancy to correct the distorted gradients. In this work, we consider attack models ranging from strong ones: $q$ omniscient adversaries with full knowledge of the defense protocol that can change from iteration to iteration to weak ones: $q$ randomly chosen adversaries with limited collusion abilities which only change every few iterations at a time. Our algorithms rely on redundant task assignments coupled with detection of adversarial behavior. For strong attacks, we demonstrate a reduction in the fraction of distorted gradients ranging from 16\%-99\% as compared to the prior state-of-the-art. Our top-1 classification accuracy results on the CIFAR-10 data set demonstrate 25\% advantage in accuracy (averaged over strong and weak scenarios) under the most sophisticated attacks compared to state-of-the-art methods.  ( 3 min )
    Ex-Ante Assessment of Discrimination in Dataset. (arXiv:2208.07918v1 [cs.LG])
    Data owners face increasing liability for how the use of their data could harm under-priviliged communities. Stakeholders would like to identify the characteristics of data that lead to algorithms being biased against any particular demographic groups, for example, defined by their race, gender, age, and/or religion. Specifically, we are interested in identifying subsets of the feature space where the ground truth response function from features to observed outcomes differs across demographic groups. To this end, we propose FORESEE, a FORESt of decision trEEs algorithm, which generates a score that captures how likely an individual's response varies with sensitive attributes. Empirically, we find that our approach allows us to identify the individuals who are most likely to be misclassified by several classifiers, including Random Forest, Logistic Regression, Support Vector Machine, and k-Nearest Neighbors. The advantage of our approach is that it allows stakeholders to characterize risky samples that may contribute to discrimination, as well as, use the FORESEE to estimate the risk of upcoming samples.  ( 2 min )
    Tiny-HR: Towards an interpretable machine learning pipeline for heart rate estimation on edge devices. (arXiv:2208.07981v1 [cs.LG])
    The focus of this paper is a proof of concept, machine learning (ML) pipeline that extracts heart rate from pressure sensor data acquired on low-power edge devices. The ML pipeline consists an upsampler neural network, a signal quality classifier, and a 1D-convolutional neural network optimized for efficient and accurate heart rate estimation. The models were designed so the pipeline was less than 40 kB. Further, a hybrid pipeline consisting of the upsampler and classifier, followed by a peak detection algorithm was developed. The pipelines were deployed on ESP32 edge device and benchmarked against signal processing to determine the energy usage, and inference times. The results indicate that the proposed ML and hybrid pipeline reduces energy and time per inference by 82% and 28% compared to traditional algorithms. The main trade-off for ML pipeline was accuracy, with a mean absolute error (MAE) of 3.28, compared to 2.39 and 1.17 for the hybrid and signal processing pipelines. The ML models thus show promise for deployment in energy and computationally constrained devices. Further, the lower sampling rate and computational requirements for the ML pipeline could enable custom hardware solutions to reduce the cost and energy needs of wearable devices.  ( 3 min )
    Gradient-Based Meta-Learning Using Uncertainty to Weigh Loss for Few-Shot Learning. (arXiv:2208.08135v1 [cs.LG])
    Model-Agnostic Meta-Learning (MAML) is one of the most successful meta-learning techniques for few-shot learning. It uses gradient descent to learn commonalities between various tasks, enabling the model to learn the meta-initialization of its own parameters to quickly adapt to new tasks using a small amount of labeled training data. A key challenge to few-shot learning is task uncertainty. Although a strong prior can be obtained from meta-learning with a large number of tasks, a precision model of the new task cannot be guaranteed because the volume of the training dataset is normally too small. In this study, first,in the process of choosing initialization parameters, the new method is proposed for task-specific learner adaptively learn to select initialization parameters that minimize the loss of new tasks. Then, we propose two improved methods for the meta-loss part: Method 1 generates weights by comparing meta-loss differences to improve the accuracy when there are few classes, and Method 2 introduces the homoscedastic uncertainty of each task to weigh multiple losses based on the original gradient descent,as a way to enhance the generalization ability to novel classes while ensuring accuracy improvement. Compared with previous gradient-based meta-learning methods, our model achieves better performance in regression tasks and few-shot classification and improves the robustness of the model to the learning rate and query sets in the meta-test set.  ( 3 min )
    Mixed Quantum-Classical Method For Fraud Detection with Quantum Feature Selection. (arXiv:2208.07963v1 [quant-ph])
    This paper presents a first end-to-end application of a Quantum Support Vector Machine (QSVM) algorithm for a classification problem in the financial payment industry using the IBM Safer Payments and IBM Quantum Computers via the Qiskit software stack. Based on real card payment data, a thorough comparison is performed to assess the complementary impact brought in by the current state-of-the-art Quantum Machine Learning algorithms with respect to the Classical Approach. A new method to search for best features is explored using the Quantum Support Vector Machine's feature map characteristics. The results are compared using fraud specific key performance indicators: Accuracy, Recall, and False Positive Rate, extracted from analyses based on human expertise (rule decisions), classical machine learning algorithms (Random Forest, XGBoost) and quantum based machine learning algorithms using QSVM. In addition, a hybrid classical-quantum approach is explored by using an ensemble model that combines classical and quantum algorithms to better improve the fraud prevention decision. We found, as expected, that the results highly depend on feature selections and algorithms that are used to select them. The QSVM provides a complementary exploration of the feature space which led to an improved accuracy of the mixed quantum-classical method for fraud detection, on a drastically reduced data set to fit current state of Quantum Hardware.  ( 3 min )
    AHEAD: A Triple Attention Based Heterogeneous Graph Anomaly Detection Approach. (arXiv:2208.08200v1 [cs.SI])
    Graph anomaly detection on attributed networks has become a prevalent research topic due to its broad applications in many influential domains. In real-world scenarios, nodes and edges in attributed networks usually display distinct heterogeneity, i.e. attributes of different types of nodes show great variety, different types of relations represent diverse meanings. Anomalies usually perform differently from the majority in various perspectives of heterogeneity in these networks. However, existing graph anomaly detection approaches do not leverage heterogeneity in attributed networks, which is highly related to anomaly detection. In light of this problem, we propose AHEAD: a heterogeneity-aware unsupervised graph anomaly detection approach based on the encoder-decoder framework. Specifically, for the encoder, we design three levels of attention, i.e. attribute level, node type level, and edge level attentions to capture the heterogeneity of network structure, node properties and information of a single node, respectively. In the decoder, we exploit structure, attribute, and node type reconstruction terms to obtain an anomaly score for each node. Extensive experiments show the superiority of AHEAD on several real-world heterogeneous information networks compared with the state-of-arts in the unsupervised setting. Further experiments verify the effectiveness and robustness of our triple attention, model backbone, and decoder in general.  ( 3 min )
    A Monotonicity Constrained Attention Module for Emotion Classification with Limited EEG Data. (arXiv:2208.08155v1 [eess.SP])
    In this work, a parameter-efficient attention module is presented for emotion classification using a limited, or relatively small, number of electroencephalogram (EEG) signals. This module is called the Monotonicity Constrained Attention Module (MCAM) due to its capability of incorporating priors on the monotonicity when converting features' Gram matrices into attention matrices for better feature refinement. Our experiments have shown that MCAM's effectiveness is comparable to state-of-the-art attention modules in boosting the backbone network's performance in prediction while requiring less parameters. Several accompanying sensitivity analyses on trained models' prediction concerning different attacks are also performed. These attacks include various frequency domain filtering levels and gradually morphing between samples associated with multiple labels. Our results can help better understand different modules' behaviour in prediction and can provide guidance in applications where data is limited and are with noises.  ( 2 min )
    Random Search Hyper-Parameter Tuning: Expected Improvement Estimation and the Corresponding Lower Bound. (arXiv:2208.08170v1 [cs.LG])
    Hyperparameter tuning is a common technique for improving the performance of neural networks. Most techniques for hyperparameter search involve an iterated process where the model is retrained at every iteration. However, the expected accuracy improvement from every additional search iteration, is still unknown. Calculating the expected improvement can help create stopping rules for hyperparameter tuning and allow for a wiser allocation of a project's computational budget. In this paper, we establish an empirical estimate for the expected accuracy improvement from an additional iteration of hyperparameter search. Our results hold for any hyperparameter tuning method which is based on random search \cite{bergstra2012random} and samples hyperparameters from a fixed distribution. We bound our estimate with an error of $O\left(\sqrt{\frac{\log k}{k}}\right)$ w.h.p. where $k$ is the current number of iterations. To the best of our knowledge this is the first bound on the expected gain from an additional iteration of hyperparameter search. Finally, we demonstrate that the optimal estimate for the expected accuracy will still have an error of $\frac{1}{k}$.  ( 2 min )
    Autonomous Resource Management in Construction Companies Using Deep Reinforcement Learning Based on IoT. (arXiv:2208.08087v1 [cs.LG])
    Resource allocation is one of the most critical issues in planning construction projects, due to its direct impact on cost, time, and quality. There are usually specific allocation methods for autonomous resource management according to the projects objectives. However, integrated planning and optimization of utilizing resources in an entire construction organization are scarce. The purpose of this study is to present an automatic resource allocation structure for construction companies based on Deep Reinforcement Learning (DRL), which can be used in various situations. In this structure, Data Harvesting (DH) gathers resource information from the distributed Internet of Things (IoT) sensor devices all over the companys projects to be employed in the autonomous resource management approach. Then, Coverage Resources Allocation (CRA) is compared to the information obtained from DH in which the Autonomous Resource Management (ARM) determines the project of interest. Likewise, Double Deep Q-Networks (DDQNs) with similar models are trained on two distinct assignment situations based on structured resource information of the company to balance objectives with resource constraints. The suggested technique in this paper can efficiently adjust to large resource management systems by combining portfolio information with adopted individual project information. Also, the effects of important information processing parameters on resource allocation performance are analyzed in detail. Moreover, the results of the generalizability of management approaches are presented, indicating no need for additional training when the variables of situations change.  ( 3 min )
    DeepSportradar-v1: Computer Vision Dataset for Sports Understanding with High Quality Annotations. (arXiv:2208.08190v1 [cs.CV])
    With the recent development of Deep Learning applied to Computer Vision, sport video understanding has gained a lot of attention, providing much richer information for both sport consumers and leagues. This paper introduces DeepSportradar-v1, a suite of computer vision tasks, datasets and benchmarks for automated sport understanding. The main purpose of this framework is to close the gap between academic research and real world settings. To this end, the datasets provide high-resolution raw images, camera parameters and high quality annotations. DeepSportradar currently supports four challenging tasks related to basketball: ball 3D localization, camera calibration, player instance segmentation and player re-identification. For each of the four tasks, a detailed description of the dataset, objective, performance metrics, and the proposed baseline method are provided. To encourage further research on advanced methods for sport understanding, a competition is organized as part of the MMSports workshop from the ACM Multimedia 2022 conference, where participants have to develop state-of-the-art methods to solve the above tasks. The four datasets, development kits and baselines are publicly available.  ( 3 min )
    Enhancing Audio Perception of Music By AI Picked Room Acoustics. (arXiv:2208.07994v1 [cs.SD])
    Every sound that we hear is the result of successive convolutional operations (e.g. room acoustics, microphone characteristics, resonant properties of the instrument itself, not to mention characteristics and limitations of the sound reproduction system). In this work we seek to determine the best room in which to perform a particular piece using AI. Additionally, we use room acoustics as a way to enhance the perceptual qualities of a given sound. Historically, rooms (particularly Churches and concert halls) were designed to host and serve specific musical functions. In some cases the architectural acoustical qualities enhanced the music performed there. We try to mimic this, as a first step, by designating room impulse responses that would correlate to producing enhanced sound quality for particular music. A convolutional architecture is first trained to take in an audio sample and mimic the ratings of experts with about 78 % accuracy for various instrument families and notes for perceptual qualities. This gives us a scoring function for any audio sample which can rate the perceptual pleasantness of a note automatically. Now, via a library of about 60,000 synthetic impulse responses mimicking all kinds of room, materials, etc, we use a simple convolution operation, to transform the sound as if it was played in a particular room. The perceptual evaluator is used to rank the musical sounds, and yield the "best room or the concert hall" to play a sound. As a byproduct it can also use room acoustics to turn a poor quality sound into a "good" sound.  ( 3 min )
    Riemannian Diffusion Models. (arXiv:2208.07949v1 [cs.LG])
    Diffusion models are recent state-of-the-art methods for image generation and likelihood estimation. In this work, we generalize continuous-time diffusion models to arbitrary Riemannian manifolds and derive a variational framework for likelihood estimation. Computationally, we propose new methods for computing the Riemannian divergence which is needed in the likelihood estimation. Moreover, in generalizing the Euclidean case, we prove that maximizing this variational lower-bound is equivalent to Riemannian score matching. Empirically, we demonstrate the expressive power of Riemannian diffusion models on a wide spectrum of smooth manifolds, such as spheres, tori, hyperboloids, and orthogonal groups. Our proposed method achieves new state-of-the-art likelihoods on all benchmarks.  ( 2 min )
    On the generalization of learning algorithms that do not converge. (arXiv:2208.07951v1 [cs.LG])
    Generalization analyses of deep learning typically assume that the training converges to a fixed point. But, recent results indicate that in practice, the weights of deep neural networks optimized with stochastic gradient descent often oscillate indefinitely. To reduce this discrepancy between theory and practice, this paper focuses on the generalization of neural networks whose training dynamics do not necessarily converge to fixed points. Our main contribution is to propose a notion of statistical algorithmic stability (SAS) that extends classical algorithmic stability to non-convergent algorithms and to study its connection to generalization. This ergodic-theoretic approach leads to new insights when compared to the traditional optimization and learning theory perspectives. We prove that the stability of the time-asymptotic behavior of a learning algorithm relates to its generalization and empirically demonstrate how loss dynamics can provide clues to generalization performance. Our findings provide evidence that networks that "train stably generalize better" even when the training continues indefinitely and the weights do not converge.  ( 2 min )
    Private Estimation with Public Data. (arXiv:2208.07984v1 [cs.LG])
    We initiate the study of differentially private (DP) estimation with access to a small amount of public data. For private estimation of d-dimensional Gaussians, we assume that the public data comes from a Gaussian that may have vanishing similarity in total variation distance with the underlying Gaussian of the private data. We show that under the constraints of pure or concentrated DP, d+1 public data samples are sufficient to remove any dependence on the range parameters of the private data distribution from the private sample complexity, which is known to be otherwise necessary without public data. For separated Gaussian mixtures, we assume that the underlying public and private distributions are the same, and we consider two settings: (1) when given a dimension-independent amount of public data, the private sample complexity can be improved polynomially in terms of the number of mixture components, and any dependence on the range parameters of the distribution can be removed in the approximate DP case; (2) when given an amount of public data linear in the dimension, the private sample complexity can be made independent of range parameters even under concentrated DP, and additional improvements can be made to the overall sample complexity.  ( 2 min )
    ShortcutLens: A Visual Analytics Approach for Exploring Shortcuts in Natural Language Understanding Dataset. (arXiv:2208.08010v1 [cs.HC])
    Benchmark datasets play an important role in evaluating Natural Language Understanding (NLU) models. However, shortcuts -- unwanted biases in the benchmark datasets -- can damage the effectiveness of benchmark datasets in revealing models' real capabilities. Since shortcuts vary in coverage, productivity, and semantic meaning, it is challenging for NLU experts to systematically understand and avoid them when creating benchmark datasets. In this paper, we develop a visual analytics system, ShortcutLens, to help NLU experts explore shortcuts in NLU benchmark datasets. The system allows users to conduct multi-level exploration of shortcuts. Specifically, Statistics View helps users grasp the statistics such as coverage and productivity of shortcuts in the benchmark dataset. Template View employs hierarchical and interpretable templates to summarize different types of shortcuts. Instance View allows users to check the corresponding instances covered by the shortcuts. We conduct case studies and expert interviews to evaluate the effectiveness and usability of the system. The results demonstrate that ShortcutLens supports users in gaining a better understanding of benchmark dataset issues through shortcuts, inspiring them to create challenging and pertinent benchmark datasets.  ( 2 min )
    Resource-aware Federated Learning using Knowledge Extraction and Multi-model Fusion. (arXiv:2208.07978v1 [cs.DC])
    With increasing concern about user data privacy, federated learning (FL) has been developed as a unique training paradigm for training machine learning models on edge devices without access to sensitive data. Traditional FL and existing methods directly employ aggregation methods on all edges of the same models and training devices for a cloud server. Although these methods protect data privacy, they are not capable of model heterogeneity, even ignore the heterogeneous computing power, and incur steep communication costs. In this paper, we purpose a resource-aware FL to aggregate an ensemble of local knowledge extracted from edge models, instead of aggregating the weights of each local model, which is then distilled into a robust global knowledge as the server model through knowledge distillation. The local model and the global knowledge are extracted into a tiny size knowledge network by deep mutual learning. Such knowledge extraction allows the edge client to deploy a resource-aware model and perform multi-model knowledge fusion while maintaining communication efficiency and model heterogeneity. Empirical results show that our approach has significantly improved over existing FL algorithms in terms of communication cost and generalization performance in heterogeneous data and models. Our approach reduces the communication cost of VGG-11 by up to 102$\times$ and ResNet-32 by up to 30$\times$ when training ResNet-20 as the knowledge network.  ( 3 min )
    Collaborative causal inference on distributed data. (arXiv:2208.07898v1 [stat.ME])
    The development of technologies for causal inference with the privacy preservation of distributed data has attracted considerable attention in recent years. To address this issue, we propose a quasi-experiment based on data collaboration (DC-QE) that enables causal inference from distributed data with privacy preservation. Our method preserves the privacy of private data by sharing only dimensionality-reduced intermediate representations, which are individually constructed by each party. Moreover, our method can reduce both random errors and biases, whereas existing methods can only reduce random errors in the estimation of treatment effects. Through numerical experiments on both artificial and real-world data, we confirmed that our method can lead to better estimation results than individual analyses. With the spread of our method, intermediate representations can be published as open data to help researchers find causalities and accumulated as a knowledge base.  ( 2 min )
    Streaming Adaptive Submodular Maximization. (arXiv:2208.08021v1 [cs.AI])
    Many sequential decision making problems can be formulated as an adaptive submodular maximization problem. However, most of existing studies in this field focus on pool-based setting, where one can pick items in any order, and there have been few studies for the stream-based setting where items arrive in an arbitrary order and one must immediately decide whether to select an item or not upon its arrival. In this paper, we introduce a new class of utility functions, semi-policywise submodular functions. We develop a series of effective algorithms to maximize a semi-policywise submodular function under the stream-based setting.  ( 2 min )
    Quantum Bayes AI. (arXiv:2208.08068v1 [stat.ML])
    Quantum Bayesian AI (Q-B) is an emerging field that levers the computational gains available in Quantum computing. The promise is an exponential speed-up in many Bayesian algorithms. Our goal is to apply these methods directly to statistical and machine learning problems. We provide a duality between classical and quantum probability for calculating of posterior quantities of interest. Our framework unifies MCMC, Deep Learning and Quantum Learning calculations from the viewpoint from von Neumann's principle of quantum measurement. Quantum embeddings and neural gates are also an important part of data encoding and feature selection. There is a natural duality with well-known kernel methods in statistical learning. We illustrate the behaviour of quantum algorithms on two simple classification algorithms. Finally, we conclude with directions for future research.  ( 2 min )
    Interference Cancellation GAN Framework for Dynamic Channels. (arXiv:2208.08019v1 [cs.LG])
    Symbol detection is a fundamental and challenging problem in modern communication systems, e.g., multiuser multiple-input multiple-output (MIMO) setting. Iterative Soft Interference Cancellation (SIC) is a state-of-the-art method for this task and recently motivated data-driven neural network models, e.g. DeepSIC, that can deal with unknown non-linear channels. However, these neural network models require thorough timeconsuming training of the networks before applying, and is thus not readily suitable for highly dynamic channels in practice. We introduce an online training framework that can swiftly adapt to any changes in the channel. Our proposed framework unifies the recent deep unfolding approaches with the emerging generative adversarial networks (GANs) to capture any changes in the channel and quickly adjust the networks to maintain the top performance of the model. We demonstrate that our framework significantly outperforms recent neural network models on highly dynamic channels and even surpasses those on the static channel in our experiments.  ( 2 min )
    Artificial Intelligence Empowered Multiple Access for Ultra Reliable and Low Latency THz Wireless Networks. (arXiv:2208.08039v1 [eess.SP])
    Terahertz (THz) wireless networks are expected to catalyze the beyond fifth generation (B5G) era. However, due to the directional nature and the line-of-sight demand of THz links, as well as the ultra-dense deployment of THz networks, a number of challenges that the medium access control (MAC) layer needs to face are created. In more detail, the need of rethinking user association and resource allocation strategies by incorporating artificial intelligence (AI) capable of providing "real-time" solutions in complex and frequently changing environments becomes evident. Moreover, to satisfy the ultra-reliability and low-latency demands of several B5G applications, novel mobility management approaches are required. Motivated by this, this article presents a holistic MAC layer approach that enables intelligent user association and resource allocation, as well as flexible and adaptive mobility management, while maximizing systems' reliability through blockage minimization. In more detail, a fast and centralized joint user association, radio resource allocation, and blockage avoidance by means of a novel metaheuristic-machine learning framework is documented, that maximizes the THz networks performance, while minimizing the association latency by approximately three orders of magnitude. To support, within the access point (AP) coverage area, mobility management and blockage avoidance, a deep reinforcement learning (DRL) approach for beam-selection is discussed. Finally, to support user mobility between coverage areas of neighbor APs, a proactive hand-over mechanism based on AI-assisted fast channel prediction is~reported.  ( 3 min )
    Paint2Pix: Interactive Painting based Progressive Image Synthesis and Editing. (arXiv:2208.08092v1 [cs.CV])
    Controllable image synthesis with user scribbles is a topic of keen interest in the computer vision community. In this paper, for the first time we study the problem of photorealistic image synthesis from incomplete and primitive human paintings. In particular, we propose a novel approach paint2pix, which learns to predict (and adapt) "what a user wants to draw" from rudimentary brushstroke inputs, by learning a mapping from the manifold of incomplete human paintings to their realistic renderings. When used in conjunction with recent works in autonomous painting agents, we show that paint2pix can be used for progressive image synthesis from scratch. During this process, paint2pix allows a novice user to progressively synthesize the desired image output, while requiring just few coarse user scribbles to accurately steer the trajectory of the synthesis process. Furthermore, we find that our approach also forms a surprisingly convenient approach for real image editing, and allows the user to perform a diverse range of custom fine-grained edits through the addition of only a few well-placed brushstrokes. Supplemental video and demo are available at https://1jsingh.github.io/paint2pix  ( 2 min )
    A Survey on Incomplete Multi-view Clustering. (arXiv:2208.08040v1 [cs.LG])
    Conventional multi-view clustering seeks to partition data into respective groups based on the assumption that all views are fully observed. However, in practical applications, such as disease diagnosis, multimedia analysis, and recommendation system, it is common to observe that not all views of samples are available in many cases, which leads to the failure of the conventional multi-view clustering methods. Clustering on such incomplete multi-view data is referred to as incomplete multi-view clustering. In view of the promising application prospects, the research of incomplete multi-view clustering has noticeable advances in recent years. However, there is no survey to summarize the current progresses and point out the future research directions. To this end, we review the recent studies of incomplete multi-view clustering. Importantly, we provide some frameworks to unify the corresponding incomplete multi-view clustering methods, and make an in-depth comparative analysis for some representative methods from theoretical and experimental perspectives. Finally, some open problems in the incomplete multi-view clustering field are offered for researchers.  ( 2 min )
    Online Learning for Mixture of Multivariate Hawkes Processes. (arXiv:2208.07961v1 [stat.ML])
    Online learning of Hawkes processes has received increasing attention in the last couple of years especially for modeling a network of actors. However, these works typically either model the rich interaction between the events or the latent cluster of the actors or the network structure between the actors. We propose to model the latent structure of the network of actors as well as their rich interaction across events for real-world settings of medical and financial applications. Experimental results on both synthetic and real-world data showcase the efficacy of our approach.  ( 2 min )
    PD-MORL: Preference-Driven Multi-Objective Reinforcement Learning Algorithm. (arXiv:2208.07914v1 [cs.LG])
    Many real-world problems involve multiple, possibly conflicting, objectives. Multi-objective reinforcement learning (MORL) approaches have emerged to tackle these problems by maximizing a joint objective function weighted by a preference vector. These approaches find fixed customized policies corresponding to preference vectors specified during training. However, the design constraints and objectives typically change dynamically in real-life scenarios. Furthermore, storing a policy for each potential preference is not scalable. Hence, obtaining a set of Pareto front solutions for the entire preference space in a given domain with a single training is critical. To this end, we propose a novel MORL algorithm that trains a single universal network to cover the entire preference space. The proposed approach, Preference-Driven MORL (PD-MORL), utilizes the preferences as guidance to update the network parameters. After demonstrating PD-MORL using classical Deep Sea Treasure and Fruit Tree Navigation benchmarks, we evaluate its performance on challenging multi-objective continuous control tasks.  ( 2 min )
    FOLD-SE: Scalable Explainable AI. (arXiv:2208.07912v1 [cs.LG])
    FOLD-R++ is a highly efficient and explainable rule-based machine learning algorithm for binary classification tasks. It generates a stratified normal logic program as an (explainable) trained model. We present an improvement over the FOLD-R++ algorithm, termed FOLD-SE, that provides scalable explainability (SE) while inheriting all the merits of FOLD-R++. Scalable explainability means that regardless of the size of the dataset, the number of learned rules and learned literals stay small and, hence, understandable by human beings, while maintaining good performance in classification. FOLD-SE is competitive in performance with state-of-the-art algorithms such as XGBoost and Multi-Layer Perceptrons (MLP). However, unlike XGBoost and MLP, the FOLD-SE algorithm generates a model with scalable explainability. The FOLD-SE algorithm outperforms FOLD-R++ and RIPPER algorithms in efficiency, performance, and explainability, especially for large datasets. The FOLD-RM algorithm is an extension of FOLD-R++ for multi-class classification tasks. An improved FOLD-RM algorithm built upon FOLD-SE is also presented.  ( 2 min )
    Measuring Statistical Dependencies via Maximum Norm and Characteristic Functions. (arXiv:2208.07934v1 [cs.LG])
    In this paper, we focus on the problem of statistical dependence estimation using characteristic functions. We propose a statistical dependence measure, based on the maximum-norm of the difference between joint and product-marginal characteristic functions. The proposed measure can detect arbitrary statistical dependence between two random vectors of possibly different dimensions, is differentiable, and easily integrable into modern machine learning and deep learning pipelines. We also conduct experiments both with simulated and real data. Our simulations show, that the proposed method can measure statistical dependencies in high-dimensional, non-linear data, and is less affected by the curse of dimensionality, compared to the previous work in this line of research. The experiments with real data demonstrate the potential applicability of our statistical measure for two different empirical inference scenarios, showing statistically significant improvement in the performance characteristics when applied for supervised feature extraction and deep neural network regularization. In addition, we provide a link to the accompanying open-source repository https://bit.ly/3d4ch5I.  ( 2 min )
  • Open

    Localized Debiased Machine Learning: Efficient Inference on Quantile Treatment Effects and Beyond. (arXiv:1912.12945v5 [stat.ML] UPDATED)
    We consider estimating a low-dimensional parameter in an estimating equation involving high-dimensional nuisances that depend on the parameter. A central example is the efficient estimating equation for the (local) quantile treatment effect ((L)QTE) in causal inference, which involves as a nuisance the covariate-conditional cumulative distribution function evaluated at the quantile to be estimated. Debiased machine learning (DML) is a data-splitting approach to estimating high-dimensional nuisances using flexible machine learning methods, but applying it to problems with parameter-dependent nuisances is impractical. For (L)QTE, DML requires we learn the whole covariate-conditional cumulative distribution function. We instead propose localized debiased machine learning (LDML), which avoids this burdensome step and needs only estimate nuisances at a single initial rough guess for the parameter. For (L)QTE, LDML involves learning just two regression functions, a standard task for machine learning methods. We prove that under lax rate conditions our estimator has the same favorable asymptotic behavior as the infeasible estimator that uses the unknown true nuisances. Thus, LDML notably enables practically-feasible and theoretically-grounded efficient estimation of important quantities in causal inference such as (L)QTEs when we must control for many covariates and/or flexible relationships, as we demonstrate in empirical studies.  ( 3 min )
    Semi-Supervised Anomaly Detection Based on Quadratic Multiform Separation. (arXiv:2208.08265v1 [stat.ML])
    In this paper we propose a novel method for semi-supervised anomaly detection (SSAD). Our classifier is named QMS22 as its inception was dated 2022 upon the framework of quadratic multiform separation (QMS), a recently introduced classification model. QMS22 tackles SSAD by solving a multi-class classification problem involving both the training set and the test set of the original problem. The classification problem intentionally includes classes with overlapping samples. One of the classes contains mixture of normal samples and outliers, and all other classes contain only normal samples. An outlier score is then calculated for every sample in the test set using the outcome of the classification problem. We also include performance evaluation of QMS22 against top performing classifiers using ninety-five benchmark imbalanced datasets from the KEEL repository. These classifiers are BRM (Bagging-Random Miner), OCKRA (One-Class K-means with Randomly-projected features Algorithm), ISOF (Isolation Forest), and ocSVM (One-Class Support Vector Machine). It is shown by using the area under the curve of the receiver operating characteristic curve as the performance measure, QMS22 significantly outperforms ISOF and ocSVM. Moreover, the Wilcoxon signed-rank tests reveal that there is no statistically significant difference when testing QMS22 against BRM nor QMS22 against OCKRA.  ( 2 min )
    Domain Knowledge in A*-Based Causal Discovery. (arXiv:2208.08247v1 [stat.ML])
    Causal discovery has become a vital tool for scientists and practitioners wanting to discover causal relationships from observational data. While most previous approaches to causal discovery have implicitly assumed that no expert domain knowledge is available, practitioners can often provide such domain knowledge from prior experience. Recent work has incorporated domain knowledge into constraint-based causal discovery. The majority of such constraint-based methods, however, assume causal faithfulness, which has been shown to be frequently violated in practice. Consequently, there has been renewed attention towards exact-search score-based causal discovery methods, which do not assume causal faithfulness, such as A*-based methods. However, there has been no consideration of these methods in the context of domain knowledge. In this work, we focus on efficiently integrating several types of domain knowledge into A*-based causal discovery. In doing so, we discuss and explain how domain knowledge can reduce the graph search space and then provide an analysis of the potential computational gains. We support these findings with experiments on synthetic and real data, showing that even small amounts of domain knowledge can dramatically speed up A*-based causal discovery and improve its performance and practicality.  ( 2 min )
    CoSimGNN: Towards Large-scale Graph Similarity Computation. (arXiv:2005.07115v7 [cs.LG] UPDATED)
    The ability to compute similarity scores between graphs based on metrics such as Graph Edit Distance (GED) is important in many real-world applications. Computing exact GED values is typically an NP-hard problem and traditional algorithms usually achieve an unsatisfactory trade-off between accuracy and efficiency. Recently, Graph Neural Networks (GNNs) provide a data-driven solution for this task, which is more efficient while maintaining prediction accuracy in small graph (around 10 nodes per graph) similarity computation. Existing GNN-based methods, which either respectively embeds two graphs (lack of low-level cross-graph interactions) or deploy cross-graph interactions for whole graph pairs (redundant and time-consuming), are still not able to achieve competitive results when the number of nodes in graphs increases. In this paper, we focus on similarity computation for large-scale graphs and propose the "embedding-coarsening-matching" framework CoSimGNN, which first embeds and coarsens large graphs with adaptive pooling operation and then deploys fine-grained interactions on the coarsened graphs for final similarity scores. Furthermore, we create several synthetic datasets which provide new benchmarks for graph similarity computation. Detailed experiments on both synthetic and real-world datasets have been conducted and CoSimGNN achieves the best performance while the inference time is at most 1/3 of that of previous state-of-the-art.  ( 3 min )
    Supervised PCA: A Multiobjective Approach. (arXiv:2011.05309v4 [stat.ML] UPDATED)
    Methods for supervised principal component analysis (SPCA) aim to incorporate label information into principal component analysis (PCA), so that the extracted features are more useful for a prediction task of interest. Prior work on SPCA has focused primarily on optimizing prediction error, and has neglected the value of maximizing variance explained by the extracted features. We propose a new method for SPCA that addresses both of these objectives jointly, and demonstrate empirically that our approach dominates existing approaches, i.e., outperforms them with respect to both prediction error and variation explained. Our approach accommodates arbitrary supervised learning losses and, through a statistical reformulation, provides a novel low-rank extension of generalized linear models.  ( 2 min )
    Deep Gaussian Process Emulation using Stochastic Imputation. (arXiv:2107.01590v2 [stat.ML] UPDATED)
    Deep Gaussian processes (DGPs) provide a rich class of models that can better represent functions with varying regimes or sharp changes, compared to conventional GPs. In this work, we propose a novel inference method for DGPs for computer model emulation. By stochastically imputing the latent layers, our approach transforms a DGP into a linked GP: a novel emulator developed for systems of linked computer models. This transformation permits an efficient DGP training procedure that only involves optimizations of conventional GPs. In addition, predictions from DGP emulators can be made in a fast and analytically tractable manner by naturally utilizing the closed form predictive means and variances of linked GP emulators. We demonstrate the method in a series of synthetic examples and empirical applications, and show that it is a competitive candidate for DGP surrogate inference, combining efficiency that is comparable to doubly stochastic variational inference and uncertainty quantification that is comparable to the fully-Bayesian approach. A $\texttt{Python}$ package $\texttt{dgpsi}$ implementing the method is also produced and available at https://github.com/mingdeyu/DGP.  ( 2 min )
    Using Machine Learning to Test Causal Hypotheses in Conjoint Analysis. (arXiv:2201.08343v2 [stat.ME] UPDATED)
    Conjoint analysis is a popular experimental design used to measure multidimensional preferences. Researchers examine how varying a factor of interest, while controlling for other relevant factors, influences decision-making. Currently, there exist two methodological approaches to analyzing data from a conjoint experiment. The first focuses on estimating the average marginal effects of each factor while averaging over the other factors. Although this allows for straightforward design-based estimation, the results critically depend on the distribution of other factors and how interaction effects are aggregated. An alternative model-based approach can compute various quantities of interest, but requires researchers to correctly specify the model, a challenging task for conjoint analysis with many factors and possible interactions. In addition, a commonly used logistic regression has poor statistical properties even with a moderate number of factors when incorporating interactions. We propose a new hypothesis testing approach based on the conditional randomization test to answer the most fundamental question of conjoint analysis: Does a factor of interest matter in any way given the other factors? Our methodology is solely based on the randomization of factors, and hence is free from assumptions. Yet, it allows researchers to use any test statistic, including those based on complex machine learning algorithms. As a result, we are able to combine the strengths of the existing design-based and model-based approaches. We illustrate the proposed methodology through conjoint analysis of immigration preferences and political candidate evaluation. We also extend the proposed approach to test for regularity assumptions commonly used in conjoint analysis. An open-source software package is available for implementing the proposed methodology.  ( 3 min )
    Quantum Bayes AI. (arXiv:2208.08068v1 [stat.ML])
    Quantum Bayesian AI (Q-B) is an emerging field that levers the computational gains available in Quantum computing. The promise is an exponential speed-up in many Bayesian algorithms. Our goal is to apply these methods directly to statistical and machine learning problems. We provide a duality between classical and quantum probability for calculating of posterior quantities of interest. Our framework unifies MCMC, Deep Learning and Quantum Learning calculations from the viewpoint from von Neumann's principle of quantum measurement. Quantum embeddings and neural gates are also an important part of data encoding and feature selection. There is a natural duality with well-known kernel methods in statistical learning. We illustrate the behaviour of quantum algorithms on two simple classification algorithms. Finally, we conclude with directions for future research.  ( 2 min )
    Quadratic Multiform Separation: A New Classification Model in Machine Learning. (arXiv:2110.04925v2 [stat.ML] UPDATED)
    In this paper we present a new classification model in machine learning. Our result is threefold: 1) The model produces comparable predictive accuracy to that of most common classification models. 2) It runs significantly faster than most common classification models. 3) It has the ability to identify a portion of unseen samples for which class labels can be found with much higher predictive accuracy. Currently there are several patents pending on the proposed model.  ( 2 min )
    A Framework for Machine Learning of Model Error in Dynamical Systems. (arXiv:2107.06658v3 [math.DS] UPDATED)
    The development of data-informed predictive models for dynamical systems is of widespread interest in many disciplines. We present a unifying framework for blending mechanistic and machine-learning approaches to identify dynamical systems from noisily and partially observed data. We compare pure data-driven learning with hybrid models which incorporate imperfect domain knowledge. Our formulation is agnostic to the chosen machine learning model, is presented in both continuous- and discrete-time settings, and is compatible both with model errors that exhibit substantial memory and errors that are memoryless. First, we study memoryless linear (w.r.t. parametric-dependence) model error from a learning theory perspective, defining excess risk and generalization error. For ergodic continuous-time systems, we prove that both excess risk and generalization error are bounded above by terms that diminish with the square-root of T, the time-interval over which training data is specified. Secondly, we study scenarios that benefit from modeling with memory, proving universal approximation theorems for two classes of continuous-time recurrent neural networks (RNNs): both can learn memory-dependent model error. In addition, we connect one class of RNNs to reservoir computing, thereby relating learning of memory-dependent error to recent work on supervised learning between Banach spaces using random features. Numerical results are presented (Lorenz '63, Lorenz '96 Multiscale systems) to compare purely data-driven and hybrid approaches, finding hybrid methods less data-hungry and more parametrically efficient. Finally, we demonstrate numerically how data assimilation can be leveraged to learn hidden dynamics from noisy, partially-observed data, and illustrate challenges in representing memory by this approach, and in the training of such models.  ( 3 min )
    Debiased Inference on Identified Linear Functionals of Underidentified Nuisances via Penalized Minimax Estimation. (arXiv:2208.08291v1 [stat.ME])
    We study generic inference on identified linear functionals of nonunique nuisances defined as solutions to underidentified conditional moment restrictions. This problem appears in a variety of applications, including nonparametric instrumental variable models, proximal causal inference under unmeasured confounding, and missing-not-at-random data with shadow variables. Although the linear functionals of interest, such as average treatment effect, are identifiable under suitable conditions, nonuniqueness of nuisances pose serious challenges to statistical inference, since in this setting common nuisance estimators can be unstable and lack fixed limits. In this paper, we propose penalized minimax estimators for the nuisance functions and show they enable valid inference in this challenging setting. The proposed nuisance estimators can accommodate flexible function classes, and importantly, they can converge to fixed limits determined by the penalization, regardless of whether the nuisances are unique or not. We use the penalized nuisance estimators to form a debiased estimator for the linear functional of interest and prove its asymptotic normality under generic high-level conditions, which provide for asymptotically valid confidence intervals.  ( 2 min )
    Online Learning for Mixture of Multivariate Hawkes Processes. (arXiv:2208.07961v1 [stat.ML])
    Online learning of Hawkes processes has received increasing attention in the last couple of years especially for modeling a network of actors. However, these works typically either model the rich interaction between the events or the latent cluster of the actors or the network structure between the actors. We propose to model the latent structure of the network of actors as well as their rich interaction across events for real-world settings of medical and financial applications. Experimental results on both synthetic and real-world data showcase the efficacy of our approach.  ( 2 min )
    Score-Based Generative Models Detect Manifolds. (arXiv:2206.01018v2 [stat.ML] UPDATED)
    Score-based generative models (SGMs) need to approximate the scores $\nabla \log p_t$ of the intermediate distributions as well as the final distribution $p_T$ of the forward process. The theoretical underpinnings of the effects of these approximations are still lacking. We find precise conditions under which SGMs are able to produce samples from an underlying (low-dimensional) data manifold $\mathcal{M}$. This assures us that SGMs are able to generate the "right kind of samples". For example, taking $\mathcal{M}$ to be the subset of images of faces, we find conditions under which the SGM robustly produces an image of a face, even though the relative frequencies of these images might not accurately represent the true data generating distribution. Moreover, this analysis is a first step towards understanding the generalization properties of SGMs: Taking $\mathcal{M}$ to be the set of all training samples, our results provide a precise description of when the SGM memorizes its training data.  ( 2 min )
    Sparse Nonnegative Tucker Decomposition and Completion under Noisy Observations. (arXiv:2208.08287v1 [cs.LG])
    Tensor decomposition is a powerful tool for extracting physically meaningful latent factors from multi-dimensional nonnegative data, and has been an increasing interest in a variety of fields such as image processing, machine learning, and computer vision. In this paper, we propose a sparse nonnegative Tucker decomposition and completion method for the recovery of underlying nonnegative data under noisy observations. Here the underlying nonnegative data tensor is decomposed into a core tensor and several factor matrices with all entries being nonnegative and the factor matrices being sparse. The loss function is derived by the maximum likelihood estimation of the noisy observations, and the $\ell_0$ norm is employed to enhance the sparsity of the factor matrices. We establish the error bound of the estimator of the proposed model under generic noise scenarios, which is then specified to the observations with additive Gaussian noise, additive Laplace noise, and Poisson observations, respectively. Our theoretical results are better than those by existing tensor-based or matrix-based methods. Moreover, the minimax lower bounds are shown to be matched with the derived upper bounds up to logarithmic factors. Numerical examples on both synthetic and real-world data sets demonstrate the superiority of the proposed method for nonnegative tensor data completion.  ( 2 min )
    Learning low-rank latent mesoscale structures in networks. (arXiv:2102.06984v3 [cs.SI] UPDATED)
    It is common to use networks to encode the architecture of interactions between entities in complex systems in applications in the physical, biological, social, and information sciences. To study the large-scale behavior of complex systems, it is useful to study mesoscale structures in networks as building blocks that influence such behavior. We present a new approach for describing low-rank mesoscale structure in networks, and we illustrate our approach using several synthetic network models and empirical friendship, collaboration, and protein--protein interaction (PPI) networks. We find that these networks possess a relatively small number of `latent motifs' that together can successfully approximate most subgraphs of a network at a fixed mesoscale. We use an algorithm that we call `network dictionary learning' (NDL), which combines a network-sampling method and nonnegative matrix factorization, to learn the latent motifs of a given network. The ability to encode a network using a set of latent motifs has a wide variety of applications to network-analysis tasks, such as comparison, denoising, and edge inference. Additionally, using our new network denoising and reconstruction (NDR) algorithm, we demonstrate how to denoise a corrupted network by using only the latent motifs that one learns directly from the corrupted networks.  ( 3 min )
    Two-Stage Robust and Sparse Distributed Statistical Inference for Large-Scale Data. (arXiv:2208.08230v1 [stat.ML])
    In this paper, we address the problem of conducting statistical inference in settings involving large-scale data that may be high-dimensional and contaminated by outliers. The high volume and dimensionality of the data require distributed processing and storage solutions. We propose a two-stage distributed and robust statistical inference procedures coping with high-dimensional models by promoting sparsity. In the first stage, known as model selection, relevant predictors are locally selected by applying robust Lasso estimators to the distinct subsets of data. The variable selections from each computation node are then fused by a voting scheme to find the sparse basis for the complete data set. It identifies the relevant variables in a robust manner. In the second stage, the developed statistically robust and computationally efficient bootstrap methods are employed. The actual inference constructs confidence intervals, finds parameter estimates and quantifies standard deviation. Similar to stage 1, the results of local inference are communicated to the fusion center and combined there. By using analytical methods, we establish the favorable statistical properties of the robust and computationally efficient bootstrap methods including consistency for a fixed number of predictors, and robustness. The proposed two-stage robust and distributed inference procedures demonstrate reliable performance and robustness in variable selection, finding confidence intervals and bootstrap approximations of standard deviations even when data is high-dimensional and contaminated by outliers.  ( 3 min )
    Expressivity of Hidden Markov Chains vs. Recurrent Neural Networks from a system theoretic viewpoint. (arXiv:2208.08175v1 [eess.SY])
    Hidden Markov Chains (HMC) and Recurrent Neural Networks (RNN) are two well known tools for predicting time series. Even though these solutions were developed independently in distinct communities, they share some similarities when considered as probabilistic structures. So in this paper we first consider HMC and RNN as generative models, and we embed both structures in a common generative unified model (GUM). We next address a comparative study of the expressivity of these models. To that end we assume that the models are furthermore linear and Gaussian. The probability distributions produced by these models are characterized by structured covariance series, and as a consequence expressivity reduces to comparing sets of structured covariance series, which enables us to call for stochastic realization theory (SRT). We finally provide conditions under which a given covariance series can be realized by a GUM, an HMC or an RNN.  ( 2 min )
    On the generalization of learning algorithms that do not converge. (arXiv:2208.07951v1 [cs.LG])
    Generalization analyses of deep learning typically assume that the training converges to a fixed point. But, recent results indicate that in practice, the weights of deep neural networks optimized with stochastic gradient descent often oscillate indefinitely. To reduce this discrepancy between theory and practice, this paper focuses on the generalization of neural networks whose training dynamics do not necessarily converge to fixed points. Our main contribution is to propose a notion of statistical algorithmic stability (SAS) that extends classical algorithmic stability to non-convergent algorithms and to study its connection to generalization. This ergodic-theoretic approach leads to new insights when compared to the traditional optimization and learning theory perspectives. We prove that the stability of the time-asymptotic behavior of a learning algorithm relates to its generalization and empirically demonstrate how loss dynamics can provide clues to generalization performance. Our findings provide evidence that networks that "train stably generalize better" even when the training continues indefinitely and the weights do not converge.  ( 2 min )
    Shallow neural network representation of polynomials. (arXiv:2208.08138v1 [stat.ML])
    We show that $d$-variate polynomials of degree $R$ can be represented on $[0,1]^d$ as shallow neural networks of width $d+1+\sum_{r=2}^R\binom{r+d-1}{d-1}[\binom{r+d-1}{d-1}+1]$. Also, by SNN representation of localized Taylor polynomials of univariate $C^\beta$-smooth functions, we derive for shallow networks the minimax optimal rate of convergence, up to a logarithmic factor, to unknown univariate regression function.  ( 2 min )
    Superior generalization of smaller models in the presence of significant label noise. (arXiv:2208.08003v1 [cs.LG])
    The benefits of over-parameterization in achieving superior generalization performance have been shown in several recent studies, justifying the trend of using larger models in practice. In the context of robust learning however, the effect of neural network size has not been well studied. In this work, we find that in the presence of a substantial fraction of mislabeled examples, increasing the network size beyond some point can be harmful. In particular, the originally monotonic or `double descent' test loss curve (w.r.t. network width) turns into a U-shaped or a double U-shaped curve when label noise increases, suggesting that the best generalization is achieved by some model with intermediate size. We observe that when network size is controlled by density through random pruning, similar test loss behaviour is observed. We also take a closer look into both phenomenon through bias-variance decomposition and theoretically characterize how label noise shapes the variance term. Similar behavior of the test loss can be observed even when state-of-the-art robust methods are applied, indicating that limiting the network size could further boost existing methods. Finally, we empirically examine the effect of network size on the smoothness of learned functions, and find that the originally negative correlation between size and smoothness is flipped by label noise.  ( 3 min )
    Private Estimation with Public Data. (arXiv:2208.07984v1 [cs.LG])
    We initiate the study of differentially private (DP) estimation with access to a small amount of public data. For private estimation of d-dimensional Gaussians, we assume that the public data comes from a Gaussian that may have vanishing similarity in total variation distance with the underlying Gaussian of the private data. We show that under the constraints of pure or concentrated DP, d+1 public data samples are sufficient to remove any dependence on the range parameters of the private data distribution from the private sample complexity, which is known to be otherwise necessary without public data. For separated Gaussian mixtures, we assume that the underlying public and private distributions are the same, and we consider two settings: (1) when given a dimension-independent amount of public data, the private sample complexity can be improved polynomially in terms of the number of mixture components, and any dependence on the range parameters of the distribution can be removed in the approximate DP case; (2) when given an amount of public data linear in the dimension, the private sample complexity can be made independent of range parameters even under concentrated DP, and additional improvements can be made to the overall sample complexity.  ( 2 min )

  • Open

    [N] NeurIPS 2022 Temporal Graph Learning Workshop
    We are pleased to announce the NeurIPS 2022 Temporal Graph Learning Workshop. The workshop aims to share understanding and techniques to facilitate the development of novel temporal graph learning methods. For more details, please see workshop website: https://sites.google.com/view/tglworkshop2022/home. We will also give updates through Twitter @tgl_workshop. Key Dates: The planned dates are as follows: Submission deadline: Sep. 19th, 2022 Accept/reject notification: Oct. 12th, 2022 Camera ready deadline: Nov. 3rd, 2022 Workshop date: Dec. 3rd, 2022; in-person in New Orleans, US Call for Papers: We encourage researchers to submit their papers broadly related to temporal graph learning. We also welcome papers that present benchmark datasets, evaluation protocols, and challenges …  ( 89 min )
    Best Budget GPU for ai training [D]
    Hi, I have a budget of 200€-400€ what would be your Recommendation ? submitted by /u/Mo_187_ [link] [comments]  ( 88 min )
    [P] - VkFFT now supports Rader's algorithm - A100 and MI250 benchmarks
    Hello, I am the creator of the VkFFT - GPU Fast Fourier Transform library for Vulkan/CUDA/HIP/OpenCL and Level Zero. In the latest update, I have implemented my take on Rader's FFT algorithm, which allows VkFFT to do FFTs of sequences representable as a multiplication of primes up to 83, just like you would with powers of two. Rader's FFT algorithm represents an FFT of prime length sequence as a convolution of length N-1. Inlining these convolutions as a step in the Stockham algorithm, makes it possible to have radix kernels of extremely high prime lengths - VkFFT currently uses primes up to 83. Previously, VkFFT had to switch to Bluestein's algorithm if a sequence had primes bigger than 13. Bluestein's algorithm does FFT of arbitrary length as a zero-padded convolution of a length at le…  ( 92 min )
    [Discussion] Advice on Toolkit/Framework?
    Hey guys, Kind of a noob question here. I am working on a pilot project that involves a combination of circumstances that make it rather peculiar (at least to me). I can certainly appreciate some fresh eyes and pointers on what kind of tools/libraries you would choose to approach the problem. Relevant details: Fairly large dataset (at least a couple hundred million records) Will require significant amount of stateful transformations (ie: transformations like standardization where you have to learn the mean and stddev from your training data and persist the learned states during inference time, but potentially MUCH more involved). Part of the transformation pipeline can inference on bulk data with relatively high latency, part of the transformation pipeline requires inference on SI…  ( 91 min )
    [D] Looking for resources on feedback control systems with a DNN Plant
    We have a deep neural network model and some feedback that modifies the input to that model over time. This system currently has some undesirable properties. We’d like to retrain it to not have those properties, but may not be able to for a variety of reasons. So I’m looking for any resources on developing feedback controllers where the plant is / includes a neural network. Note: I’m not looking to use a NN as my controller, just looking for any techniques that can be applied when the plant is a NN. submitted by /u/nlman0 [link] [comments]  ( 108 min )
    [D] Anyone has Deep Learning benchmark for 6800XT with Rocm?
    I suppose currently 6800XT is about $600, which is the price I am willing to pay. That money can net me a 3070 Ti. However, the Nvidia choice has like half the amount of VRAM, and I am kinda get bored with the CUDA lock down system anyway. On top of that, my 1080 TI for ML training is getting older. I can feel it... Anyway, does anyone have any data about how AMD offerings (I know Rocm right now only support Rx 6800 and above) can compete with Nvidia cards? Any comparable data between a 6800XT and 3070 Ti in deep learning would be nice. Thank you. submitted by /u/ffleader1 [link] [comments]  ( 88 min )
    [P] Solving Boggle in the Browser Using TensorFlow.js and WebAssembly!
    Hi everyone! Here's a project I've been working on for the last few months. It's a Boggle solver that can take a photo of a Boggle board and attempt to extract the letters of the board using TensorFlow.js and OpenCV.js. The actual solving code is written in Rust and compiled to WebAssembly. Link to Blog Post: https://prowe.ca/blog/wasm-rust-boggle-solver Link to project: https://roggle.prowe.ca/ Any advice on how I could improve the accuracy of my model would be greatly appreciated. I spent some time trying to increase the accuracy but it still struggles to figure out each letter a lot of the time. submitted by /u/Parkuman [link] [comments]  ( 89 min )
    [D] What framework are you using?
    I realized recently that PyTorch overtook Tensorflow on Google Trends: https://trends.google.com/trends/explore?date=all&geo=US&q=tensorflow,pytorch What are you using? View Poll submitted by /u/wnorrisii [link] [comments]  ( 89 min )
    [D] If there’s one practical tip you wish should have been drilled deeply into you when you first started out learning about deep learning, what would it be?
    For example, things that you found are really important when you're in the industry but not well-covered / explained in a typical DL course (undergrad level). Best if that thing changed how you approached DL projects, or something that made you much more productive. ​ I'll start the ball rolling: one of the biggest pains / time-consuming aspects of ML/DL projects so far have been poorly documented code - not just from others but your own. Having no documentation is bad but documenting / tracking every single thing you've tried is equally bad (revisiting such a repo is horrifying...). Writing just enough documentation so that someone else knows how to prep the data correctly and get the model running on them + reproduce the best results reported + be aware about what have been tried before (and do all these 3 things in the shortest time possible) - that'd do the trick but it's not always easy to get the right balance, especially when writing it for someone else to read. I guess the lesson learnt is to (dedicate some time to) clean up the repo before ending any projects, because you never know when you / someone else have to revisit it. Perhaps some kind of standardisation / template for documentation writing would become a thing as the field matures... there are probably such stuff from software engineering but something specific to data + models + ML/DL code is needed. submitted by /u/manzaikid [link] [comments]  ( 114 min )
    [D] Bias-variance tradeoff in human perception?
    Wanted to start a discussion about the bias-variance tradeoff in human perception. Are human perceptual systems (an example is object recognition) extremely biased, with little to no variance? For example - I never fail to recognize a water bottle (little variance) under any environmental conditions submitted by /u/liqui_date_me [link] [comments]  ( 90 min )
    [D] Fool me once, shame on you; fool me twice, shame on me: Exponential Smoothing vs. Facebook's Neural-Prophet.
    ​ https://preview.redd.it/put2itbz1bi91.png?width=920&format=png&auto=webp&s=10c905054f14214d1caaaf7765dc5693efad4a14 History tends to repeat itself. But FB-Prophet's tainted memory is too recent and should act as a warning not to repeat the same mistakes. This post compares Neural-Prophet's performance with Exponential Smoothing (ETS), a half-century-old forecasting method part of every practitioner's toolkit. Our comparison covers Tourism, M3, M4, ERCOT, and ETTm2 datasets, following the authors' recommended hyperparameter and network configuration settings. Despite Neural-Prophet's outstanding success over its unreliable predecessor, its errors are still 30 percent larger than ETS' while doubling its computation time. https://preview.redd.it/34d42nc8lai91.png?width=2008&format=png&auto=webp&s=38fe03059107b7054fe60a464c701e10d1ac3330 We hope this exercise helps the community evaluation of forecasting tools. And help us avoid adopting yet another overpromising and unproven forecasting method. As always, if you find our work helpful, your starring support ⭐ is greatly appreciated https://github.com/Nixtla/statsforecast. submitted by /u/fedegarzar [link] [comments]  ( 109 min )
    [P] MinImagen: A Minimal Imagen Implementation
    This year has been great for text-to-image models, but given that SoTA models like DALL-E 2 and Imagen depend on the recent advances in Diffusion Models, there seems to be a relative lack of good resources on building these models. I created MinImagen - a minimal Imagen implementation - to fill this gap. It includes a thoroughly commented repository and the above linked associated article, and is intended to help elucidate how Imagen (and related models) work internally. Here are some links if you want to take a look: Build Your Own Imagen Text-to-Image Model MinImagen GitHub Respository MinImagen Documentation MinImagen PyPi Package The implementation strips off all of the bells and whistles to isolate the salient and essential components of Imagen for educational purposes, and so it does not use modern best practices for maximum efficiency. Looking forward to seeing what you think and answering any questions! submitted by /u/SleekEagle [link] [comments]  ( 89 min )
    [P] Yet another deep learning based natural language processing APIs focused on Korean and English
    We released a collection of NLP APIs, dubbed TUNiBridge. Currently, it consists of 11 modules--safety check, de-identification, text analytics, image analytics, acrostic poem generation, etc. Check them here: https://tunibridge.ai/ submitted by /u/longinglove [link] [comments]  ( 111 min )
    [P] Cleanlab Vizzy — learn how to automatically find label errors and out-of-distribution data
    If you’ve seen the Cleanlab open-source package for automatically finding issues in datasets and training an ML model on bad labels as if you had error-free data — if you’re like me, you may have been curious — how does it work? It might seem surprising that it’s possible to automatically identify label errors and out-of-distribution data, using any model and for any modality of dataset (described as "black magic" by some). Cleanlab accomplishes this using "confident learning" algorithms backed by solid theory and peer-reviewed research. To help myself (and others!) build intuition for how they work, I built Vizzy, an interactive visualization playground that runs in the browser. Vizzy lets you experiment with an example dataset, tweak the labels, and run Cleanlab to automatically find issues like label errors and out-of-distribution data. Screenshot of Cleanlab Vizzy (https://playground.cleanlab.ai) Vizzy includes a JavaScript port of (a part of) cleanlab, which implements the algorithms described in https://arxiv.org/abs/1911.00068. There are other neat technical nuggets in the implementation of Vizzy as well, including ML model training in the browser (using features from a pretrained ResNet-18, performing truncated SVD, and using an SVM model for speed). If you’re interested in the details of how Vizzy works, check out the blog post. Happy to answer any questions related to Vizzy, cleanlab, or confident learning and data-centric AI in general! Vizzy: https://playground.cleanlab.ai/ Blog post: https://cleanlab.ai/blog/cleanlab-vizzy/ Source code: https://github.com/cleanlab/vizzy submitted by /u/Calebchiam [link] [comments]  ( 109 min )
    [P] CodeStamper - Ensuring traceability between ML experiments and Code
    In an ideal world an ML engineer should be able to rerun a past experiment at any moment in time and re-obtain the same results. CodeStamper tries to help the user in this direction in a non-ideal world. There are full fledged projects like DVC which aim to version everything (code, datasets & models) in order to ensure reproducibility. CodeStamper is a lightweight solution, which provides seamless integration into existing projects and targets to ensure code traceability (so it is not a magic bullet for all aspects of an ML experiment like datasets). When things can go wrong. An ML experiment is started but it might not be reproducible in the future because: Issue CodeStamper's approach The experiment itself does not contain any information related to the code with which it wa…  ( 89 min )
    [P] The table extraction tool: PP-Structure
    PP-Structure is an OCR toolkit that can be used for document analysis and processing with complex structures, designed to help developers better complete document understanding tasks ​ * Support the layout analysis of documents, divide the documents into 5 types of areas **text, title, table, image and list** (conjunction with Layout-Parser) * Support to extract the texts from the text, title, picture and list areas (used in conjunction with PP-OCR) * Support to extract excel files from the table areas * Support python WHL package and command line usage, easy to use * Support custom training for layout analysis and table structure tasks * Support Document Visual Question Answering (DOC-VQA) tasks: Semantic Entity Recognition (SER) and Relation Extraction (RE) submitted by /u/osicli [link] [comments]  ( 88 min )
    [P] The spelled-out intro to neural networks and backpropagation: building micrograd (Andrej Karpathy 2h25m lecture)
    A new lecture from Andrej Karpathy on his YouTube channel: https://www.youtube.com/watch?v=VMj-3S1tku0 This is the most step-by-step spelled-out explanation of backpropagation and training of neural networks. It only assumes basic knowledge of Python and a vague recollection of calculus from high school. According to Karpathy, "this is the culmination of about 8 years of obsessing about the best way to explain neural nets and backprop." He also mentions, "If you know Python, have a vague recollection of taking some derivatives in your high school, watch this video and not understand backpropagation and the core of neural nets by the end then I will eat a shoe :D" Link to the YouTube video: https://www.youtube.com/watch?v=VMj-3S1tku0 submitted by /u/hardmaru [link] [comments]  ( 89 min )
  • Open

    Learn Deep Learning Concepts From Minions
    submitted by /u/BuilderPrior4707 [link] [comments]  ( 86 min )
    whole proces in layers. after that i deside to make the painting in water. i love water elements to use in paintings. and you?
    submitted by /u/katomic_tattoo [link] [comments]  ( 89 min )
    Designing APIs for AI
    It’s estimated that anywhere from 50-90% of AI models developed never make it past the AI “valley of death” that exists between the lab and production deployment. This tech talk covers how an API-based approach to building and maintaining AI-enabled applications can bridge the divide between data scientists, software developers, and infrastructure managers building them. You'll learn best practices for API design, as well as pitfalls to avoid. https://youtu.be/tYStPrwRY4w submitted by /u/modzykirsten [link] [comments]  ( 87 min )
    i made a mnist spin off with geometric shapes
    It is basically a program to generate labeled geometric shapes, it generates as much data as you want... The first input is about the size of the image, the second one is how many images do you want when clicking on download. https://preview.redd.it/h4wdl8sqkbi91.png?width=560&format=png&auto=webp&s=0fbcd4751602918f4d25adbfb2c0e232c36e896e Example of low resolution (recomended) https://preview.redd.it/y137s3hdkbi91.png?width=1158&format=png&auto=webp&s=5e74cb7b2027c089ab3535aa65265a383c726569 high resolution example: https://preview.redd.it/4wk62kbfkbi91.png?width=1086&format=png&auto=webp&s=326a731d575b136108bab0f7a72dc385c987ade9 The main goal of this project is to create my own object recognition (with boundary) AI, but for that I still need to figure out how to make a dynamic output AI, and improve on the noise / gradient area of this program. link source code submitted by /u/Small-Ad-1694 [link] [comments]  ( 87 min )
    DALL-E 2 Art: Experiments with Prompts or How I Got My New Wallpaper
    submitted by /u/strikingLoo [link] [comments]  ( 86 min )
    Question about AI research back in 80's
    When I was younger, in my early 20's, I was into programming and algorithms and very interested in AI and learning systems. I remember reading an article in a magazine (may have been Computing magazine or something like that) and there was a researcher that had created a progam that he intended to learn as if it was a child. I am thinking this was in Berkley but cannot be sure but I think it was a University in California at least. I have since tried to find references or an update to this but have never been able to find anything. Looking at you folks here for some type of info on this. Always been curious if this had been scrapped, moved into an advanced computer system, etc. submitted by /u/AbruptGravy [link] [comments]  ( 89 min )
    look at this cool animation please
    submitted by /u/mech010001 [link] [comments]  ( 88 min )
    Real-world AI assistant: Google combines a large language model with an everyday robot
    submitted by /u/much_successes [link] [comments]  ( 86 min )
    Character generation using Midjourney AI
    submitted by /u/guilds-and-blades [link] [comments]  ( 86 min )
    If you are a data scientist, analyst or simply looking to make your analytics skills more efficient, Blinx is the tool for you!
    submitted by /u/SamuelSmith1416 [link] [comments]  ( 86 min )
    White house in the forrest
    submitted by /u/widgia [link] [comments]  ( 86 min )
    Lion family on a scooter !
    submitted by /u/Remarkable_Owl_2058 [link] [comments]  ( 89 min )
    Computer Vision News of August 2022
    Dear all, Here is Computer Vision News of August 2022. Many great articles about Artificial Intelligence, Deep Learning, Computer Vision and more (with code!) HTML5 version (recommended) PDF version Dilbert on page 2. Free subscription on page 60. Enjoy! https://preview.redd.it/o7m3v844q8i91.jpg?width=400&format=pjpg&auto=webp&s=fb1f9ed4ab3c4131154038390d7d1fd9be660441 submitted by /u/Gletta [link] [comments]  ( 86 min )
    Workshops in the first day of AGI-2022 conference on August 19 - video streaming links
    submitted by /u/akolonin [link] [comments]  ( 87 min )
    [Repost]Survey on AI Ethics and Readiness (for high school and first-year bachelor students)
    https://forms.gle/rPKmuN611VeLmZaNA I'm conducting a survey to understand awareness, attitudes and readiness of high school students towards Artificial Intelligence. The study will look at different aspects such as opportunities, risks, and ethics of AI, and also education necessary for high schoolers to improve their understanding. The results will be published as part of a detailed report. Your inputs are valuable in understanding how students learn and think about AI. All responses will be kept confidential and data is analysed only at the aggregate level. The “best” five responses will get a Rs 1000 amazon gift card each. The winners will be selected by, you guessed it, an algorithm. Thank You! submitted by /u/divijadurga [link] [comments]  ( 94 min )
    Andrew NG Machine Learning New Specialization
    submitted by /u/ampankajsharma [link] [comments]  ( 86 min )
    My second attempt at creating wallpapers for my phone: Chaos Forest Nº 1 & 2 | Using MidJourney AI (Image Creator bot for Discord)
    submitted by /u/Potato_Player_BR [link] [comments]  ( 86 min )
  • Open

    Project or Research topic to get a job?
    Hi everyone, I am currently a graduate student learning RL and I am intended to look for a RL engineer (not researcher) position in a game studio. I have learned some typical models, including PPO, SAC, etc, but am not familiar with state-of-art algorithms in specific areas. Now I work with my friend in an open-source project, which is a robot-learning environment. Most of the work is about data communication and data transformation. ​ At the same time, my recent attempts to look for a job all failed because of lacking project experience, especially experience in algorithm design and implementation. So I want to search for a project or research topic, which is relative to RL and game (or robot). Could anyone give me some instructions or suggestions? Thank you very much! submitted by /u/ZavierTi2021 [link] [comments]  ( 87 min )
    Why might agent be unable to learn training environment? Possible constraints?
    Hi everyone! I'm working on an RL problem using PPO. The environment's data/states/variables come from an actual, finite data table (time series). Therefore, I have split the data table into "training" and "testing", so that after training, I can see how the agent performs in states that it's never seen during training (just like a train/test split in supervised learning). The problem is that I am actually unable to get good performance on the training environment. Some details: My training dataset consists of 1,400,000 rows/unique states, with around 140 state-space variables I am using PPO with both (value and policy) networks having the following architecture: 192-128-64 I have tried gamma ranging from 0.9825 to 0.995 I am implementing a "dynamic" learning rate schedule, where the learning rate is halved if performance doesn't improve in 250K timesteps of training I have trained for up to 4M timesteps. The agent DOES learn, but the performance isn't good enough on training set (performance either plateaus or ends up crashing as training goes on) Does anyone have any ideas for what might be the problem? I would expect that the agent would be able to "overfit" on the training set, as I have seen before with smaller datasets. What might I be missing? Why wouldn't the agent be able to get insanely good scores on the finite training environment? Any suggestions would be appreciated! I know I'm coming at this from a "supervised learning" perspective, but given that it's not a simulated environment with endless possible states, I think it only makes sense. submitted by /u/VladimirB-98 [link] [comments]  ( 90 min )
    Reinforcement learning models are prone to membership inference attacks
    submitted by /u/bendee983 [link] [comments]  ( 86 min )
    For a Multi-Agent Swarm, would you have different RL models for each agent or one master RL model that takes in data of all the agents and outputs actions for all the agent, or are both the same thing?
    submitted by /u/FailedMesh [link] [comments]  ( 87 min )
    Reducing Exploitability with Population Based Training
    submitted by /u/Caffeinated-Scholar [link] [comments]  ( 87 min )
    How does off-policy monte-carlo explore and converge?
    Premises to question: Behavior Policy: e-greedy (stochastic) & Target Policy: greedy, (deterministic) Importance Sampling Included In Off-policy Monte-Carlo control, the behavior policy chooses actions to follow, and the target policy learns from those actions. However, because of importance sampling, if the behavior policy chooses an action that is not believed to be the "best" action by the target policy, then the importance sampling ratio is 0 and the algorithm disregards any learning. My question, then, is how the target policy can ever change its preferred action if the action value is only updated when the behavior policy chooses the same action as the target policy? How is there any exploration if the target policy is greedy, because the importance sampling ratio zeros out every action from the behavior policy that is not chosen by the target policy? Sutton's RL book says that "learning will be slow... if non-greedy actions are common" What I don't understand is how the target policy can ever choose a different action if the only actions that count are the actions that are the same from target and behavior policy? I've been struggling on this for the past few days, please help. Thank you. submitted by /u/JonathanMonathan62 [link] [comments]  ( 100 min )
  • Open

    AWS Localization uses Amazon Translate to scale localization
    The AWS website is currently available in 16 languages (12 for the AWS Management Console and for technical documentation): Arabic, Chinese Simplified, Chinese Traditional, English, French, German, Indonesian, Italian, Japanese, Korean, Portuguese, Russian, Spanish, Thai, Turkish, and Vietnamese. Customers all over the world gain hands-on experience with the AWS platform, products, and services in their […]  ( 6 min )
    Incrementally update a dataset with a bulk import mechanism in Amazon Personalize
    We are excited to announce that Amazon Personalize now supports incremental bulk dataset imports; a new option for updating your data and improving the quality of your recommendations. Keeping your datasets current is an important part of maintaining the relevance of your recommendations. Prior to this new feature launch, Amazon Personalize offered two mechanisms for […]  ( 5 min )
  • Open

    Is the Matrix Coming to Life?
    In June 2018, the metaverse was first announced and the world went crazy. This caused cryptocurrencies to go crazy and Elon Musk could not…  ( 9 min )
  • Open

    Code Released: Conformal Training
    The code for our ICLR'22 paper on learning optimal conformal classifiers is now available on GitHub. The repository not only includes our implementation of conformal training but also relevant baselines such as coverage training and several conformal predictors for evaluation. Furthermore, it allows to reproduce the majority of experiments from the paper. The post Code Released: Conformal Training appeared first on David Stutz.  ( 3 min )
  • Open

    Cross platform muscle memory
    I’ve mostly used Windows and Linux for the last several years. When I needed to get a new laptop recently I got a MacBook Pro. I’ve used a Mac before, but it’s been a while, and so I’m starting over. I can move between Windows and Linux and almost forget what OS I’m using because […] Cross platform muscle memory first appeared on John D. Cook.  ( 5 min )
    More on near-integer decibels
    In base 10, four decibel values are approximately integers. Yesterday I explored whether base 10 was unique in this regard. I defined the value of n decibels in base b to be g(n, b) = bn/b. Sticking in 10 for b gives the usual definition of decibel levels. There are two ways to quantify what […] More on near-integer decibels first appeared on John D. Cook.  ( 5 min )
  • Open

    Immunai Co-Founder Luis Voloch on Using Deep Learning to Develop New Drugs
    Mapping the immune system could lead to the creation of drugs that help our bodies win the fight against cancer and other diseases. That’s the big idea behind immunotherapy. The problem: the immune system is incredibly complex. Enter Immunai, a biotech company that’s using cutting-edge genomics & ML technology to map the human immune system Read article > The post Immunai Co-Founder Luis Voloch on Using Deep Learning to Develop New Drugs appeared first on NVIDIA Blog.  ( 4 min )
  • Open

    Hyperparameter Optimization of Generative Adversarial Network Models for High-Energy Physics Simulations. (arXiv:2208.07715v1 [hep-ex])
    The Generative Adversarial Network (GAN) is a powerful and flexible tool that can generate high-fidelity synthesized data by learning. It has seen many applications in simulating events in High Energy Physics (HEP), including simulating detector responses and physics events. However, training GANs is notoriously hard and optimizing their hyperparameters even more so. It normally requires many trial-and-error training attempts to force a stable training and reach a reasonable fidelity. Significant tuning work has to be done to achieve the accuracy required by physics analyses. This work uses the physics-agnostic and high-performance-computer-friendly hyperparameter optimization tool HYPPO to optimize and examine the sensitivities of the hyperparameters of a GAN for two independent HEP datasets. This work provides the first insights into efficiently tuning GANs for Large Hadron Collider data. We show that given proper hyperparameter tuning, we can find GANs that provide high-quality approximations of the desired quantities. We also provide guidelines for how to go about GAN architecture tuning using the analysis tools in HYPPO.
    Proceedings of the ICML 2022 Expressive Vocalizations Workshop and Competition: Recognizing, Generating, and Personalizing Vocal Bursts. (arXiv:2207.06958v2 [cs.SD] UPDATED)
    This is the Proceedings of the ICML Expressive Vocalization (ExVo) Competition. The ExVo competition focuses on understanding and generating vocal bursts: laughs, gasps, cries, and other non-verbal vocalizations that are central to emotional expression and communication. ExVo 2022, included three competition tracks using a large-scale dataset of 59,201 vocalizations from 1,702 speakers. The first, ExVo-MultiTask, requires participants to train a multi-task model to recognize expressed emotions and demographic traits from vocal bursts. The second, ExVo-Generate, requires participants to train a generative model that produces vocal bursts conveying ten different emotions. The third, ExVo-FewShot, requires participants to leverage few-shot learning incorporating speaker identity to train a model for the recognition of 10 emotions conveyed by vocal bursts.
    Counterfactual Supervision-based Information Bottleneck for Out-of-Distribution Generalization. (arXiv:2208.07798v1 [cs.LG])
    Learning invariant (causal) features for out-of-distribution (OOD) generalization has attracted extensive attention recently, and among the proposals invariant risk minimization (IRM) (Arjovsky et al., 2019) is a notable solution. In spite of its theoretical promise for linear regression, the challenges of using IRM in linear classification problems yet remain (Rosenfeld et al.,2020, Nagarajan et al., 2021). Along this line, a recent study (Arjovsky et al., 2019) has made a first step and proposes a learning principle of information bottleneck based invariant risk minimization (IB-IRM). In this paper, we first show that the key assumption of support overlap of invariant features used in (Arjovsky et al., 2019) is rather strong for the guarantee of OOD generalization and it is still possible to achieve the optimal solution without such assumption. To further answer the question of whether IB-IRM is sufficient for learning invariant features in linear classification problems, we show that IB-IRM would still fail in two cases whether or not the invariant features capture all information about the label. To address such failures, we propose a \textit{Counterfactual Supervision-based Information Bottleneck (CSIB)} learning algorithm that provably recovers the invariant features. The proposed algorithm works even when accessing data from a single environment, and has theoretically consistent results for both binary and multi-class problems. We present empirical experiments on three synthetic datasets that verify the efficacy of our proposed method.
    Learning Facial Liveness Representation for Domain Generalized Face Anti-spoofing. (arXiv:2208.07828v1 [cs.CV])
    Face anti-spoofing (FAS) aims at distinguishing face spoof attacks from the authentic ones, which is typically approached by learning proper models for performing the associated classification task. In practice, one would expect such models to be generalized to FAS in different image domains. Moreover, it is not practical to assume that the type of spoof attacks would be known in advance. In this paper, we propose a deep learning model for addressing the aforementioned domain-generalized face anti-spoofing task. In particular, our proposed network is able to disentangle facial liveness representation from the irrelevant ones (i.e., facial content and image domain features). The resulting liveness representation exhibits sufficient domain invariant properties, and thus it can be applied for performing domain-generalized FAS. In our experiments, we conduct experiments on five benchmark datasets with various settings, and we verify that our model performs favorably against state-of-the-art approaches in identifying novel types of spoof attacks in unseen image domains.
    Near Optimal Adversarial Attack on UCB Bandits. (arXiv:2008.09312v2 [cs.LG] UPDATED)
    We consider a stochastic multi-arm bandit problem where rewards are subject to adversarial corruption. We propose a novel attack strategy that manipulates a UCB principle into pulling some non-optimal target arm $T - o(T)$ times with a cumulative cost that scales as $\sqrt{\log T}$, where $T$ is the number of rounds. We also prove the first lower bound on the cumulative attack cost. Our lower bound matches our upper bound up to $\log \log T$ factors, showing our attack to be near optimal.
    Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting. (arXiv:2206.09113v2 [cs.LG] UPDATED)
    Multivariate Time Series (MTS) forecasting plays a vital role in a wide range of applications. Recently, Spatial-Temporal Graph Neural Networks (STGNNs) have become increasingly popular MTS forecasting methods. STGNNs jointly model the spatial and temporal patterns of MTS through graph neural networks and sequential models, significantly improving the prediction accuracy. But limited by model complexity, most STGNNs only consider short-term historical MTS data, such as data over the past one hour. However, the patterns of time series and the dependencies between them (i.e., the temporal and spatial patterns) need to be analyzed based on long-term historical MTS data. To address this issue, we propose a novel framework, in which STGNN is Enhanced by a scalable time series Pre-training model (STEP). Specifically, we design a pre-training model to efficiently learn temporal patterns from very long-term history time series (e.g., the past two weeks) and generate segment-level representations. These representations provide contextual information for short-term time series input to STGNNs and facilitate modeling dependencies between time series. Experiments on three public real-world datasets demonstrate that our framework is capable of significantly enhancing downstream STGNNs, and our pre-training model aptly captures temporal patterns.
    Learning Representations with Contrastive Self-Supervised Learning for Histopathology Applications. (arXiv:2112.05760v2 [eess.IV] UPDATED)
    Unsupervised learning has made substantial progress over the last few years, especially by means of contrastive self-supervised learning. The dominating dataset for benchmarking self-supervised learning has been ImageNet, for which recent methods are approaching the performance achieved by fully supervised training. The ImageNet dataset is however largely object-centric, and it is not clear yet what potential those methods have on widely different datasets and tasks that are not object-centric, such as in digital pathology. While self-supervised learning has started to be explored within this area with encouraging results, there is reason to look closer at how this setting differs from natural images and ImageNet. In this paper we make an in-depth analysis of contrastive learning for histopathology, pin-pointing how the contrastive objective will behave differently due to the characteristics of histopathology data. We bring forward a number of considerations, such as view generation for the contrastive objective and hyper-parameter tuning. In a large battery of experiments, we analyze how the downstream performance in tissue classification will be affected by these considerations. The results point to how contrastive learning can reduce the annotation effort within digital pathology, but that the specific dataset characteristics need to be considered. To take full advantage of the contrastive learning objective, different calibrations of view generation and hyper-parameters are required. Our results pave the way for realizing the full potential of self-supervised learning for histopathology applications.
    Hierarchical Kickstarting for Skill Transfer in Reinforcement Learning. (arXiv:2207.11584v2 [cs.LG] UPDATED)
    Practising and honing skills forms a fundamental component of how humans learn, yet artificial agents are rarely specifically trained to perform them. Instead, they are usually trained end-to-end, with the hope being that useful skills will be implicitly learned in order to maximise discounted return of some extrinsic reward function. In this paper, we investigate how skills can be incorporated into the training of reinforcement learning (RL) agents in complex environments with large state-action spaces and sparse rewards. To this end, we created SkillHack, a benchmark of tasks and associated skills based on the game of NetHack. We evaluate a number of baselines on this benchmark, as well as our own novel skill-based method Hierarchical Kickstarting (HKS), which is shown to outperform all other evaluated methods. Our experiments show that learning with a prior knowledge of useful skills can significantly improve the performance of agents on complex problems. We ultimately argue that utilising predefined skills provides a useful inductive bias for RL problems, especially those with large state-action spaces and sparse rewards.
    Interactive and Visual Prompt Engineering for Ad-hoc Task Adaptation with Large Language Models. (arXiv:2208.07852v1 [cs.CL])
    State-of-the-art neural language models can now be used to solve ad-hoc language tasks through zero-shot prompting without the need for supervised training. This approach has gained popularity in recent years, and researchers have demonstrated prompts that achieve strong accuracy on specific NLP tasks. However, finding a prompt for new tasks requires experimentation. Different prompt templates with different wording choices lead to significant accuracy differences. PromptIDE allows users to experiment with prompt variations, visualize prompt performance, and iteratively optimize prompts. We developed a workflow that allows users to first focus on model feedback using small data before moving on to a large data regime that allows empirical grounding of promising prompts using quantitative measures of the task. The tool then allows easy deployment of the newly created ad-hoc models. We demonstrate the utility of PromptIDE (demo at this http URL) and our workflow using several real-world use cases.
    Do Invariances in Deep Neural Networks Align with Human Perception?. (arXiv:2111.14726v3 [cs.CV] UPDATED)
    An evaluation criterion for safe and trustworthy deep learning is how well the invariances captured by representations of deep neural networks (DNNs) are shared with humans. We identify challenges in measuring these invariances. Prior works used gradient-based methods to generate \textit{identically represented inputs} (IRIs), \ie, inputs which have identical representations (on a given layer) of a neural network, and thus capture invariances of a given network. One necessary criterion for a network's invariances to align with human perception is for its IRIs look `similar` to humans. Prior works, however, have mixed takeaways; some argue that later layers of DNNs do not learn human-like invariances (\cite{jenelle2019metamers}) yet others seem to indicate otherwise (\cite{mahendran2014understanding}). We argue that the loss function used to generate IRIs can heavily affect takeaways about invariances of the network and is the primary reason for these conflicting findings. We propose an \textit{adversarial} regularizer on the IRI generation loss that finds IRIs that make any model appear to have very little shared invariance with humans. Based on this evidence, we argue that there is scope for improving models to have human-like invariances, and further, to have meaningful comparisons between models one should use IRIs generated using the \textit{regularizer-free} loss. We then conduct an in-depth investigation of how different components (\eg~architectures, training losses, data augmentations) of the deep learning pipeline contribute to learning models that have good alignment with humans. We find that architectures with residual connections trained using a (self-supervised) contrastive loss with $\ell_p$ ball adversarial data augmentation tend to learn invariances that are most aligned with humans.
    QuickSkill: Novice Skill Estimation in Online Multiplayer Games. (arXiv:2208.07704v1 [cs.LG])
    Matchmaking systems are vital for creating fair matches in online multiplayer games, which directly affects players' satisfactions and game experience. Most of the matchmaking systems largely rely on precise estimation of players' game skills to construct equitable games. However, the skill rating of a novice is usually inaccurate, as current matchmaking rating algorithms require considerable amount of games for learning the true skill of a new player. Using these unreliable skill scores at early stages for matchmaking usually leads to disparities in terms of team performance, which causes negative game experience. This is known as the ''cold-start'' problem for matchmaking rating algorithms. To overcome this conundrum, this paper proposes QuickSKill, a deep learning based novice skill estimation framework to quickly probe abilities of new players in online multiplayer games. QuickSKill extracts sequential performance features from initial few games of a player to predict his/her future skill rating with a dedicated neural network, thus delivering accurate skill estimation at the player's early game stage. By employing QuickSKill for matchmaking, game fairness can be dramatically improved in the initial cold-start period. We conduct experiments in a popular mobile multiplayer game in both offline and online scenarios. Results obtained with two real-world anonymized gaming datasets demonstrate that proposed QuickSKill delivers precise estimation of game skills for novices, leading to significantly lower team skill disparities and better player game experience. To the best of our knowledge, proposed QuickSKill is the first framework that tackles the cold-start problem for traditional skill rating algorithms.
    A Latent Feature Analysis-based Approach for Spatio-Temporal Traffic Data Recovery. (arXiv:2208.07739v1 [eess.SP])
    Missing data is an inevitable and common problem in data-driven intelligent transportation systems (ITS). In the past decade, scholars have done many research on the recovery of missing traffic data, however how to make full use of spatio-temporal traffic patterns to improve the recovery performance is still an open problem. Aiming at the spatio-temporal characteristics of traffic speed data, this paper regards the recovery of missing data as a matrix completion problem, and proposes a spatio-temporal traffic data completion method based on hidden feature analysis, which discovers spatio-temporal patterns and underlying structures from incomplete data to complete the recovery task. Therefore, we introduce spatial and temporal correlation to capture the main underlying features of each dimension. Finally, these latent features are applied to recovery traffic data through latent feature analysis. The experimental and evaluation results show that the evaluation criterion value of the model is small, which indicates that the model has better performance. The results show that the model can accurately estimate the continuous missing data.
    Active Bucketized Learning for ACOPF Optimization Proxies. (arXiv:2208.07497v1 [cs.LG])
    This paper considers optimization proxies for Optimal Power Flow (OPF), i.e., machine-learning models that approximate the input/output relationship of OPF. Recent work has focused on showing that such proxies can be of high fidelity. However, their training requires significant data, each instance necessitating the (offline) solving of an OPF for a sample of the input distribution. To meet the requirements of market-clearing applications, this paper proposes Active Bucketized Sampling (ABS), a novel active learning framework that aims at training the best possible OPF proxy within a time limit. ABS partitions the input distribution into buckets and uses an acquisition function to determine where to sample next. It relies on an adaptive learning rate that increases and decreases over time. Experimental results demonstrate the benefits of ABS.
    Subtype-Aware Dynamic Unsupervised Domain Adaptation. (arXiv:2208.07754v1 [cs.CV])
    Unsupervised domain adaptation (UDA) has been successfully applied to transfer knowledge from a labeled source domain to target domains without their labels. Recently introduced transferable prototypical networks (TPN) further addresses class-wise conditional alignment. In TPN, while the closeness of class centers between source and target domains is explicitly enforced in a latent space, the underlying fine-grained subtype structure and the cross-domain within-class compactness have not been fully investigated. To counter this, we propose a new approach to adaptively perform a fine-grained subtype-aware alignment to improve performance in the target domain without the subtype label in both domains. The insight of our approach is that the unlabeled subtypes in a class have the local proximity within a subtype, while exhibiting disparate characteristics, because of different conditional and label shifts. Specifically, we propose to simultaneously enforce subtype-wise compactness and class-wise separation, by utilizing intermediate pseudo-labels. In addition, we systematically investigate various scenarios with and without prior knowledge of subtype numbers, and propose to exploit the underlying subtype structure. Furthermore, a dynamic queue framework is developed to evolve the subtype cluster centroids steadily using an alternative processing scheme. Experimental results, carried out with multi-view congenital heart disease data and VisDA and DomainNet, show the effectiveness and validity of our subtype-aware UDA, compared with state-of-the-art UDA methods.
    Modeling Occasion Evolution in Frequency Domain for Promotion-Aware Click-Through Rate Prediction. (arXiv:2112.13747v3 [cs.LG] UPDATED)
    Promotions are becoming very important and frequent in e-commerce platforms to attract customers and boost sales, resulting in various occasions which drive users to behave differently. Due to the frequent changes of occasions, existing Click-Through Rate (CTR) prediction methods are not able to generalize well to online serving because the data distribution is uncertain. Besides, with training data collected from different occasions, the assumption of identical distribution does not hold, imposing extra difficulties on model learning. In this paper, we propose a novel CTR model named MOEF for recommendation under frequent changes of occasions. Firstly, we generate occasion signals from the online business scenario with a proper sampling interval. For each occasion signal, we obtain a sequence of frequency spectrum via Fast Fourier Transformation applied on sliding time windows. Occasion signals are more discriminative in frequency domain, so we can model occasion evolution with sequences of frequency spectrum via LSTM more easily to learn a better occasion representation, helping tackle the online distribution uncertainty. To ease the difficulties of model learning introduced by non-identically distributed training data, we adopt multiple experts to learn feature representations from multiple aspects, which are guided by the occasion representation via an attention mechanism. Accordingly, a mixture of feature representations is obtained adaptively for different occasions and used for the final CTR prediction. Experimental results on real-world datasets validate the superiority of our MOEF model. Online A/B tests also show MOEF achieves significant gains of 4.23% on CTR and 6.47% on IPV during promotion periods as well as 4.61% and 6.96% in normal days, respectively. The code will be made publicly available.
    $L^p$ sampling numbers for the Fourier-analytic Barron space. (arXiv:2208.07605v1 [math.FA])
    In this paper, we consider Barron functions $f : [0,1]^d \to \mathbb{R}$ of smoothness $\sigma > 0$, which are functions that can be written as \[ f(x) = \int_{\mathbb{R}^d} F(\xi) \, e^{2 \pi i \langle x, \xi \rangle} \, d \xi \quad \text{with} \quad \int_{\mathbb{R}^d} |F(\xi)| \cdot (1 + |\xi|)^{\sigma} \, d \xi < \infty. \] For $\sigma = 1$, these functions play a prominent role in machine learning, since they can be efficiently approximated by (shallow) neural networks without suffering from the curse of dimensionality. For these functions, we study the following question: Given $m$ point samples $f(x_1),\dots,f(x_m)$ of an unknown Barron function $f : [0,1]^d \to \mathbb{R}$ of smoothness $\sigma$, how well can $f$ be recovered from these samples, for an optimal choice of the sampling points and the reconstruction procedure? Denoting the optimal reconstruction error measured in $L^p$ by $s_m (\sigma; L^p)$, we show that \[ m^{- \frac{1}{\max \{ p,2 \}} - \frac{\sigma}{d}} \lesssim s_m(\sigma;L^p) \lesssim (\ln (e + m))^{\alpha(\sigma,d) / p} \cdot m^{- \frac{1}{\max \{ p,2 \}} - \frac{\sigma}{d}} , \] where the implied constants only depend on $\sigma$ and $d$ and where $\alpha(\sigma,d)$ stays bounded as $d \to \infty$.
    On Optimizing Back-Substitution Methods for Neural Network Verification. (arXiv:2208.07669v1 [cs.LG])
    With the increasing application of deep learning in mission-critical systems, there is a growing need to obtain formal guarantees about the behaviors of neural networks. Indeed, many approaches for verifying neural networks have been recently proposed, but these generally struggle with limited scalability or insufficient accuracy. A key component in many state-of-the-art verification schemes is computing lower and upper bounds on the values that neurons in the network can obtain for a specific input domain -- and the tighter these bounds, the more likely the verification is to succeed. Many common algorithms for computing these bounds are variations of the symbolic-bound propagation method; and among these, approaches that utilize a process called back-substitution are particularly successful. In this paper, we present an approach for making back-substitution produce tighter bounds. To achieve this, we formulate and then minimize the imprecision errors incurred during back-substitution. Our technique is general, in the sense that it can be integrated into numerous existing symbolic-bound propagation techniques, with only minor modifications. We implement our approach as a proof-of-concept tool, and present favorable results compared to state-of-the-art verifiers that perform back-substitution.
    On Efficient Real-Time Semantic Segmentation: A Survey. (arXiv:2206.08605v2 [cs.CV] UPDATED)
    Semantic segmentation is the problem of assigning a class label to every pixel in an image, and is an important component of an autonomous vehicle vision stack for facilitating scene understanding and object detection. However, many of the top performing semantic segmentation models are extremely complex and cumbersome, and as such are not suited to deployment onboard autonomous vehicle platforms where computational resources are limited and low-latency operation is a vital requirement. In this survey, we take a thorough look at the works that aim to address this misalignment with more compact and efficient models capable of deployment on low-memory embedded systems while meeting the constraint of real-time inference. We discuss several of the most prominent works in the field, placing them within a taxonomy based on their major contributions, and finally we evaluate the inference speed of the discussed models under consistent hardware and software setups that represent a typical research environment with high-end GPU and a realistic deployed scenario using low-memory embedded GPU hardware. Our experimental results demonstrate that many works are capable of real-time performance on resource-constrained hardware, while illustrating the consistent trade-off between latency and accuracy.
    Parametric Scattering Networks. (arXiv:2107.09539v4 [cs.LG] UPDATED)
    The wavelet scattering transform creates geometric invariants and deformation stability. In multiple signal domains, it has been shown to yield more discriminative representations compared to other non-learned representations and to outperform learned representations in certain tasks, particularly on limited labeled data and highly structured signals. The wavelet filters used in the scattering transform are typically selected to create a tight frame via a parameterized mother wavelet. In this work, we investigate whether this standard wavelet filterbank construction is optimal. Focusing on Morlet wavelets, we propose to learn the scales, orientations, and aspect ratios of the filters to produce problem-specific parameterizations of the scattering transform. We show that our learned versions of the scattering transform yield significant performance gains in small-sample classification settings over the standard scattering transform. Moreover, our empirical results suggest that traditional filterbank constructions may not always be necessary for scattering transforms to extract effective representations.
    Efficient Multimodal Transformer with Dual-Level Feature Restoration for Robust Multimodal Sentiment Analysis. (arXiv:2208.07589v1 [cs.LG])
    With the proliferation of user-generated online videos, Multimodal Sentiment Analysis (MSA) has attracted increasing attention recently. Despite significant progress, there are still two major challenges on the way towards robust MSA: 1) inefficiency when modeling cross-modal interactions in unaligned multimodal data; and 2) vulnerability to random modality feature missing which typically occurs in realistic settings. In this paper, we propose a generic and unified framework to address them, named Efficient Multimodal Transformer with Dual-Level Feature Restoration (EMT-DLFR). Concretely, EMT employs utterance-level representations from each modality as the global multimodal context to interact with local unimodal features and mutually promote each other. It not only avoids the quadratic scaling cost of previous local-local cross-modal interaction methods but also leads to better performance. To improve model robustness in the incomplete modality setting, on the one hand, DLFR performs low-level feature reconstruction to implicitly encourage the model to learn semantic information from incomplete data. On the other hand, it innovatively regards complete and incomplete data as two different views of one sample and utilizes siamese representation learning to explicitly attract their high-level representations. Comprehensive experiments on three popular datasets demonstrate that our method achieves superior performance in both complete and incomplete modality settings.
    A Large-Scale Dataset of Twitter Chatter about Online Learning during the Current COVID-19 Omicron Wave. (arXiv:2208.07810v1 [cs.SI])
    The COVID-19 Omicron variant, reported to be the most immune evasive variant of COVID-19, is resulting in a surge of COVID-19 cases globally. This has caused schools, colleges, and universities in different parts of the world to transition to online learning. As a result, social media platforms such as Twitter are seeing an increase in conversations related to online learning in the form of tweets. Mining such tweets to develop a dataset can serve as a data resource for different applications and use-cases related to the analysis of interest, views, opinions, perspectives, attitudes, and feedback towards online learning during the current surge of COVID-19 cases caused by the Omicron variant. Therefore, this work presents a large-scale open-access Twitter dataset of conversations about online learning from different parts of the world since the first detected case of the COVID-19 Omicron variant in November 2021. The dataset is compliant with the privacy policy, developer agreement, and guidelines for content redistribution of Twitter, as well as with the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) principles for scientific data management. The paper also briefly outlines some potential applications in the fields of Big Data, Data Mining, Natural Language Processing, and their related disciplines, with a specific focus on online learning during this Omicron wave that may be studied, explored, and investigated by using this dataset.
    Predicting student performance using sequence classification with time-based windows. (arXiv:2208.07749v1 [cs.LG])
    A growing number of universities worldwide use various forms of online and blended learning as part of their academic curricula. Furthermore, the recent changes caused by the COVID-19 pandemic have led to a drastic increase in importance and ubiquity of online education. Among the major advantages of e-learning is not only improving students' learning experience and widening their educational prospects, but also an opportunity to gain insights into students' learning processes with learning analytics. This study contributes to the topic of improving and understanding e-learning processes in the following ways. First, we demonstrate that accurate predictive models can be built based on sequential patterns derived from students' behavioral data, which are able to identify underperforming students early in the course. Second, we investigate the specificity-generalizability trade-off in building such predictive models by investigating whether predictive models should be built for every course individually based on course-specific sequential patterns, or across several courses based on more general behavioral patterns. Finally, we present a methodology for capturing temporal aspects in behavioral data and analyze its influence on the predictive performance of the models. The results of our improved sequence classification technique are capable to predict student performance with high levels of accuracy, reaching 90 percent for course-specific models.
    Notes on Worst-case Inefficiency of Gradient Descent Even in R^2. (arXiv:2008.07513v2 [cs.LG] UPDATED)
    Gradient descent is a popular algorithm in optimization, and its performance in convex settings is mostly well understood. In non-convex settings, it has been shown that gradient descent is able to escape saddle points asymptotically and converge to local minimizers [Lee et. al. 2016]. Recent studies also show a perturbed version of gradient descent is enough to escape saddle points efficiently [Jin et. al. 2015, Ge et. al. 2017]. In this paper we show a negative result: gradient descent may take exponential time to escape saddle points, with non-pathological two dimensional functions. While our focus is theoretical, we also conduct experiments verifying our theoretical result. Through our analysis we demonstrate that stochasticity is essential to escape saddle points efficiently.
    Mask and Reason: Pre-Training Knowledge Graph Transformers for Complex Logical Queries. (arXiv:2208.07638v1 [cs.LG])
    Knowledge graph (KG) embeddings have been a mainstream approach for reasoning over incomplete KGs. However, limited by their inherently shallow and static architectures, they can hardly deal with the rising focus on complex logical queries, which comprise logical operators, imputed edges, multiple source entities, and unknown intermediate entities. In this work, we present the Knowledge Graph Transformer (kgTransformer) with masked pre-training and fine-tuning strategies. We design a KG triple transformation method to enable Transformer to handle KGs, which is further strengthened by the Mixture-of-Experts (MoE) sparse activation. We then formulate the complex logical queries as masked prediction and introduce a two-stage masked pre-training strategy to improve transferability and generalizability. Extensive experiments on two benchmarks demonstrate that kgTransformer can consistently outperform both KG embedding-based baselines and advanced encoders on nine in-domain and out-of-domain reasoning tasks. Additionally, kgTransformer can reason with explainability via providing the full reasoning paths to interpret given answers.
    Deletion Robust Non-Monotone Submodular Maximization over Matroids. (arXiv:2208.07582v1 [cs.DS])
    Maximizing a submodular function is a fundamental task in machine learning and in this paper we study the deletion robust version of the problem under the classic matroids constraint. Here the goal is to extract a small size summary of the dataset that contains a high value independent set even after an adversary deleted some elements. We present constant-factor approximation algorithms, whose space complexity depends on the rank $k$ of the matroid and the number $d$ of deleted elements. In the centralized setting we present a $(4.597+O(\varepsilon))$-approximation algorithm with summary size $O( \frac{k+d}{\varepsilon^2}\log \frac{k}{\varepsilon})$ that is improved to a $(3.582+O(\varepsilon))$-approximation with $O(k + \frac{d}{\varepsilon^2}\log \frac{k}{\varepsilon})$ summary size when the objective is monotone. In the streaming setting we provide a $(9.435 + O(\varepsilon))$-approximation algorithm with summary size and memory $O(k + \frac{d}{\varepsilon^2}\log \frac{k}{\varepsilon})$; the approximation factor is then improved to $(5.582+O(\varepsilon))$ in the monotone case.
    High Fidelity Visualization of What Your Self-Supervised Representation Knows About. (arXiv:2112.09164v2 [cs.LG] UPDATED)
    Discovering what is learned by neural networks remains a challenge. In self-supervised learning, classification is the most common task used to evaluate how good a representation is. However, relying only on such downstream task can limit our understanding of what information is retained in the representation of a given input. In this work, we showcase the use of a Representation Conditional Diffusion Model (RCDM) to visualize in data space the representations learned by self-supervised models. The use of RCDM is motivated by its ability to generate high-quality samples -- on par with state-of-the-art generative models -- while ensuring that the representations of those samples are faithful i.e. close to the one used for conditioning. By using RCDM to analyze self-supervised models, we are able to clearly show visually that i) SSL (backbone) representation are not invariant to the data augmentations they were trained with -- thus debunking an often restated but mistaken belief; ii) SSL post-projector embeddings appear indeed invariant to these data augmentation, along with many other data symmetries; iii) SSL representations appear more robust to small adversarial perturbation of their inputs than representations trained in a supervised manner; and iv) that SSL-trained representations exhibit an inherent structure that can be explored thanks to RCDM visualization and enables image manipulation.
    Representation Learning on Graphs to Identifying Circular Trading in Goods and Services Tax. (arXiv:2208.07660v1 [cs.LG])
    Circular trading is a form of tax evasion in Goods and Services Tax where a group of fraudulent taxpayers (traders) aims to mask illegal transactions by superimposing several fictitious transactions (where no value is added to the goods or service) among themselves in a short period. Due to the vast database of taxpayers, it is infeasible for authorities to manually identify groups of circular traders and the illegitimate transactions they are involved in. This work uses big data analytics and graph representation learning techniques to propose a framework to identify communities of circular traders and isolate the illegitimate transactions in the respective communities. Our approach is tested on real-life data provided by the Department of Commercial Taxes, Government of Telangana, India, where we uncovered several communities of circular traders.
    Towards Certified Robustness of Distance Metric Learning. (arXiv:2006.05945v2 [stat.ML] UPDATED)
    Metric learning aims to learn a distance metric such that semantically similar instances are pulled together while dissimilar instances are pushed away. Many existing methods consider maximizing or at least constraining a distance margin in the feature space that separates similar and dissimilar pairs of instances to guarantee their generalization ability. In this paper, we advocate imposing an adversarial margin in the input space so as to improve the generalization and robustness of metric learning algorithms. We first show that, the adversarial margin, defined as the distance between training instances and their closest adversarial examples in the input space, takes account of both the distance margin in the feature space and the correlation between the metric and triplet constraints. Next, to enhance robustness to instance perturbation, we propose to enlarge the adversarial margin through minimizing a derived novel loss function termed the perturbation loss. The proposed loss can be viewed as a data-dependent regularizer and easily plugged into any existing metric learning methods. Finally, we show that the enlarged margin is beneficial to the generalization ability by using the theoretical technique of algorithmic robustness. Experimental results on 16 datasets demonstrate the superiority of the proposed method over existing state-of-the-art methods in both discrimination accuracy and robustness against possible noise.
    Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting. (arXiv:2206.09112v3 [cs.LG] UPDATED)
    We all depend on mobility, and vehicular transportation affects the daily lives of most of us. Thus, the ability to forecast the state of traffic in a road network is an important functionality and a challenging task. Traffic data is often obtained from sensors deployed in a road network. Recent proposals on spatial-temporal graph neural networks have achieved great progress at modeling complex spatial-temporal correlations in traffic data, by modeling traffic data as a diffusion process. However, intuitively, traffic data encompasses two different kinds of hidden time series signals, namely the diffusion signals and inherent signals. Unfortunately, nearly all previous works coarsely consider traffic signals entirely as the outcome of the diffusion, while neglecting the inherent signals, which impacts model performance negatively. To improve modeling performance, we propose a novel Decoupled Spatial-Temporal Framework (DSTF) that separates the diffusion and inherent traffic information in a data-driven manner, which encompasses a unique estimation gate and a residual decomposition mechanism. The separated signals can be handled subsequently by the diffusion and inherent modules separately. Further, we propose an instantiation of DSTF, Decoupled Dynamic Spatial-Temporal Graph Neural Network (D2STGNN), that captures spatial-temporal correlations and also features a dynamic graph learning module that targets the learning of the dynamic characteristics of traffic networks. Extensive experiments with four real-world traffic datasets demonstrate that the framework is capable of advancing the state-of-the-art.
    Training Latent Variable Models with Auto-encoding Variational Bayes: A Tutorial. (arXiv:2208.07818v1 [cs.LG])
    Auto-encoding Variational Bayes (AEVB) is a powerful and general algorithm for fitting latent variable models (a promising direction for unsupervised learning), and is well-known for training the Variational Auto-Encoder (VAE). In this tutorial, we focus on motivating AEVB from the classic Expectation Maximization (EM) algorithm, as opposed to from deterministic auto-encoders. Though natural and somewhat self-evident, the connection between EM and AEVB is not emphasized in the recent deep learning literature, and we believe that emphasizing this connection can improve the community's understanding of AEVB. In particular, we find it especially helpful to view (1) optimizing the evidence lower bound (ELBO) with respect to inference parameters as approximate E-step and (2) optimizing ELBO with respect to generative parameters as approximate M-step; doing both simultaneously as in AEVB is then simply tightening and pushing up ELBO at the same time. We discuss how approximate E-step can be interpreted as performing variational inference. Important concepts such as amortization and the reparametrization trick are discussed in great detail. Finally, we derive from scratch the AEVB training procedures of a non-deep and several deep latent variable models, including VAE, Conditional VAE, Gaussian Mixture VAE and Variational RNN. It is our hope that readers would recognize AEVB as a general algorithm that can be used to fit a wide range of latent variable models (not just VAE), and apply AEVB to such models that arise in their own fields of research. PyTorch code for all included models are publicly available.
    Combining Predictions under Uncertainty: The Case of Random Decision Trees. (arXiv:2208.07403v1 [cs.LG])
    A common approach to aggregate classification estimates in an ensemble of decision trees is to either use voting or to average the probabilities for each class. The latter takes uncertainty into account, but not the reliability of the uncertainty estimates (so to say, the "uncertainty about the uncertainty"). More generally, much remains unknown about how to best combine probabilistic estimates from multiple sources. In this paper, we investigate a number of alternative prediction methods. Our methods are inspired by the theories of probability, belief functions and reliable classification, as well as a principle that we call evidence accumulation. Our experiments on a variety of data sets are based on random decision trees which guarantees a high diversity in the predictions to be combined. Somewhat unexpectedly, we found that taking the average over the probabilities is actually hard to beat. However, evidence accumulation showed consistently better results on all but very small leafs.
    Machine Learning-Based Test Smell Detection. (arXiv:2208.07574v1 [cs.SE])
    Context: Test smells are symptoms of sub-optimal design choices adopted when developing test cases. Previous studies have proved their harmfulness for test code maintainability and effectiveness. Therefore, researchers have been proposing automated, heuristic-based techniques to detect them. However, the performance of such detectors is still limited and dependent on thresholds to be tuned. Objective: We propose the design and experimentation of a novel test smell detection approach based on machine learning to detect four test smells. Method: We plan to develop the largest dataset of manually-validated test smells. This dataset will be leveraged to train six machine learners and assess their capabilities in within- and cross-project scenarios. Finally, we plan to compare our approach with state-of-the-art heuristic-based techniques.
    Learning Operators with Ignore Effects for Bilevel Planning in Continuous Domains. (arXiv:2208.07737v1 [cs.AI])
    Bilevel planning, in which a high-level search over an abstraction of an environment is used to guide low-level decision making, is an effective approach to solving long-horizon tasks in continuous state and action spaces. Recent work has shown that action abstractions that enable such bilevel planning can be learned in the form of symbolic operators and neural samplers given symbolic predicates and demonstrations that achieve known goals. In this work, we show that existing approaches fall short in environments where actions tend to cause a large number of predicates to change. To address this issue, we propose to learn operators with ignore effects. The key idea motivating our approach is that modeling every observed change in the predicates is unnecessary; the only changes that need be modeled are those that are necessary for high-level search to achieve the specified goal. Experimentally, we show that our approach is able to learn operators with ignore effects across six hybrid robotic domains that enable an agent to solve novel variations of a task, with different initial states, goals, and numbers of objects, significantly more efficiently than several baselines.
    An initial alignment between neural network and target is needed for gradient descent to learn. (arXiv:2202.12846v2 [cs.LG] UPDATED)
    This paper introduces the notion of ``Initial Alignment'' (INAL) between a neural network at initialization and a target function. It is proved that if a network and a Boolean target function do not have a noticeable INAL, then noisy gradient descent on a fully connected network with normalized i.i.d. initialization will not learn in polynomial time. Thus a certain amount of knowledge about the target (measured by the INAL) is needed in the architecture design. This also provides an answer to an open problem posed in [AS20]. The results are based on deriving lower-bounds for descent algorithms on symmetric neural networks without explicit knowledge of the target function beyond its INAL.
    CARD: Classification and Regression Diffusion Models. (arXiv:2206.07275v2 [stat.ML] UPDATED)
    Learning the distribution of a continuous or categorical response variable $\boldsymbol y$ given its covariates $\boldsymbol x$ is a fundamental problem in statistics and machine learning. Deep neural network-based supervised learning algorithms have made great progress in predicting the mean of $\boldsymbol y$ given $\boldsymbol x$, but they are often criticized for their ability to accurately capture the uncertainty of their predictions. In this paper, we introduce classification and regression diffusion (CARD) models, which combine a denoising diffusion-based conditional generative model and a pre-trained conditional mean estimator, to accurately predict the distribution of $\boldsymbol y$ given $\boldsymbol x$. We demonstrate the outstanding ability of CARD in conditional distribution prediction with both toy examples and real-world datasets, the experimental results on which show that CARD in general outperforms state-of-the-art methods, including Bayesian neural network-based ones that are designed for uncertainty estimation, especially when the conditional distribution of $\boldsymbol y$ given $\boldsymbol x$ is multi-modal.
    TAR: Neural Logical Reasoning across TBox and ABox. (arXiv:2205.14591v2 [cs.AI] UPDATED)
    Many ontologies, i.e., Description Logic (DL) knowledge bases, have been developed to provide rich knowledge about various domains. An ontology consists of an ABox, i.e., assertion axioms between two entities or between a concept and an entity, and a TBox, i.e., terminology axioms between two concepts. Neural logical reasoning (NLR) is a fundamental task to explore such knowledge bases, which aims at answering multi-hop queries with logical operations based on distributed representations of queries and answers. While previous NLR methods can give specific entity-level answers, i.e., ABox answers, they are not able to provide descriptive concept-level answers, i.e., TBox answers, where each concept is a description of a set of entities. In other words, previous NLR methods only reason over the ABox of an ontology while ignoring the TBox. In particular, providing TBox answers enables inferring the explanations of each query with descriptive concepts, which make answers comprehensible to users and are of great usefulness in the field of applied ontology. In this work, we formulate the problem of neural logical reasoning across TBox and ABox (TA-NLR), solving which needs to address challenges in incorporating, representing, and operating on concepts. We propose an original solution named TAR for TA-NLR. Firstly, we incorporate description logic based ontological axioms to provide the source of concepts. Then, we represent concepts and queries as fuzzy sets, i.e., sets whose elements have degrees of membership, to bridge concepts and queries with entities. Moreover, we design operators involving concepts on top of fuzzy set representation of concepts and queries for optimization and inference. Extensive experimental results on two real-world datasets demonstrate the effectiveness of TAR for TA-NLR.
    Estimating the Mixing Time of Ergodic Markov Chains. (arXiv:1902.01224v4 [math.ST] UPDATED)
    We address the problem of estimating the mixing time $t_{\mathsf{mix}}$ of an arbitrary ergodic finite-state Markov chain from a single trajectory of length $m$. The reversible case was addressed by Hsu et al. [2019], who left the general case as an open problem. In the reversible case, the analysis is greatly facilitated by the fact that the Markov operator is self-adjoint, and Weyl's inequality allows for a dimension-free perturbation analysis of the empirical eigenvalues. As Hsu et al. point out, in the absence of reversibility (which induces asymmetric pair probabilities matrices), the existing perturbation analysis has a worst-case exponential dependence on the number of states $d$. Furthermore, even if an eigenvalue perturbation analysis with better dependence on $d$ were available, in the non-reversible case the connection between the spectral gap and the mixing time is not nearly as straightforward as in the reversible case. Our key insight is to estimate the pseudo-spectral gap $\gamma_{\mathsf{ps}}$ instead, which allows us to overcome the loss of symmetry and to achieve a polynomial dependence on the minimal stationary probability $\pi_\star$ and $\gamma_{\mathsf{ps}}$. Additionally, in the reversible case, we obtain simultaneous nearly (up to logarithmic factors) minimax rates in $t_{\mathsf{mix}}$ and precision $\varepsilon$, closing a gap in Hsu et al., who treated $\varepsilon$ as constant in the lower bounds. Finally, we construct fully empirical confidence intervals for $\gamma_{\mathsf{ps}}$, which shrink to zero at a rate of roughly $1/\sqrt{m}$, and improve the state of the art in even the reversible case.
    Knowledge-Injected Federated Learning. (arXiv:2208.07530v1 [cs.LG])
    Federated learning is an emerging technique for training models from decentralized data sets. In many applications, data owners participating in the federated learning system hold not only the data but also a set of domain knowledge. Such knowledge includes human know-how and craftsmanship that can be extremely helpful to the federated learning task. In this work, we propose a federated learning framework that allows the injection of participants' domain knowledge, where the key idea is to refine the global model with knowledge locally. The scenario we consider is motivated by a real industry-level application, and we demonstrate the effectiveness of our approach to this application.
    Multi-Point Integrated Sensing and Communication: Fusion Model and Functionality Selection. (arXiv:2208.07592v1 [cs.IT])
    Integrated sensing and communication (ISAC) represents a paradigm shift, where previously competing wireless transmissions are jointly designed to operate in harmony via the shared use of the hardware platform for improving the spectral, energy, and hardware efficiencies. However, due to adversarial factors such as fading and blockages, ISAC without fusion may suffer from high sensing uncertainties. This paper presents a multi-point ISAC (MPISAC) system that fuses the outputs from multiple ISAC devices for achieving higher sensing performance by exploiting multi-radar data redundancy. Furthermore, we propose to effectively explore the performance trade-off between sensing and communication via a functionality selection module that adaptively determines the working state (i.e., sensing or communication) of an ISAC device. The crux of our approach is to adopt a fusion model that predicts the fusion accuracy via hypothesis testing and optimal voting analysis. Simulation results demonstrate the superiority of MPISAC over various benchmark schemes and show that the proposed approach can effectively span the trade-off region in ISAC systems.
    End-to-End Video-To-Speech Synthesis using Generative Adversarial Networks. (arXiv:2104.13332v3 [cs.LG] UPDATED)
    Video-to-speech is the process of reconstructing the audio speech from a video of a spoken utterance. Previous approaches to this task have relied on a two-step process where an intermediate representation is inferred from the video, and is then decoded into waveform audio using a vocoder or a waveform reconstruction algorithm. In this work, we propose a new end-to-end video-to-speech model based on Generative Adversarial Networks (GANs) which translates spoken video to waveform end-to-end without using any intermediate representation or separate waveform synthesis algorithm. Our model consists of an encoder-decoder architecture that receives raw video as input and generates speech, which is then fed to a waveform critic and a power critic. The use of an adversarial loss based on these two critics enables the direct synthesis of raw audio waveform and ensures its realism. In addition, the use of our three comparative losses helps establish direct correspondence between the generated audio and the input video. We show that this model is able to reconstruct speech with remarkable realism for constrained datasets such as GRID, and that it is the first end-to-end model to produce intelligible speech for LRW (Lip Reading in the Wild), featuring hundreds of speakers recorded entirely `in the wild'. We evaluate the generated samples in two different scenarios -- seen and unseen speakers -- using four objective metrics which measure the quality and intelligibility of artificial speech. We demonstrate that the proposed approach outperforms all previous works in most metrics on GRID and LRW.
    Role of Data Augmentation in Unsupervised Anomaly Detection. (arXiv:2208.07734v1 [cs.LG])
    Self-supervised learning (SSL) has emerged as a promising alternative to create supervisory signals to real-world tasks, avoiding extensive cost of careful labeling. SSL is particularly attractive for unsupervised problems such as anomaly detection (AD), where labeled anomalies are costly to secure, difficult to simulate, or even nonexistent. A large catalog of augmentation functions have been used for SSL-based AD (SSAD), and recent works have observed that the type of augmentation has a significant impact on performance. Motivated by those, this work sets out to put SSAD under a larger lens and carefully investigate the role of data augmentation in AD through extensive experiments on many testbeds. Our main finding is that self-supervision acts as a yet-another model hyperparameter, and should be chosen carefully in regards to the nature of true anomalies in the data. That is, the alignment between the augmentation and the underlying anomaly-generating mechanism is the key for the success of SSAD, and in the lack thereof, SSL can even impair (!) detection performance. Moving beyond proposing another SSAD method, our study contributes to the better understanding of this growing area and lays out new directions for future research.
    GNNear: Accelerating Full-Batch Training of Graph Neural Networks with Near-Memory Processing. (arXiv:2111.00680v2 [cs.LG] UPDATED)
    Recently, Graph Neural Networks (GNNs) have become state-of-the-art algorithms for analyzing non-euclidean graph data. However, to realize efficient GNN training is challenging, especially on large graphs. The reasons are many-folded: 1) GNN training incurs a substantial memory footprint. Full-batch training on large graphs even requires hundreds to thousands of gigabytes of memory. 2) GNN training involves both memory-intensive and computation-intensive operations, challenging current CPU/GPU platforms. 3) The irregularity of graphs can result in severe resource under-utilization and load-imbalance problems. This paper presents a GNNear accelerator to tackle these challenges. GNNear adopts a DIMM-based memory system to provide sufficient memory capacity. To match the heterogeneous nature of GNN training, we offload the memory-intensive Reduce operations to in-DIMM Near-Memory-Engines (NMEs), making full use of the high aggregated local bandwidth. We adopt a Centralized-Acceleration-Engine (CAE) to process the computation-intensive Update operations. We further propose several optimization strategies to deal with the irregularity of input graphs and improve GNNear's performance. Comprehensive evaluations on 16 GNN training tasks demonstrate that GNNear achieves 30.8$\times$/2.5$\times$ geomean speedup and 79.6$\times$/7.3$\times$(geomean) higher energy efficiency compared to Xeon E5-2698-v4 CPU and NVIDIA V100 GPU.
    CYBORGS: Contrastively Bootstrapping Object Representations by Grounding in Segmentation. (arXiv:2203.09343v2 [cs.CV] UPDATED)
    Many recent approaches in contrastive learning have worked to close the gap between pretraining on iconic images like ImageNet and pretraining on complex scenes like COCO. This gap exists largely because commonly used random crop augmentations obtain semantically inconsistent content in crowded scene images of diverse objects. Previous works use preprocessing pipelines to localize salient objects for improved cropping, but an end-to-end solution is still elusive. In this work, we propose a framework which accomplishes this goal via joint learning of representations and segmentation. We leverage segmentation masks to train a model with a mask-dependent contrastive loss, and use the partially trained model to bootstrap better masks. By iterating between these two components, we ground the contrastive updates in segmentation information, and simultaneously improve segmentation throughout pretraining. Experiments show our representations transfer robustly to downstream tasks in classification, detection and segmentation.
    CTI4AI: Threat Intelligence Generation and Sharing after Red Teaming AI Models. (arXiv:2208.07476v1 [cs.CR])
    As the practicality of Artificial Intelligence (AI) and Machine Learning (ML) based techniques grow, there is an ever increasing threat of adversarial attacks. There is a need to red team this ecosystem to identify system vulnerabilities, potential threats, characterize properties that will enhance system robustness, and encourage the creation of effective defenses. A secondary need is to share this AI security threat intelligence between different stakeholders like, model developers, users, and AI/ML security professionals. In this paper, we create and describe a prototype system CTI4AI, to overcome the need to methodically identify and share AI/ML specific vulnerabilities and threat intelligence.
    Federated Learning with Partial Model Personalization. (arXiv:2204.03809v2 [cs.LG] UPDATED)
    We consider two federated learning algorithms for training partially personalized models, where the shared and personal parameters are updated either simultaneously or alternately on the devices. Both algorithms have been proposed in the literature, but their convergence properties are not fully understood, especially for the alternating variant. We provide convergence analyses of both algorithms in the general nonconvex setting with partial participation and delineate the regime where one dominates the other. Our experiments on real-world image, text, and speech datasets demonstrate that (a) partial personalization can obtain most of the benefits of full model personalization with a small fraction of personal parameters, and, (b) the alternating update algorithm often outperforms the simultaneous update algorithm by a small but consistent margin.
    Generating a Terrain-Robustness Benchmark for Legged Locomotion: A Prototype via Terrain Authoring and Active Learning. (arXiv:2208.07681v1 [cs.RO])
    Terrain-aware locomotion has become an emerging topic in legged robotics. However, it is hard to generate challenging and realistic terrains in simulation, which limits the way researchers evaluate their locomotion policies. In this paper, we prototype the generation of a terrain dataset via terrain authoring and active learning, and the learned samplers can stably generate diverse high-quality terrains. Hopefully, the generated dataset can make a terrain-robustness benchmark for legged locomotion. The dataset and the code implementation are released at https://bit.ly/3bn4j7f.
    Deep learning for enhanced free-space optical communications. (arXiv:2208.07712v1 [cs.LG])
    Atmospheric effects, such as turbulence and background thermal noise, inhibit the propagation of coherent light used in ON-OFF keying free-space optical communication. Here we present and experimentally validate a convolutional neural network to reduce the bit error rate of free-space optical communication in post-processing that is significantly simpler and cheaper than existing solutions based on advanced optics. Our approach consists of two neural networks, the first determining the presence of coherent bit sequences in thermal noise and turbulence and the second demodulating the coherent bit sequences. All data used for training and testing our network is obtained experimentally by generating ON-OFF keying bit streams of coherent light, combining these with thermal light, and passing the resultant light through a turbulent water tank which we have verified mimics turbulence in the air to a high degree of accuracy. Our convolutional neural network improves detection accuracy over threshold classification schemes and has the capability to be integrated with current demodulation and error correction schemes.
    Towards Domain-Independent and Real-Time Gesture Recognition Using mmWave Signal. (arXiv:2111.06195v2 [cs.CV] UPDATED)
    Human gesture recognition using millimeter-wave (mmWave) signals provides attractive applications including smart home and in-car interfaces. While existing works achieve promising performance under controlled settings, practical applications are still limited due to the need of intensive data collection, extra training efforts when adapting to new domains, and poor performance for real-time recognition. In this paper, we propose DI-Gesture, a domain-independent and real-time mmWave gesture recognition system. Specifically, we first derive signal variations corresponding to human gestures with spatial-temporal processing. To enhance the robustness of the system and reduce data collecting efforts, we design a data augmentation framework for mmWave signals based on correlations between signal patterns and gesture variations. Furthermore, a spatial-temporal gesture segmentation algorithm is employed for real-time recognition. Extensive experimental results show DI-Gesture achieves an average accuracy of 97.92\%, 99.18\%, and 98.76\% for new users, environments, and locations, respectively. We also evaluate DI-Gesture in challenging scenarios like real-time recognition and sensing at extreme angles, all of which demonstrates the superior robustness and effectiveness of our system.
    SVTS: Scalable Video-to-Speech Synthesis. (arXiv:2205.02058v2 [cs.SD] UPDATED)
    Video-to-speech synthesis (also known as lip-to-speech) refers to the translation of silent lip movements into the corresponding audio. This task has received an increasing amount of attention due to its self-supervised nature (i.e., can be trained without manual labelling) combined with the ever-growing collection of audio-visual data available online. Despite these strong motivations, contemporary video-to-speech works focus mainly on small- to medium-sized corpora with substantial constraints in both vocabulary and setting. In this work, we introduce a scalable video-to-speech framework consisting of two components: a video-to-spectrogram predictor and a pre-trained neural vocoder, which converts the mel-frequency spectrograms into waveform audio. We achieve state-of-the art results for GRID and considerably outperform previous approaches on LRW. More importantly, by focusing on spectrogram prediction using a simple feedforward model, we can efficiently and effectively scale our method to very large and unconstrained datasets: To the best of our knowledge, we are the first to show intelligible results on the challenging LRS3 dataset.
    Supernet Training for Federated Image Classification under System Heterogeneity. (arXiv:2206.01366v4 [cs.LG] UPDATED)
    Efficient deployment of deep neural networks across many devices and resource constraints, especially on edge devices, is one of the most challenging problems in the presence of data-privacy preservation issues. Conventional approaches have evolved to either improve a single global model while keeping each local training data decentralized (i.e., data-heterogeneity) or to train a once-for-all network that supports diverse architectural settings to address heterogeneous systems equipped with different computational capabilities (i.e., model-heterogeneity). However, little research has considered both directions simultaneously. In this work, we propose a novel framework to consider both scenarios, namely Federation of Supernet Training (FedSup), where clients send and receive a supernet whereby it contains all possible architectures sampled from itself. It is inspired by how averaging parameters in the model aggregation stage of Federated Learning (FL) is similar to weight-sharing in supernet training. Specifically, in the FedSup framework, a weight-sharing approach widely used in the training single shot model is combined with the averaging of Federated Learning (FedAvg). Under our framework, we present an efficient algorithm (E-FedSup) by sending the sub-model to clients in the broadcast stage for reducing communication costs and training overhead. We demonstrate several strategies to enhance supernet training in the FL environment and conduct extensive empirical evaluations. The resulting framework is shown to pave the way for the robustness of both data- and model-heterogeneity on several standard benchmarks.
    Enhancing Dynamic Mode Decomposition Workflow with In-Situ Visualization and Data Compression. (arXiv:2208.07767v1 [cs.GR])
    Modern computational science and engineering applications are being improved by the advances in scientific machine learning. Data-driven methods such as Dynamic Mode Decomposition (DMD) can extract coherent structures from spatio-temporal data generated from dynamical systems and infer different scenarios for said systems. The spatio-temporal data comes as snapshots containing spatial information for each time instant. In modern engineering applications, the generation of high-dimensional snapshots can be time and/or resource-demanding. In the present study, we consider two strategies for enhancing DMD workflow in large numerical simulations: (i) snapshots compression to relieve disk pressure; (ii) the use of in situ visualization images to reconstruct the dynamics (or part of) in runtime. We evaluate our approaches with two 3D fluid dynamics simulations and consider DMD to reconstruct the solutions. Results reveal that snapshot compression considerably reduces the required disk space. We have observed that lossy compression reduces storage by almost $50\%$ with low relative errors in the signal reconstructions and other quantities of interest. We also extend our analysis to data generated on-the-fly, using in-situ visualization tools to generate image files of our state vectors during runtime. On large simulations, the generation of snapshots may be slow enough to use batch algorithms for inference. Streaming DMD takes advantage of the incremental SVD algorithm and updates the modes with the arrival of each new snapshot. We use streaming DMD to reconstruct the dynamics from in-situ generated images. We show that this process is efficient, and the reconstructed dynamics are accurate.
    SupMAE: Supervised Masked Autoencoders Are Efficient Vision Learners. (arXiv:2205.14540v2 [cs.CV] UPDATED)
    Recently, self-supervised Masked Autoencoders (MAE) have attracted unprecedented attention for their impressive representation learning ability. However, the pretext task, Masked Image Modeling (MIM), reconstructs the missing local patches, lacking the global understanding of the image. This paper extends MAE to a fully-supervised setting by adding a supervised classification branch, thereby enabling MAE to effectively learn global features from golden labels. The proposed Supervised MAE (SupMAE) only exploits a visible subset of image patches for classification, unlike the standard supervised pre-training where all image patches are used. Through experiments, we demonstrate that not only is SupMAE more training efficient but also it learns more robust and transferable features. Specifically, SupMAE achieves comparable performance with MAE using only 30% of compute when evaluated on ImageNet with the ViT-B/16 model. SupMAE's robustness on ImageNet variants and transfer learning performance outperforms MAE and standard supervised pre-training counterparts. Code will be made publicly available.
    Uncertainty-guided Source-free Domain Adaptation. (arXiv:2208.07591v1 [cs.CV])
    Source-free domain adaptation (SFDA) aims to adapt a classifier to an unlabelled target data set by only using a pre-trained source model. However, the absence of the source data and the domain shift makes the predictions on the target data unreliable. We propose quantifying the uncertainty in the source model predictions and utilizing it to guide the target adaptation. For this, we construct a probabilistic source model by incorporating priors on the network parameters inducing a distribution over the model predictions. Uncertainties are estimated by employing a Laplace approximation and incorporated to identify target data points that do not lie in the source manifold and to down-weight them when maximizing the mutual information on the target data. Unlike recent works, our probabilistic treatment is computationally lightweight, decouples source training and target adaptation, and requires no specialized source training or changes of the model architecture. We show the advantages of uncertainty-guided SFDA over traditional SFDA in the closed-set and open-set settings and provide empirical evidence that our approach is more robust to strong domain shifts even without tuning.
    An Overview and Prospective Outlook on Robust Training and Certification of Machine Learning Models. (arXiv:2208.07464v1 [cs.LG])
    In this discussion paper, we survey recent research surrounding robustness of machine learning models. As learning algorithms become increasingly more popular in data-driven control systems, their robustness to data uncertainty must be ensured in order to maintain reliable safety-critical operations. We begin by reviewing common formalisms for such robustness, and then move on to discuss popular and state-of-the-art techniques for training robust machine learning models as well as methods for provably certifying such robustness. From this unification of robust machine learning, we identify and discuss pressing directions for future research in the area.
    Algorithmic Assistance with Recommendation-Dependent Preferences. (arXiv:2208.07626v1 [cs.LG])
    When we use algorithms to produce recommendations, we typically think of these recommendations as providing helpful information, such as when risk assessments are presented to judges or doctors. But when a decision-maker obtains a recommendation, they may not only react to the information. The decision-maker may view the recommendation as a default action, making it costly for them to deviate, for example when a judge is reluctant to overrule a high-risk assessment of a defendant or a doctor fears the consequences of deviating from recommended procedures. In this article, we consider the effect and design of recommendations when they affect choices not just by shifting beliefs, but also by altering preferences. We motivate our model from institutional factors, such as a desire to avoid audits, as well as from well-established models in behavioral science that predict loss aversion relative to a reference point, which here is set by the algorithm. We show that recommendation-dependent preferences create inefficiencies where the decision-maker is overly responsive to the recommendation, which changes the optimal design of the algorithm towards providing less conservative recommendations. As a potential remedy, we discuss an algorithm that strategically withholds recommendations, and show how it can improve the quality of final decisions.
    Does lossy image compression affect racial bias within face recognition?. (arXiv:2208.07613v1 [cs.CV])
    Yes - This study investigates the impact of commonplace lossy image compression on face recognition algorithms with regard to the racial characteristics of the subject. We adopt a recently proposed racial phenotype-based bias analysis methodology to measure the effect of varying levels of lossy compression across racial phenotype categories. Additionally, we determine the relationship between chroma-subsampling and race-related phenotypes for recognition performance. Prior work investigates the impact of lossy JPEG compression algorithm on contemporary face recognition performance. However, there is a gap in how this impact varies with different race-related inter-sectional groups and the cause of this impact. Via an extensive experimental setup, we demonstrate that common lossy image compression approaches have a more pronounced negative impact on facial recognition performance for specific racial phenotype categories such as darker skin tones (by up to 34.55\%). Furthermore, removing chroma-subsampling during compression improves the false matching rate (up to 15.95\%) across all phenotype categories affected by the compression, including darker skin tones, wide noses, big lips, and monolid eye categories. In addition, we outline the characteristics that may be attributable as the underlying cause of such phenomenon for lossy compression algorithms such as JPEG.
    Certified Robustness via Randomized Smoothing over Multiplicative Parameters of Input Transformations. (arXiv:2106.14432v3 [cs.LG] UPDATED)
    Currently the most popular method of providing robustness certificates is randomized smoothing where an input is smoothed via some probability distribution. We propose a novel approach to randomized smoothing over multiplicative parameters. Using this method we construct certifiably robust classifiers with respect to a gamma correction perturbation and compare the result with classifiers obtained via other smoothing distributions (Gaussian, Laplace, uniform). The experiments show that asymmetrical Rayleigh distribution allows to obtain better certificates for some values of perturbation parameters. To the best of our knowledge it is the first work concerning certified robustness against the multiplicative gamma correction transformation and the first to study effects of asymmetrical distributions in randomized smoothing.
    On Sample Complexity of Offline Reinforcement Learning with Deep ReLU Networks in Besov Spaces. (arXiv:2103.06671v4 [stat.ML] UPDATED)
    Offline reinforcement learning (RL) leverages previously collected data for policy optimization without any further active exploration. Despite the recent interest in this problem, its theoretical results in neural network function approximation setting remain limited. In this paper, we study the statistical theory of offline RL with deep ReLU network function approximation. In particular, we establish the sample complexity of $\tilde{\mathcal{O}}\left( \kappa^{1 + d/\alpha} \cdot \epsilon^{-2 - 2d/\alpha} \right)$ for offline RL with deep ReLU networks, where $\kappa$ is a measure of distributional shift, $d$ is the dimension of the state-action space, $\alpha$ is a (possibly fractional) smoothness parameter of the underlying Markov decision process (MDP), and $\epsilon$ is a user-specified error. Notably, our sample complexity holds under two novel considerations, namely the Besov dynamic closure and the correlated structure that arises from value regression for offline RL. While the Besov dynamic closure generalizes the dynamic conditions for offline RL in the prior works, the correlated structure renders the prior works of offline RL with general/neural network function approximation improper or inefficient. To the best of our knowledge, this is the first theoretical characterization of the sample complexity of offline RL with deep neural network function approximation under the general Besov regularity condition that goes beyond the traditional Reproducing Hilbert kernel spaces and Neural Tangent Kernels.
    ProtoRes: Proto-Residual Network for Pose Authoring via Learned Inverse Kinematics. (arXiv:2106.01981v6 [cs.CV] UPDATED)
    Our work focuses on the development of a learnable neural representation of human pose for advanced AI assisted animation tooling. Specifically, we tackle the problem of constructing a full static human pose based on sparse and variable user inputs (e.g. locations and/or orientations of a subset of body joints). To solve this problem, we propose a novel neural architecture that combines residual connections with prototype encoding of a partially specified pose to create a new complete pose from the learned latent space. We show that our architecture outperforms a baseline based on Transformer, both in terms of accuracy and computational efficiency. Additionally, we develop a user interface to integrate our neural model in Unity, a real-time 3D development platform. Furthermore, we introduce two new datasets representing the static human pose modeling problem, based on high-quality human motion capture data, which will be released publicly along with model code.
    Semidefinite Programming versus Burer-Monteiro Factorization for Matrix Sensing. (arXiv:2208.07469v1 [math.OC])
    Many fundamental low-rank optimization problems, such as matrix completion, phase synchronization/retrieval, power system state estimation, and robust PCA, can be formulated as the matrix sensing problem. Two main approaches for solving matrix sensing are based on semidefinite programming (SDP) and Burer-Monteiro (B-M) factorization. The SDP method suffers from high computational and space complexities, whereas the B-M method may return a spurious solution due to the non-convexity of the problem. The existing theoretical guarantees for the success of these methods have led to similar conservative conditions, which may wrongly imply that these methods have comparable performances. In this paper, we shed light on some major differences between these two methods. First, we present a class of structured matrix completion problems for which the B-M methods fail with an overwhelming probability, while the SDP method works correctly. Second, we identify a class of highly sparse matrix completion problems for which the B-M method works and the SDP method fails. Third, we prove that although the B-M method exhibits the same performance independent of the rank of the unknown solution, the success of the SDP method is correlated to the rank of the solution and improves as the rank increases. Unlike the existing literature that has mainly focused on those instances of matrix sensing for which both SDP and B-M work, this paper offers the first result on the unique merit of each method over the alternative approach.
    New drugs and stock market: how to predict pharma market reaction to clinical trial announcements. (arXiv:2208.07248v2 [q-fin.ST] UPDATED)
    Pharmaceutical companies operate in a strictly regulated and highly risky environment in which a single slip can lead to serious financial implications. Accordingly, the announcements of clinical trial results tend to determine the future course of events, hence being closely monitored by the public. In this work, we provide statistical evidence for the result promulgation influence on the public pharma market value. Whereas most works focus on retrospective impact analysis, the present research aims to predict the numerical values of announcement-induced changes in stock prices. For this purpose, we develop a pipeline that includes a BERT-based model for extracting sentiment polarity of announcements, a Temporal Fusion Transformer for forecasting the expected return, a graph convolution network for capturing event relationships, and gradient boosting for predicting the price change. The challenge of the problem lies in inherently different patterns of responses to positive and negative announcements, reflected in a stronger and more pronounced reaction to the negative news. Moreover, such phenomenon as the drop in stocks after the positive announcements affirms the counterintuitiveness of the price behavior. Importantly, we discover two crucial factors that should be considered while working within a predictive framework. The first factor is the drug portfolio size of the company, indicating the greater susceptibility to an announcement in the case of small drug diversification. The second one is the network effect of the events related to the same company or nosology. All findings and insights are gained on the basis of one of the biggest FDA (the Food and Drug Administration) announcement datasets, consisting of 5436 clinical trial announcements from 681 companies over the last five years.
    Uconv-Conformer: High Reduction of Input Sequence Length for End-to-End Speech Recognition. (arXiv:2208.07657v1 [eess.AS])
    Optimization of modern ASR architectures is among the highest priority tasks since it saves many computational resources for model training and inference. The work proposes a new Uconv-Conformer architecture based on the standard Conformer model that consistently reduces the input sequence length by 16 times, which results in speeding up the work of the intermediate layers. To solve the convergence problem with such a significant reduction of the time dimension, we use upsampling blocks similar to the U-Net architecture to ensure the correct CTC loss calculation and stabilize network training. The Uconv-Conformer architecture appears to be not only faster in terms of training and inference but also shows better WER compared to the baseline Conformer. Our best Uconv-Conformer model showed 40.3% epoch training time reduction, 47.8%, and 23.5% inference acceleration on the CPU and GPU, respectively. Relative WER on Librispeech test_clean and test_other decreased by 7.3% and 9.2%.
    Delaunay-Triangulation-Based Learning with Hessian Total-Variation Regularization. (arXiv:2208.07787v1 [eess.SP])
    Regression is one of the core problems tackled in supervised learning. Rectified linear unit (ReLU) neural networks generate continuous and piecewise-linear (CPWL) mappings and are the state-of-the-art approach for solving regression problems. In this paper, we propose an alternative method that leverages the expressivity of CPWL functions. In contrast to deep neural networks, our CPWL parameterization guarantees stability and is interpretable. Our approach relies on the partitioning of the domain of the CPWL function by a Delaunay triangulation. The function values at the vertices of the triangulation are our learnable parameters and identify the CPWL function uniquely. Formulating the learning scheme as a variational problem, we use the Hessian total variation (HTV) as regularizer to favor CPWL functions with few affine pieces. In this way, we control the complexity of our model through a single hyperparameter. By developing a computational framework to compute the HTV of any CPWL function parameterized by a triangulation, we discretize the learning problem as the generalized least absolute shrinkage and selection operator (LASSO). Our experiments validate the usage of our method in low-dimensional scenarios.
    Deep convolutional surrogates and degrees of freedom in thermal design. (arXiv:2208.07482v1 [cs.LG])
    We present surrogate models for heat transfer and pressure drop prediction of complex fin geometries generated using composite Bezier curves. Thermal design process includes iterative high fidelity simulation which is complex, computationally expensive, and time-consuming. With the advancement in machine learning algorithms as well as Graphics Processing Units (GPUs), we can utilize the parallel processing architecture of GPUs rather than solely relying on CPUs to accelerate the thermo-fluid simulation. In this study, Convolutional Neural Networks (CNNs) are used to predict results of Computational Fluid Dynamics (CFD) directly from topologies saved as images. The case with a single fin as well as multiple morphable fins are studied. A comparison of Xception network and regular CNN is presented for the case with a single fin design. Results show that high accuracy in prediction is observed for single fin design particularly using Xception network. Increasing design freedom to multiple fins increases the error in prediction. This error, however, remains within three percent for pressure drop and heat transfer estimation which is valuable for design purpose.
    Model Optimization in Imbalanced Regression. (arXiv:2206.09991v2 [cs.LG] UPDATED)
    Imbalanced domain learning aims to produce accurate models in predicting instances that, though underrepresented, are of utmost importance for the domain. Research in this field has been mainly focused on classification tasks. Comparatively, the number of studies carried out in the context of regression tasks is negligible. One of the main reasons for this is the lack of loss functions capable of focusing on minimizing the errors of extreme (rare) values. Recently, an evaluation metric was introduced: Squared Error Relevance Area (SERA). This metric posits a bigger emphasis on the errors committed at extreme values while also accounting for the performance in the overall target variable domain, thus preventing severe bias. However, its effectiveness as an optimization metric is unknown. In this paper, our goal is to study the impacts of using SERA as an optimization criterion in imbalanced regression tasks. Using gradient boosting algorithms as proof of concept, we perform an experimental study with 36 data sets of different domains and sizes. Results show that models that used SERA as an objective function are practically better than the models produced by their respective standard boosting algorithms at the prediction of extreme values. This confirms that SERA can be embedded as a loss function into optimization-based learning algorithms for imbalanced regression scenarios.
    A Library for Representing Python Programs as Graphs for Machine Learning. (arXiv:2208.07461v1 [cs.LG])
    Graph representations of programs are commonly a central element of machine learning for code research. We introduce an open source Python library python_graphs that applies static analysis to construct graph representations of Python programs suitable for training machine learning models. Our library admits the construction of control-flow graphs, data-flow graphs, and composite ``program graphs'' that combine control-flow, data-flow, syntactic, and lexical information about a program. We present the capabilities and limitations of the library, perform a case study applying the library to millions of competitive programming submissions, and showcase the library's utility for machine learning research.
    Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere. (arXiv:2005.10242v10 [cs.LG] UPDATED)
    Contrastive representation learning has been outstandingly successful in practice. In this work, we identify two key properties related to the contrastive loss: (1) alignment (closeness) of features from positive pairs, and (2) uniformity of the induced distribution of the (normalized) features on the hypersphere. We prove that, asymptotically, the contrastive loss optimizes these properties, and analyze their positive effects on downstream tasks. Empirically, we introduce an optimizable metric to quantify each property. Extensive experiments on standard vision and language datasets confirm the strong agreement between both metrics and downstream task performance. Remarkably, directly optimizing for these two metrics leads to representations with comparable or better performance at downstream tasks than contrastive learning. Project Page: https://tongzhouwang.info/hypersphere Code: https://github.com/SsnL/align_uniform , https://github.com/SsnL/moco_align_uniform
    Unsupervised Domain Adaptation for Segmentation with Black-box Source Model. (arXiv:2208.07769v1 [cs.CV])
    Unsupervised domain adaptation (UDA) has been widely used to transfer knowledge from a labeled source domain to an unlabeled target domain to counter the difficulty of labeling in a new domain. The training of conventional solutions usually relies on the existence of both source and target domain data. However, privacy of the large-scale and well-labeled data in the source domain and trained model parameters can become the major concern of cross center/domain collaborations. In this work, to address this, we propose a practical solution to UDA for segmentation with a black-box segmentation model trained in the source domain only, rather than original source data or a white-box source model. Specifically, we resort to a knowledge distillation scheme with exponential mixup decay (EMD) to gradually learn target-specific representations. In addition, unsupervised entropy minimization is further applied to regularization of the target domain confidence. We evaluated our framework on the BraTS 2018 database, achieving performance on par with white-box source model adaptation approaches.
    Do As I Can, Not As I Say: Grounding Language in Robotic Affordances. (arXiv:2204.01691v2 [cs.RO] UPDATED)
    Large language models can encode a wealth of semantic knowledge about the world. Such knowledge could be extremely useful to robots aiming to act upon high-level, temporally extended instructions expressed in natural language. However, a significant weakness of language models is that they lack real-world experience, which makes it difficult to leverage them for decision making within a given embodiment. For example, asking a language model to describe how to clean a spill might result in a reasonable narrative, but it may not be applicable to a particular agent, such as a robot, that needs to perform this task in a particular environment. We propose to provide real-world grounding by means of pretrained skills, which are used to constrain the model to propose natural language actions that are both feasible and contextually appropriate. The robot can act as the language model's "hands and eyes," while the language model supplies high-level semantic knowledge about the task. We show how low-level skills can be combined with large language models so that the language model provides high-level knowledge about the procedures for performing complex and temporally-extended instructions, while value functions associated with these skills provide the grounding necessary to connect this knowledge to a particular physical environment. We evaluate our method on a number of real-world robotic tasks, where we show the need for real-world grounding and that this approach is capable of completing long-horizon, abstract, natural language instructions on a mobile manipulator. The project's website and the video can be found at https://say-can.github.io/.
    Langevin Diffusion Variational Inference. (arXiv:2208.07743v1 [cs.LG])
    Many methods that build powerful variational distributions based on unadjusted Langevin transitions exist. Most of these were developed using a wide range of different approaches and techniques. Unfortunately, the lack of a unified analysis and derivation makes developing new methods and reasoning about existing ones a challenging task. We address this giving a single analysis that unifies and generalizes these existing techniques. The main idea is to augment the target and variational by numerically simulating the underdamped Langevin diffusion process and its time reversal. The benefits of this approach are twofold: it provides a unified formulation for many existing methods, and it simplifies the development of new ones. In fact, using our formulation we propose a new method that combines the strengths of previously existing algorithms; it uses underdamped Langevin transitions and powerful augmentations parameterized by a score network. Our empirical evaluation shows that our proposed method consistently outperforms relevant baselines in a wide range of tasks.
    Towards Informed Design and Validation Assistance in Computer Games Using Imitation Learning. (arXiv:2208.07811v1 [cs.SE])
    In games, as in and many other domains, design validation and testing is a huge challenge as systems are growing in size and manual testing is becoming infeasible. This paper proposes a new approach to automated game validation and testing. Our method leverages a data-driven imitation learning technique, which requires little effort and time and no knowledge of machine learning or programming, that designers can use to efficiently train game testing agents. We investigate the validity of our approach through a user study with industry experts. The survey results show that our method is indeed a valid approach to game validation and that data-driven programming would be a useful aid to reducing effort and increasing quality of modern playtesting. The survey also highlights several open challenges. With the help of the most recent literature, we analyze the identified challenges and propose future research directions suitable for supporting and maximizing the utility of our approach.
    Fair Machine Learning in Healthcare: A Review. (arXiv:2206.14397v2 [cs.LG] UPDATED)
    Benefiting from the digitization of healthcare data and the development of computing power, machine learning methods are increasingly used in the healthcare domain. Fairness problems have been identified in machine learning for healthcare, resulting in an unfair allocation of limited healthcare resources or excessive health risks for certain groups. Therefore, addressing the fairness problems has recently attracted increasing attention from the healthcare community. However, the intersection of machine learning for healthcare and fairness in machine learning remains understudied. In this review, we build the bridge by exposing fairness problems, summarizing possible biases, sorting out mitigation methods and pointing out challenges along with opportunities for the future.
    FRAug: Tackling Federated Learning with Non-IID Features via Representation Augmentation. (arXiv:2205.14900v2 [cs.LG] UPDATED)
    Federated Learning (FL) is a decentralized learning paradigm, in which multiple clients collaboratively train deep learning models without centralizing their local data, and hence preserve data privacy. Real-world applications usually involve a distribution shift across the datasets of the different clients, which hurts the generalization ability of the clients to unseen samples from their respective data distributions. In this work, we address the recently proposed feature shift problem where the clients have different feature distributions, while the label distribution is the same. We propose Federated Representation Augmentation (FRAug) to tackle this practical and challenging problem. Our approach generates synthetic client-specific samples in the embedding space to augment the usually small client datasets. For that, we train a shared generative model to fuse the clients knowledge learned from their different feature distributions. This generator synthesizes client-agnostic embeddings, which are then locally transformed into client-specific embeddings by Representation Transformation Networks (RTNets). By transferring knowledge across the clients, the generated embeddings act as a regularizer for the client models and reduce overfitting to the local original datasets, hence improving generalization. Our empirical evaluation on public benchmarks and a real-world medical dataset demonstrates the effectiveness of the proposed method, which substantially outperforms the current state-of-the-art FL methods for non-IID features, including PartialFed and FedBN.
    tile2tile: Learning Game Filters for Platformer Style Transfer. (arXiv:2208.07699v1 [cs.LG])
    We present tile2tile, an approach for style transfer between levels of tile-based platformer games. Our method involves training models that translate levels from a lower-resolution sketch representation based on tile affordances to the original tile representation for a given game. This enables these models, which we refer to as filters, to translate level sketches into the style of a specific game. Moreover, by converting a level of one game into sketch form and then translating the resulting sketch into the tiles of another game, we obtain a method of style transfer between two games. We use Markov random fields and autoencoders for learning the game filters and apply them to demonstrate style transfer between levels of Super Mario Bros, Kid Icarus, Mega Man and Metroid.
    Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectives. (arXiv:2208.07422v1 [cs.CV])
    Deep learning has become the method of choice to tackle real-world problems in different domains, partly because of its ability to learn from data and achieve impressive performance on a wide range of applications. However, its success usually relies on two assumptions: (i) vast troves of labeled datasets are required for accurate model fitting, and (ii) training and testing data are independent and identically distributed. Its performance on unseen target domains, thus, is not guaranteed, especially when encountering out-of-distribution data at the adaptation stage. The performance drop on data in a target domain is a critical problem in deploying deep neural networks that are successfully trained on data in a source domain. Unsupervised domain adaptation (UDA) is proposed to counter this, by leveraging both labeled source domain data and unlabeled target domain data to carry out various tasks in the target domain. UDA has yielded promising results on natural image processing, video analysis, natural language processing, time-series data analysis, medical image analysis, etc. In this review, as a rapidly evolving topic, we provide a systematic comparison of its methods and applications. In addition, the connection of UDA with its closely related tasks, e.g., domain generalization and out-of-distribution detection, has also been discussed. Furthermore, deficiencies in current methods and possible promising directions are highlighted.
    Online Learning for Non-monotone Submodular Maximization: From Full Information to Bandit Feedback. (arXiv:2208.07632v1 [cs.LG])
    In this paper, we revisit the online non-monotone continuous DR-submodular maximization problem over a down-closed convex set, which finds wide real-world applications in the domain of machine learning, economics, and operations research. At first, we present the Meta-MFW algorithm achieving a $1/e$-regret of $O(\sqrt{T})$ at the cost of $T^{3/2}$ stochastic gradient evaluations per round. As far as we know, Meta-MFW is the first algorithm to obtain $1/e$-regret of $O(\sqrt{T})$ for the online non-monotone continuous DR-submodular maximization problem over a down-closed convex set. Furthermore, in sharp contrast with ODC algorithm \citep{thang2021online}, Meta-MFW relies on the simple online linear oracle without discretization, lifting, or rounding operations. Considering the practical restrictions, we then propose the Mono-MFW algorithm, which reduces the per-function stochastic gradient evaluations from $T^{3/2}$ to 1 and achieves a $1/e$-regret bound of $O(T^{4/5})$. Next, we extend Mono-MFW to the bandit setting and propose the Bandit-MFW algorithm which attains a $1/e$-regret bound of $O(T^{8/9})$. To the best of our knowledge, Mono-MFW and Bandit-MFW are the first sublinear-regret algorithms to explore the one-shot and bandit setting for online non-monotone continuous DR-submodular maximization problem over a down-closed convex set, respectively. Finally, we conduct numerical experiments on both synthetic and real-world datasets to verify the effectiveness of our methods.
    Entity Anchored ICD Coding. (arXiv:2208.07444v1 [cs.LG])
    Medical coding is a complex task, requiring assignment of a subset of over 72,000 ICD codes to a patient's notes. Modern natural language processing approaches to these tasks have been challenged by the length of the input and size of the output space. We limit our model inputs to a small window around medical entities found in our documents. From those local contexts, we build contextualized representations of both ICD codes and entities, and aggregate over these representations to form document-level predictions. In contrast to existing methods which use a representation fixed either in size or by codes seen in training, we represent ICD codes by encoding the code description with local context. We discuss metrics appropriate to deploying coding systems in practice. We show that our approach is superior to existing methods in both standard and deployable measures, including performance on rare and unseen codes.
    Investigating and Explaining the Frequency Bias in Image Classification. (arXiv:2205.03154v2 [cs.CV] UPDATED)
    CNNs exhibit many behaviors different from humans, one of which is the capability of employing high-frequency components. This paper discusses the frequency bias phenomenon in image classification tasks: the high-frequency components are actually much less exploited than the low- and mid-frequency components. We first investigate the frequency bias phenomenon by presenting two observations on feature discrimination and learning priority. Furthermore, we hypothesize that (i) the spectral density, (ii) class consistency directly affect the frequency bias. Specifically, our investigations verify that the spectral density of datasets mainly affects the learning priority, while the class consistency mainly affects the feature discrimination.
    How can spherical CNNs benefit ML-based diffusion MRI parameter estimation?. (arXiv:2207.00572v2 [eess.IV] UPDATED)
    This paper demonstrates spherical convolutional neural networks (S-CNN) offer distinct advantages over conventional fully-connected networks (FCN) at estimating scalar parameters of tissue microstructure from diffusion MRI (dMRI). Such microstructure parameters are valuable for identifying pathology and quantifying its extent. However, current clinical practice commonly acquires dMRI data consisting of only 6 diffusion weighted images (DWIs), limiting the accuracy and precision of estimated microstructure indices. Machine learning (ML) has been proposed to address this challenge. However, existing ML-based methods are not robust to differing dMRI gradient sampling schemes, nor are they rotation equivariant. Lack of robustness to sampling schemes requires a new network to be trained for each scheme, complicating the analysis of data from multiple sources. A possible consequence of the lack of rotational equivariance is that the training dataset must contain a diverse range of microstucture orientations. Here, we show spherical CNNs represent a compelling alternative that is robust to new sampling schemes as well as offering rotational equivariance. We show the latter can be leveraged to decrease the number of training datapoints required.
    Learnable Filters for Geometric Scattering Modules. (arXiv:2208.07458v1 [cs.LG])
    We propose a new graph neural network (GNN) module, based on relaxations of recently proposed geometric scattering transforms, which consist of a cascade of graph wavelet filters. Our learnable geometric scattering (LEGS) module enables adaptive tuning of the wavelets to encourage band-pass features to emerge in learned representations. The incorporation of our LEGS-module in GNNs enables the learning of longer-range graph relations compared to many popular GNNs, which often rely on encoding graph structure via smoothness or similarity between neighbors. Further, its wavelet priors result in simplified architectures with significantly fewer learned parameters compared to competing GNNs. We demonstrate the predictive performance of LEGS-based networks on graph classification benchmarks, as well as the descriptive quality of their learned features in biochemical graph data exploration tasks. Our results show that LEGS-based networks match or outperforms popular GNNs, as well as the original geometric scattering construction, on many datasets, in particular in biochemical domains, while retaining certain mathematical properties of handcrafted (non-learned) geometric scattering.
    Making Reinforcement Learning Work on Swimmer. (arXiv:2208.07587v1 [cs.LG])
    The SWIMMER environment is a standard benchmark in reinforcement learning (RL). In particular, it is often used in papers comparing or combining RL methods with direct policy search methods such as genetic algorithms or evolution strategies. A lot of these papers report poor performance on SWIMMER from RL methods and much better performance from direct policy search methods. In this technical report we show that the low performance of RL methods on SWIMMER simply comes from the inadequate tuning of an important hyper-parameter and that, by setting this hyper-parameter to a correct value, the issue can be very easily fixed.
    Value-Consistent Representation Learning for Data-Efficient Reinforcement Learning. (arXiv:2206.12542v2 [cs.LG] UPDATED)
    Deep reinforcement learning (RL) algorithms suffer severe performance degradation when the interaction data is scarce, which limits their real-world application. Recently, visual representation learning has been shown to be effective and promising for boosting sample efficiency in RL. These methods usually rely on contrastive learning and data augmentation to train a transition model for state prediction, which is different from how the model is used in RL--performing value-based planning. Accordingly, the learned representation by these visual methods may be good for recognition but not optimal for estimating state value and solving the decision problem. To address this issue, we propose a novel method, called value-consistent representation learning (VCR), to learn representations that are directly related to decision-making. More specifically, VCR trains a model to predict the future state (also referred to as the ''imagined state'') based on the current one and a sequence of actions. Instead of aligning this imagined state with a real state returned by the environment, VCR applies a $Q$-value head on both states and obtains two distributions of action values. Then a distance is computed and minimized to force the imagined state to produce a similar action value prediction as that by the real state. We develop two implementations of the above idea for the discrete and continuous action spaces respectively. We conduct experiments on Atari 100K and DeepMind Control Suite benchmarks to validate their effectiveness for improving sample efficiency. It has been demonstrated that our methods achieve new state-of-the-art performance for search-free RL algorithms.
    Coil2Coil: Self-supervised MR image denoising using phased-array coil images. (arXiv:2208.07552v1 [eess.IV])
    Denoising of magnetic resonance images is beneficial in improving the quality of low signal-to-noise ratio images. Recently, denoising using deep neural networks has demonstrated promising results. Most of these networks, however, utilize supervised learning, which requires large training images of noise-corrupted and clean image pairs. Obtaining training images, particularly clean images, is expensive and time-consuming. Hence, methods such as Noise2Noise (N2N) that require only pairs of noise-corrupted images have been developed to reduce the burden of obtaining training datasets. In this study, we propose a new self-supervised denoising method, Coil2Coil (C2C), that does not require the acquisition of clean images or paired noise-corrupted images for training. Instead, the method utilizes multichannel data from phased-array coils to generate training images. First, it divides and combines multichannel coil images into two images, one for input and the other for label. Then, they are processed to impose noise independence and sensitivity normalization such that they can be used for the training images of N2N. For inference, the method inputs a coil-combined image (e.g., DICOM image), enabling a wide application of the method. When evaluated using synthetic noise-added images, C2C shows the best performance against several self-supervised methods, reporting comparable outcomes to supervised methods. When testing the DICOM images, C2C successfully denoised real noise without showing structure-dependent residuals in the error maps. Because of the significant advantage of not requiring additional scans for clean or paired images, the method can be easily utilized for various clinical applications.
    Self-paced learning to improve text row detection in historical documents with missing labels. (arXiv:2201.12216v3 [cs.CV] UPDATED)
    An important preliminary step of optical character recognition systems is the detection of text rows. To address this task in the context of historical data with missing labels, we propose a self-paced learning algorithm capable of improving the row detection performance. We conjecture that pages with more ground-truth bounding boxes are less likely to have missing annotations. Based on this hypothesis, we sort the training examples in descending order with respect to the number of ground-truth bounding boxes, and organize them into k batches. Using our self-paced learning method, we train a row detector over k iterations, progressively adding batches with less ground-truth annotations. At each iteration, we combine the ground-truth bounding boxes with pseudo-bounding boxes (bounding boxes predicted by the model itself) using non-maximum suppression, and we include the resulting annotations at the next training iteration. We demonstrate that our self-paced learning strategy brings significant performance gains on two data sets of historical documents, improving the average precision of YOLOv4 with more than 12% on one data set and 39% on the other.
    Reward Design For An Online Reinforcement Learning Algorithm Supporting Oral Self-Care. (arXiv:2208.07406v1 [cs.AI])
    Dental disease is one of the most common chronic diseases despite being largely preventable. However, professional advice on optimal oral hygiene practices is often forgotten or abandoned by patients. Therefore patients may benefit from timely and personalized encouragement to engage in oral self-care behaviors. In this paper, we develop an online reinforcement learning (RL) algorithm for use in optimizing the delivery of mobile-based prompts to encourage oral hygiene behaviors. One of the main challenges in developing such an algorithm is ensuring that the algorithm considers the impact of the current action on the effectiveness of future actions (i.e., delayed effects), especially when the algorithm has been made simple in order to run stably and autonomously in a constrained, real-world setting (i.e., highly noisy, sparse data). We address this challenge by designing a quality reward which maximizes the desired health outcome (i.e., high-quality brushing) while minimizing user burden. We also highlight a procedure for optimizing the hyperparameters of the reward by building a simulation environment test bed and evaluating candidates using the test bed. The RL algorithm discussed in this paper will be deployed in Oralytics, an oral self-care app that provides behavioral strategies to boost patient engagement in oral hygiene practices.
    A Review of the Convergence of 5G/6G Architecture and Deep Learning. (arXiv:2208.07643v1 [cs.LG])
    The convergence of 5G architecture and deep learning has gained a lot of research interests in both the fields of wireless communication and artificial intelligence. This is because deep learning technologies have been identified to be the potential driver of the 5G technologies, that make up the 5G architecture. Hence, there have been extensive surveys on the convergence of 5G architecture and deep learning. However, most of the existing survey papers mainly focused on how deep learning can converge with a specific 5G technology, thus, not covering the full spectrum of the 5G architecture. Although there is a recent survey paper that appears to be robust, a review of that paper shows that it is not well structured to specifically cover the convergence of deep learning and the 5G technologies. Hence, this paper provides a robust overview of the convergence of the key 5G technologies and deep learning. The challenges faced by such convergence are discussed. In addition, a brief overview of the future 6G architecture, and how it can converge with deep learning is also discussed.
    A unifying partially-interpretable framework for neural network-based extreme quantile regression. (arXiv:2208.07581v1 [stat.ML])
    Risk management in many environmental settings requires an understanding of the mechanisms that drive extreme events. Useful metrics for quantifying such risk are extreme quantiles of response variables conditioned on predictor variables that describe e.g., climate, biosphere and environmental states. Typically these quantiles lie outside the range of observable data and so, for estimation, require specification of parametric extreme value models within a regression framework. Classical approaches in this context utilise linear or additive relationships between predictor and response variables and suffer in either their predictive capabilities or computational efficiency; moreover, their simplicity is unlikely to capture the truly complex structures that lead to the creation of extreme wildfires. In this paper, we propose a new methodological framework for performing extreme quantile regression using artificial neutral networks, which are able to capture complex non-linear relationships and scale well to high-dimensional data. The "black box" nature of neural networks means that they lack the desirable trait of interpretability often favoured by practitioners; thus, we combine aspects of linear, and additive, models with deep learning to create partially interpretable neural networks that can be used for statistical inference but retain high prediction accuracy. To complement this methodology, we further propose a novel point process model for extreme values which overcomes the finite lower-endpoint problem associated with the generalised extreme value class of distributions. Efficacy of our unified framework is illustrated on U.S. wildfire data with a high-dimensional predictor set and we illustrate vast improvements in predictive performance over linear and spline-based regression techniques.
    Scalable Quantum Neural Networks for Classification. (arXiv:2208.07719v1 [quant-ph])
    Many recent machine learning tasks resort to quantum computing to improve classification accuracy and training efficiency by taking advantage of quantum mechanics, known as quantum machine learning (QML). The variational quantum circuit (VQC) is frequently utilized to build a quantum neural network (QNN), which is a counterpart to the conventional neural network. Due to hardware limitations, however, current quantum devices only allow one to use few qubits to represent data and perform simple quantum computations. The limited quantum resource on a single quantum device degrades the data usage and limits the scale of the quantum circuits, preventing quantum advantage to some extent. To alleviate this constraint, we propose an approach to implementing a scalable quantum neural network (SQNN) by utilizing the quantum resource of multiple small-size quantum devices cooperatively. In an SQNN system, several quantum devices are used as quantum feature extractors, extracting local features from an input instance in parallel, and a quantum device works as a quantum predictor, performing prediction over the local features collected through classical communication channels. The quantum feature extractors in the SQNN system are independent of each other, so one can flexibly use quantum devices of varying sizes, with larger quantum devices extracting more local features. Especially, the SQNN can be performed on a single quantum device in a modular fashion. Our work is exploratory and carried out on a quantum system simulator using the TensorFlow Quantum library. The evaluation conducts a binary classification on the MNIST dataset. It shows that the SQNN model achieves a comparable classification accuracy to a regular QNN model of the same scale. Furthermore, it demonstrates that the SQNN model with more quantum resources can significantly improve classification accuracy.
    Private Query Release via the Johnson-Lindenstrauss Transform. (arXiv:2208.07410v1 [cs.DS])
    We introduce a new method for releasing answers to statistical queries with differential privacy, based on the Johnson-Lindenstrauss lemma. The key idea is to randomly project the query answers to a lower dimensional space so that the distance between any two vectors of feasible query answers is preserved up to an additive error. Then we answer the projected queries using a simple noise-adding mechanism, and lift the answers up to the original dimension. Using this method, we give, for the first time, purely differentially private mechanisms with optimal worst case sample complexity under average error for answering a workload of $k$ queries over a universe of size $N$. As other applications, we give the first purely private efficient mechanisms with optimal sample complexity for computing the covariance of a bounded high-dimensional distribution, and for answering 2-way marginal queries. We also show that, up to the dependence on the error, a variant of our mechanism is nearly optimal for every given query workload.
    Enhancement to Training of Bidirectional GAN : An Approach to Demystify Tax Fraud. (arXiv:2208.07675v1 [cs.LG])
    Outlier detection is a challenging activity. Several machine learning techniques are proposed in the literature for outlier detection. In this article, we propose a new training approach for bidirectional GAN (BiGAN) to detect outliers. To validate the proposed approach, we train a BiGAN with the proposed training approach to detect taxpayers, who are manipulating their tax returns. For each taxpayer, we derive six correlation parameters and three ratio parameters from tax returns submitted by him/her. We train a BiGAN with the proposed training approach on this nine-dimensional derived ground-truth data set. Next, we generate the latent representation of this data set using the $encoder$ (encode this data set using the $encoder$) and regenerate this data set using the $generator$ (decode back using the $generator$) by giving this latent representation as the input. For each taxpayer, compute the cosine similarity between his/her ground-truth data and regenerated data. Taxpayers with lower cosine similarity measures are potential return manipulators. We applied our method to analyze the iron and steel taxpayers data set provided by the Commercial Taxes Department, Government of Telangana, India.
    Score-Based Diffusion meets Annealed Importance Sampling. (arXiv:2208.07698v1 [stat.ML])
    More than twenty years after its introduction, Annealed Importance Sampling (AIS) remains one of the most effective methods for marginal likelihood estimation. It relies on a sequence of distributions interpolating between a tractable initial distribution and the target distribution of interest which we simulate from approximately using a non-homogeneous Markov chain. To obtain an importance sampling estimate of the marginal likelihood, AIS introduces an extended target distribution to reweight the Markov chain proposal. While much effort has been devoted to improving the proposal distribution used by AIS, by changing the intermediate distributions and corresponding Markov kernels, an underappreciated issue is that AIS uses a convenient but suboptimal extended target distribution. This can hinder its performance. We here leverage recent progress in score-based generative modeling (SGM) to approximate the optimal extended target distribution for AIS proposals corresponding to the discretization of Langevin and Hamiltonian dynamics. We demonstrate these novel, differentiable, AIS procedures on a number of synthetic benchmark distributions and variational auto-encoders.
    LEMON: Explainable Entity Matching. (arXiv:2110.00516v2 [cs.DB] UPDATED)
    State-of-the-art entity matching (EM) methods are hard to interpret, and there is significant value in bringing explainable AI to EM. Unfortunately, most popular explainability methods do not work well out of the box for EM and need adaptation. In this paper, we identify three challenges of applying local post hoc feature attribution methods to entity matching: cross-record interaction effects, non-match explanations, and variation in sensitivity. We propose our novel model-agnostic and schema-flexible method LEMON that addresses all three challenges by (i) producing dual explanations to avoid cross-record interaction effects, (ii) introducing the novel concept of attribution potential to explain how two records could have matched, and (iii) automatically choosing explanation granularity to match the sensitivity of the matcher and record pair in question. Experiments on public datasets demonstrate that the proposed method is more faithful to the matcher and does a better job of helping users understand the decision boundary of the matcher than previous work. Furthermore, user studies show that the rate at which human subjects can construct counterfactual examples after seeing an explanation from our proposed method increases from 54% to 64% for matches and from 15% to 49% for non-matches compared to explanations from a standard adaptation of LIME.
    GFlowNet Foundations. (arXiv:2111.09266v3 [cs.LG] UPDATED)
    Generative Flow Networks (GFlowNets) have been introduced as a method to sample a diverse set of candidates in an active learning context, with a training objective that makes them approximately sample in proportion to a given reward function. In this paper, we show a number of additional theoretical properties of GFlowNets. They can be used to estimate joint probability distributions and the corresponding marginal distributions where some variables are unspecified and, of particular interest, can represent distributions over composite objects like sets and graphs. GFlowNets amortize the work typically done by computationally expensive MCMC methods in a single but trained generative pass. They could also be used to estimate partition functions and free energies, conditional probabilities of supersets (supergraphs) given a subset (subgraph), as well as marginal distributions over all supersets (supergraphs) of a given set (graph). We introduce variations enabling the estimation of entropy and mutual information, sampling from a Pareto frontier, connections to reward-maximizing policies, and extensions to stochastic environments, continuous actions and modular energy functions.
    A Deep Reinforcement Learning-based Adaptive Charging Policy for Wireless Rechargeable Sensor Networks. (arXiv:2208.07824v1 [cs.LG])
    Wireless sensor networks consist of randomly distributed sensor nodes for monitoring targets or areas of interest. Maintaining the network for continuous surveillance is a challenge due to the limited battery capacity in each sensor. Wireless power transfer technology is emerging as a reliable solution for energizing the sensors by deploying a mobile charger (MC) to recharge the sensor. However, designing an optimal charging path for the MC is challenging because of uncertainties arising in the networks. The energy consumption rate of the sensors may fluctuate significantly due to unpredictable changes in the network topology, such as node failures. These changes also lead to shifts in the importance of each sensor, which are often assumed to be the same in existing works. We address these challenges in this paper by proposing a novel adaptive charging scheme using a deep reinforcement learning (DRL) approach. Specifically, we endow the MC with a charging policy that determines the next sensor to charge conditioning on the current state of the network. We then use a deep neural network to parametrize this charging policy, which will be trained by reinforcement learning techniques. Our model can adapt to spontaneous changes in the network topology. The empirical results show that the proposed algorithm outperforms the existing on-demand algorithms by a significant margin.
    Universal Solutions of Feedforward ReLU Networks for Interpolations. (arXiv:2208.07498v1 [cs.LG])
    This paper provides a theoretical framework on the solution of feedforward ReLU networks for interpolations, in terms of what is called an interpolation matrix, which is the summary, extension and generalization of our three preceding works, with the expectation that the solution of engineering could be included in this framework and finally understood. To three-layer networks, we classify different kinds of solutions and model them in a normalized form; the solution finding is investigated by three dimensions, including data, networks and the training; the mechanism of overparameterization solutions is interpreted. To deep-layer networks, we present a general result called sparse-matrix principle, which could describe some basic behavior of deep layers and explain the phenomenon of the sparse-activation mode that appears in engineering applications associated with brain science; an advantage of deep layers compared to shallower ones is manifested in this principle. As applications, a general solution of deep neural networks for classification is constructed by that principle; and we also use the principle to study the data-disentangling property of encoders. Analogous to the three-layer case, the solution of deep layers is also explored through several dimensions. The mechanism of multi-output neural networks is explained from the perspective of interpolation matrices.
    SOLBP: Second-Order Loopy Belief Propagation for Inference in Uncertain Bayesian Networks. (arXiv:2208.07368v1 [cs.AI])
    In second-order uncertain Bayesian networks, the conditional probabilities are only known within distributions, i.e., probabilities over probabilities. The delta-method has been applied to extend exact first-order inference methods to propagate both means and variances through sum-product networks derived from Bayesian networks, thereby characterizing epistemic uncertainty, or the uncertainty in the model itself. Alternatively, second-order belief propagation has been demonstrated for polytrees but not for general directed acyclic graph structures. In this work, we extend Loopy Belief Propagation to the setting of second-order Bayesian networks, giving rise to Second-Order Loopy Belief Propagation (SOLBP). For second-order Bayesian networks, SOLBP generates inferences consistent with those generated by sum-product networks, while being more computationally efficient and scalable.
    Efficient Randomized Subspace Embeddings for Distributed Optimization under a Communication Budget. (arXiv:2103.07578v4 [cs.LG] UPDATED)
    We study first-order optimization algorithms under the constraint that the descent direction is quantized using a pre-specified budget of $R$-bits per dimension, where $R \in (0 ,\infty)$. We propose computationally efficient optimization algorithms with convergence rates matching the information-theoretic performance lower bounds for: (i) Smooth and Strongly-Convex objectives with access to an Exact Gradient oracle, as well as (ii) General Convex and Non-Smooth objectives with access to a Noisy Subgradient oracle. The crux of these algorithms is a polynomial complexity source coding scheme that embeds a vector into a random subspace before quantizing it. These embeddings are such that with high probability, their projection along any of the canonical directions of the transform space is small. As a consequence, quantizing these embeddings followed by an inverse transform to the original space yields a source coding method with optimal covering efficiency while utilizing just $R$-bits per dimension. Our algorithms guarantee optimality for arbitrary values of the bit-budget $R$, which includes both the sub-linear budget regime ($R < 1$), as well as the high-budget regime ($R \geq 1$), while requiring $O\left(n^2\right)$ multiplications, where $n$ is the dimension. We also propose an efficient relaxation of this coding scheme using Hadamard subspaces that requires a near-linear time, i.e., $O\left(n \log n\right)$ additions.Furthermore, we show that the utility of our proposed embeddings can be extended to significantly improve the performance of gradient sparsification schemes. Numerical simulations validate our theoretical claims. Our implementations are available at https://github.com/rajarshisaha95/DistOptConstrComm.
    Reliable Decision from Multiple Subtasks through Threshold Optimization: Content Moderation in the Wild. (arXiv:2208.07522v1 [cs.LG])
    Social media platforms struggle to protect users from harmful content through content moderation. These platforms have recently leveraged machine learning models to cope with the vast amount of user-generated content daily. Since moderation policies vary depending on countries and types of products, it is common to train and deploy the models per policy. However, this approach is highly inefficient, especially when the policies change, requiring dataset re-labeling and model re-training on the shifted data distribution. To alleviate this cost inefficiency, social media platforms often employ third-party content moderation services that provide prediction scores of multiple subtasks, such as predicting the existence of underage personnel, rude gestures, or weapons, instead of directly providing final moderation decisions. However, making a reliable automated moderation decision from the prediction scores of the multiple subtasks for a specific target policy has not been widely explored yet. In this study, we formulate real-world scenarios of content moderation and introduce a simple yet effective threshold optimization method that searches the optimal thresholds of the multiple subtasks to make a reliable moderation decision in a cost-effective way. Extensive experiments demonstrate that our approach shows better performance in content moderation compared to existing threshold optimization methods and heuristics.
    FedMR: Fedreated Learning via Model Recombination. (arXiv:2208.07677v1 [cs.LG])
    As a promising privacy-preserving machine learning method, Federated Learning (FL) enables global model training across clients without compromising their confidential local data. However, existing FL methods suffer from the problem of low inference performance for unevenly distributed data, since most of them rely on Federated Averaging (FedAvg)-based aggregation. By averaging model parameters in a coarse manner, FedAvg eclipses the individual characteristics of local models, which strongly limits the inference capability of FL. Worse still, in each round of FL training, FedAvg dispatches the same initial local models to clients, which can easily result in stuck-at-local-search for optimal global models. To address the above issues, this paper proposes a novel and effective FL paradigm named FedMR (Federating Model Recombination). Unlike conventional FedAvg-based methods, the cloud server of FedMR shuffles each layer of collected local models and recombines them to achieve new models for local training on clients. Due to the fine-grained model recombination and local training in each FL round, FedMR can quickly figure out one globally optimal model for all the clients. Comprehensive experimental results demonstrate that, compared with state-of-the-art FL methods, FedMR can significantly improve the inference accuracy without causing extra communication overhead.
    FALSE: Fake News Automatic and Lightweight Solution. (arXiv:2208.07686v1 [cs.LG])
    Fake news existed ever since there was news, from rumors to printed media then radio and television. Recently, the information age, with its communications and Internet breakthroughs, exacerbated the spread of fake news. Additionally, aside from e-Commerce, the current Internet economy is dependent on advertisements, views and clicks, which prompted many developers to bait the end users to click links or ads. Consequently, the wild spread of fake news through social media networks has impacted real world issues from elections to 5G adoption and the handling of the Covid- 19 pandemic. Efforts to detect and thwart fake news has been there since the advent of fake news, from fact checkers to artificial intelligence-based detectors. Solutions are still evolving as more sophisticated techniques are employed by fake news propagators. In this paper, R code have been used to study and visualize a modern fake news dataset. We use clustering, classification, correlation and various plots to analyze and present the data. The experiments show high efficiency of classifiers in telling apart real from fake news.
    Hypergraphs with Edge-Dependent Vertex Weights: p-Laplacians and Spectral Clustering. (arXiv:2208.07457v1 [cs.LG])
    We study p-Laplacians and spectral clustering for a recently proposed hypergraph model that incorporates edge-dependent vertex weights (EDVWs). These weights can reflect different importance of vertices within a hyperedge, thus conferring the hypergraph model higher expressivity and flexibility. By constructing submodular EDVWs-based splitting functions, we convert hypergraphs with EDVWs into submodular hypergraphs for which the spectral theory is better developed. In this way, existing concepts and theorems such as p-Laplacians and Cheeger inequalities proposed under the submodular hypergraph setting can be directly extended to hypergraphs with EDVWs. For submodular hypergraphs with EDVWs-based splitting functions, we propose an efficient algorithm to compute the eigenvector associated with the second smallest eigenvalue of the hypergraph 1-Laplacian. We then utilize this eigenvector to cluster the vertices, achieving higher clustering accuracy than traditional spectral clustering based on the 2-Laplacian. More broadly, the proposed algorithm works for all submodular hypergraphs that are graph reducible. Numerical experiments using real-world data demonstrate the effectiveness of combining spectral clustering based on the 1-Laplacian and EDVWs.
    Reinforcement Learning for Branch-and-Bound Optimisation using Retrospective Trajectories. (arXiv:2205.14345v2 [cs.LG] UPDATED)
    Combinatorial optimisation problems framed as mixed integer linear programmes (MILPs) are ubiquitous across a range of real-world applications. The canonical branch-and-bound algorithm seeks to exactly solve MILPs by constructing a search tree of increasingly constrained sub-problems. In practice, its solving time performance is dependent on heuristics, such as the choice of the next variable to constrain ('branching'). Recently, machine learning (ML) has emerged as a promising paradigm for branching. However, prior works have struggled to apply reinforcement learning (RL), citing sparse rewards, difficult exploration, and partial observability as significant challenges. Instead, leading ML methodologies resort to approximating high quality handcrafted heuristics with imitation learning (IL), which precludes the discovery of novel policies and requires expensive data labelling. In this work, we propose retro branching; a simple yet effective approach to RL for branching. By retrospectively deconstructing the search tree into multiple paths each contained within a sub-tree, we enable the agent to learn from shorter trajectories with more predictable next states. In experiments on four combinatorial tasks, our approach enables learning-to-branch without any expert guidance or pre-training. We outperform the current state-of-the-art RL branching algorithm by 3-5x and come within 20% of the best IL method's performance on MILPs with 500 constraints and 1000 variables, with ablations verifying that our retrospectively constructed trajectories are essential to achieving these results.
    Self-Supervised Learning for Anomalous Channel Detection in EEG Graphs: Application to Seizure Analysis. (arXiv:2208.07448v1 [cs.LG])
    Electroencephalogram (EEG) signals are effective tools towards seizure analysis where one of the most important challenges is accurate detection of seizure events and brain regions in which seizure happens or initiates. However, all existing machine learning-based algorithms for seizure analysis require access to the labeled seizure data while acquiring labeled data is very labor intensive, expensive, as well as clinicians dependent given the subjective nature of the visual qualitative interpretation of EEG signals. In this paper, we propose to detect seizure channels and clips in a self-supervised manner where no access to the seizure data is needed. The proposed method considers local structural and contextual information embedded in EEG graphs by employing positive and negative sub-graphs. We train our method through minimizing contrastive and generative losses. The employ of local EEG sub-graphs makes the algorithm an appropriate choice when accessing to the all EEG channels is impossible due to complications such as skull fractures. We conduct an extensive set of experiments on the largest seizure dataset and demonstrate that our proposed framework outperforms the state-of-the-art methods in the EEG-based seizure study. The proposed method is the only study that requires no access to the seizure data in its training phase, yet establishes a new state-of-the-art to the field, and outperforms all related supervised methods.
    Towards Better Data Augmentation using Wasserstein Distance in Variational Auto-encoder. (arXiv:2109.14795v2 [cs.LG] UPDATED)
    VAE, or variational auto-encoder, compresses data into latent attributes, and generates new data of different varieties. VAE based on KL divergence has been considered as an effective technique for data augmentation. In this paper, we propose the use of Wasserstein distance as a measure of distributional similarity for the latent attributes, and show its superior theoretical lower bound (ELBO) compared with that of KL divergence under mild conditions. Using multiple experiments, we demonstrate that the new loss function exhibits better convergence property and generates artificial images that could better aid the image classification tasks.
  • Open

    An Overview and Prospective Outlook on Robust Training and Certification of Machine Learning Models. (arXiv:2208.07464v1 [cs.LG])
    In this discussion paper, we survey recent research surrounding robustness of machine learning models. As learning algorithms become increasingly more popular in data-driven control systems, their robustness to data uncertainty must be ensured in order to maintain reliable safety-critical operations. We begin by reviewing common formalisms for such robustness, and then move on to discuss popular and state-of-the-art techniques for training robust machine learning models as well as methods for provably certifying such robustness. From this unification of robust machine learning, we identify and discuss pressing directions for future research in the area.
    Higher-order accurate two-sample network inference and network hashing. (arXiv:2208.07573v1 [stat.ME])
    Two-sample hypothesis testing for comparing two networks is an important yet difficult problem. Major challenges include: potentially different sizes and sparsity levels; non-repeated observations of adjacency matrices; computational scalability; and theoretical investigations, especially on finite-sample accuracy and minimax optimality. In this article, we propose the first provably higher-order accurate two-sample inference method by comparing network moments. Our method extends the classical two-sample t-test to the network setting. We make weak modeling assumptions and can effectively handle networks of different sizes and sparsity levels. We establish strong finite-sample theoretical guarantees, including rate-optimality properties. Our method is easy to implement and computes fast. We also devise a novel nonparametric framework of offline hashing and fast querying particularly effective for maintaining and querying very large network databases. We demonstrate the effectiveness of our method by comprehensive simulations. We apply our method to two real-world data sets and discover interesting novel structures.
    A unifying partially-interpretable framework for neural network-based extreme quantile regression. (arXiv:2208.07581v1 [stat.ML])
    Risk management in many environmental settings requires an understanding of the mechanisms that drive extreme events. Useful metrics for quantifying such risk are extreme quantiles of response variables conditioned on predictor variables that describe e.g., climate, biosphere and environmental states. Typically these quantiles lie outside the range of observable data and so, for estimation, require specification of parametric extreme value models within a regression framework. Classical approaches in this context utilise linear or additive relationships between predictor and response variables and suffer in either their predictive capabilities or computational efficiency; moreover, their simplicity is unlikely to capture the truly complex structures that lead to the creation of extreme wildfires. In this paper, we propose a new methodological framework for performing extreme quantile regression using artificial neutral networks, which are able to capture complex non-linear relationships and scale well to high-dimensional data. The "black box" nature of neural networks means that they lack the desirable trait of interpretability often favoured by practitioners; thus, we combine aspects of linear, and additive, models with deep learning to create partially interpretable neural networks that can be used for statistical inference but retain high prediction accuracy. To complement this methodology, we further propose a novel point process model for extreme values which overcomes the finite lower-endpoint problem associated with the generalised extreme value class of distributions. Efficacy of our unified framework is illustrated on U.S. wildfire data with a high-dimensional predictor set and we illustrate vast improvements in predictive performance over linear and spline-based regression techniques.
    Private Query Release via the Johnson-Lindenstrauss Transform. (arXiv:2208.07410v1 [cs.DS])
    We introduce a new method for releasing answers to statistical queries with differential privacy, based on the Johnson-Lindenstrauss lemma. The key idea is to randomly project the query answers to a lower dimensional space so that the distance between any two vectors of feasible query answers is preserved up to an additive error. Then we answer the projected queries using a simple noise-adding mechanism, and lift the answers up to the original dimension. Using this method, we give, for the first time, purely differentially private mechanisms with optimal worst case sample complexity under average error for answering a workload of $k$ queries over a universe of size $N$. As other applications, we give the first purely private efficient mechanisms with optimal sample complexity for computing the covariance of a bounded high-dimensional distribution, and for answering 2-way marginal queries. We also show that, up to the dependence on the error, a variant of our mechanism is nearly optimal for every given query workload.
    CARD: Classification and Regression Diffusion Models. (arXiv:2206.07275v2 [stat.ML] UPDATED)
    Learning the distribution of a continuous or categorical response variable $\boldsymbol y$ given its covariates $\boldsymbol x$ is a fundamental problem in statistics and machine learning. Deep neural network-based supervised learning algorithms have made great progress in predicting the mean of $\boldsymbol y$ given $\boldsymbol x$, but they are often criticized for their ability to accurately capture the uncertainty of their predictions. In this paper, we introduce classification and regression diffusion (CARD) models, which combine a denoising diffusion-based conditional generative model and a pre-trained conditional mean estimator, to accurately predict the distribution of $\boldsymbol y$ given $\boldsymbol x$. We demonstrate the outstanding ability of CARD in conditional distribution prediction with both toy examples and real-world datasets, the experimental results on which show that CARD in general outperforms state-of-the-art methods, including Bayesian neural network-based ones that are designed for uncertainty estimation, especially when the conditional distribution of $\boldsymbol y$ given $\boldsymbol x$ is multi-modal.
    Accelerating nanomaterials discovery with artificial intelligence at the HPC centers. (arXiv:2208.07612v1 [cond-mat.mtrl-sci])
    Study of properties of chemicals, drugs, biomaterials and alloys requires decades of dedicated work. Often times the outcome, however, is not what is expected for practical applications. This research procedure can be inverted by the new artificial intelligence and optimization methods. Instead of studying the properties of a material and its structurally close derivatives, the chemical and structural parameter space that contains all possible derivatives of that material can be scanned in a fast and smart way at the HPC centers. As a result of this, the particular material that has the specific physical or chemical properties can be found. Here we show how Bayesian optimization, Gaussian regression and artificial neural networks can be used towards this goal. We present an example smart search on the doped graphene quantum dot parameter space.
    GFlowNet Foundations. (arXiv:2111.09266v3 [cs.LG] UPDATED)
    Generative Flow Networks (GFlowNets) have been introduced as a method to sample a diverse set of candidates in an active learning context, with a training objective that makes them approximately sample in proportion to a given reward function. In this paper, we show a number of additional theoretical properties of GFlowNets. They can be used to estimate joint probability distributions and the corresponding marginal distributions where some variables are unspecified and, of particular interest, can represent distributions over composite objects like sets and graphs. GFlowNets amortize the work typically done by computationally expensive MCMC methods in a single but trained generative pass. They could also be used to estimate partition functions and free energies, conditional probabilities of supersets (supergraphs) given a subset (subgraph), as well as marginal distributions over all supersets (supergraphs) of a given set (graph). We introduce variations enabling the estimation of entropy and mutual information, sampling from a Pareto frontier, connections to reward-maximizing policies, and extensions to stochastic environments, continuous actions and modular energy functions.
    Model Optimization in Imbalanced Regression. (arXiv:2206.09991v2 [cs.LG] UPDATED)
    Imbalanced domain learning aims to produce accurate models in predicting instances that, though underrepresented, are of utmost importance for the domain. Research in this field has been mainly focused on classification tasks. Comparatively, the number of studies carried out in the context of regression tasks is negligible. One of the main reasons for this is the lack of loss functions capable of focusing on minimizing the errors of extreme (rare) values. Recently, an evaluation metric was introduced: Squared Error Relevance Area (SERA). This metric posits a bigger emphasis on the errors committed at extreme values while also accounting for the performance in the overall target variable domain, thus preventing severe bias. However, its effectiveness as an optimization metric is unknown. In this paper, our goal is to study the impacts of using SERA as an optimization criterion in imbalanced regression tasks. Using gradient boosting algorithms as proof of concept, we perform an experimental study with 36 data sets of different domains and sizes. Results show that models that used SERA as an objective function are practically better than the models produced by their respective standard boosting algorithms at the prediction of extreme values. This confirms that SERA can be embedded as a loss function into optimization-based learning algorithms for imbalanced regression scenarios.
    Near Optimal Adversarial Attack on UCB Bandits. (arXiv:2008.09312v2 [cs.LG] UPDATED)
    We consider a stochastic multi-arm bandit problem where rewards are subject to adversarial corruption. We propose a novel attack strategy that manipulates a UCB principle into pulling some non-optimal target arm $T - o(T)$ times with a cumulative cost that scales as $\sqrt{\log T}$, where $T$ is the number of rounds. We also prove the first lower bound on the cumulative attack cost. Our lower bound matches our upper bound up to $\log \log T$ factors, showing our attack to be near optimal.
    Estimating the Mixing Time of Ergodic Markov Chains. (arXiv:1902.01224v4 [math.ST] UPDATED)
    We address the problem of estimating the mixing time $t_{\mathsf{mix}}$ of an arbitrary ergodic finite-state Markov chain from a single trajectory of length $m$. The reversible case was addressed by Hsu et al. [2019], who left the general case as an open problem. In the reversible case, the analysis is greatly facilitated by the fact that the Markov operator is self-adjoint, and Weyl's inequality allows for a dimension-free perturbation analysis of the empirical eigenvalues. As Hsu et al. point out, in the absence of reversibility (which induces asymmetric pair probabilities matrices), the existing perturbation analysis has a worst-case exponential dependence on the number of states $d$. Furthermore, even if an eigenvalue perturbation analysis with better dependence on $d$ were available, in the non-reversible case the connection between the spectral gap and the mixing time is not nearly as straightforward as in the reversible case. Our key insight is to estimate the pseudo-spectral gap $\gamma_{\mathsf{ps}}$ instead, which allows us to overcome the loss of symmetry and to achieve a polynomial dependence on the minimal stationary probability $\pi_\star$ and $\gamma_{\mathsf{ps}}$. Additionally, in the reversible case, we obtain simultaneous nearly (up to logarithmic factors) minimax rates in $t_{\mathsf{mix}}$ and precision $\varepsilon$, closing a gap in Hsu et al., who treated $\varepsilon$ as constant in the lower bounds. Finally, we construct fully empirical confidence intervals for $\gamma_{\mathsf{ps}}$, which shrink to zero at a rate of roughly $1/\sqrt{m}$, and improve the state of the art in even the reversible case.
    Towards Certified Robustness of Distance Metric Learning. (arXiv:2006.05945v2 [stat.ML] UPDATED)
    Metric learning aims to learn a distance metric such that semantically similar instances are pulled together while dissimilar instances are pushed away. Many existing methods consider maximizing or at least constraining a distance margin in the feature space that separates similar and dissimilar pairs of instances to guarantee their generalization ability. In this paper, we advocate imposing an adversarial margin in the input space so as to improve the generalization and robustness of metric learning algorithms. We first show that, the adversarial margin, defined as the distance between training instances and their closest adversarial examples in the input space, takes account of both the distance margin in the feature space and the correlation between the metric and triplet constraints. Next, to enhance robustness to instance perturbation, we propose to enlarge the adversarial margin through minimizing a derived novel loss function termed the perturbation loss. The proposed loss can be viewed as a data-dependent regularizer and easily plugged into any existing metric learning methods. Finally, we show that the enlarged margin is beneficial to the generalization ability by using the theoretical technique of algorithmic robustness. Experimental results on 16 datasets demonstrate the superiority of the proposed method over existing state-of-the-art methods in both discrimination accuracy and robustness against possible noise.
    SOLBP: Second-Order Loopy Belief Propagation for Inference in Uncertain Bayesian Networks. (arXiv:2208.07368v1 [cs.AI])
    In second-order uncertain Bayesian networks, the conditional probabilities are only known within distributions, i.e., probabilities over probabilities. The delta-method has been applied to extend exact first-order inference methods to propagate both means and variances through sum-product networks derived from Bayesian networks, thereby characterizing epistemic uncertainty, or the uncertainty in the model itself. Alternatively, second-order belief propagation has been demonstrated for polytrees but not for general directed acyclic graph structures. In this work, we extend Loopy Belief Propagation to the setting of second-order Bayesian networks, giving rise to Second-Order Loopy Belief Propagation (SOLBP). For second-order Bayesian networks, SOLBP generates inferences consistent with those generated by sum-product networks, while being more computationally efficient and scalable.  ( 2 min )
    On Sample Complexity of Offline Reinforcement Learning with Deep ReLU Networks in Besov Spaces. (arXiv:2103.06671v4 [stat.ML] UPDATED)
    Offline reinforcement learning (RL) leverages previously collected data for policy optimization without any further active exploration. Despite the recent interest in this problem, its theoretical results in neural network function approximation setting remain limited. In this paper, we study the statistical theory of offline RL with deep ReLU network function approximation. In particular, we establish the sample complexity of $\tilde{\mathcal{O}}\left( \kappa^{1 + d/\alpha} \cdot \epsilon^{-2 - 2d/\alpha} \right)$ for offline RL with deep ReLU networks, where $\kappa$ is a measure of distributional shift, $d$ is the dimension of the state-action space, $\alpha$ is a (possibly fractional) smoothness parameter of the underlying Markov decision process (MDP), and $\epsilon$ is a user-specified error. Notably, our sample complexity holds under two novel considerations, namely the Besov dynamic closure and the correlated structure that arises from value regression for offline RL. While the Besov dynamic closure generalizes the dynamic conditions for offline RL in the prior works, the correlated structure renders the prior works of offline RL with general/neural network function approximation improper or inefficient. To the best of our knowledge, this is the first theoretical characterization of the sample complexity of offline RL with deep neural network function approximation under the general Besov regularity condition that goes beyond the traditional Reproducing Hilbert kernel spaces and Neural Tangent Kernels.  ( 3 min )
    Deletion Robust Non-Monotone Submodular Maximization over Matroids. (arXiv:2208.07582v1 [cs.DS])
    Maximizing a submodular function is a fundamental task in machine learning and in this paper we study the deletion robust version of the problem under the classic matroids constraint. Here the goal is to extract a small size summary of the dataset that contains a high value independent set even after an adversary deleted some elements. We present constant-factor approximation algorithms, whose space complexity depends on the rank $k$ of the matroid and the number $d$ of deleted elements. In the centralized setting we present a $(4.597+O(\varepsilon))$-approximation algorithm with summary size $O( \frac{k+d}{\varepsilon^2}\log \frac{k}{\varepsilon})$ that is improved to a $(3.582+O(\varepsilon))$-approximation with $O(k + \frac{d}{\varepsilon^2}\log \frac{k}{\varepsilon})$ summary size when the objective is monotone. In the streaming setting we provide a $(9.435 + O(\varepsilon))$-approximation algorithm with summary size and memory $O(k + \frac{d}{\varepsilon^2}\log \frac{k}{\varepsilon})$; the approximation factor is then improved to $(5.582+O(\varepsilon))$ in the monotone case.  ( 2 min )
    Estimating Appearance Models for Image Segmentation via Tensor Factorization. (arXiv:2208.07853v1 [cs.CV])
    Image Segmentation is one of the core tasks in Computer Vision and solving it often depends on modeling the image appearance data via the color distributions of each it its constituent regions. Whereas many segmentation algorithms handle the appearance models dependence using alternation or implicit methods, we propose here a new approach to directly estimate them from the image without prior information on the underlying segmentation. Our method uses local high order color statistics from the image as an input to tensor factorization-based estimator for latent variable models. This approach is able to estimate models in multiregion images and automatically output the regions proportions without prior user interaction, overcoming the drawbacks from a prior attempt to this problem. We also demonstrate the performance of our proposed method in many challenging synthetic and real imaging scenarios and show that it leads to an efficient segmentation algorithm.  ( 2 min )
    Neural Networks for Extreme Quantile Regression with an Application to Forecasting of Flood Risk. (arXiv:2208.07590v1 [stat.ME])
    Risk assessment for extreme events requires accurate estimation of high quantiles that go beyond the range of historical observations. When the risk depends on the values of observed predictors, regression techniques are used to interpolate in the predictor space. We propose the EQRN model that combines tools from neural networks and extreme value theory into a method capable of extrapolation in the presence of complex predictor dependence. Neural networks can naturally incorporate additional structure in the data. We develop a recurrent version of EQRN that is able to capture complex sequential dependence in time series. We apply this method to forecasting of flood risk in the Swiss Aare catchment. It exploits information from multiple covariates in space and time to provide one-day-ahead predictions of return levels and exceedances probabilities. This output complements the static return level from a traditional extreme value analysis and the predictions are able to adapt to distributional shifts as experienced in a changing climate. Our model can help authorities to manage flooding more effectively and to minimize their disastrous impacts through early warning systems.  ( 2 min )
    Training Latent Variable Models with Auto-encoding Variational Bayes: A Tutorial. (arXiv:2208.07818v1 [cs.LG])
    Auto-encoding Variational Bayes (AEVB) is a powerful and general algorithm for fitting latent variable models (a promising direction for unsupervised learning), and is well-known for training the Variational Auto-Encoder (VAE). In this tutorial, we focus on motivating AEVB from the classic Expectation Maximization (EM) algorithm, as opposed to from deterministic auto-encoders. Though natural and somewhat self-evident, the connection between EM and AEVB is not emphasized in the recent deep learning literature, and we believe that emphasizing this connection can improve the community's understanding of AEVB. In particular, we find it especially helpful to view (1) optimizing the evidence lower bound (ELBO) with respect to inference parameters as approximate E-step and (2) optimizing ELBO with respect to generative parameters as approximate M-step; doing both simultaneously as in AEVB is then simply tightening and pushing up ELBO at the same time. We discuss how approximate E-step can be interpreted as performing variational inference. Important concepts such as amortization and the reparametrization trick are discussed in great detail. Finally, we derive from scratch the AEVB training procedures of a non-deep and several deep latent variable models, including VAE, Conditional VAE, Gaussian Mixture VAE and Variational RNN. It is our hope that readers would recognize AEVB as a general algorithm that can be used to fit a wide range of latent variable models (not just VAE), and apply AEVB to such models that arise in their own fields of research. PyTorch code for all included models are publicly available.  ( 3 min )
    Score-Based Diffusion meets Annealed Importance Sampling. (arXiv:2208.07698v1 [stat.ML])
    More than twenty years after its introduction, Annealed Importance Sampling (AIS) remains one of the most effective methods for marginal likelihood estimation. It relies on a sequence of distributions interpolating between a tractable initial distribution and the target distribution of interest which we simulate from approximately using a non-homogeneous Markov chain. To obtain an importance sampling estimate of the marginal likelihood, AIS introduces an extended target distribution to reweight the Markov chain proposal. While much effort has been devoted to improving the proposal distribution used by AIS, by changing the intermediate distributions and corresponding Markov kernels, an underappreciated issue is that AIS uses a convenient but suboptimal extended target distribution. This can hinder its performance. We here leverage recent progress in score-based generative modeling (SGM) to approximate the optimal extended target distribution for AIS proposals corresponding to the discretization of Langevin and Hamiltonian dynamics. We demonstrate these novel, differentiable, AIS procedures on a number of synthetic benchmark distributions and variational auto-encoders.  ( 2 min )
    Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere. (arXiv:2005.10242v10 [cs.LG] UPDATED)
    Contrastive representation learning has been outstandingly successful in practice. In this work, we identify two key properties related to the contrastive loss: (1) alignment (closeness) of features from positive pairs, and (2) uniformity of the induced distribution of the (normalized) features on the hypersphere. We prove that, asymptotically, the contrastive loss optimizes these properties, and analyze their positive effects on downstream tasks. Empirically, we introduce an optimizable metric to quantify each property. Extensive experiments on standard vision and language datasets confirm the strong agreement between both metrics and downstream task performance. Remarkably, directly optimizing for these two metrics leads to representations with comparable or better performance at downstream tasks than contrastive learning. Project Page: https://tongzhouwang.info/hypersphere Code: https://github.com/SsnL/align_uniform , https://github.com/SsnL/moco_align_uniform  ( 3 min )
    Notes on Worst-case Inefficiency of Gradient Descent Even in R^2. (arXiv:2008.07513v2 [cs.LG] UPDATED)
    Gradient descent is a popular algorithm in optimization, and its performance in convex settings is mostly well understood. In non-convex settings, it has been shown that gradient descent is able to escape saddle points asymptotically and converge to local minimizers [Lee et. al. 2016]. Recent studies also show a perturbed version of gradient descent is enough to escape saddle points efficiently [Jin et. al. 2015, Ge et. al. 2017]. In this paper we show a negative result: gradient descent may take exponential time to escape saddle points, with non-pathological two dimensional functions. While our focus is theoretical, we also conduct experiments verifying our theoretical result. Through our analysis we demonstrate that stochasticity is essential to escape saddle points efficiently.  ( 2 min )
    $L^p$ sampling numbers for the Fourier-analytic Barron space. (arXiv:2208.07605v1 [math.FA])
    In this paper, we consider Barron functions $f : [0,1]^d \to \mathbb{R}$ of smoothness $\sigma > 0$, which are functions that can be written as \[ f(x) = \int_{\mathbb{R}^d} F(\xi) \, e^{2 \pi i \langle x, \xi \rangle} \, d \xi \quad \text{with} \quad \int_{\mathbb{R}^d} |F(\xi)| \cdot (1 + |\xi|)^{\sigma} \, d \xi < \infty. \] For $\sigma = 1$, these functions play a prominent role in machine learning, since they can be efficiently approximated by (shallow) neural networks without suffering from the curse of dimensionality. For these functions, we study the following question: Given $m$ point samples $f(x_1),\dots,f(x_m)$ of an unknown Barron function $f : [0,1]^d \to \mathbb{R}$ of smoothness $\sigma$, how well can $f$ be recovered from these samples, for an optimal choice of the sampling points and the reconstruction procedure? Denoting the optimal reconstruction error measured in $L^p$ by $s_m (\sigma; L^p)$, we show that \[ m^{- \frac{1}{\max \{ p,2 \}} - \frac{\sigma}{d}} \lesssim s_m(\sigma;L^p) \lesssim (\ln (e + m))^{\alpha(\sigma,d) / p} \cdot m^{- \frac{1}{\max \{ p,2 \}} - \frac{\sigma}{d}} , \] where the implied constants only depend on $\sigma$ and $d$ and where $\alpha(\sigma,d)$ stays bounded as $d \to \infty$.  ( 2 min )

  • Open

    Google AI Open-Sources ‘Rax’, A Python Library for LTR (Learning to Rank) in the JAX ecosystem
    submitted by /u/ai-lover [link] [comments]  ( 88 min )
    Dall-e Mini generated tour of Nashville
    submitted by /u/iTieRoomsTogether [link] [comments]  ( 89 min )
    First time trying out midjourney
    submitted by /u/OryonBlack [link] [comments]  ( 86 min )
    IBM Releases Deep Search For Scientific Discovery
    submitted by /u/pmz [link] [comments]  ( 86 min )
    Somwhere in the future
    submitted by /u/widgia [link] [comments]  ( 86 min )
    Entire AI lifecycle in one platform! You can turn data into Apps in just minutes!
    submitted by /u/SamuelSmith1416 [link] [comments]  ( 86 min )
    Using DALLE to visualise the concept of a Paperclip Maximizer
    submitted by /u/newt-s [link] [comments]  ( 86 min )
    The Latest Language Model From Meta AI, ‘Atlas,’ Has Outperformed Previous Models Like ‘Palm’ And Reached Over 42% Accuracy On Natural Questions Using Only 64 Examples
    submitted by /u/ai-lover [link] [comments]  ( 90 min )
    My first attempt at creating wallpapers for my phone: The God Emperor | Using MidJourney AI (Image Creator bot for Discord)
    submitted by /u/Potato_Player_BR [link] [comments]  ( 86 min )
    AI-Designed Graphic Tees
    submitted by /u/cityofgoul [link] [comments]  ( 87 min )
    Midjourney – AI Revolution in Art and Graphic Design
    submitted by /u/kbf_ [link] [comments]  ( 86 min )
    How do you feel when someone participates on your art on MJ
    submitted by /u/MuralPassport [link] [comments]  ( 86 min )
    What are your favourite AI/ML RSS feeds (Podcasts, Blogs, News)?
    What are your favourite RSS/Atom feeds you consume to stay up to date with all things AI/ML in terms of News sites, Podcasts, Blogs or even YouTube channels? Ideally anything that isn't already covered by sites like allainews.com. Things like: research, innovations new libraries & how to use them products/tech related to AI/ML educational stuff, how to get into AI/Data Science anything else that's related submitted by /u/ai_jobs [link] [comments]  ( 86 min )
    The Walter White Scene From the Better Call Saul Finale Upscaled to 4K 60FPS
    submitted by /u/hirep14316 [link] [comments]  ( 86 min )
  • Open

    Announcing the launch of the model copy feature for Amazon Rekognition Custom Labels
    Amazon Rekognition Custom Labels is a fully managed computer vision service that allows developers to build custom models to classify and identify objects in images that are specific and unique to your business. Rekognition Custom Labels doesn’t require you to have any prior computer vision expertise. For example, you can find your logo in social […]  ( 9 min )
    Cloud-based medical imaging reconstruction using deep neural networks
    Medical imaging techniques like computed tomography (CT), magnetic resonance imaging (MRI), medical x-ray imaging, ultrasound imaging, and others are commonly used by doctors for various reasons. Some examples include detecting changes in the appearance of organs, tissues, and vessels, and detecting abnormalities such as tumors and various other type of pathologies. Before doctors can use […]  ( 7 min )
  • Open

    [R] Transframer: Arbitrary Frame Prediction with Generative Models - DeepMind 2022 - Can generate 30 Second Videos from a single frame while also being able to do 8 different Vision tasks including depth estimation, object detection and instance segmentation.
    Paper: https://arxiv.org/abs/2203.09494 Abstract: We present a general-purpose framework for image modelling and vision tasks based on probabilistic frame prediction. Our approach unifies a broad range of tasks, from image segmentation, to novel view synthesis and video interpolation. We pair this framework with an architecture we term Transframer, which uses U-Net and Transformer components to condition on annotated context frames, and outputs sequences of sparse, compressed image features. Transframer is the state-of-the-art on a variety of video generation benchmarks, is competitive with the strongest models on few-shot view synthesis, and can generate coherent 30 second videos from a single image without any explicit geometric information. A single generalist Transframer simultaneously produces promising results on 8 tasks, including semantic segmentation, image classification and optical flow prediction with no task-specific architectural components, demonstrating that multi-task computer vision can be tackled using probabilistic image models. Our approach can in principle be applied to a wide range of applications that require learning the conditional structure of annotated image-formatted data. https://preview.redd.it/zmaxnf27k5i91.jpg?width=957&format=pjpg&auto=webp&s=e757c94a5f31cb5760919844aabca042ae810a5c https://preview.redd.it/p9p5em27k5i91.jpg?width=967&format=pjpg&auto=webp&s=05c4c547289be0cfea56a378067632645b22d8e2 https://preview.redd.it/653mvm27k5i91.jpg?width=1321&format=pjpg&auto=webp&s=7e93c66ac40d1fbb9211dad967847fc435e1c9a4 https://i.redd.it/32952t27k5i91.gif submitted by /u/Singularian2501 [link] [comments]  ( 110 min )
    Why does the CNN model accuracy vary too much when the dataset is the same? [P]
    I am training a CNN model to classify among 6 categories. My image size is not square but 14x100 pixel. Actually there are no real image but I converted some numbers (time series data) to an image array. The problem is after training when I evaluate the model, on different run it shows me different accuracy even though the dataset is same. I am using sklearn train_test_split and used random_state = 0 and 42 to keep the data same on different run. But it is showing varying accuracy from 50% to even 100%. Could someone explain what could be the reason and how to solve this? The CNN model is shown below: model_2 = tf.keras.Sequential([ tf.keras.layers.Conv2D(filters=3, kernel_size=3, strides=1, padding="same", activation="relu",input_shape=(X_train_scaled[0].shape)), tf.keras.layers.MaxPool2D(pool_size=2), tf.keras.layers.Conv2D(6,3, padding="same", activation='relu'), tf.keras.layers.MaxPool2D(pool_size=2), tf.keras.layers.Conv2D(12,3, padding="same", activation='relu'), tf.keras.layers.MaxPool2D(pool_size=2), tf.keras.layers.Flatten(), tf.keras.layers.Dense(72, activation='relu'), tf.keras.layers.Dense(36, activation='relu'), tf.keras.layers.Dense(2, activation= 'softmax') #Output layer ]) #Training the model model_2.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) model_2.fit(X_train_scaled, y_train, epochs=300) #Accuracy and loss loss, accuracy= model_2.evaluate(X_test_scaled, y_test) print(f'Loss: {loss}, Accuracy: {accuracy}') The model summary: Model Summary submitted by /u/alam1001 [link] [comments]  ( 89 min )
    [D] [R] Fine tuning a model with batch size equal to 2
    Hello, So I‘m trying to fine tune a CNN based model. But my gpu can only hold a batch size equal to 2. (I can’t use gradient checkpointing here ..) The problem is that the model contains batchnorm layers. So as I see it I has two solutions : Train as usual, this means that my statistics will be based on two images. -Make the batchnorm layer behave in training as in testing that means I will be using the mean nad variance in training as well. is this case I fear that the regularization effect will not be the same. Anyone can enlighten me on this please ? Thanks ! submitted by /u/Meddhouib10 [link] [comments]  ( 88 min )
    [D] How do contrastive loss functions avoid the issue of duplicates / near duplicates?
    Contrastive loss functions work as follows: You have a grid of (image, image) (SiMCLR), or (image, text) pairs (CLIP), and you maximize the similarity between the target pairs (2 different crops of an image in the (image, image) case), or (image, text) pairs in the (image, text) case. My question is, in the (image, image) case, how do you deal with the following issue: imagine you have 2 images that are near duplicates of each other. In this case, the contrastive loss would push those 2 images apart in embedding space, when in reality, you might want to those 2 images to be similar in embedding space. submitted by /u/vanilla-acc [link] [comments]  ( 89 min )
    [R] PAINT: Patching models with weight interpolations
    Even the best pre-trained models are not perfect. PAINT is a simple, fast and effective method to improve your model on a new downstream task, without overspecialization. Paper: https://arxiv.org/abs/2208.05592 Code: https://github.com/mlfoundations/patching Website: https://model-patching.github.io/ Abstract: Open-vocabulary models like CLIP achieve high accuracy across many image classification tasks. However, there are still settings where their zero-shot performance is far from optimal. We study model patching, where the goal is to improve accuracy on specific tasks without degrading accuracy on tasks where performance is already adequate. Towards this goal, we introduce PAINT, a patching method that uses interpolations between the weights of a model before fine-tuning and the weights after fine-tuning on a task to be patched. On nine tasks where zero-shot CLIP performs poorly, PAINT increases accuracy by 15 to 60 percentage points while preserving accuracy on ImageNet within one percentage point of the zero-shot model. PAINT also allows a single model to be patched on multiple tasks and improves with model scale. Furthermore, we identify cases of broad transfer, where patching on one task increases accuracy on other tasks even when the tasks have disjoint classes. Finally, we investigate applications beyond common benchmarks such as counting or reducing the impact of typographic attacks on CLIP. Our findings demonstrate that it is possible to expand the set of tasks on which open-vocabulary models achieve high accuracy without re-training them from scratch. ​ https://preview.redd.it/z1s6mgf0b5i91.png?width=984&format=png&auto=webp&s=b3936585bd0f14b4fa8994068718d0bdb954a963 submitted by /u/hakoka [link] [comments]  ( 89 min )
    [D] References regarding the learning behaviour of neural networks?
    I’m asked to give a talk about the black box nature of neural networks and what we know about how networks learn. One part is the standard stuff about explainable AI including SHAP values and visualising activation maps. However, I want to dig a little more into the theoretical work what we know about the inner optimisations of networks. What behaviour seems unexpected? What impresses us in a positive way? When and where do networks come to trivial solutions? One example I’m thinking of is the lottery ticket hypothesis which I want to include. The target audience is techy but not necessarily familiar with AI research. Do you have some other references that come to mind? Have you heard a similar talk elsewhere? Thank you submitted by /u/gopietz [link] [comments]  ( 88 min )
    [D] Should I ask someone before using their model for benchmarking?
    So I'm going to be publishing a paper soon and, for benchmarking our model in the paper, I've reimplemented someone else's model to compare against. Is it polite for me to ask permission of that model's creator before publishing? submitted by /u/Spentworth [link] [comments]  ( 89 min )
    [D] WACV 2023 Round-1 Paper Notification
    Discussions regarding accepted/rejected/revision for papers submitted @ WACV 2023 (Round-1). Results are supposed to be out today. submitted by /u/jarvvvis [link] [comments]  ( 88 min )
    [P] First public pretrained ViT-VQGAN released
    hi folks, 4 months ago I have announced a unofficial implementation of ViT-VQGAN at here and now I'm happily to announce that first public ViT-VQGAN (base config) weight ever is released. I hope it can be useful to you guys. Below is some images and its reconstruction: https://preview.redd.it/yebww82s04i91.png?width=1034&format=png&auto=webp&s=fbc0aaf74be06511e8028843b1a7cf153ad52a3f https://preview.redd.it/5xhyd4mj14i91.png?width=1034&format=png&auto=webp&s=8ee9be1e650bce9763ac63d959b6d0a9574a6eaa submitted by /u/ThunaClone [link] [comments]  ( 87 min )
    [P] Data pipelines for data scientists - looking for contributors for new open-source notebook
    We're currently working on a new open-source notebook to shape the future of building data pipelines. We would love for you to test out our current version in a collaborative effort to create better workflows for data scientists (and other data and machine learning professionals). Repo: https://github.com/mage-ai/mage-ai More about Mage: https://mage.ai Join our slack community: https://mage.ai/chat submitted by /u/tchungry [link] [comments]  ( 88 min )
    [D] Is UK PhD as good as US one?
    Despite equally great univs in machine learning in both UK (also include Europe) and US, the PhD duration differs a lot (UK typically 3-4 years while US typically 6). Is UK PhD less valued in the ml community than US? I do believe that there is more coursework in the initial years in US than UK, but is that the only difference. If both are equally good then why don't people opt more for UK PhDs? submitted by /u/kush2196 [link] [comments]  ( 92 min )
    [D] Why is Ordinal Regression so overlooked?
    Ordinal Regression (Ordinal Classification) is such a prevalent problem in business and academia, e.g. ranking of products/risk/health/drugs, however there is very little out there in terms of papers and libraries. The most recent and usable DL attempt I have found is the CORAL/CORN frameworks (keras, pytorch) which have just a few stars, and that's it. Is it a problem that all big companies have "solved" internally, or is it just that no one bothers and it's approximated by clipping regular regression outputs, or using regular multiclass classification? submitted by /u/koorm [link] [comments]  ( 91 min )
    [P] Deploy Detectron2 models with Triton inference server
    Detectron2 is a very popular PyTorch-based library for detection tasks. Although it has optimal implementations of many detection and segmentation models, it does not provide a good way to deploy the models to production. I share in the post below how I deployed Detectron2 models with Triton inference server (an inference system developed by NVIDIA). Hope you find it's helpful! https://tintn.github.io/deploy-detectron2-with-triton/ submitted by /u/Tin_Ng [link] [comments]  ( 87 min )
    [D] Anyone using Run AI to manage their GPU servers?
    Hi, We recently were building a simple model training as a service for GPU jobs and came across run.ai. There has been mentions of GPU virtualization but not quite sure how to use it with the existing Kubernetes setup of ours. Are there any organisations using Run AI currently? Are there any opensource alternatives for the same? submitted by /u/scb_11 [link] [comments]  ( 105 min )
    [D] What are your favourite AI/ML RSS feeds (Podcasts, Blogs, News)?
    What are your favourite RSS/Atom feeds you consume to stay up to date with all things AI/ML in terms of News sites, Podcasts, Blogs or even YouTube channels? Ideally anything that isn't already covered by sites like allainews.com. Things like: research, innovations new libraries & how to use them products/tech related to AI/ML educational stuff, how to get into AI/Data Science anything else that's related submitted by /u/ai_jobs [link] [comments]  ( 88 min )
    [P] OcrPy: A Python Library to OCR, Archive, Index and Search any documents with ease.
    Hey there, We wanted to share a library that we've been working on for a while. It's called OcrPy - a Python library for doing OCR++. At its core the library aims to unify a multitude of OCR systems (commercial & open-source) and provides a consistent and unified interface, along with a lot of additional functionalities that are usually really useful in practice - such as identifying the type of documents, the different types of layout's they come in etc and in turn processing them in different approaches. Once you parse these docs, it also lets you index and enables searching over these collections via semantic search. with that said, here are the links to the python library, documentation & tutorials. Github: https://github.com/maxent-ai/ocrpy Documentation: https://maxentlabs…  ( 90 min )
    [P] dataclass_array: Dataclasses which can be manipulated like numpy arrays (batched, reshaped, sliced,...) (for TF, Jax, Numpy,...)
    Available at: https://github.com/google-research/dataclass_array `dataclass_array` allow to have structured data that can be of arbitrary batch shape. For example: Defining a dataclass array: @dataclasses.dataclass(frozen=True) class Ray(dca.DataclassArray): pos: FloatArray['*batch_shape 3'] dir: FloatArray['*batch_shape 3'] Dataclass array can then be manipulated as if they were ndarray, while keeping the internal semantic structure rays = camera.rays() # Returns `Ray` with shape `(h, w)` rays.shape == (h, w) rays.pos.shape == (h, w, 3) # Individual ndarray fields accessible rays = rays.reshape('h w -> w h') # Native einops support rays = rays.flatten() rays = rays[..., :30] rays = rays[rays.norm() > 0] # Masking, filtering rays = rays.as_jax() # Native Jax, TF, NumPy... support For an example of dataclass_array used in practice, see: https://github.com/google-research/visu3d submitted by /u/Conchylicultor [link] [comments]  ( 88 min )
    [D] Doing similarity search for images?
    One way you can do similarity search is by training your own CLIP. But let's say you only have images, and you want to make a good similarity search algorithm, like https://same.energy/. What might you do? Is there any good model that can be trained on just images? Maybe you just use a Vision Transformer to produce embeddings? The thing is, same.energy says: Same Energy's core search uses deep learning. The most similar published work is CLIP by OpenAI. which indicates he is doing something more fancy than a vision transformer. submitted by /u/throwaway119284 [link] [comments]  ( 90 min )
  • Open

    Locating content of highest value groups in SQL
    Quite often, while skimming through SQL to prepare for interviews, one comes across the question of finding the employee with the highest or 2nd highest salary by joining a table that holds employee information with another that contains department information. This begs the question: what about finding the employee who earns the nth highest salary department-wise?… Read More »Locating content of highest value groups in SQL The post Locating content of highest value groups in SQL appeared first on Data Science Central.  ( 21 min )
    A Single Source of Truth: The 360 Customer View
    An average consumer uses various marketing channels while interacting with a brand. The numbers were calculated in Upland BlueVenn’s latest Digital Divide Report. They examined 500 marketers and 4000 consumers across the UK and US in 2021, and found that at least 20 marketing channels were used. When data comes from so many ends, the… Read More »A Single Source of Truth: The 360 Customer View The post A Single Source of Truth: The 360 Customer View appeared first on Data Science Central.  ( 20 min )
    DSC Weekly 16 August 2022: You Are Your Business
    Announcements As cybersecurity risks evolve, it’s more important than ever for organizations to be aware of emerging threats and developments. In the four-day Combating Cyber Threats & Breach Prevention 2022 summit, leading security experts will share best-in-class strategies for keeping abreast of vulnerabilities, anticipating coming threats and baking security into a wide range of enterprise operations and… Read More »DSC Weekly 16 August 2022: You Are Your Business The post DSC Weekly 16 August 2022: You Are Your Business appeared first on Data Science Central.  ( 21 min )
    Cloud Security for Healthcare Sector: All You Need to Know
    Endpoint security is vital for cloud computing. Tracking and applying security protocols across the device is an ongoing process; regular checkups, in the form of audits and penetration testing, can keep your cloud security strong. As the healthcare sector and the rest of the world continues to become increasingly reliant on digital technologies, there has… Read More »Cloud Security for Healthcare Sector: All You Need to Know The post Cloud Security for Healthcare Sector: All You Need to Know appeared first on Data Science Central.  ( 18 min )
    Blockchain Technology Optimizing Early Entrants in Education
    In its initial stages of development, Blockchain Technology is predicted to top $2 billion in three years. We’re living in an era of super-smart intelligent machines, rather, now, our future lies in being more human and less like a machine. This is how the consequences of this inevitable rise of technology are engraved in our… Read More »Blockchain Technology Optimizing Early Entrants in Education The post Blockchain Technology Optimizing Early Entrants in Education appeared first on Data Science Central.  ( 19 min )
    How to Implement a Data Privacy and Protection Strategy for Remote Teams
    In today’s digital world, data privacy and protection are increasingly important. Add in the complexity of remote teams, and you have a whole new ball game. It’s undeniable that remote work is favored by employees and now employers too, with 97% of workers. So it’s essential to implement a data privacy and protection strategy that… Read More »How to Implement a Data Privacy and Protection Strategy for Remote Teams The post How to Implement a Data Privacy and Protection Strategy for Remote Teams appeared first on Data Science Central.  ( 21 min )
  • Open

    Dump a pickle file to a readable text file
    I got a data file from a client recently in “pickle” format. I happen to know that pickle is a binary format for serializing Python objects, but trying to open a pickle file could be a puzzle if you didn’t know this. There are a couple problems with using pickle files for data transfer. First […] Dump a pickle file to a readable text file first appeared on John D. Cook.  ( 5 min )
    Duodecibels
    It’s a curious and convenient fact that many decibel values are close to integers [1]: 3 dB ≈ 2 6 dB ≈ 4 7 dB ≈ 5 9 dB ≈ 8 Is base 10 unique in this regard? If we were to look at the analogs of decibels in other bases, would we see a […] Duodecibels first appeared on John D. Cook.  ( 6 min )
  • Open

    What is the relationship between state value V and action Q function?
    https://preview.redd.it/5rj64g4zj3i91.png?width=667&format=png&auto=webp&s=4d148c9b5c57c9d050b60bd69a67f57677e5e45a submitted by /u/souhaielbensalem [link] [comments]  ( 87 min )
    Advantage clipping in PPO
    Per mini batch normalization of advantages seems to be quite common and is discussed in many papers such as Andrychowicz 2020 . However, especially with very large batch sizes, which are generally favourable for a smooth learning progress, outliers can still slip trough. My experiments showed, this can lead to very high KL-Divergence and therefore unlearning the previously learned policy. I had the idea to just clip the advantages by the 99th percentile or something semilar. But I was wondering if there are some papers already about this method, as I couldn't find some. Any thoughts, experience and papers would be much appreciated! ​ ​ At ~21k steps you can see the mean advantages are dropping. Unfortunately I did not record the max. advantages. I try to replicate this now. ​ https://preview.redd.it/kaf6q7xr53i91.png?width=344&format=png&auto=webp&s=6839d1cb73518563868328a442ac4812f9b5a589 ​ https://preview.redd.it/8jzzvntu53i91.png?width=345&format=png&auto=webp&s=5c31aee4382c8ee3cbb293a709766ff6a3e52d9d submitted by /u/flxh13 [link] [comments]  ( 87 min )
    Thompson Sampling for Contextual Bandits with Linear Payoffs- building a recommender system
    Hi guys, I am trying to implement the algorithm in paper "Thompson Sampling for Contextual Bandits with Linear Payoffs" (https://arxiv.org/pdf/1209.3352.pdf). I am just unsure of the linear algebra aspect of this paper. If you look at section 2.2, https://preview.redd.it/it9nuvyh40i91.png?width=1058&format=png&auto=webp&s=cbd2a01dd5b29f8cdec5f406e3bc7da2d01c8fe9 Here, we see that mu_tilde is sampled from a normal (Gaussian) distribution with mean mu_hat and standard deviation v^2*B(t)^-1. Just looking at matrix dimensions for simplicity, B(t) = identity[d x d] + features[d x 1] * features.T[1 x d] = [d x d] + [d x d] = [d x d] mu_hat = B(t)^-1 [d x d] * ( [d x 1] * [1 x 1]) = [d x d] * [d x 1] = [d x 1] This is all cool. Now we get to the actual algorithm, which samples mu_tilde from: Gaussian(mean=mu_hat, std=v^2*B(t)^-1) In Python implementation, I use np.random.normal(mean=mu_hat, std=v^2*B(t)^-1). Here is the problem: v^2 is a constant that is set in initialization. So, v^2*B(t)^-1 = [1x1] * [d x d] = [d x d] That means the sample from the gaussian will also be [dxd]. This means that the second part of the algo, which calculates features [1 x d] * mu_tilde [dxd] = [1xd]. From my understanding, we want this final term that goes into the argmax to be [1x1] (aka 1 number) because we want to pick the arm that maximizes this term. Given 3 [1xd] matrices, I cannot compute the argmax. Has anyone implemented this algorithm? ​ Is there a problem with my logic here? I am really struggling to implement this... can anyone help? ​ My code is here ​ https://preview.redd.it/de134mvr80i91.png?width=1786&format=png&auto=webp&s=67242107e22d281ae63bd339a9354c738389e21a submitted by /u/Prestigious-Energy26 [link] [comments]  ( 89 min )
  • Open

    Breakthrough AI + Robotic Arm + Nvidia | 3D Printed Materials Feel Movements | AI To Cure Epilepsy
    submitted by /u/kenickh [link] [comments]  ( 93 min )
  • Open

    Smart Devices, Smart Manufacturing: Pegatron Taps AI, Digital Twins
    In the fast-paced field of making the world’s tech devices, Pegatron Corp. initially harnessed AI to gain an edge. Now, it’s on the cusp of creating digital twins to further streamline its efficiency. Whether or not they’re familiar with the name, most people have probably used smartphones, tablets, Wi-Fi routers or other products that Taiwan-based Read article > The post Smart Devices, Smart Manufacturing: Pegatron Taps AI, Digital Twins appeared first on NVIDIA Blog.  ( 6 min )
    AI Shows the Way: Seoul Robotics Helps Cars Move, Park on Their Own
    Imagine driving a car — one without self-driving capabilities — to a mall, airport or parking garage, and using an app to have the car drive off to park itself. Software company Seoul Robotics is using NVIDIA technology to make this possible — turning non-autonomous cars into self-driving vehicles. Headquartered in Korea, the company’s initial Read article > The post AI Shows the Way: Seoul Robotics Helps Cars Move, Park on Their Own appeared first on NVIDIA Blog.  ( 6 min )
    Digital Art Professor Kate Parsons Inspires Next Generation of Creators This Week ‘In the NVIDIA Studio’
    Many artists can edit a video, paint a picture or build a model — but transforming one’s imagination into stunning creations can now involve breakthrough design technologies. Kate Parsons, a digital art professor at Pepperdine University and this week’s featured In the NVIDIA Studio artist, helped bring a music video for How Do I Get to Invincible to life using virtual reality and NVIDIA GeForce RTX GPUs. The post Digital Art Professor Kate Parsons Inspires Next Generation of Creators This Week ‘In the NVIDIA Studio’ appeared first on NVIDIA Blog.  ( 8 min )
  • Open

    Towards Helpful Robots: Grounding Language in Robotic Affordances
    Posted by Brian Ichter and Karol Hausman, Research Scientists, Google Research, Brain Team Over the last several years, we have seen significant progress in applying machine learning to robotics. However, robotic systems today are capable of executing only very short, hard-coded commands, such as “Pick up an apple,” because they tend to perform best with clear tasks and rewards. They struggle with learning to perform long-horizon tasks and reasoning about abstract goals, such as a user prompt like “I just worked out, can you get me a healthy snack?” Meanwhile, recent progress in training language models (LMs) has led to systems that can perform a wide range of language understanding and generation tasks with impressive results. However, these language models are inherently not grounded i…  ( 27 min )
  • Open

    Generative Art Systems*
    * an epistemological approach  ( 16 min )
  • Open

    Plasticity Neural Network Based on Astrocytic Influence at Critical Period, Synaptic Competition and Compensation by Current and Mnemonic Brain Plasticity and Synapse Formation. (arXiv:2203.11740v3 [cs.NE] UPDATED)
    The mechanism of our NN is very well in line with the results of the latest MIT brain plasticity study, in which researchers found that as a synapse strengthens, neighboring synapses automatically weaken themselves to compensate. Regarding the importance of this mechanism, Dr. Luo's team at Stanford University has put forward that competition regarding synapse formation for dendritic morphogenesis is crucial. We try to conduct research on the mechanism of failure in brain plasticity by model at the closure of critical period in details by contrasting with studies before. Cutting edge imaging and genetic tools are combined in their experimental studies, whereas our research lays more emphasis on the model, derivation and simulation of a new NN. In tests, which demonstrate that dendrite generation, to a certain extent, is curbed by synapse formation. Current and mnemonic brain plasticity as well as synaptic action range are also taken into account in the study. Furthermore, the frame of the new NN is based on current gradient informational and mnemonic negative and positive gradient informational synapse formation. The mnemonic gradient information needs to take into account the forgotten memory-astrocytic synapse formation memory persistence factor (including both negative and positive memories - i.e. the optimal gradient information so far and relatively inferior gradient information). We found that the astrocytic memory persistence factor, like the phagocytosis factor, produces the effect of reducing the local accumulation of synapses. The PNN in which only the synaptic phagocytosis effect is considered regardless of the gradients update, and whether the synaptic phagocytosis of different variables and synaptic positions is cancelled is determined by the correlation coefficient of the corresponding time interval, proves simple and effective.  ( 3 min )
    RangeUDF: Semantic Surface Reconstruction from 3D Point Clouds. (arXiv:2204.09138v1 [cs.CV] CROSS LISTED)
    We present RangeUDF, a new implicit representation based framework to recover the geometry and semantics of continuous 3D scene surfaces from point clouds. Unlike occupancy fields or signed distance fields which can only model closed 3D surfaces, our approach is not restricted to any type of topology. Being different from the existing unsigned distance fields, our framework does not suffer from any surface ambiguity. In addition, our RangeUDF can jointly estimate precise semantics for continuous surfaces. The key to our approach is a range-aware unsigned distance function together with a surface-oriented semantic segmentation module. Extensive experiments show that RangeUDF clearly surpasses state-of-the-art approaches for surface reconstruction on four point cloud datasets. Moreover, RangeUDF demonstrates superior generalization capability across multiple unseen datasets, which is nearly impossible for all existing approaches.  ( 2 min )
    Siamese neural networks for a generalized, quantitative comparison of complex model outputs. (arXiv:2208.06530v1 [cs.LG])
    Computational models are quantitative representations of systems. By analyzing and comparing the outputs of such models, it is possible to gain a better understanding of the system itself. Though, as the complexity of model outputs increases, it becomes increasingly difficult to compare simulations to each other. While it is straightforward to only compare a few specific model outputs across multiple simulations, it is more informative to be able to compare model simulations as a whole. However, it is difficult to holistically compare model simulations in an unbiased manner. To address these limitations, we use Siamese neural networks to compare model simulations as a single value, with the neural networks capturing the relationships between the model outputs. We provide an approach to training Siamese networks on model simulations and display how the trained networks can then be used to provide a holistic comparison of model outputs. This approach can be applied to a wide range of model types, providing a quantitative method of analyzing the complex outputs of computational models.  ( 2 min )
    Optimistic No-regret Algorithms for Discrete Caching. (arXiv:2208.06414v1 [cs.LG])
    We take a systematic look at the problem of storing whole files in a cache with limited capacity in the context of optimistic learning, where the caching policy has access to a prediction oracle (provided by, e.g., a Neural Network). The successive file requests are assumed to be generated by an adversary, and no assumption is made on the accuracy of the oracle. In this setting, we provide a universal lower bound for prediction-assisted online caching and proceed to design a suite of policies with a range of performance-complexity trade-offs. All proposed policies offer sublinear regret bounds commensurate with the accuracy of the oracle. Our results substantially improve upon all recently-proposed online caching policies, which, being unable to exploit the oracle predictions, offer only $O(\sqrt{T})$ regret. In this pursuit, we design, to the best of our knowledge, the first comprehensive optimistic Follow-the-Perturbed leader policy, which generalizes beyond the caching problem. We also study the problem of caching files with different sizes and the bipartite network caching problem. Finally, we evaluate the efficacy of the proposed policies through extensive numerical experiments using real-world traces.  ( 2 min )
    Unifying supervised learning and VAEs -- automating statistical inference in (astro-)particle physics with amortized conditional normalizing flows. (arXiv:2008.05825v3 [cs.LG] UPDATED)
    A KL-divergence objective of the joint distribution of data and labels allows to unify supervised learning and variational autoencoders (VAEs) under one umbrella of stochastic variational inference. The unification motivates an extended supervised scheme which allows to calculate a goodness-of-fit p-value for the neural network model. Conditional normalizing flows amortized with a neural network are crucial in this construction. We discuss how they allow to rigorously define coverage for posteriors defined jointly on a product space, e.g. $\mathbb{R}^n \times \mathcal{S}^m$, which encompasses posteriors over directions. Finally, systematic uncertainties are naturally included in the variational viewpoint. In classical likelihood approaches or other machine learning models, the ingredients of (1) systematics, (2) coverage and (3) goodness-of-fit are typically not all available or at least one of them strongly constrained. In contrast, the proposed extended supervised training with amortized normalizing flows accommodates all three of them for variational inference of arbitrary statistical distributions defined on product spaces like $\mathbb{R}^n \times \ldots \times \mathcal{S}^m$ and no fundamental barrier in terms of complexity of the underlying data. It therefore has great potential for the statistical toolbox of the contemporary (astro-)particle physicist.
    Court Judgement Labeling Using Topic Modeling and Syntactic Parsing. (arXiv:2208.04225v2 [cs.IR] UPDATED)
    In regions that practice common law, relevant historical cases are essential references for sentencing. To help legal practitioners find previous judgement easier, this paper aims to label each court judgement by some tags. These tags are legally important to summarize the judgement and can guide the user to similar judgements. We introduce a heuristic system to solve the problem, which starts from Aspect-driven Topic Modeling and uses Dependency Parsing and Constituency Parsing for phrase generation. We also construct a legal term tree for Hong Kong and implemented a sentence simplification module to support the system. Finally, we propose a similar document recommendation algorithm based on the generated tags. It enables users to find similar documents based on a few selected aspects rather than the whole passage. Experiment results show that this system is the best approach for this specific task. It is better than simple term extraction method in terms of summarizing the document, and the recommendation algorithm is more effective than full-text comparison approaches. We believe that the system has huge potential in law as well as in other areas.
    When do Models Generalize? A Perspective from Data-Algorithm Compatibility. (arXiv:2202.06054v2 [cs.LG] UPDATED)
    One of the major open problems in machine learning theory is to characterize generalization in the overparameterized regime, where most traditional generalization bounds become inconsistent. In many scenarios, their failure can be attributed to obscuring the crucial interplay between the training algorithm and the underlying data distribution. To address this shortcoming, we propose a concept named compatibility, which quantitatively characterizes generalization in a both data-relevant and algorithm-relevant manner. By considering the entire training trajectory and focusing on early-stopping iterates, compatibility fully exploits the algorithm information and therefore yields better generalization guarantees. We validate this by theoretically studying compatibility under the setting of overparameterized linear regression with gradient descent. Specifically, we perform a data-dependent trajectory analysis and derive a sufficient condition for compatibility under such a setting. Our theoretical results show that in the sense of compatibility, generalization holds with significantly weaker restrictions on the problem instance than the previous last iterate analysis.
    LM-CORE: Language Models with Contextually Relevant External Knowledge. (arXiv:2208.06458v1 [cs.CL])
    Large transformer-based pre-trained language models have achieved impressive performance on a variety of knowledge-intensive tasks and can capture factual knowledge in their parameters. We argue that storing large amounts of knowledge in the model parameters is sub-optimal given the ever-growing amounts of knowledge and resource requirements. We posit that a more efficient alternative is to provide explicit access to contextually relevant structured knowledge to the model and train it to use that knowledge. We present LM-CORE -- a general framework to achieve this -- that allows \textit{decoupling} of the language model training from the external knowledge source and allows the latter to be updated without affecting the already trained model. Experimental results show that LM-CORE, having access to external knowledge, achieves significant and robust outperformance over state-of-the-art knowledge-enhanced language models on knowledge probing tasks; can effectively handle knowledge updates; and performs well on two downstream tasks. We also present a thorough error analysis highlighting the successes and failures of LM-CORE.
    The SVD of Convolutional Weights: A CNN Interpretability Framework. (arXiv:2208.06894v1 [cs.CV])
    Deep neural networks used for image classification often use convolutional filters to extract distinguishing features before passing them to a linear classifier. Most interpretability literature focuses on providing semantic meaning to convolutional filters to explain a model's reasoning process and confirm its use of relevant information from the input domain. Fully connected layers can be studied by decomposing their weight matrices using a singular value decomposition, in effect studying the correlations between the rows in each matrix to discover the dynamics of the map. In this work we define a singular value decomposition for the weight tensor of a convolutional layer, which provides an analogous understanding of the correlations between filters, exposing the dynamics of the convolutional map. We validate our definition using recent results in random matrix theory. By applying the decomposition across the linear layers of an image classification network we suggest a framework against which interpretability methods might be applied using hypergraphs to model class separation. Rather than looking to the activations to explain the network, we use the singular vectors with the greatest corresponding singular values for each linear layer to identify those features most important to the network. We illustrate our approach with examples and introduce the DeepDataProfiler library, the analysis tool used for this study.
    Class-attention Video Transformer for Engagement Intensity Prediction. (arXiv:2208.07216v1 [cs.CV])
    In order to deal with variant-length long videos, prior works extract multi-modal features and fuse them to predict students' engagement intensity. In this paper, we present a new end-to-end method Class Attention in Video Transformer (CavT), which involves a single vector to process class embedding and to uniformly perform end-to-end learning on variant-length long videos and fixed-length short videos. Furthermore, to address the lack of sufficient samples, we propose a binary-order representatives sampling method (BorS) to add multiple video sequences of each video to augment the training set. BorS+CavT not only achieves the state-of-the-art MSE (0.0495) on the EmotiW-EP dataset, but also obtains the state-of-the-art MSE (0.0377) on the DAiSEE dataset. The code and models will be made publicly available at https://github.com/mountainai/cavt.
    RandomSCM: interpretable ensembles of sparse classifiers tailored for omics data. (arXiv:2208.06436v1 [cs.LG])
    Background: Understanding the relationship between the Omics and the phenotype is a central problem in precision medicine. The high dimensionality of metabolomics data challenges learning algorithms in terms of scalability and generalization. Most learning algorithms do not produce interpretable models -- Method: We propose an ensemble learning algorithm based on conjunctions or disjunctions of decision rules. -- Results : Applications on metabolomics data shows that it produces models that achieves high predictive performances. The interpretability of the models makes them useful for biomarker discovery and patterns discovery in high dimensional data.
    Recent Advances in Reinforcement Learning in Finance. (arXiv:2112.04553v3 [q-fin.MF] UPDATED)
    The rapid changes in the finance industry due to the increasing amount of data have revolutionized the techniques on data processing and data analysis and brought new theoretical and computational challenges. In contrast to classical stochastic control theory and other analytical approaches for solving financial decision-making problems that heavily reply on model assumptions, new developments from reinforcement learning (RL) are able to make full use of the large amount of financial data with fewer model assumptions and to improve decisions in complex financial environments. This survey paper aims to review the recent developments and use of RL approaches in finance. We give an introduction to Markov decision processes, which is the setting for many of the commonly used RL approaches. Various algorithms are then introduced with a focus on value and policy based methods that do not require any model assumptions. Connections are made with neural networks to extend the framework to encompass deep RL algorithms. Our survey concludes by discussing the application of these RL algorithms in a variety of decision-making problems in finance, including optimal execution, portfolio optimization, option pricing and hedging, market making, smart order routing, and robo-advising.
    PatchDropout: Economizing Vision Transformers Using Patch Dropout. (arXiv:2208.07220v1 [cs.CV])
    Vision transformers have demonstrated the potential to outperform CNNs in a variety of vision tasks. But the computational and memory requirements of these models prohibit their use in many applications, especially those that depend on high-resolution images, such as medical image classification. Efforts to train ViTs more efficiently are overly complicated, necessitating architectural changes or intricate training schemes. In this work, we show that standard ViT models can be efficiently trained at high resolution by randomly dropping input image patches. This simple approach, PatchDropout, reduces FLOPs and memory by at least 50% in standard natural image datasets such as ImageNet, and those savings only increase with image size. On CSAW, a high-resolution medical dataset, we observe a 5 times savings in computation and memory using PatchDropout, along with a boost in performance. For practitioners with a fixed computational or memory budget, PatchDropout makes it possible to choose image resolution, hyperparameters, or model size to get the most performance out of their model.
    Model Generalization: A Sharpness Aware Optimization Perspective. (arXiv:2208.06915v1 [cs.LG])
    Sharpness-Aware Minimization (SAM) and adaptive sharpness-aware minimization (ASAM) aim to improve the model generalization. And in this project, we proposed three experiments to valid their generalization from the sharpness aware perspective. And our experiments show that sharpness aware-based optimization techniques could help to provide models with strong generalization ability. Our experiments also show that ASAM could improve the generalization performance on un-normalized data, but further research is needed to confirm this.
    Self-supervised Contrastive Representation Learning for Semi-supervised Time-Series Classification. (arXiv:2208.06616v1 [cs.LG])
    Learning time-series representations when only unlabeled data or few labeled samples are available can be a challenging task. Recently, contrastive self-supervised learning has shown great improvement in extracting useful representations from unlabeled data via contrasting different augmented views of data. In this work, we propose a novel Time-Series representation learning framework via Temporal and Contextual Contrasting (TS-TCC) that learns representations from unlabeled data with contrastive learning. Specifically, we propose time-series specific weak and strong augmentations and use their views to learn robust temporal relations in the proposed temporal contrasting module, besides learning discriminative representations by our proposed contextual contrasting module. Additionally, we conduct a systematic study of time-series data augmentation selection, which is a key part of contrastive learning. We also extend TS-TCC to the semi-supervised learning settings and propose a Class-Aware TS-TCC (CA-TCC) that benefits from the available few labeled data to further improve representations learned by TS-TCC. Specifically, we leverage robust pseudo labels produced by TS-TCC to realize class-aware contrastive loss. Extensive experiments show that the linear evaluation of the features learned by our proposed framework performs comparably with the fully supervised training. Additionally, our framework shows high efficiency in few labeled data and transfer learning scenarios. The code is publicly available at \url{https://github.com/emadeldeen24/TS-TCC}.
    Privacy-Preserving Logistic Regression Training with A Faster Gradient Variant. (arXiv:2201.10838v2 [cs.CR] UPDATED)
    Logistic regression training over encrypted data has been an attractive idea to security concerns for years. In this paper, we propose a faster gradient variant called $\texttt{quadratic gradient}$ to implement logistic regression training in a homomorphic encryption domain, the core of which can be seen as an extension of the simplified fixed Hessian. We enhance Nesterov's accelerated gradient (NAG) and Adaptive Gradient Algorithm (Adagrad) respectively with this gradient variant and evaluate the enhanced algorithms on several datasets. Experimental results show that the enhanced methods have a state-of-the-art performance in convergence speed compared to the naive first-order gradient methods. We then adopt the enhanced NAG method to implement homomorphic logistic regression training and obtain a comparable result by only $3$ iterations.
    Pruning Self-attentions into Convolutional Layers in Single Path. (arXiv:2111.11802v3 [cs.CV] UPDATED)
    Vision Transformers (ViTs) have achieved impressive performance over various computer vision tasks. However, modelling global correlations with multi-head self-attention (MSA) layers leads to two widely recognized issues: the massive computational resource consumption and the lack of intrinsic inductive bias for modelling local visual patterns. To solve both issues, we devise a simple yet effective method named Single-Path Vision Transformer pruning (SPViT), to efficiently and automatically compress the pre-trained ViTs into compact models with proper locality added. Specifically, we first propose a novel weight-sharing scheme between MSA and convolutional operations, delivering a single-path space to encode all candidate operations. In this way, we cast the operation search problem as finding which subset of parameters to use in each MSA layer, which significantly reduces the computational cost and optimization difficulty, and the convolution kernels can be well initialized using pre-trained MSA parameters. Relying on the single-path space, we further introduce learnable binary gates to encode the operation choices, which are jointly optimized with network parameters to automatically determine the configuration of each layer. We conduct extensive experiments on two representative ViTs showing that our SPViT achieves a new SOTA for pruning on ImageNet-1k. For example, our SPViT can trim 52.0% FLOPs for DeiT-B and get an impressive 0.6% top-1 accuracy gain simultaneously. The source code is available at https://github.com/ziplab/SPViT.
    Finite Expression Method for Solving High-Dimensional Partial Differential Equations. (arXiv:2206.10121v2 [math.NA] UPDATED)
    Designing efficient and accurate numerical solvers for high-dimensional partial differential equations (PDEs) remains a challenging and important topic in computational science and engineering, mainly due to the "curse of dimensionality" in designing numerical schemes that scale in dimension. This paper introduces a new methodology that seeks an approximate PDE solution in the space of functions with finitely many analytic expressions and, hence, this methodology is named the finite expression method (FEX). It is proved in approximation theory that FEX can avoid the curse of dimensionality. As a proof of concept, a deep reinforcement learning method is proposed to implement FEX for various high-dimensional PDEs in different dimensions, achieving high and even machine accuracy with a memory complexity polynomial in dimension and an amenable time complexity. An approximate solution with finite analytic expressions also provides interpretable insights into the ground truth PDE solution, which can further help to advance the understanding of physical systems and design postprocessing techniques for a refined solution.
    Diffusion Models for Video Prediction and Infilling. (arXiv:2206.07696v2 [cs.CV] UPDATED)
    Predicting and anticipating future outcomes or reasoning about missing information in a sequence are critical skills for agents to be able to make intelligent decisions. This requires strong, temporally coherent generative capabilities. Diffusion models have shown remarkable success in several generative tasks, but have not been extensively explored in the video domain. We present Random-Mask Video Diffusion (RaMViD), which extends image diffusion models to videos using 3D convolutions, and introduces a new conditioning technique during training. By varying the mask we condition on, the model is able to perform video prediction, infilling, and upsampling. Due to our simple conditioning scheme, we can utilize the same architecture as used for unconditional training, which allows us to train the model in a conditional and unconditional fashion at the same time. We evaluate the model on two benchmark datasets for video prediction, on which we achieve state-of-the-art results, and one for video generation.
    Bounding Membership Inference. (arXiv:2202.12232v3 [cs.LG] UPDATED)
    Differential Privacy (DP) is the de facto standard for reasoning about the privacy guarantees of a training algorithm. Despite the empirical observation that DP reduces the vulnerability of models to existing membership inference (MI) attacks, a theoretical underpinning as to why this is the case is largely missing in the literature. In practice, this means that models need to be trained with DP guarantees that greatly decrease their accuracy. In this paper, we provide a tighter bound on the positive accuracy (i.e., attack precision) of any MI adversary when a training algorithm provides $\epsilon$-DP or $(\epsilon, \delta)$-DP. Our bound informs the design of a novel privacy amplification scheme, where an effective training set is sub-sampled from a larger set prior to the beginning of training, to greatly reduce the bound on MI accuracy. As a result, our scheme enables DP users to employ looser DP guarantees when training their model to limit the success of any MI adversary; this ensures that the model's accuracy is less impacted by the privacy guarantee. Finally, we discuss implications of our MI bound on the field of machine unlearning.
    The Enforced Transfer: An Instance-Based Divide-and-Conquer Unsupervised Domain Adaptation Algorithm. (arXiv:2201.10001v3 [cs.LG] UPDATED)
    Existing Domain Adaptation (DA) algorithms train target models and then use the target models to classify all samples in the target dataset. While this approach attempts to address the problem that the source and the target data are from different distributions, it fails to recognize the possibility that, within the target domain, some samples are closer to the distribution of the source domain than the distribution of the target domain. In this paper, we develop a novel DA algorithm, the Enforced Transfer, that deals with this situation. A straightforward but effective idea to deal with this dilemma is to use an out-of-distribution detection algorithm to decide if, during the testing phase, a given sample is closer to the distribution of the source domain, the target domain, or neither. In the first case, this sample is given to a machine learning classifier trained on source samples. In the second case, this sample is given to a machine learning classifier trained on target samples. In the third case, this sample is discarded as neither an ML model trained on source nor an ML model trained on target is suitable to classify it. It is widely known that the first few layers in a neural network extract low-level features, so the aforementioned approach can be extended from classifying samples in three different scenarios to classifying the samples' activations after an empirically determined layer in three different scenarios. The Enforced Transfer implements the idea. On three types of DA tasks, we outperform the state-of-the-art algorithms that we compare against.
    Prospects of federated machine learning in fluid dynamics. (arXiv:2208.07017v1 [cs.LG])
    Physics-based models have been mainstream in fluid dynamics for developing predictive models. In recent years, machine learning has offered a renaissance to the fluid community due to the rapid developments in data science, processing units, neural network based technologies, and sensor adaptations. So far in many applications in fluid dynamics, machine learning approaches have been mostly focused on a standard process that requires centralizing the training data on a designated machine or in a data center. In this letter, we present a federated machine learning approach that enables localized clients to collaboratively learn an aggregated and shared predictive model while keeping all the training data on each edge device. We demonstrate the feasibility and prospects of such decentralized learning approach with an effort to forge a deep learning surrogate model for reconstructing spatiotemporal fields. Our results indicate that federated machine learning might be a viable tool for designing highly accurate predictive decentralized digital twins relevant to fluid dynamics.
    Hide and Seek: on the Stealthiness of Attacks against Deep Learning Systems. (arXiv:2205.15944v2 [cs.CR] UPDATED)
    With the growing popularity of artificial intelligence and machine learning, a wide spectrum of attacks against deep learning models have been proposed in the literature. Both the evasion attacks and the poisoning attacks attempt to utilize adversarially altered samples to fool the victim model to misclassify the adversarial sample. While such attacks claim to be or are expected to be stealthy, i.e., imperceptible to human eyes, such claims are rarely evaluated. In this paper, we present the first large-scale study on the stealthiness of adversarial samples used in the attacks against deep learning. We have implemented 20 representative adversarial ML attacks on six popular benchmarking datasets. We evaluate the stealthiness of the attack samples using two complementary approaches: (1) a numerical study that adopts 24 metrics for image similarity or quality assessment; and (2) a user study of 3 sets of questionnaires that has collected 20,000+ annotations from 1,000+ responses. Our results show that the majority of the existing attacks introduce nonnegligible perturbations that are not stealthy to human eyes. We further analyze the factors that contribute to attack stealthiness. We further examine the correlation between the numerical analysis and the user studies, and demonstrate that some image quality metrics may provide useful guidance in attack designs, while there is still a significant gap between assessed image quality and visual stealthiness of attacks.
    Learning Physics-Consistent Particle Interactions. (arXiv:2202.00299v2 [cs.LG] UPDATED)
    Interacting particle systems play a key role in science and engineering. Access to the governing particle interaction law is fundamental for a complete understanding of such systems. However, the inherent system complexity keeps the particle interaction hidden in many cases. Machine learning methods have the potential to learn the behavior of interacting particle systems by combining experiments with data analysis methods. However, most existing algorithms focus on learning the kinetics at the particle level. Learning pairwise interaction, e.g., pairwise force or pairwise potential energy, remains an open challenge. Here, we propose an algorithm that adapts the Graph Networks framework, which contains an edge part to learn the pairwise interaction and a node part to model the dynamics at particle level. Different from existing approaches that use neural networks in both parts, we design a deterministic operator in the node part that allows to precisely infer the pairwise interactions that are consistent with underlying physical laws by only being trained to predict the particle acceleration. We test the proposed methodology on multiple datasets and demonstrate that it achieves superior performance in inferring correctly the pairwise interactions while also being consistent with the underlying physics on all the datasets. The proposed framework is scalable to larger systems and transferable to any type of particle interactions. The developed methodology can support a better understanding and discovery of the underlying particle interaction laws, and hence guide the design of materials with targeted properties.
    Deception for Cyber Defence: Challenges and Opportunities. (arXiv:2208.07127v1 [cs.CR])
    Deception is rapidly growing as an important tool for cyber defence, complementing existing perimeter security measures to rapidly detect breaches and data theft. One of the factors limiting the use of deception has been the cost of generating realistic artefacts by hand. Recent advances in Machine Learning have, however, created opportunities for scalable, automated generation of realistic deceptions. This vision paper describes the opportunities and challenges involved in developing models to mimic many common elements of the IT stack for deception effects.
    A Hybrid Model and Learning-Based Adaptive Navigation Filter. (arXiv:2207.12082v2 [eess.SY] UPDATED)
    The fusion between an inertial navigation system and global navigation satellite systems is regularly used in many platforms such as drones, land vehicles, and marine vessels. The fusion is commonly carried out in a model-based extended Kalman filter framework. One of the critical parameters of the filter is the process noise covariance. It is responsible for the real-time solution accuracy, as it considers both vehicle dynamics uncertainty and the inertial sensors quality. In most situations, the process noise is covariance assumed to be constant. Yet, due to vehicle dynamics and sensor measurement variations throughout the trajectory, the process noise covariance is subject to change. To cope with such situations, several adaptive model-based Kalman filters were suggested in the literature. In this paper, we propose a hybrid model and learning-based adaptive navigation filter. We rely on the model-based Kalman filter and design a deep neural network model to tune the momentary system noise covariance matrix, based only on the inertial sensor readings. Once the process noise covariance is learned, it is plugged into the well-established, model-based Kalman filter. After deriving the proposed hybrid framework, field experiment results using a quadrotor are presented and a comparison to model-based adaptive approaches is given. We show that the proposed method obtained an improvement of 25% in the position error. Furthermore, the proposed hybrid learning method can be used in any navigation filter and also in any relevant estimation problem.
    Defense against Backdoor Attacks via Identifying and Purifying Bad Neurons. (arXiv:2208.06537v1 [cs.LG])
    The opacity of neural networks leads their vulnerability to backdoor attacks, where hidden attention of infected neurons is triggered to override normal predictions to the attacker-chosen ones. In this paper, we propose a novel backdoor defense method to mark and purify the infected neurons in the backdoored neural networks. Specifically, we first define a new metric, called benign salience. By combining the first-order gradient to retain the connections between neurons, benign salience can identify the infected neurons with higher accuracy than the commonly used metric in backdoor defense. Then, a new Adaptive Regularization (AR) mechanism is proposed to assist in purifying these identified infected neurons via fine-tuning. Due to the ability to adapt to different magnitudes of parameters, AR can provide faster and more stable convergence than the common regularization mechanism in neuron purifying. Extensive experimental results demonstrate that our method can erase the backdoor in neural networks with negligible performance degradation.
    Z-BERT-A: a zero-shot Pipeline for Unknown Intent detection. (arXiv:2208.07084v1 [cs.CL])
    Intent discovery is a fundamental task in NLP, and it is increasingly relevant for a variety of industrial applications (Quarteroni 2018). The main challenge resides in the need to identify from input utterances novel unseen in-tents. Herein, we propose Z-BERT-A, a two-stage method for intent discovery relying on a Transformer architecture (Vaswani et al. 2017; Devlin et al. 2018), fine-tuned with Adapters (Pfeiffer et al. 2020), initially trained for Natural Language Inference (NLI), and later applied for unknown in-tent classification in a zero-shot setting. In our evaluation, we firstly analyze the quality of the model after adaptive fine-tuning on known classes. Secondly, we evaluate its performance casting intent classification as an NLI task. Lastly, we test the zero-shot performance of the model on unseen classes, showing how Z-BERT-A can effectively perform in-tent discovery by generating intents that are semantically similar, if not equal, to the ground truth ones. Our experiments show how Z-BERT-A is outperforming a wide variety of baselines in two zero-shot settings: known intents classification and unseen intent discovery. The proposed pipeline holds the potential to be widely applied in a variety of application for customer care. It enables automated dynamic triage using a lightweight model that, unlike large language models, can be easily deployed and scaled in a wide variety of business scenarios. Especially when considering a setting with limited hardware availability and performance whereon-premise or low resource cloud deployments are imperative. Z-BERT-A, predicting novel intents from a single utterance, represents an innovative approach for intent discovery, enabling online generation of novel intents. The pipeline is available as an installable python package at the following link: https://github.com/GT4SD/zberta.
    UAV-CROWD: Violent and non-violent crowd activity simulator from the perspective of UAV. (arXiv:2208.06702v1 [cs.CV])
    Unmanned Aerial Vehicle (UAV) has gained significant traction in the recent years, particularly the context of surveillance. However, video datasets that capture violent and non-violent human activity from aerial point-of-view is scarce. To address this issue, we propose a novel, baseline simulator which is capable of generating sequences of photo-realistic synthetic images of crowds engaging in various activities that can be categorized as violent or non-violent. The crowd groups are annotated with bounding boxes that are automatically computed using semantic segmentation. Our simulator is capable of generating large, randomized urban environments and is able to maintain an average of 25 frames per second on a mid-range computer with 150 concurrent crowd agents interacting with each other. We also show that when synthetic data from the proposed simulator is augmented with real world data, binary video classification accuracy is improved by 5% on average across two different models.
    Transformer-Empowered 6G Intelligent Networks: From Massive MIMO Processing to Semantic Communication. (arXiv:2205.03770v2 [cs.IT] UPDATED)
    6G wireless networks are foreseen to speed up the convergence of the physical and cyber worlds and to enable a paradigm-shift in the way we deploy and exploit communication networks. Machine learning, in particular deep learning (DL), is going to be one of the key technological enablers of 6G by offering a new paradigm for the design and optimization of networks with a high level of intelligence. In this article, we introduce an emerging DL architecture, known as the transformer, and discuss its potential impact on 6G network design. We first discuss the differences between the transformer and classical DL architectures, and emphasize the transformer's self-attention mechanism and strong representation capabilities, which make it particularly appealing in tackling various challenges in wireless network design. Specifically, we propose transformer-based solutions for massive multiple-input multiple-output (MIMO) systems and various semantic communication problems in 6G networks. Finally, we discuss key challenges and open issues in transformer-based solutions, and identify future research directions for their deployment in intelligent 6G networks.
    Theoretical Exploration of Solutions of Feedforward ReLU Networks. (arXiv:2202.01919v6 [cs.LG] UPDATED)
    This paper aims to interpret the mechanism of feedforward ReLU networks by exploring their solutions for piecewise linear functions, through the deduction from basic rules. The constructed solution should be universal enough to explain some network architectures of engineering; in order for that, several ways are provided to enhance the solution universality. Some of the consequences of our theories include: Under affine-geometry background, the solutions of both three-layer networks and deep-layer networks are given, particularly for those architectures applied in practice, such as multilayer feedforward neural networks and decoders; We give clear and intuitive interpretations of each component of network architectures; The parameter-sharing mechanism for multi-outputs is investigated; We provide an explanation of overparameterization solutions in terms of affine transforms; Under our framework, an advantage of deep layers compared to shallower ones is natural to be obtained. Some intermediate results are the basic knowledge for the modeling or understanding of neural networks, such as the classification of data embedded in higher-dimensional space, the generalization of affine transforms, the probabilistic model of matrix ranks, the concepts of distinguishable data sets and interference among hyperplanes, and so on.
    Fast & Furious: Modelling Malware Detection as Evolving Data Streams. (arXiv:2205.12311v2 [cs.CR] UPDATED)
    Malware is a major threat to computer systems and imposes many challenges to cyber security. Targeted threats, such as ransomware, cause millions of dollars in losses every year. The constant increase of malware infections has been motivating popular antiviruses (AVs) to develop dedicated detection strategies, which include meticulously crafted machine learning (ML) pipelines. However, malware developers unceasingly change their samples' features to bypass detection. This constant evolution of malware samples causes changes to the data distribution (i.e., concept drifts) that directly affect ML model detection rates, something not considered in the majority of the literature work. In this work, we evaluate the impact of concept drift on malware classifiers for two Android datasets: DREBIN (about 130K apps) and a subset of AndroZoo (about 350K apps). We used these datasets to train an Adaptive Random Forest (ARF) classifier, as well as a Stochastic Gradient Descent (SGD) classifier. We also ordered all datasets samples using their VirusTotal submission timestamp and then extracted features from their textual attributes using two algorithms (Word2Vec and TF-IDF). Then, we conducted experiments comparing both feature extractors, classifiers, as well as four drift detectors (DDM, EDDM, ADWIN, and KSWIN) to determine the best approach for real environments. Finally, we compare some possible approaches to mitigate concept drift and propose a novel data stream pipeline that updates both the classifier and the feature extractor. To do so, we conducted a longitudinal evaluation by (i) classifying malware samples collected over nine years (2009-2018), (ii) reviewing concept drift detection algorithms to attest its pervasiveness, (iii) comparing distinct ML approaches to mitigate the issue, and (iv) proposing an ML data stream pipeline that outperformed literature approaches.
    A Near-Optimal Algorithm for Univariate Zeroth-Order Budget Convex Optimization. (arXiv:2208.06720v1 [math.OC])
    This paper studies a natural generalization of the problem of minimizing a univariate convex function $f$ by querying its values sequentially. At each time-step $t$, the optimizer can invest a budget $b_t$ in a query point $X_t$ of their choice to obtain a fuzzy evaluation of $f$ at $X_t$ whose accuracy depends on the amount of budget invested in $X_t$ across times. This setting is motivated by the minimization of objectives whose values can only be determined approximately through lengthy or expensive computations. We design an any-time parameter-free algorithm called Dyadic Search, for which we prove near-optimal optimization error guarantees. As a byproduct of our analysis, we show that the classical dependence on the global Lipschitz constant in the error bounds is an artifact of the granularity of the budget. Finally, we illustrate our theoretical findings with numerical simulations.
    An Efficient and Reliable Asynchronous Federated Learning Scheme for Smart Public Transportation. (arXiv:2208.07194v1 [cs.LG])
    Machine Learning (ML) is a distributed approach for training predictive models on the Internet of Vehicles (IoV) to enable smart public transportation. Since the traffic conditions change over time, the ML model that predicts traffic flows and the time passengers wait at stops must be updated continuously and efficiently. Federated learning (FL) is a distributed machine learning scheme that allows vehicles to receive continuous model updates without having to upload raw data to the cloud and wait for models to be trained. However, FL in smart public transportation is vulnerable to poisoning or DDoS attacks since vehicles travel in public. Besides, due to device heterogeneity and imbalanced data distributions, the synchronized aggregation strategy that collects local models from specific vehicles before aggregation is inefficient. Although Asynchronous Federated Learning (AFL) schemes are developed to improve efficiency by aggregating local models as soon as they are received, the stale local models remain unreasonably weighted, resulting in poor learning performance. To enable smarter public transportation, this paper offers a blockchain-based asynchronous federated learning scheme with a dynamic scaling factor (DBAFL). Specifically, the novel committee-based consensus algorithm for blockchain improves reliability at the lowest possible cost of time. Meanwhile, the devised dynamic scaling factor allows AFL to assign reasonable weight to stale local models. Extensive experiments conducted on heterogeneous devices validate outperformed learning performance, efficiency, and reliability of DBAFL.
    GANzilla: User-Driven Direction Discovery in Generative Adversarial Networks. (arXiv:2207.08320v2 [cs.HC] UPDATED)
    Generative Adversarial Network (GAN) is widely adopted in numerous application areas, such as data preprocessing, image editing, and creativity support. However, GAN's 'black box' nature prevents non-expert users from controlling what data a model generates, spawning a plethora of prior work that focused on algorithm-driven approaches to extract editing directions to control GAN. Complementarily, we propose a GANzilla: a user-driven tool that empowers a user with the classic scatter/gather technique to iteratively discover directions to meet their editing goals. In a study with 12 participants, GANzilla users were able to discover directions that (i) edited images to match provided examples (closed-ended tasks) and that (ii) met a high-level goal, e.g., making the face happier, while showing diversity across individuals (open-ended tasks).
    Analysis of impact of emotions on target speech extraction and speech separation. (arXiv:2208.07091v1 [cs.SD])
    Recently, the performance of blind speech separation (BSS) and target speech extraction (TSE) has greatly progressed. Most works, however, focus on relatively well-controlled conditions using, e.g., read speech. The performance may degrade in more realistic situations. One of the factors causing such degradation may be intrinsic speaker variability, such as emotions, occurring commonly in realistic speech. In this paper, we investigate the influence of emotions on TSE and BSS. We create a new test dataset of emotional mixtures for the evaluation of TSE and BSS. This dataset combines LibriSpeech and Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS). Through controlled experiments, we can analyze the impact of different emotions on the performance of BSS and TSE. We observe that BSS is relatively robust to emotions, while TSE, which requires identifying and extracting the speech of a target speaker, is much more sensitive to emotions. On comparative speaker verification experiments we show that identifying the target speaker may be particularly challenging when dealing with emotional speech. Using our findings, we outline potential future directions that could improve the robustness of BSS and TSE systems toward emotional speech.
    Continuous Active Learning Using Pretrained Transformers. (arXiv:2208.06955v1 [cs.IR])
    Pre-trained and fine-tuned transformer models like BERT and T5 have improved the state of the art in ad-hoc retrieval and question-answering, but not as yet in high-recall information retrieval, where the objective is to retrieve substantially all relevant documents. We investigate whether the use of transformer-based models for reranking and/or featurization can improve the Baseline Model Implementation of the TREC Total Recall Track, which represents the current state of the art for high-recall information retrieval. We also introduce CALBERT, a model that can be used to continuously fine-tune a BERT-based model based on relevance feedback.
    Novel Ordering-based Approaches for Causal Structure Learning in the Presence of Unobserved Variables. (arXiv:2208.06935v1 [cs.LG])
    We propose ordering-based approaches for learning the maximal ancestral graph (MAG) of a structural equation model (SEM) up to its Markov equivalence class (MEC) in the presence of unobserved variables. Existing ordering-based methods in the literature recover a graph through learning a causal order (c-order). We advocate for a novel order called removable order (r-order) as they are advantageous over c-orders for structure learning. This is because r-orders are the minimizers of an appropriately defined optimization problem that could be either solved exactly (using a reinforcement learning approach) or approximately (using a hill-climbing search). Moreover, the r-orders (unlike c-orders) are invariant among all the graphs in a MEC and include c-orders as a subset. Given that set of r-orders is often significantly larger than the set of c-orders, it is easier for the optimization problem to find an r-order instead of a c-order. We evaluate the performance and the scalability of our proposed approaches on both real-world and randomly generated networks.
    Graph Neural Networks as Gradient Flows. (arXiv:2206.10991v2 [cs.LG] UPDATED)
    Dynamical systems minimizing an energy are ubiquitous in geometry and physics. We propose a novel framework for GNNs where we parametrize (and {\em learn}) an energy functional and then take the GNN equations to be the gradient flow of such energy. This approach allows to analyse the GNN evolution from a multi-particle perspective as learning attractive and repulsive forces in feature space via the positive and negative eigenvalues of a symmetric `channel-mixing' matrix. We conduct spectral analysis of the solutions and provide a better understanding of the role of the channel-mixing in (residual) graph convolutional models and of its ability to steer the diffusion away from over-smoothing. We perform thorough ablation studies corroborating our theory and show competitive performance of simple models on homophilic and heterophilic datasets.
    Inductive Biases for Object-Centric Representations in the Presence of Complex Textures. (arXiv:2204.08479v3 [cs.CV] UPDATED)
    Understanding which inductive biases could be helpful for the unsupervised learning of object-centric representations of natural scenes is challenging. In this paper, we systematically investigate the performance of two models on datasets where neural style transfer was used to obtain objects with complex textures while still retaining ground-truth annotations. We find that by using a single module to reconstruct both the shape and visual appearance of each object, the model learns more useful representations and achieves better object separation. In addition, we observe that adjusting the latent space size is insufficient to improve segmentation performance. Finally, the downstream usefulness of the representations is significantly more strongly correlated with segmentation quality than with reconstruction accuracy.
    A Novel Regularization Approach to Fair ML. (arXiv:2208.06557v1 [cs.LG])
    A number of methods have been introduced for the fair ML issue, most of them complex and many of them very specific to the underlying ML moethodology. Here we introduce a new approach that is simple, easily explained, and potentially applicable to a number of standard ML algorithms. Explicitly Deweighted Features (EDF) reduces the impact of each feature among the proxies of sensitive variables, allowing a different amount of deweighting applied to each such feature. The user specifies the deweighting hyperparameters, to achieve a given point in the Utility/Fairness tradeoff spectrum. We also introduce a new, simple criterion for evaluating the degree of protection afforded by any fair ML method.
    Feasibility Layer Aided Machine Learning Approach for Day-Ahead Operations. (arXiv:2208.06742v1 [eess.SY])
    Day-ahead operations involves a complex and computationally intensive optimization process to determine the generator commitment schedule and dispatch. The optimization process is a mixed-integer linear program (MILP) also known as security-constrained unit commitment (SCUC). Independent system operators (ISOs) run SCUC daily and require state-of-the-art algorithms to speed up the process. Existing patterns in historical information can be leveraged for model reduction of SCUC, which can provide significant time savings. In this paper, machine learning (ML) based classification approaches, namely logistic regression, neural networks, random forest and K-nearest neighbor, were studied for model reduction of SCUC. The ML was then aided with a feasibility layer (FL) and post-process technique to ensure high-quality solutions. The proposed approach is validated on several test systems namely, IEEE 24-Bus system, IEEE-73 Bus system, IEEE 118-Bus system, 500-Bus system, and Polish 2383-Bus system. Moreover, model reduction of a stochastic SCUC (SSCUC) was demonstrated utilizing a modified IEEE 24-Bus system with renewable generation. Simulation results demonstrate a high training accuracy to identify commitment schedule while FL and post-process ensure ML predictions do not lead to infeasible solutions with minimal loss in solution quality.
    A Theory for Knowledge Transfer in Continual Learning. (arXiv:2208.06931v1 [cs.LG])
    Continual learning of a stream of tasks is an active area in deep neural networks. The main challenge investigated has been the phenomenon of catastrophic forgetting or interference of newly acquired knowledge with knowledge from previous tasks. Recent work has investigated forward knowledge transfer to new tasks. Backward transfer for improving knowledge gained during previous tasks has received much less attention. There is in general limited understanding of how knowledge transfer could aid tasks learned continually. We present a theory for knowledge transfer in continual supervised learning, which considers both forward and backward transfer. We aim at understanding their impact for increasingly knowledgeable learners. We derive error bounds for each of these transfer mechanisms. These bounds are agnostic to specific implementations (e.g. deep neural networks). We demonstrate that, for a continual learner that observes related tasks, both forward and backward transfer can contribute to an increasing performance as more tasks are observed.
    Memory-Driven Text-to-Image Generation. (arXiv:2208.07022v1 [cs.CV])
    We introduce a memory-driven semi-parametric approach to text-to-image generation, which is based on both parametric and non-parametric techniques. The non-parametric component is a memory bank of image features constructed from a training set of images. The parametric component is a generative adversarial network. Given a new text description at inference time, the memory bank is used to selectively retrieve image features that are provided as basic information of target images, which enables the generator to produce realistic synthetic results. We also incorporate the content information into the discriminator, together with semantic features, allowing the discriminator to make a more reliable prediction. Experimental results demonstrate that the proposed memory-driven semi-parametric approach produces more realistic images than purely parametric approaches, in terms of both visual fidelity and text-image semantic consistency.
    Opinion Market Model: Stemming Far-Right Opinion Spread using Positive Interventions. (arXiv:2208.06620v1 [cs.SI])
    Recent years have seen the rise of extremist views in the opinion ecosystem we call social media. Allowing online extremism to persist has dire societal consequences, and efforts to mitigate it are continuously explored. Positive interventions, controlled signals that add attention to the opinion ecosystem with the aim of boosting certain opinions, are one such pathway for mitigation. This work proposes a platform to test the effectiveness of positive interventions, through the Opinion Market Model (OMM), a two-tier model of the online opinion ecosystem jointly accounting for both inter-opinion interactions and the role of positive interventions. The first tier models the size of the opinion attention market using the multivariate discrete-time Hawkes process; the second tier leverages the market share attraction model to model opinions cooperating and competing for market share given limited attention. On a synthetic dataset, we show the convergence of our proposed estimation scheme. On a dataset of Facebook and Twitter discussions containing moderate and far-right opinions about bushfires and climate change, we show superior predictive performance over the state-of-the-art and the ability to uncover latent opinion interactions. Lastly, we use OMM to demonstrate the effectiveness of mainstream media coverage as a positive intervention in suppressing far-right opinions.
    Fundamental limitations on optimization in variational quantum algorithms. (arXiv:2205.05056v2 [quant-ph] UPDATED)
    Exploring quantum applications of near-term quantum devices is a rapidly growing field of quantum information science with both theoretical and practical interests. A leading paradigm to establish such near-term quantum applications is variational quantum algorithms (VQAs). These algorithms use a classical optimizer to train a parameterized quantum circuit to accomplish certain tasks, where the circuits are usually randomly initialized. In this work, we prove that for a broad class of such random circuits, the variation range of the cost function via adjusting any local quantum gate within the circuit vanishes exponentially in the number of qubits with a high probability. This result can unify the restrictions on gradient-based and gradient-free optimizations in a natural manner and reveal extra harsh constraints on the training landscapes of VQAs. Hence a fundamental limitation on the trainability of VQAs is unraveled, indicating the essential mechanism of the optimization hardness in the Hilbert space with exponential dimension. We further showcase the validity of our results with numerical simulations of representative VQAs. We believe that these results would deepen our understanding of the scalability of VQAs and shed light on the search for near-term quantum applications with advantages.
    Towards out of distribution generalization for problems in mechanics. (arXiv:2206.14917v2 [stat.ML] UPDATED)
    There has been a massive increase in research interest towards applying data driven methods to problems in mechanics. While traditional machine learning (ML) methods have enabled many breakthroughs, they rely on the assumption that the training (observed) data and testing (unseen) data are independent and identically distributed (i.i.d). Thus, traditional ML approaches often break down when applied to real world mechanics problems with unknown test environments and data distribution shifts. In contrast, out-of-distribution (OOD) generalization assumes that the test data may shift (i.e., violate the i.i.d. assumption). To date, multiple methods have been proposed to improve the OOD generalization of ML methods. However, because of the lack of benchmark datasets for OOD regression problems, the efficiency of these OOD methods on regression problems, which dominate the mechanics field, remains unknown. To address this, we investigate the performance of OOD generalization methods for regression problems in mechanics. Specifically, we identify three OOD problems: covariate shift, mechanism shift, and sampling bias. For each problem, we create two benchmark examples that extend the Mechanical MNIST dataset collection, and we investigate the performance of popular OOD generalization methods on these mechanics-specific regression problems. Our numerical experiments show that in most cases, while the OOD generalization algorithms perform better compared to traditional ML methods on these OOD problems, there is a compelling need to develop more robust OOD generalization methods that are effective across multiple OOD scenarios. Overall, we expect that this study, as well as the associated open access benchmark datasets, will enable further development of OOD generalization methods for mechanics specific regression problems.
    Guided Evolutionary Neural Architecture Search With Efficient Performance Estimation. (arXiv:2208.06475v1 [cs.NE])
    Neural Architecture Search (NAS) methods have been successfully applied to image tasks with excellent results. However, NAS methods are often complex and tend to converge to local minima as soon as generated architectures seem to yield good results. This paper proposes GEA, a novel approach for guided NAS. GEA guides the evolution by exploring the search space by generating and evaluating several architectures in each generation at initialisation stage using a zero-proxy estimator, where only the highest-scoring architecture is trained and kept for the next generation. Subsequently, GEA continuously extracts knowledge about the search space without increased complexity by generating several off-springs from an existing architecture at each generation. More, GEA forces exploitation of the most performant architectures by descendant generation while simultaneously driving exploration through parent mutation and favouring younger architectures to the detriment of older ones. Experimental results demonstrate the effectiveness of the proposed method, and extensive ablation studies evaluate the importance of different parameters. Results show that GEA achieves state-of-the-art results on all data sets of NAS-Bench-101, NAS-Bench-201 and TransNAS-Bench-101 benchmarks.
    Predictive Data Calibration for Linear Correlation Significance Testing. (arXiv:2208.07081v1 [stat.ME])
    Inferring linear relationships lies at the heart of many empirical investigations. A measure of linear dependence should correctly evaluate the strength of the relationship as well as qualify whether it is meaningful for the population. Pearson's correlation coefficient (PCC), the \textit{de-facto} measure for bivariate relationships, is known to lack in both regards. The estimated strength $r$ maybe wrong due to limited sample size, and nonnormality of data. In the context of statistical significance testing, erroneous interpretation of a $p$-value as posterior probability leads to Type I errors -- a general issue with significance testing that extends to PCC. Such errors are exacerbated when testing multiple hypotheses simultaneously. To tackle these issues, we propose a machine-learning-based predictive data calibration method which essentially conditions the data samples on the expected linear relationship. Calculating PCC using calibrated data yields a calibrated $p$-value that can be interpreted as posterior probability together with a calibrated $r$ estimate, a desired outcome not provided by other methods. Furthermore, the ensuing independent interpretation of each test might eliminate the need for multiple testing correction. We provide empirical evidence favouring the proposed method using several simulations and application to real-world data.
    Deep Neural Networks with ReLU-Sine-Exponential Activations Break Curse of Dimensionality in Approximation on H\"older Class. (arXiv:2103.00542v6 [cs.LG] UPDATED)
    In this paper, we construct neural networks with ReLU, sine and $2^x$ as activation functions. For general continuous $f$ defined on $[0,1]^d$ with continuity modulus $\omega_f(\cdot)$, we construct ReLU-sine-$2^x$ networks that enjoy an approximation rate $\mathcal{O}(\omega_f(\sqrt{d})\cdot2^{-M}+\omega_{f}\left(\frac{\sqrt{d}}{N}\right))$, where $M,N\in \mathbb{N}^{+}$ denote the hyperparameters related to widths of the networks. As a consequence, we can construct ReLU-sine-$2^x$ network with the depth $5$ and width $\max\left\{\left\lceil2d^{3/2}\left(\frac{3\mu}{\epsilon}\right)^{1/{\alpha}}\right\rceil,2\left\lceil\log_2\frac{3\mu d^{\alpha/2}}{2\epsilon}\right\rceil+2\right\}$ that approximates $f\in \mathcal{H}_{\mu}^{\alpha}([0,1]^d)$ within a given tolerance $\epsilon >0$ measured in $L^p$ norm $p\in[1,\infty)$, where $\mathcal{H}_{\mu}^{\alpha}([0,1]^d)$ denotes the H\"older continuous function class defined on $[0,1]^d$ with order $\alpha \in (0,1]$ and constant $\mu > 0$. Therefore, the ReLU-sine-$2^x$ networks overcome the curse of dimensionality on $\mathcal{H}_{\mu}^{\alpha}([0,1]^d)$. In addition to its supper expressive power, functions implemented by ReLU-sine-$2^x$ networks are (generalized) differentiable, enabling us to apply SGD to train.
    WiFi Based Distance Estimation Using Supervised Machine Learning. (arXiv:2208.07190v1 [cs.LG])
    In recent years WiFi became the primary source of information to locate a person or device indoor. Collecting RSSI values as reference measurements with known positions, known as WiFi fingerprinting, is commonly used in various positioning methods and algorithms that appear in literature. However, measuring the spatial distance between given set of WiFi fingerprints is heavily affected by the selection of the signal distance function used to model signal space as geospatial distance. In this study, the authors proposed utilization of machine learning to improve the estimation of geospatial distance between fingerprints. This research examined data collected from 13 different open datasets to provide a broad representation aiming for general model that can be used in any indoor environment. The proposed novel approach extracted data features by examining a set of commonly used signal distance metrics via feature selection process that includes feature analysis and genetic algorithm. To demonstrate that the output of this research is venue independent, all models were tested on datasets previously excluded during the training and validation phase. Finally, various machine learning algorithms were compared using wide variety of evaluation metrics including ability to scale out the test bed to real world unsolicited datasets.
    RLang: A Declarative Language for Expression Prior Knowledge for Reinforcement Learning. (arXiv:2208.06448v1 [cs.AI])
    Communicating useful background knowledge to reinforcement learning (RL) agents is an important and effective method for accelerating learning. We introduce RLang, a domain-specific language (DSL) for communicating domain knowledge to an RL agent. Unlike other existing DSLs proposed by the RL community that ground to single elements of a decision-making formalism (e.g., the reward function or policy function), RLang can specify information about every element of a Markov decision process. We define precise syntax and grounding semantics for RLang, and provide a parser implementation that grounds RLang programs to an algorithm-agnostic partial world model and policy that can be exploited by an RL agent. We provide a series of example RLang programs, and demonstrate how different RL methods can exploit the resulting knowledge, including model-free and model-based tabular algorithms, hierarchical approaches, and deep RL algorithms (including both policy gradient and value-based methods).
    Cost-effective Framework for Gradual Domain Adaptation with Multifidelity. (arXiv:2202.04359v2 [stat.ML] UPDATED)
    In domain adaptation, when there is a large distance between the source and target domains, the prediction performance will degrade. Gradual domain adaptation is one of the solutions to such an issue, assuming that we have access to intermediate domains, which shift gradually from the source to the target domain. In previous works, it was assumed that the number of samples in the intermediate domains was sufficiently large; hence, self-training was possible without the need for labeled data. If the number of accessible intermediate domains is restricted, the distances between domains become large, and self-training will fail. Practically, the cost of samples in intermediate domains will vary, and it is natural to consider that the closer an intermediate domain is to the target domain, the higher the cost of obtaining samples from the intermediate domain is. To solve the trade-off between cost and accuracy, we propose a framework that combines multifidelity and active domain adaptation. The effectiveness of the proposed method is evaluated by experiments with real-world datasets.
    A Deep Reinforcement Learning Approach to Supply Chain Inventory Management. (arXiv:2204.09603v2 [cs.LG] UPDATED)
    This paper leverages recent developments in reinforcement learning and deep learning to solve the supply chain inventory management (SCIM) problem, a complex sequential decision-making problem consisting of determining the optimal quantity of products to produce and ship to different warehouses over a given time horizon. A mathematical formulation of the stochastic two-echelon supply chain environment is given, which allows an arbitrary number of warehouses and product types to be managed. Additionally, an open-source library that interfaces with deep reinforcement learning (DRL) algorithms is developed and made publicly available for solving the SCIM problem. Performances achieved by state-of-the-art DRL algorithms are compared through a rich set of numerical experiments on synthetically generated data. The experimental plan is designed and performed, including different structures, topologies, demands, capacities, and costs of the supply chain. Results show that the PPO algorithm adapts very well to different characteristics of the environment. The VPG algorithm almost always converges to a local maximum, even if it typically achieves an acceptable performance level. Finally, A3C is the fastest algorithm, but just like VPG, it never achieves the best performance when compared to PPO. In conclusion, numerical experiments show that DRL performs consistently better than standard reorder policies, such as the static (s, Q)-policy. Thus, it can be considered a practical and effective option for solving real-world instances of the stochastic two-echelon SCIM problem.
    DuETA: Traffic Congestion Propagation Pattern Modeling via Efficient Graph Learning for ETA Prediction at Baidu Maps. (arXiv:2208.06979v1 [cs.LG])
    Estimated time of arrival (ETA) prediction, also known as travel time estimation, is a fundamental task for a wide range of intelligent transportation applications, such as navigation, route planning, and ride-hailing services. To accurately predict the travel time of a route, it is essential to take into account both contextual and predictive factors, such as spatial-temporal interaction, driving behavior, and traffic congestion propagation inference. The ETA prediction models previously deployed at Baidu Maps have addressed the factors of spatial-temporal interaction (ConSTGAT) and driving behavior (SSML). In this work, we focus on modeling traffic congestion propagation patterns to improve ETA performance. Traffic congestion propagation pattern modeling is challenging, and it requires accounting for impact regions over time and cumulative effect of delay variations over time caused by traffic events on the road network. In this paper, we present a practical industrial-grade ETA prediction framework named DuETA. Specifically, we construct a congestion-sensitive graph based on the correlations of traffic patterns, and we develop a route-aware graph transformer to directly learn the long-distance correlations of the road segments. This design enables DuETA to capture the interactions between the road segment pairs that are spatially distant but highly correlated with traffic conditions. Extensive experiments are conducted on large-scale, real-world datasets collected from Baidu Maps. Experimental results show that ETA prediction can significantly benefit from the learned traffic congestion propagation patterns. In addition, DuETA has already been deployed in production at Baidu Maps, serving billions of requests every day. This demonstrates that DuETA is an industrial-grade and robust solution for large-scale ETA prediction services.
    MM-GNN: Mix-Moment Graph Neural Network towards Modeling Neighborhood Feature Distribution. (arXiv:2208.07012v1 [cs.LG])
    Graph Neural Networks (GNNs) have shown expressive performance on graph representation learning by aggregating information from neighbors. Recently, some studies have discussed the importance of modeling neighborhood distribution on the graph. However, most existing GNNs aggregate neighbors' features through single statistic (e.g., mean, max, sum), which loses the information related to neighbor's feature distribution and therefore degrades the model performance. In this paper, inspired by the method of moment in statistical theory, we propose to model neighbor's feature distribution with multi-order moments. We design a novel GNN model, namely Mix-Moment Graph Neural Network (MM-GNN), which includes a Multi-order Moment Embedding (MME) module and an Element-wise Attention-based Moment Adaptor module. MM-GNN first calculates the multi-order moments of the neighbors for each node as signatures, and then use an Element-wise Attention-based Moment Adaptor to assign larger weights to important moments for each node and update node representations. We conduct extensive experiments on 15 real-world graphs (including social networks, citation networks and web-page networks etc.) to evaluate our model, and the results demonstrate the superiority of MM-GNN over existing state-of-the-art models.
    Learning Controllable 3D Level Generators. (arXiv:2206.13623v3 [cs.AI] UPDATED)
    Procedural Content Generation via Reinforcement Learning (PCGRL) foregoes the need for large human-authored data-sets and allows agents to train explicitly on functional constraints, using computable, user-defined measures of quality instead of target output. We explore the application of PCGRL to 3D domains, in which content-generation tasks naturally have greater complexity and potential pertinence to real-world applications. Here, we introduce several PCGRL tasks for the 3D domain, Minecraft (Mojang Studios, 2009). These tasks will challenge RL-based generators using affordances often found in 3D environments, such as jumping, multiple dimensional movement, and gravity. We train an agent to optimize each of these tasks to explore the capabilities of previous research in PCGRL. This agent is able to generate relatively complex and diverse levels, and generalize to random initial states and control targets. Controllability tests in the presented tasks demonstrate their utility to analyze success and failure for 3D generators.
    Overcoming Oversmoothness in Graph Convolutional Networks via Hybrid Scattering Networks. (arXiv:2201.08932v2 [stat.ML] UPDATED)
    Geometric deep learning has made great strides towards generalizing the design of structure-aware neural networks from traditional domains to non-Euclidean ones, giving rise to graph neural networks (GNN) that can be applied to graph-structured data arising in, e.g., social networks, biochemistry, and material science. Graph convolutional networks (GCNs) in particular, inspired by their Euclidean counterparts, have been successful in processing graph data by extracting structure-aware features. However, current GNN models are often constrained by various phenomena that limit their expressive power and ability to generalize to more complex graph datasets. Most models essentially rely on low-pass filtering of graph signals via local averaging operations, leading to oversmoothing. Moreover, to avoid severe oversmoothing, most popular GCN-style networks tend to be shallow, with narrow receptive fields, leading to underreaching. Here, we propose a hybrid GNN framework that combines traditional GCN filters with band-pass filters defined via geometric scattering. We further introduce an attention framework that allows the model to locally attend over combined information from different filters at the node level. Our theoretical results establish the complementary benefits of the scattering filters to leverage structural information from the graph, while our experiments show the benefits of our method on various learning tasks.
    Evaluating Dense Passage Retrieval using Transformers. (arXiv:2208.06959v1 [cs.IR])
    Although representational retrieval models based on Transformers have been able to make major advances in the past few years, and despite the widely accepted conventions and best-practices for testing such models, a $\textit{standardized}$ evaluation framework for testing them has not been developed. In this work, we formalize the best practices and conventions followed by researchers in the literature, paving the path for more standardized evaluations - and therefore more fair comparisons between the models. Our framework (1) embeds the documents and queries; (2) for each query-document pair, computes the relevance score based on the dot product of the document and query embedding; (3) uses the $\texttt{dev}$ set of the MSMARCO dataset to evaluate the models; (4) uses the $\texttt{trec_eval}$ script to calculate MRR@100, which is the primary metric used to evaluate the models. Most importantly, we showcase the use of this framework by experimenting on some of the most well-known dense retrieval models.
    Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models. (arXiv:2208.06677v1 [cs.LG])
    Adaptive gradient algorithms borrow the moving average idea of heavy ball acceleration to estimate accurate first- and second-order moments of gradient for accelerating convergence. However, Nesterov acceleration which converges faster than heavy ball acceleration in theory and also in many empirical cases is much less investigated under the adaptive gradient setting. In this work, we propose the ADAptive Nesterov momentum algorithm, Adan for short, to effectively speedup the training of deep neural networks. Adan first reformulates the vanilla Nesterov acceleration to develop a new Nesterov momentum estimation (NME) method, which avoids the extra computation and memory overhead of computing gradient at the extrapolation point. Then Adan adopts NME to estimate the first- and second-order moments of the gradient in adaptive gradient algorithms for convergence acceleration. Besides, we prove that Adan finds an $\epsilon$-approximate first-order stationary point within $O(\epsilon^{-3.5})$ stochastic gradient complexity on the nonconvex stochastic problems (e.g., deep learning problems), matching the best-known lower bound. Extensive experimental results show that Adan surpasses the corresponding SoTA optimizers on both vision transformers (ViTs) and CNNs, and sets new SoTAs for many popular networks, e.g., ResNet, ConvNext, ViT, Swin, MAE, LSTM, Transformer-XL, and BERT. More surprisingly, Adan can use half of the training cost (epochs) of SoTA optimizers to achieve higher or comparable performance on ViT and ResNet, e.t.c., and also shows great tolerance to a large range of minibatch size, e.g., from 1k to 32k. We hope Adan can contribute to the development of deep learning by reducing training cost and relieving engineering burden of trying different optimizers on various architectures. Code will be released at https://github.com/sail-sg/Adan.
    Cloud-Based Real-Time Molecular Screening Platform with MolFormer. (arXiv:2208.06665v1 [cs.LG])
    With the prospect of automating a number of chemical tasks with high fidelity, chemical language processing models are emerging at a rapid speed. Here, we present a cloud-based real-time platform that allows users to virtually screen molecules of interest. For this purpose, molecular embeddings inferred from a recently proposed large chemical language model, named MolFormer, are leveraged. The platform currently supports three tasks: nearest neighbor retrieval, chemical space visualization, and property prediction. Based on the functionalities of this platform and results obtained, we believe that such a platform can play a pivotal role in automating chemistry and chemical engineering research, as well as assist in drug discovery and material design tasks. A demo of our platform is provided at \url{www.ibm.biz/molecular_demo}.
    Applying Regularized Schr\"odinger-Bridge-Based Stochastic Process in Generative Modeling. (arXiv:2208.07131v1 [cs.LG])
    Compared to the existing function-based models in deep generative modeling, the recently proposed diffusion models have achieved outstanding performance with a stochastic-process-based approach. But a long sampling time is required for this approach due to many timesteps for discretization. Schr\"odinger bridge (SB)-based models attempt to tackle this problem by training bidirectional stochastic processes between distributions. However, they still have a slow sampling speed compared to generative models such as generative adversarial networks. And due to the training of the bidirectional stochastic processes, they require a relatively long training time. Therefore, this study tried to reduce the number of timesteps and training time required and proposed regularization terms to the existing SB models to make the bidirectional stochastic processes consistent and stable with a reduced number of timesteps. Each regularization term was integrated into a single term to enable more efficient training in computation time and memory usage. Applying this regularized stochastic process to various generation tasks, the desired translations between different distributions were obtained, and accordingly, the possibility of generative modeling based on a stochastic process with faster sampling speed could be confirmed. The code is available at https://github.com/KiUngSong/RSB.
    Grasping Core Rules of Time Series through Pure Models. (arXiv:2208.07105v1 [cs.LG])
    Time series underwent the transition from statistics to deep learning, as did many other machine learning fields. Although it appears that the accuracy has been increasing as the model is updated in a number of publicly available datasets, it typically only increases the scale by several times in exchange for a slight difference in accuracy. Through this experiment, we point out a different line of thinking, time series, especially long-term forecasting, may differ from other fields. It is not necessary to use extensive and complex models to grasp all aspects of time series, but to use pure models to grasp the core rules of time series changes. With this simple but effective idea, we created PureTS, a network with three pure linear layers that achieved state-of-the-art in 80% of the long sequence prediction tasks while being nearly the lightest model and having the fastest running speed. On this basis, we discuss the potential of pure linear layers in both phenomena and essence. The ability to understand the core law contributes to the high precision of long-distance prediction, and reasonable fluctuation prevents it from distorting the curve in multi-step prediction like mainstream deep learning models, which is summarized as a pure linear neural network that avoids over-fluctuating. Finally, we suggest the fundamental design standards for lightweight long-step time series tasks: input and output should try to have the same dimension, and the structure avoids fragmentation and complex operations.
    Double Auctions with Two-sided Bandit Feedback. (arXiv:2208.06536v1 [cs.LG])
    Double Auction enables decentralized transfer of goods between multiple buyers and sellers, thus underpinning functioning of many online marketplaces. Buyers and sellers compete in these markets through bidding, but do not often know their own valuation a-priori. As the allocation and pricing happens through bids, the profitability of participants, hence sustainability of such markets, depends crucially on learning respective valuations through repeated interactions. We initiate the study of Double Auction markets under bandit feedback on both buyers' and sellers' side. We show with confidence bound based bidding, and `Average Pricing' there is an efficient price discovery among the participants. In particular, the buyers and sellers exchanging goods attain $O(\sqrt{T})$ regret in $T$ rounds. The buyers and sellers who do not benefit from exchange in turn only experience $O(\log{T}/ \Delta)$ regret in $T$ rounds where $\Delta$ is the minimum price gap. We augment our upper bound by showing that even with a known fixed price of the good -- a simpler learning problem than Double Auction -- $\omega(\sqrt{T})$ regret is unattainable in certain markets.
    Teacher Guided Training: An Efficient Framework for Knowledge Transfer. (arXiv:2208.06825v1 [cs.LG])
    The remarkable performance gains realized by large pretrained models, e.g., GPT-3, hinge on the massive amounts of data they are exposed to during training. Analogously, distilling such large models to compact models for efficient deployment also necessitates a large amount of (labeled or unlabeled) training data. In this paper, we propose the teacher-guided training (TGT) framework for training a high-quality compact model that leverages the knowledge acquired by pretrained generative models, while obviating the need to go through a large volume of data. TGT exploits the fact that the teacher has acquired a good representation of the underlying data domain, which typically corresponds to a much lower dimensional manifold than the input space. Furthermore, we can use the teacher to explore input space more efficiently through sampling or gradient-based methods; thus, making TGT especially attractive for limited data or long-tail settings. We formally capture this benefit of proposed data-domain exploration in our generalization bounds. We find that TGT can improve accuracy on several image classification benchmarks as well as a range of text classification and retrieval tasks.
    Overcoming the Long Horizon Barrier for Sample-Efficient Reinforcement Learning with Latent Low-Rank Structure. (arXiv:2206.03569v2 [cs.LG] UPDATED)
    The practicality of reinforcement learning algorithms has been limited due to poor scaling with respect to the problem size, as the sample complexity of learning an $\epsilon$-optimal policy is $\Tilde{\Omega}\left(|S||A|H^3 / \eps^2\right)$ over worst case instances of an MDP with state space $S$, action space $A$, and horizon $H$. We consider a class of MDPs that exhibit low rank structure, where the latent features are unknown. We argue that a natural combination of value iteration and low-rank matrix estimation results in an estimation error that grows doubly exponentially in the horizon $H$. We then provide a new algorithm along with statistical guarantees that efficiently exploits low rank structure given access to a generative model, achieving a sample complexity of $\Tilde{O}\left(d^5(|S|+|A|)\mathrm{poly}(H)/\eps^2\right)$ for a rank $d$ setting, which is minimax optimal with respect to the scaling of $|S|, |A|$, and $\eps$. In contrast to literature on linear and low-rank MDPs, we do not require a known feature mapping, our algorithm is computationally simple, and our results hold for long time horizons. Our results provide insights on the minimal low-rank structural assumptions required on the MDP with respect to the transition kernel versus the optimal action-value function.
    Robust Contrastive Active Learning with Feature-guided Query Strategies. (arXiv:2109.06873v2 [cs.LG] UPDATED)
    We introduce supervised contrastive active learning (SCAL) and propose efficient query strategies in active learning based on the feature similarity (featuresim) and principal component analysis based feature-reconstruction error (fre) to select informative data samples with diverse feature representations. We demonstrate our proposed method achieves state-of-the-art accuracy, model calibration and reduces sampling bias in an active learning setup for balanced and imbalanced datasets on image classification tasks. We also evaluate robustness of model to distributional shift derived from different query strategies in active learning setting. Using extensive experiments, we show that our proposed approach outperforms high performing compute-intensive methods by a big margin resulting in 9.9% lower mean corruption error, 7.2% lower expected calibration error under dataset shift and 8.9% higher AUROC for out-of-distribution detection.
    Expert Aggregation for Financial Forecasting. (arXiv:2111.15365v3 [q-fin.ST] UPDATED)
    Machine learning algorithms dedicated to financial time series forecasting have gained a lot of interest over the last few years. One difficulty lies in the choice between several algorithms, as their estimation accuracy may be unstable over time. Aggregation combines a finite set of forecasting models, called experts, without making assumptions about the models and dynamically adapts to market conditions. We apply expert aggregation to the construction of long-short strategies, built from the individual stock return forecasts. The online mixture outperforms individual algorithms in terms of both portfolio performance and stability. Extensions to both expert and aggregation specializations are also proposed and improve the overall mixture on portfolio evaluation metrics.
    Lifelong Neural Predictive Coding: Learning Cumulatively Online without Forgetting. (arXiv:1905.10696v4 [cs.LG] UPDATED)
    In lifelong learning systems based on artificial neural networks, one of the biggest obstacles is the inability to retain old knowledge as new information is encountered. This phenomenon is known as catastrophic forgetting. In this paper, we propose a new kind of connectionist architecture, the Sequential Neural Coding Network, that is robust to forgetting when learning from streams of data points and, unlike networks of today, does not learn via the popular back-propagation of errors. Grounded in the neurocognitive theory of predictive processing, our model adapts synapses in a biologically-plausible fashion while another neural system learns to direct and control this cortex-like structure, mimicking some of the task-executive control functionality of the basal ganglia. In our experiments, we demonstrate that our self-organizing system experiences significantly less forgetting compared to standard neural models, outperforming a swath of previously proposed methods, including rehearsal/data buffer-based methods, on both standard (SplitMNIST, Split Fashion MNIST, etc.) and custom benchmarks even though it is trained in a stream-like fashion. Our work offers evidence that emulating mechanisms in real neuronal systems, e.g., local learning, lateral competition, can yield new directions and possibilities for tackling the grand challenge of lifelong machine learning.
    Towards Interpretable Sleep Stage Classification Using Cross-Modal Transformers. (arXiv:2208.06991v1 [cs.LG])
    Accurate sleep stage classification is significant for sleep health assessment. In recent years, several deep learning and machine learning based sleep staging algorithms have been developed and they have achieved performance on par with human annotation. Despite improved performance, a limitation of most deep-learning based algorithms is their Black-box behavior, which which have limited their use in clinical settings. Here, we propose Cross-Modal Transformers, which is a transformer-based method for sleep stage classification. Our models achieve both competitive performance with the state-of-the-art approaches and eliminates the Black-box behavior of deep-learning models by utilizing the interpretability aspect of the attention modules. The proposed cross-modal transformers consist of a novel cross-modal transformer encoder architecture along with a multi-scale 1-dimensional convolutional neural network for automatic representation learning. Our sleep stage classifier based on this design was able to achieve sleep stage classification performance on par with or better than the state-of-the-art approaches, along with interpretability, a fourfold reduction in the number of parameters and a reduced training time compared to the current state-of-the-art. Our code is available at https://github.com/Jathurshan0330/Cross-Modal-Transformer.
    Near Real-Time Social Distance Estimation in London. (arXiv:2012.07751v4 [cs.CY] UPDATED)
    During the COVID-19 pandemic, policy makers at the Greater London Authority, the regional governance body of London, UK, are reliant upon prompt and accurate data sources. Large well-defined heterogeneous compositions of activity throughout the city are sometimes difficult to acquire, yet are a necessity in order to learn 'busyness' and consequently make safe policy decisions. One component of our project within this space is to utilise existing infrastructure to estimate social distancing adherence by the general public. Our method enables near immediate sampling and contextualisation of activity and physical distancing on the streets of London via live traffic camera feeds. We introduce a framework for inspecting and improving upon existing methods, whilst also describing its active deployment on over 900 real-time feeds.
    A Unified Causal View of Domain Invariant Representation Learning. (arXiv:2208.06987v1 [stat.ML])
    Machine learning methods can be unreliable when deployed in domains that differ from the domains on which they were trained. To address this, we may wish to learn representations of data that are domain-invariant in the sense that we preserve data structure that is stable across domains, but throw out spuriously-varying parts. There are many representation-learning approaches of this type, including methods based on data augmentation, distributional invariances, and risk invariance. Unfortunately, when faced with any particular real-world domain shift, it is unclear which, if any, of these methods might be expected to work. The purpose of this paper is to show how the different methods relate to each other, and clarify the real-world circumstances under which each is expected to succeed. The key tool is a new notion of domain shift relying on the idea that causal relationships are invariant, but non-causal relationships (e.g., due to confounding) may vary.
    Generalization Bounds for Gradient Methods via Discrete and Continuous Prior. (arXiv:2205.13799v3 [cs.LG] UPDATED)
    Proving algorithm-dependent generalization error bounds for gradient-type optimization methods has attracted significant attention recently in learning theory. However, most existing trajectory-based analyses require either restrictive assumptions on the learning rate (e.g., fast decreasing learning rate), or continuous injected noise (such as the Gaussian noise in Langevin dynamics). In this paper, we introduce a new discrete data-dependent prior to the PAC-Bayesian framework, and prove a high probability generalization bound of order $O(\frac{1}{n}\cdot \sum_{t=1}^T(\gamma_t/\varepsilon_t)^2\left\|{\mathbf{g}_t}\right\|^2)$ for Floored GD (i.e. a version of gradient descent with precision level $\varepsilon_t$), where $n$ is the number of training samples, $\gamma_t$ is the learning rate at step $t$, $\mathbf{g}_t$ is roughly the difference of the gradient computed using all samples and that using only prior samples. $\left\|{\mathbf{g}_t}\right\|$ is upper bounded by and and typical much smaller than the gradient norm $\left\|{\nabla f(W_t)}\right\|$. We remark that our bound holds for nonconvex and nonsmooth scenarios. Moreover, our theoretical results provide numerically favorable upper bounds of testing errors (e.g., $0.037$ on MNIST). Using a similar technique, we can also obtain new generalization bounds for certain variants of SGD. Furthermore, we study the generalization bounds for gradient Langevin Dynamics (GLD). Using the same framework with a carefully constructed continuous prior, we show a new high probability generalization bound of order $O(\frac{1}{n} + \frac{L^2}{n^2}\sum_{t=1}^T(\gamma_t/\sigma_t)^2)$ for GLD. The new $1/n^2$ rate is due to the concentration of the difference between the gradient of training samples and that of the prior.
    Revocable Deep Reinforcement Learning with Affinity Regularization for Outlier-Robust Graph Matching. (arXiv:2012.08950v4 [cs.CV] UPDATED)
    Graph matching (GM) has been a building block in many areas including computer vision and pattern recognition. Despite the recent impressive progress, existing deep GM methods often have difficulty in handling outliers in both graphs, which are ubiquitous in practice. We propose a deep reinforcement learning (RL) based approach RGM for weighted graph matching, whose sequential node matching scheme naturally fits with the strategy for selective inlier matching against outliers. A revocable action scheme is devised to improve the agent's flexibility against the complex constrained matching task. Moreover, we propose a quadratic approximation technique to regularize the affinity matrix, in the presence of outliers. As such, the RL agent can finish inlier matching timely when the objective score stops growing, for which otherwise an additional hyperparameter i.e. the number of common inliers is needed to avoid matching outliers. In this paper, we focus on learning the back-end solver for the most general form of GM: the Lawler's QAP, whose input is the affinity matrix. Our approach can also boost other solvers using the affinity input. Experimental results on both synthetic and real-world datasets showcase its superior performance regarding both matching accuracy and robustness.
    Virgo: Scalable Unsupervised Classification of Cosmological Shock Waves. (arXiv:2208.06859v1 [astro-ph.IM])
    Cosmological shock waves are essential to understanding the formation of cosmological structures. To study them, scientists run computationally expensive high-resolution 3D hydrodynamic simulations. Interpreting the simulation results is challenging because the resulting data sets are enormous, and the shock wave surfaces are hard to separate and classify due to their complex morphologies and multiple shock fronts intersecting. We introduce a novel pipeline, Virgo, combining physical motivation, scalability, and probabilistic robustness to tackle this unsolved unsupervised classification problem. To this end, we employ kernel principal component analysis with low-rank matrix approximations to denoise data sets of shocked particles and create labeled subsets. We perform supervised classification to recover full data resolution with stochastic variational deep kernel learning. We evaluate on three state-of-the-art data sets with varying complexity and achieve good results. The proposed pipeline runs automatically, has only a few hyperparameters, and performs well on all tested data sets. Our results are promising for large-scale applications, and we highlight now enabled future scientific work.
    Compositional Clustering for Multi-Label Few-Shot Learning. (arXiv:2109.04160v3 [cs.LG] UPDATED)
    We consider a new kind of clustering problem in which clusters need not be independent of each other, but rather can have compositional relationships with other clusters (e.g., a dataset contains images of rectangles, images of circles, and images of both). This task is motivated by recent work on compositional few-shot learning and embedding models that are optimized to distinguish the label sets, not just the individual labels, assigned to the examples. To tackle this clustering problem, we propose three new algorithms: Compositional Affinity Propagation (CAP), Compositional k-means (CKM), and Greedy Compositional Reassignment (GCR). These new methods can both partition examples into coherent groups and infer the compositional structure among the groups automatically. We show promising results, compared to popular algorithms such as Gaussian mixtures, Fuzzy c-means, and Agglomerative Clustering, on the OmniGlot and LibriSpeech datasets that are widely used in few-shot learning research. Our work has applications to open-world multi-object image recognition and speaker diarization with simultaneous speech from multiple speakers.
    Explainable Artificial Intelligence for Assault Sentence Prediction in New Zealand. (arXiv:2208.06981v1 [cs.LG])
    The judiciary has historically been conservative in its use of Artificial Intelligence, but recent advances in machine learning have prompted scholars to reconsider such use in tasks like sentence prediction. This paper investigates by experimentation the potential use of explainable artificial intelligence for predicting imprisonment sentences in assault cases in New Zealand's courts. We propose a proof-of-concept explainable model and verify in practice that it is fit for purpose, with predicted sentences accurate to within one year. We further analyse the model to understand the most influential phrases in sentence length prediction. We conclude the paper with an evaluative discussion of the future benefits and risks of different ways of using such an AI model in New Zealand's courts.
    Distributed Robust Principal Component Analysis. (arXiv:2207.11669v2 [cs.DC] UPDATED)
    We study the robust principal component analysis (RPCA) problem in a distributed setting. The goal of RPCA is to find an underlying low-rank estimation for a raw data matrix when the data matrix is subject to the corruption of gross sparse errors. Previous studies have developed RPCA algorithms that provide stable solutions with fast convergence. However, these algorithms are typically hard to scale and cannot be implemented distributedly, due to the use of either SVD or large matrix multiplication. In this paper, we propose the first distributed robust principal analysis algorithm based on consensus factorization, dubbed DCF-PCA. We prove the convergence of DCF-PCA and evaluate DCF-PCA on various problem setting
    Succinct Differentiation of Disparate Boosting Ensemble Learning Methods for Prognostication of Polycystic Ovary Syndrome Diagnosis. (arXiv:2201.00418v2 [cs.LG] UPDATED)
    Prognostication of medical problems using the clinical data by leveraging the Machine Learning techniques with stellar precision is one of the most important real world challenges at the present time. Considering the medical problem of Polycystic Ovary Syndrome also known as PCOS is an emerging problem in women aged from 15 to 49. Diagnosing this disorder by using various Boosting Ensemble Methods is something we have presented in this paper. A detailed and compendious differentiation between Adaptive Boost, Gradient Boosting Machine, XGBoost and CatBoost with their respective performance metrics highlighting the hidden anomalies in the data and its effects on the result is something we have presented in this paper. Metrics like Confusion Matrix, Precision, Recall, F1 Score, FPR, RoC Curve and AUC have been used in this paper.
    Riemannian accelerated gradient methods via extrapolation. (arXiv:2208.06619v1 [math.OC])
    In this paper, we propose a simple acceleration scheme for Riemannian gradient methods by extrapolating iterates on manifolds. We show when the iterates are generated from Riemannian gradient descent method, the accelerated scheme achieves the optimal convergence rate asymptotically and is computationally more favorable than the recently proposed Riemannian Nesterov accelerated gradient methods. Our experiments verify the practical benefit of the novel acceleration strategy.
    A Hybrid Approach on Conditional GAN for Portfolio Analysis. (arXiv:2208.07159v1 [q-fin.PM])
    Over the decades, the Markowitz framework has been used extensively in portfolio analysis though it puts too much emphasis on the analysis of the market uncertainty rather than on the trend prediction. While generative adversarial network (GAN), conditional GAN (CGAN), and autoencoding CGAN (ACGAN) have been explored to generate financial time series and extract features that can help portfolio analysis. The limitation of the CGAN or ACGAN framework stands in putting too much emphasis on generating series and finding the internal trends of the series rather than predicting the future trends. In this paper, we introduce a hybrid approach on conditional GAN based on deep generative models that learns the internal trend of historical data while modeling market uncertainty and future trends. We evaluate the model on several real-world datasets from both the US and Europe markets, and show that the proposed HybridCGAN and HybridACGAN models lead to better portfolio allocation compared to the existing Markowitz, CGAN, and ACGAN approaches.
    Deep Reinforcement Learning Approach for Trading Automation in The Stock Market. (arXiv:2208.07165v1 [q-fin.TR])
    Deep Reinforcement Learning (DRL) algorithms can scale to previously intractable problems. The automation of profit generation in the stock market is possible using DRL, by combining the financial assets price "prediction" step and the "allocation" step of the portfolio in one unified process to produce fully autonomous systems capable of interacting with their environment to make optimal decisions through trial and error. This work represents a DRL model to generate profitable trades in the stock market, effectively overcoming the limitations of supervised learning approaches. We formulate the trading problem as a Partially Observed Markov Decision Process (POMDP) model, considering the constraints imposed by the stock market, such as liquidity and transaction costs. We then solve the formulated POMDP problem using the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm reporting a 2.68 Sharpe Ratio on unseen data set (test data). From the point of view of stock market forecasting and the intelligent decision-making mechanism, this paper demonstrates the superiority of DRL in financial markets over other types of machine learning and proves its credibility and advantages of strategic decision-making.
    Rethinking Graph Neural Networks for the Graph Coloring Problem. (arXiv:2208.06975v1 [cs.LG])
    Graph coloring, a classical and critical NP-hard problem, is the problem of assigning connected nodes as different colors as possible. However, we observe that state-of-the-art GNNs are less successful in the graph coloring problem. We analyze the reasons from two perspectives. First, most GNNs fail to generalize the task under homophily to heterophily, i.e., graphs where connected nodes are assigned different colors. Second, GNNs are bounded by the network depth, making them possible to be a local method, which has been demonstrated to be non-optimal in Maximum Independent Set (MIS) problem. In this paper, we focus on the aggregation-combine GNNs (AC-GNNs), a popular class of GNNs. We first define the power of AC-GNNs in the coloring problem as the capability to assign nodes different colors. The definition is different with previous one that is based on the assumption of homophily. We identify node pairs that AC-GNNs fail to discriminate. Furthermore, we show that any AC-GNN is a local coloring method, and any local coloring method is non-optimal by exploring the limits of local methods over sparse random graphs, thereby demonstrating the non-optimality of AC-GNNs due to its local property. We then prove the positive correlation between model depth and its coloring power. Moreover, we discuss the color equivariance of graphs to tackle some practical constraints such as the pre-fixing constraints. Following the discussions above, we summarize a series of rules a series of rules that make a GNN color equivariant and powerful in the coloring problem. Then, we propose a simple AC-GNN variation satisfying these rules. We empirically validate our theoretical findings and demonstrate that our simple model substantially outperforms state-of-the-art heuristic algorithms in both quality and runtime.
    Acceleration of Subspace Learning Machine via Particle Swarm Optimization and Parallel Processing. (arXiv:2208.07023v1 [cs.LG])
    Built upon the decision tree (DT) classification and regression idea, the subspace learning machine (SLM) has been recently proposed to offer higher performance in general classification and regression tasks. Its performance improvement is reached at the expense of higher computational complexity. In this work, we investigate two ways to accelerate SLM. First, we adopt the particle swarm optimization (PSO) algorithm to speed up the search of a discriminant dimension that is expressed as a linear combination of current dimensions. The search of optimal weights in the linear combination is computationally heavy. It is accomplished by probabilistic search in original SLM. The acceleration of SLM by PSO requires 10-20 times fewer iterations. Second, we leverage parallel processing in the SLM implementation. Experimental results show that the accelerated SLM method achieves a speed up factor of 577 in training time while maintaining comparable classification/regression performance of original SLM.
    Efficient Adaptive Regret Minimization. (arXiv:2207.00646v3 [cs.LG] UPDATED)
    In online convex optimization the player aims to minimize her regret against a fixed comparator over the entire repeated game. Algorithms that minimize standard regret may converge to a fixed decision, which is undesireable in changing or dynamic environments. This motivates the stronger metric of adaptive regret, or the maximum regret over any continuous sub-interval in time. Existing adaptive regret algorithms suffer from a computational penalty - typically on the order of a multiplicative factor that grows logarithmically in the number of game iterations. In this paper we show how to reduce this computational penalty to be doubly logarithmic in the number of game iterations, and with minimal degradation to the optimal attainable adaptive regret bounds.
    HyP$^2$ Loss: Beyond Hypersphere Metric Space for Multi-label Image Retrieval. (arXiv:2208.06866v1 [cs.CV])
    Image retrieval has become an increasingly appealing technique with broad multimedia application prospects, where deep hashing serves as the dominant branch towards low storage and efficient retrieval. In this paper, we carried out in-depth investigations on metric learning in deep hashing for establishing a powerful metric space in multi-label scenarios, where the pair loss suffers high computational overhead and converge difficulty, while the proxy loss is theoretically incapable of expressing the profound label dependencies and exhibits conflicts in the constructed hypersphere space. To address the problems, we propose a novel metric learning framework with Hybrid Proxy-Pair Loss (HyP$^2$ Loss) that constructs an expressive metric space with efficient training complexity w.r.t. the whole dataset. The proposed HyP$^2$ Loss focuses on optimizing the hypersphere space by learnable proxies and excavating data-to-data correlations of irrelevant pairs, which integrates sufficient data correspondence of pair-based methods and high-efficiency of proxy-based methods. Extensive experiments on four standard multi-label benchmarks justify the proposed method outperforms the state-of-the-art, is robust among different hash bits and achieves significant performance gains with a faster, more stable convergence speed. Our code is available at https://github.com/JerryXu0129/HyP2-Loss.
    An Edge-Cloud Integrated Framework for Flexible and Dynamic Stream Analytics. (arXiv:2205.04622v3 [cs.DC] UPDATED)
    With the popularity of Internet of Things (IoT), edge computing and cloud computing, more and more stream analytics applications are being developed including real-time trend prediction and object detection on top of IoT sensing data. One popular type of stream analytics is the recurrent neural network (RNN) deep learning model based time series or sequence data prediction and forecasting. Different from traditional analytics that assumes data are available ahead of time and will not change, stream analytics deals with data that are being generated continuously and data trend/distribution could change (a.k.a. concept drift), which will cause prediction/forecasting accuracy to drop over time. One other challenge is to find the best resource provisioning for stream analytics to achieve good overall latency. In this paper, we study how to best leverage edge and cloud resources to achieve better accuracy and latency for stream analytics using a type of RNN model called long short-term memory (LSTM). We propose a novel edge-cloud integrated framework for hybrid stream analytics that supports low latency inference on the edge and high capacity training on the cloud. To achieve flexible deployment, we study different approaches of deploying our hybrid learning framework including edge-centric, cloud-centric and edge-cloud integrated. Further, our hybrid learning framework can dynamically combine inference results from an LSTM model pre-trained based on historical data and another LSTM model re-trained periodically based on the most recent data. Using real-world and simulated stream datasets, our experiments show the proposed edge-cloud deployment is the best among all three deployment types in terms of latency. For accuracy, the experiments show our dynamic learning approach performs the best among all learning approaches for all three concept drift scenarios.
    Conformalized Online Learning: Online Calibration Without a Holdout Set. (arXiv:2205.09095v3 [cs.LG] UPDATED)
    We develop a framework for constructing uncertainty sets with a valid coverage guarantee in an online setting, in which the underlying data distribution can drastically -- and even adversarially -- shift over time. The technique we propose is highly flexible as it can be integrated with any online learning algorithm, requiring minimal implementation effort and computational cost. A key advantage of our method over existing alternatives -- which also build on conformal inference -- is that we do not need to split the data into training and holdout calibration sets. This allows us to fit the predictive model in a fully online manner, utilizing the most recent observation for constructing calibrated uncertainty sets. Consequently, and in contrast with existing techniques, (i) the sets we build can quickly adapt to new changes in the distribution; and (ii) our procedure does not require refitting the model at each time step. Using synthetic and real-world benchmark data sets, we demonstrate the validity of our theory and the improved performance of our proposal over existing techniques. To demonstrate the greater flexibility of the proposed method, we show how to construct valid intervals for a multiple-output regression problem that previous sequential calibration methods cannot handle due to impractical computational and memory requirements.
    Active Learning with Label Comparisons. (arXiv:2204.04670v2 [cs.LG] UPDATED)
    Supervised learning typically relies on manual annotation of the true labels. When there are many potential classes, searching for the best one can be prohibitive for a human annotator. On the other hand, comparing two candidate labels is often much easier. We focus on this type of pairwise supervision and ask how it can be used effectively in learning, and in particular in active learning. We obtain several insightful results in this context. In principle, finding the best of $k$ labels can be done with $k-1$ active queries. We show that there is a natural class where this approach is sub-optimal, and that there is a more comparison-efficient active learning scheme. A key element in our analysis is the "label neighborhood graph" of the true distribution, which has an edge between two classes if they share a decision boundary. We also show that in the PAC setting, pairwise comparisons cannot provide improved sample complexity in the worst case. We complement our theoretical results with experiments, clearly demonstrating the effect of the neighborhood graph on sample complexity.
    Covert Message Passing over Public Internet Platforms Using Model-Based Format-Transforming Encryption. (arXiv:2110.07009v2 [cs.CR] UPDATED)
    We introduce a new type of format-transforming encryption where the format of ciphertexts is implicitly encoded within a machine-learned generative model. Around this primitive, we build a system for covert messaging over large, public internet platforms (e.g., Twitter). Loosely, our system composes an authenticated encryption scheme, with a method for encoding random ciphertext bits into samples from the generative model's family of seed-indexed token-distributions. By fixing a deployment scenario, we are forced to consider system-level and algorithmic solutions to real challenges -- ~such as receiver-side parsing ambiguities, and the low information-carrying capacity of actual token-distributions~ -- that were elided in prior work. We use GPT-2 as our generative model so that our system cryptographically transforms plaintext bitstrings into natural-language covertexts suitable for posting to public platforms. We consider adversaries with full view of the internet platform's content, whose goal is to surface posts that are using our system for covert messaging. We carry out a suite of experiments to provide heuristic evidence of security and to explore tradeoffs between operational efficiency and detectability.
    Learning Linear Non-Gaussian Polytree Models. (arXiv:2208.06701v1 [stat.ML])
    In the context of graphical causal discovery, we adapt the versatile framework of linear non-Gaussian acyclic models (LiNGAMs) to propose new algorithms to efficiently learn graphs that are polytrees. Our approach combines the Chow--Liu algorithm, which first learns the undirected tree structure, with novel schemes to orient the edges. The orientation schemes assess algebraic relations among moments of the data-generating distribution and are computationally inexpensive. We establish high-dimensional consistency results for our approach and compare different algorithmic versions in numerical experiments.
    MaskBlock: Transferable Adversarial Examples with Bayes Approach. (arXiv:2208.06538v1 [cs.LG])
    The transferability of adversarial examples (AEs) across diverse models is of critical importance for black-box adversarial attacks, where attackers cannot access the information about black-box models. However, crafted AEs always present poor transferability. In this paper, by regarding the transferability of AEs as generalization ability of the model, we reveal that vanilla black-box attacks craft AEs via solving a maximum likelihood estimation (MLE) problem. For MLE, the results probably are model-specific local optimum when available data is small, i.e., limiting the transferability of AEs. By contrast, we re-formulate crafting transferable AEs as the maximizing a posteriori probability estimation problem, which is an effective approach to boost the generalization of results with limited available data. Because Bayes posterior inference is commonly intractable, a simple yet effective method called MaskBlock is developed to approximately estimate. Moreover, we show that the formulated framework is a generalization version for various attack methods. Extensive experiments illustrate MaskBlock can significantly improve the transferability of crafted adversarial examples by up to about 20%.
    Locating disparities in machine learning. (arXiv:2208.06680v1 [cs.LG])
    Machine learning was repeatedly proven to provide predictions with disparate outcomes, in which subgroups of the population (e.g., defined by age, gender, or other sensitive attributes) are systematically disadvantaged. Previous literature has focused on detecting such disparities through statistical procedures for when the sensitive attribute is specified a priori. However, this limits applicability in real-world settings where datasets are high dimensional and, on top of that, sensitive attributes may be unknown. As a remedy, we propose a data-driven framework called Automatic Location of Disparities (ALD) which aims at locating disparities in machine learning. ALD meets several demands from machine learning practice: ALD (1) is applicable to arbitrary machine learning classifiers; (2) operates on different definitions of disparities (e.g., statistical parity or equalized odds); (3) deals with both categorical and continuous predictors; (4) is suitable to handle high-dimensional settings; and (5) even identifies disparities due to intersectionality where disparities arise from complex and multi-way interactions (e.g., age above 60 and female). ALD produces interpretable fairness reports as output. We demonstrate the effectiveness of ALD based on both synthetic and real-world datasets. As a result, ALD helps practitioners and researchers of algorithmic fairness to detect disparities in machine learning algorithms, so that disparate -- or even unfair -- outcomes can be mitigated. Moreover, ALD supports practitioners in conducting algorithmic audits and protecting individuals from discrimination.
    Comparison of Forecasting Methods of House Electricity Consumption for Honda Smart Home. (arXiv:2208.07217v1 [cs.LG])
    The electricity consumption of buildings composes a major part of the city's energy consumption. Electricity consumption forecasting enables the development of home energy management systems resulting in the future design of more sustainable houses and a decrease in total energy consumption. Energy performance in buildings is influenced by many factors like ambient temperature, humidity, and a variety of electrical devices. Therefore, multivariate prediction methods are preferred rather than univariate. The Honda Smart Home US data set was selected to compare three methods for minimizing forecasting errors, MAE and RMSE: Artificial Neural Networks, Support Vector Regression, and Fuzzy Rule-Based Systems for Regression by constructing many models for each method on a multivariate data set in different time terms. The comparison shows that SVR is a superior method over the alternatives.
    An Adam-adjusting-antennae BAS Algorithm for Refining Latent Factors. (arXiv:2208.06603v1 [cs.LG])
    Extracting the latent information in high-dimensional and incomplete matrices is an important and challenging issue. The Latent Factor Analysis (LFA) model can well handle the high-dimensional matrices analysis. Recently, Particle Swarm Optimization (PSO)-incorporated LFA models have been proposed to tune the hyper-parameters adaptively with high efficiency. However, the incorporation of PSO causes the premature problem. To address this issue, we propose a sequential Adam-adjusting-antennae BAS (A2BAS) optimization algorithm, which refines the latent factors obtained by the PSO-incorporated LFA model. The A2BAS algorithm consists of two sub-algorithms. First, we design an improved BAS algorithm which adjusts beetles' antennae and step-size with Adam; Second, we implement the improved BAS algorithm to optimize all the row and column latent factors sequentially. With experimental results on two real high-dimensional matrices, we demonstrate that our algorithm can effectively solve the premature convergence issue.
    Confidence-Guided Learning Process for Continuous Classification of Time Series. (arXiv:2208.06883v1 [cs.LG])
    In the real world, the class of a time series is usually labeled at the final time, but many applications require to classify time series at every time point. e.g. the outcome of a critical patient is only determined at the end, but he should be diagnosed at all times for timely treatment. Thus, we propose a new concept: Continuous Classification of Time Series (CCTS). It requires the model to learn data in different time stages. But the time series evolves dynamically, leading to different data distributions. When a model learns multi-distribution, it always forgets or overfits. We suggest that meaningful learning scheduling is potential due to an interesting observation: Measured by confidence, the process of model learning multiple distributions is similar to the process of human learning multiple knowledge. Thus, we propose a novel Confidence-guided method for CCTS (C3TS). It can imitate the alternating human confidence described by the Dunning-Kruger Effect. We define the objective- confidence to arrange data, and the self-confidence to control the learning duration. Experiments on four real-world datasets show that C3TS is more accurate than all baselines for CCTS.
    PAC Generalization via Invariant Representations. (arXiv:2205.15196v3 [cs.LG] UPDATED)
    One method for obtaining generalizable solutions to machine learning tasks when presented with diverse training environments is to find \textit{invariant representations} of the data. These are representations of the covariates such that the best model on top of the representation is invariant across training environments. In the context of linear Structural Equation Models (SEMs), invariant representations might allow us to learn models with out-of-distribution guarantees, i.e., models that are robust to interventions in the SEM. To address the invariant representation problem in a {\em finite sample} setting, we consider the notion of $\epsilon$-approximate invariance. We study the following question: If a representation is approximately invariant with respect to a given number of training interventions, will it continue to be approximately invariant on a larger collection of unseen SEMs? This larger collection of SEMs is generated through a parameterized family of interventions. Inspired by PAC learning, we obtain finite-sample out-of-distribution generalization guarantees for approximate invariance that holds \textit{probabilistically} over a family of linear SEMs without faithfulness assumptions. Our results show bounds that do not scale in ambient dimension when intervention sites are restricted to lie in a constant size subset of in-degree bounded nodes. We also show how to extend our results to a linear indirect observation model that incorporates latent variables.
    Agreement or Disagreement in Noise-tolerant Mutual Learning?. (arXiv:2203.15317v2 [cs.CV] UPDATED)
    Deep learning has made many remarkable achievements in many fields but suffers from noisy labels in datasets. The state-of-the-art learning with noisy label method Co-teaching and Co-teaching+ confronts the noisy label by mutual-information between dual-network. However, the dual network always tends to convergent which would weaken the dual-network mechanism to resist the noisy labels. In this paper, we proposed a noise-tolerant framework named MLC in an end-to-end manner. It adjusts the dual-network with divergent regularization to ensure the effectiveness of the mechanism. In addition, we correct the label distribution according to the agreement between dual-networks. The proposed method can utilize the noisy data to improve the accuracy, generalization, and robustness of the network. We test the proposed method on the simulate noisy dataset MNIST, CIFAR-10, and the real-world noisy dataset Clothing1M. The experimental result shows that our method outperforms the previous state-of-the-art method. Besides, our method is network-free thus it is applicable to many tasks. Our code can be found at https://github.com/JiarunLiu/MLC.
    Accelerated and instance-optimal policy evaluation with linear function approximation. (arXiv:2112.13109v2 [stat.ML] UPDATED)
    We study the problem of policy evaluation with linear function approximation and present efficient and practical algorithms that come with strong optimality guarantees. We begin by proving lower bounds that establish baselines on both the deterministic error and stochastic error in this problem. In particular, we prove an oracle complexity lower bound on the deterministic error in an instance-dependent norm associated with the stationary distribution of the transition kernel, and use the local asymptotic minimax machinery to prove an instance-dependent lower bound on the stochastic error in the i.i.d. observation model. Existing algorithms fail to match at least one of these lower bounds: To illustrate, we analyze a variance-reduced variant of temporal difference learning, showing in particular that it fails to achieve the oracle complexity lower bound. To remedy this issue, we develop an accelerated, variance-reduced fast temporal difference algorithm (VRFTD) that simultaneously matches both lower bounds and attains a strong notion of instance-optimality. Finally, we extend the VRFTD algorithm to the setting with Markovian observations, and provide instance-dependent convergence results. Our theoretical guarantees of optimality are corroborated by numerical experiments.
    Frouros: A Python library for drift detection in Machine Learning problems. (arXiv:2208.06868v1 [cs.LG])
    Frouros is a Python library capable of detecting drift in machine learning problems. It provides a combination of classical and more recent algorithms for drift detection: both supervised and unsupervised, as well as some capable of acting in a semi-supervised manner. We have designed it with the objective of being easily integrated with the scikit-learn library, implementing the same application programming interface. The library is developed following a set of best development and continuous integration practices to ensure ease of maintenance and extensibility. The source code is available at https://github.com/IFCA/frouros.
    When Does Differentially Private Learning Not Suffer in High Dimensions?. (arXiv:2207.00160v3 [cs.LG] UPDATED)
    Large pretrained models can be privately fine-tuned to achieve performance approaching that of non-private models. A common theme in these results is the surprising observation that high-dimensional models can achieve favorable privacy-utility trade-offs. This seemingly contradicts known results on the model-size dependence of differentially private convex learning and raises the following research question: When does the performance of differentially private learning not degrade with increasing model size? We identify that the magnitudes of gradients projected onto subspaces is a key factor that determines performance. To precisely characterize this for private convex learning, we introduce a condition on the objective that we term \emph{restricted Lipschitz continuity} and derive improved bounds for the excess empirical and population risks that are dimension-independent under additional conditions. We empirically show that in private fine-tuning of large language models, gradients obtained during fine-tuning are mostly controlled by a few principal components. This behavior is similar to conditions under which we obtain dimension-independent bounds in convex settings. Our theoretical and empirical results together provide a possible explanation for recent successes in large-scale private fine-tuning. Code to reproduce our results can be found at \url{https://github.com/lxuechen/private-transformers/tree/main/examples/classification/spectral_analysis}.
    Orthogonal Gated Recurrent Unit with Neumann-Cayley Transformation. (arXiv:2208.06496v1 [cs.LG])
    In recent years, using orthogonal matrices has been shown to be a promising approach in improving Recurrent Neural Networks (RNNs) with training, stability, and convergence, particularly, to control gradients. While Gated Recurrent Unit (GRU) and Long Short Term Memory (LSTM) architectures address the vanishing gradient problem by using a variety of gates and memory cells, they are still prone to the exploding gradient problem. In this work, we analyze the gradients in GRU and propose the usage of orthogonal matrices to prevent exploding gradient problems and enhance long-term memory. We study where to use orthogonal matrices and we propose a Neumann series-based Scaled Cayley transformation for training orthogonal matrices in GRU, which we call Neumann-Cayley Orthogonal GRU, or simply NC-GRU. We present detailed experiments of our model on several synthetic and real-world tasks, which show that NC-GRU significantly outperforms GRU as well as several other RNNs.
    A Sparse Expansion For Deep Gaussian Processes. (arXiv:2112.05888v2 [stat.ML] UPDATED)
    In this work, we use Deep Gaussian Processes (DGPs) as statistical surrogates for stochastic processes with complex distributions. Conventional inferential methods for DGP models can suffer from high computational complexity as they require large-scale operations with kernel matrices for training and inference. In this work, we propose an efficient scheme for accurate inference and efficient training based on a range of Gaussian Processes, called the Tensor Markov Gaussian Processes (TMGP). We construct an induced approximation of TMGP referred to as the hierarchical expansion. Next, we develop a deep TMGP (DTMGP) model as the composition of multiple hierarchical expansion of TMGPs. The proposed DTMGP model has the following properties: (1) the outputs of each activation function are deterministic while the weights are chosen independently from standard Gaussian distribution; (2) in training or prediction, only polylog(M) (out of M) activation functions have non-zero outputs, which significantly boosts the computational efficiency. Our numerical experiments on synthetic models and real datasets show the superior computational efficiency of DTMGP over existing DGP models.
    PAC Reinforcement Learning for Predictive State Representations. (arXiv:2207.05738v3 [cs.LG] UPDATED)
    In this paper we study online Reinforcement Learning (RL) in partially observable dynamical systems. We focus on the Predictive State Representations (PSRs) model, which is an expressive model that captures other well-known models such as Partially Observable Markov Decision Processes (POMDP). PSR represents the states using a set of predictions of future observations and is defined entirely using observable quantities. We develop a novel model-based algorithm for PSRs that can learn a near optimal policy in sample complexity scaling polynomially with respect to all the relevant parameters of the systems. Our algorithm naturally works with function approximation to extend to systems with potentially large state and observation spaces. We show that given a realizable model class, the sample complexity of learning the near optimal policy only scales polynomially with respect to the statistical complexity of the model class, without any explicit polynomial dependence on the size of the state and observation spaces. Notably, our work is the first work that shows polynomial sample complexities to compete with the globally optimal policy in PSRs. Finally, we demonstrate how our general theorem can be directly used to derive sample complexity bounds for special models including $m$-step weakly revealing and $m$-step decodable tabular POMDPs, POMDPs with low-rank latent transition, and POMDPs with linear emission and latent transition.
    \b{eta}-Divergence-Based Latent Factorization of Tensors model for QoS prediction. (arXiv:2208.06778v1 [cs.LG])
    A nonnegative latent factorization of tensors (NLFT) model can well model the temporal pattern hidden in nonnegative quality-of-service (QoS) data for predicting the unobserved ones with high accuracy. However, existing NLFT models' objective function is based on Euclidean distance, which is only a special case of \b{eta}-divergence. Hence, can we build a generalized NLFT model via adopting \b{eta}-divergence to achieve prediction accuracy gain? To tackle this issue, this paper proposes a \b{eta}-divergence-based NLFT model (\b{eta}-NLFT). Its ideas are two-fold 1) building a learning objective with \b{eta}-divergence to achieve higher prediction accuracy, and 2) implementing self-adaptation of hyper-parameters to improve practicability. Empirical studies on two dynamic QoS datasets demonstrate that compared with state-of-the-art models, the proposed \b{eta}-NLFT model achieves the higher prediction accuracy for unobserved QoS data.
    Sharp Frequency Bounds for Sample-Based Queries. (arXiv:2208.06753v1 [cs.LG])
    A data sketch algorithm scans a big data set, collecting a small amount of data -- the sketch, which can be used to statistically infer properties of the big data set. Some data sketch algorithms take a fixed-size random sample of a big data set, and use that sample to infer frequencies of items that meet various criteria in the big data set. This paper shows how to statistically infer probably approximately correct (PAC) bounds for those frequencies, efficiently, and precisely enough that the frequency bounds are either sharp or off by only one, which is the best possible result without exact computation.
    USB: A Unified Semi-supervised Learning Benchmark. (arXiv:2208.07204v1 [cs.LG])
    Semi-supervised learning (SSL) improves model generalization by leveraging massive unlabeled data to augment limited labeled samples. However, currently, popular SSL evaluation protocols are often constrained to computer vision (CV) tasks. In addition, previous work typically trains deep neural networks from scratch, which is time-consuming and environmentally unfriendly. To address the above issues, we construct a Unified SSL Benchmark (USB) by selecting 15 diverse, challenging, and comprehensive tasks from CV, natural language processing (NLP), and audio processing (Audio), on which we systematically evaluate dominant SSL methods, and also open-source a modular and extensible codebase for fair evaluation on these SSL methods. We further provide pre-trained versions of the state-of-the-art neural models for CV tasks to make the cost affordable for further tuning. USB enables the evaluation of a single SSL algorithm on more tasks from multiple domains but with less cost. Specifically, on a single NVIDIA V100, only 37 GPU days are required to evaluate FixMatch on 15 tasks in USB while 335 GPU days (279 GPU days on 4 CV datasets except for ImageNet) are needed on 5 CV tasks with the typical protocol.
    Direct Advantage Estimation. (arXiv:2109.06093v2 [cs.LG] UPDATED)
    The predominant approach in reinforcement learning is to assign credit to actions based on the expected return. However, we show that the return may depend on the policy in a way which could lead to excessive variance in value estimation and slow down learning. Instead, we show that the advantage function can be interpreted as causal effects and shares similar properties with causal representations. Based on this insight, we propose Direct Advantage Estimation (DAE), a novel method that can model the advantage function and estimate it directly from on-policy data while simultaneously minimizing the variance of the return without requiring the (action-)value function. We also relate our method to Temporal Difference methods by showing how value functions can be seamlessly integrated into DAE. The proposed method is easy to implement and can be readily adapted by modern actor-critic methods. We evaluate DAE empirically on three discrete control domains and show that it can outperform generalized advantage estimation (GAE), a strong baseline for advantage estimation, on a majority of the environments when applied to policy optimization.
    Incoporating Weighted Board Learning System for Accurate Occupational Pneumoconiosis Staging. (arXiv:2208.06607v1 [cs.LG])
    Occupational pneumoconiosis (OP) staging is a vital task concerning the lung healthy of a subject. The staging result of a patient is depended on the staging standard and his chest X-ray. It is essentially an image classification task. However, the distribution of OP data is commonly imbalanced, which largely reduces the effect of classification models which are proposed under the assumption that data follow a balanced distribution and causes inaccurate staging results. To achieve accurate OP staging, we proposed an OP staging model who is able to handle imbalance data in this work. The proposed model adopts gray level co-occurrence matrix (GLCM) to extract texture feature of chest X-ray and implements classification with a weighted broad learning system (WBLS). Empirical studies on six data cases provided by a hospital indicate that proposed model can perform better OP staging than state-of-the-art classifiers with imbalanced data.
    DeepScalper: A Risk-Aware Reinforcement Learning Framework to Capture Fleeting Intraday Trading Opportunities. (arXiv:2201.09058v2 [q-fin.TR] UPDATED)
    Reinforcement learning (RL) techniques have shown great success in many challenging quantitative trading tasks, such as portfolio management and algorithmic trading. Especially, intraday trading is one of the most profitable and risky tasks because of the intraday behaviors of the financial market that reflect billions of rapidly fluctuating capitals. However, a vast majority of existing RL methods focus on the relatively low frequency trading scenarios (e.g., day-level) and fail to capture the fleeting intraday investment opportunities due to two major challenges: 1) how to effectively train profitable RL agents for intraday investment decision-making, which involves high-dimensional fine-grained action space; 2) how to learn meaningful multi-modality market representation to understand the intraday behaviors of the financial market at tick-level. Motivated by the efficient workflow of professional human intraday traders, we propose DeepScalper, a deep reinforcement learning framework for intraday trading to tackle the above challenges. Specifically, DeepScalper includes four components: 1) a dueling Q-network with action branching to deal with the large action space of intraday trading for efficient RL optimization; 2) a novel reward function with a hindsight bonus to encourage RL agents making trading decisions with a long-term horizon of the entire trading day; 3) an encoder-decoder architecture to learn multi-modality temporal market embedding, which incorporates both macro-level and micro-level market information; 4) a risk-aware auxiliary task to maintain a striking balance between maximizing profit and minimizing risk. Through extensive experiments on real-world market data spanning over three years on six financial futures, we demonstrate that DeepScalper significantly outperforms many state-of-the-art baselines in terms of four financial criteria.
    Privacy-Preserving Decentralized Inference with Graph Neural Networks in Wireless Networks. (arXiv:2208.06963v1 [cs.IT])
    As an efficient neural network model for graph data, graph neural networks (GNNs) recently find successful applications for various wireless optimization problems. Given that the inference stage of GNNs can be naturally implemented in a decentralized manner, GNN is a potential enabler for decentralized control/management in the next-generation wireless communications. Privacy leakage, however, may occur due to the information exchanges among neighbors during decentralized inference with GNNs. To deal with this issue, in this paper, we analyze and enhance the privacy of decentralized inference with GNNs in wireless networks. Specifically, we adopt local differential privacy as the metric, and design novel privacy-preserving signals as well as privacy-guaranteed training algorithms to achieve privacy-preserving inference. We also define the SNR-privacy trade-off function to analyze the performance upper bound of decentralized inference with GNNs in wireless networks. To further enhance the communication and computation efficiency, we adopt the over-the-air computation technique and theoretically demonstrate its advantage in privacy preservation. Through extensive simulations on the synthetic graph data, we validate our theoretical analysis, verify the effectiveness of proposed privacy-preserving wireless signaling and privacy-guaranteed training algorithm, and offer some guidance on practical implementation.
    Policy Gradient Methods Find the Nash Equilibrium in N-player General-sum Linear-quadratic Games. (arXiv:2107.13090v2 [math.OC] UPDATED)
    We consider a general-sum N-player linear-quadratic game with stochastic dynamics over a finite horizon and prove the global convergence of the natural policy gradient method to the Nash equilibrium. In order to prove the convergence of the method, we require a certain amount of noise in the system. We give a condition, essentially a lower bound on the covariance of the noise in terms of the model parameters, in order to guarantee convergence. We illustrate our results with numerical experiments to show that even in situations where the policy gradient method may not converge in the deterministic setting, the addition of noise leads to convergence.
    Class Prior Estimation under Covariate Shift: No Problem?. (arXiv:2206.02449v2 [stat.ML] UPDATED)
    We show that in the context of classification the property of source and target distributions to be related by covariate shift may be lost if the information content captured in the covariates is reduced, for instance by dropping components or mapping into a lower-dimensional or finite space. As a consequence, under covariate shift simple approaches to class prior estimation in the style of classify and count with or without adjustment are infeasible. We prove that transformations of the covariates that preserve the covariate shift property are necessarily sufficient in the statistical sense for the full set of covariates. A probing algorithm as alternative approach to class prior estimation under covariate shift is proposed.
    Self-Supervised Transformers for fMRI representation. (arXiv:2112.05761v2 [eess.IV] UPDATED)
    We present TFF, which is a Transformer framework for the analysis of functional Magnetic Resonance Imaging (fMRI) data. TFF employs a two-phase training approach. First, self-supervised training is applied to a collection of fMRI scans, where the model is trained to reconstruct 3D volume data. Second, the pre-trained model is fine-tuned on specific tasks, utilizing ground truth labels. Our results show state-of-the-art performance on a variety of fMRI tasks, including age and gender prediction, as well as schizophrenia recognition. Our code for the training, network architecture, and results is attached as supplementary material.
    Reduced Implication-bias Logic Loss for Neuro-Symbolic Learning. (arXiv:2208.06838v1 [cs.AI])
    Integrating logical reasoning and machine learning by approximating logical inference with differentiable operators is a widely used technique in Neuro-Symbolic systems. However, some differentiable operators could bring a significant bias during backpropagation and degrade the performance of Neuro-Symbolic learning. In this paper, we reveal that this bias, named \textit{Implication Bias} is common in loss functions derived from fuzzy logic operators. Furthermore, we propose a simple yet effective method to transform the biased loss functions into \textit{Reduced Implication-bias Logic Loss (RILL)} to address the above problem. Empirical study shows that RILL can achieve significant improvements compared with the biased logic loss functions, especially when the knowledge base is incomplete, and keeps more robust than the compared methods when labelled data is insufficient.
    Towards Theoretical Understandings of Robust Markov Decision Processes: Sample Complexity and Asymptotics. (arXiv:2105.03863v3 [stat.ML] UPDATED)
    In this paper, we study the non-asymptotic and asymptotic performances of the optimal robust policy and value function of robust Markov Decision Processes(MDPs), where the optimal robust policy and value function are solved only from a generative model. While prior work focusing on non-asymptotic performances of robust MDPs is restricted in the setting of the KL uncertainty set and $(s,a)$-rectangular assumption, we improve their results and also consider other uncertainty sets, including $L_1$ and $\chi^2$ balls. Our results show that when we assume $(s,a)$-rectangular on uncertainty sets, the sample complexity is about $\widetilde{O}\left(\frac{|\mathcal{S}|^2|\mathcal{A}|}{\varepsilon^2\rho^2(1-\gamma)^4}\right)$. In addition, we extend our results from $(s,a)$-rectangular assumption to $s$-rectangular assumption. In this scenario, the sample complexity varies with the choice of uncertainty sets and is generally larger than the case under $(s,a)$-rectangular assumption. Moreover, we also show that the optimal robust value function is asymptotic normal with a typical rate $\sqrt{n}$ under $(s,a)$ and $s$-rectangular assumptions from both theoretical and empirical perspectives.
    Accelerating hydrodynamic simulations of urban drainage systems with physics-guided machine learning. (arXiv:2206.01538v2 [cs.LG] UPDATED)
    We propose and demonstrate a new approach for fast and accurate surrogate modelling of urban drainage system hydraulics based on physics-guided machine learning. The surrogates are trained against a limited set of simulation results from a hydrodynamic (HiFi) model. Our approach reduces simulation times by one to two orders of magnitude compared to a HiFi model. It is thus slower than e.g. conceptual hydrological models, but it enables simulations of water levels, flows and surcharges in all nodes and links of a drainage network and thus largely preserves the level of detail provided by HiFi models. Comparing time series simulated by the surrogate and the HiFi model, R2 values in the order of 0.9 are achieved. Surrogate training times are currently in the order of one hour. However, they can likely be reduced through the application of transfer learning and graph neural networks. Our surrogate approach will be useful for interactive workshops in initial design phases of urban drainage systems, as well as for real time applications. In addition, our model formulation is generic and future research should investigate its application for simulating other water systems.
    AI for Global Climate Cooperation: Modeling Global Climate Negotiations, Agreements, and Long-Term Cooperation in RICE-N. (arXiv:2208.07004v1 [cs.LG])
    Comprehensive global cooperation is essential to limit global temperature increases while continuing economic development, e.g., reducing severe inequality or achieving long-term economic growth. Achieving long-term cooperation on climate change mitigation with n strategic agents poses a complex game-theoretic problem. For example, agents may negotiate and reach climate agreements, but there is no central authority to enforce adherence to those agreements. Hence, it is critical to design negotiation and agreement frameworks that foster cooperation, allow all agents to meet their individual policy objectives, and incentivize long-term adherence. This is an interdisciplinary challenge that calls for collaboration between researchers in machine learning, economics, climate science, law, policy, ethics, and other fields. In particular, we argue that machine learning is a critical tool to address the complexity of this domain. To facilitate this research, here we introduce RICE-N, a multi-region integrated assessment model that simulates the global climate and economy, and which can be used to design and evaluate the strategic outcomes for different negotiation and agreement frameworks. We also describe how to use multi-agent reinforcement learning to train rational agents using RICE-N. This framework underpinsAI for Global Climate Cooperation, a working group collaboration and competition on climate negotiation and agreement design. Here, we invite the scientific community to design and evaluate their solutions using RICE-N, machine learning, economic intuition, and other domain knowledge. More information can be found on www.ai4climatecoop.org.
    Combining deep learning and crowdsourcing geo-images to predict housing quality in rural China. (arXiv:2208.06997v1 [cs.LG])
    Housing quality is an essential proxy for regional wealth, security and health. Understanding the distribution of housing quality is crucial for unveiling rural development status and providing political proposals. However,present rural house quality data highly depends on a top-down, time-consuming survey at the national or provincial level but fails to unpack the housing quality at the village level. To fill the gap between accurately depicting rural housing quality conditions and deficient data,we collect massive rural images and invite users to assess their housing quality at scale. Furthermore, a deep learning framework is proposed to automatically and efficiently predict housing quality based on crowd-sourcing rural images.
    Towards Spatio-Temporal Cross-Platform Graph Embedding Fusion for Urban Traffic Flow Prediction. (arXiv:2208.06947v1 [cs.LG])
    In this paper, we have proposed STC-GEF, a novel Spatio-Temporal Cross-platform Graph Embedding Fusion approach for the urban traffic flow prediction. We have designed a spatial embedding module based on graph convolutional networks (GCN) to extract the complex spatial features within traffic flow data. Furthermore, to capture the temporal dependencies between the traffic flow data from various time intervals, we have designed a temporal embedding module based on recurrent neural networks. Based on the observations that different transportation platforms trip data (e.g., taxis, Uber, and Lyft) can be correlated, we have designed an effective fusion mechanism that combines the trip data from different transportation platforms and further uses them for cross-platform traffic flow prediction (e.g., integrating taxis and ride-sharing platforms for taxi traffic flow prediction). We have conducted extensive real-world experimental studies based on real-world trip data of yellow taxis and ride-sharing (Lyft) from the New York City (NYC), and validated the accuracy and effectiveness of STC-GEF in fusing different transportation platform data and predicting traffic flows.
    Syntax-driven Data Augmentation for Named Entity Recognition. (arXiv:2208.06957v1 [cs.CL])
    In low resource settings, data augmentation strategies are commonly leveraged to improve performance. Numerous approaches have attempted document-level augmentation (e.g., text classification), but few studies have explored token-level augmentation. Performed naively, data augmentation can produce semantically incongruent and ungrammatical examples. In this work, we compare simple masked language model replacement and an augmentation method using constituency tree mutations to improve the performance of named entity recognition in low-resource settings with the aim of preserving linguistic cohesion of the augmented sentences.
    Modeling Network-level Traffic Flow Transitions on Sparse Data. (arXiv:2208.06646v1 [cs.LG])
    Modeling how network-level traffic flow changes in the urban environment is useful for decision-making in transportation, public safety and urban planning. The traffic flow system can be viewed as a dynamic process that transits between states (e.g., traffic volumes on each road segment) over time. In the real-world traffic system with traffic operation actions like traffic signal control or reversible lane changing, the system's state is influenced by both the historical states and the actions of traffic operations. In this paper, we consider the problem of modeling network-level traffic flow under a real-world setting, where the available data is sparse (i.e., only part of the traffic system is observed). We present DTIGNN, an approach that can predict network-level traffic flows from sparse data. DTIGNN models the traffic system as a dynamic graph influenced by traffic signals, learns the transition models grounded by fundamental transition equations from transportation, and predicts future traffic states with imputation in the process. Through comprehensive experiments, we demonstrate that our method outperforms state-of-the-art methods and can better support decision-making in transportation.
    Deep-Learning-Aided Path Planning and Map Construction for Expediting Indoor Mapping. (arXiv:2011.02043v2 [cs.LG] UPDATED)
    The problem of autonomous indoor mapping is addressed. The goal is to minimize the time to achieve a predefined percentage of exposure with some desired level of certainty. The use of a pre-trained generative deep neural network, acting as a map predictor, in both the path planning and the map construction is proposed in order to expedite the mapping process. This method is examined in combination with several frontier-based path planners for two distinct floorplan datasets. Simulations are run for several configurations of the integrated map predictor, the results of which reveal that by utilizing the prediction a significant reduction in mapping time is possible. When the prediction is integrated in both path planning and map construction processes it is shown that the mapping time may in some cases be cut by over 50%.
    Asset Allocation: From Markowitz to Deep Reinforcement Learning. (arXiv:2208.07158v1 [q-fin.PM])
    Asset allocation is an investment strategy that aims to balance risk and reward by constantly redistributing the portfolio's assets according to certain goals, risk tolerance, and investment horizon. Unfortunately, there is no simple formula that can find the right allocation for every individual. As a result, investors may use different asset allocations' strategy to try to fulfil their financial objectives. In this work, we conduct an extensive benchmark study to determine the efficacy and reliability of a number of optimization techniques. In particular, we focus on traditional approaches based on Modern Portfolio Theory, and on machine-learning approaches based on deep reinforcement learning. We assess the model's performance under different market tendency, i.e., both bullish and bearish markets. For reproducibility, we provide the code implementation code in this repository.
    On the Limitations of Continual Learning for Malware Classification. (arXiv:2208.06568v1 [cs.CR])
    Malicious software (malware) classification offers a unique challenge for continual learning (CL) regimes due to the volume of new samples received on a daily basis and the evolution of malware to exploit new vulnerabilities. On a typical day, antivirus vendors receive hundreds of thousands of unique pieces of software, both malicious and benign, and over the course of the lifetime of a malware classifier, more than a billion samples can easily accumulate. Given the scale of the problem, sequential training using continual learning techniques could provide substantial benefits in reducing training and storage overhead. To date, however, there has been no exploration of CL applied to malware classification tasks. In this paper, we study 11 CL techniques applied to three malware tasks covering common incremental learning scenarios, including task, class, and domain incremental learning (IL). Specifically, using two realistic, large-scale malware datasets, we evaluate the performance of the CL methods on both binary malware classification (Domain-IL) and multi-class malware family classification (Task-IL and Class-IL) tasks. To our surprise, continual learning methods significantly underperformed naive Joint replay of the training data in nearly all settings -- in some cases reducing accuracy by more than 70 percentage points. A simple approach of selectively replaying 20% of the stored data achieves better performance, with 50% of the training time compared to Joint replay. Finally, we discuss potential reasons for the unexpectedly poor performance of the CL techniques, with the hope that it spurs further research on developing techniques that are more effective in the malware classification domain.
    Magnetic Resonance Spectroscopy Deep Learning Denoising Using Few In Vivo Data. (arXiv:2101.11442v3 [physics.med-ph] UPDATED)
    Magnetic Resonance Spectroscopy (MRS) is a noninvasive tool to reveal metabolic information. One challenge of 1H-MRS is the low Signal-Noise Ratio (SNR). To improve the SNR, a typical approach is to perform Signal Averaging (SA) with M repeated samples. The data acquisition time, however, is increased by M times accordingly, and a complete clinical MRS scan takes approximately 10 minutes at a common setting M=128. Recently, deep learning has been introduced to improve the SNR but most of them use the simulated data as the training set. This may hinder the MRS applications since some potential differences, such as acquisition system imperfections, and physiological and psychologic conditions may exist between the simulated and in vivo data. Here, we proposed a new scheme that purely used the repeated samples of realistic data. A deep learning model, Refusion Long Short-Term Memory (ReLSTM), was designed to learn the mapping from the low SNR time-domain data (24 SA) to the high SNR one (128 SA). Experiments on the in vivo brain spectra of 7 healthy subjects, 2 brain tumor patients and 1 cerebral infarction patient showed that only using 20% repeated samples, the denoised spectra by ReLSTM could provide comparable estimated concentrations of metabolites to 128 SA. Compared with the state-of-the-art low-rank denoising method, the ReLSTM achieved the lower relative error and the Cram\'er-Rao lower bounds in quantifying some important biomarkers. In summary, ReLSTM can perform high-fidelity denoising of the spectra under fast acquisition (24 SA), which would be valuable to MRS clinical studies.
    On a Mechanism Framework of Autoencoders. (arXiv:2208.06995v1 [cs.LG])
    This paper proposes a theoretical framework on the mechanism of autoencoders. To the encoder part, under the main use of dimensionality reduction, we investigate its two fundamental properties: bijective maps and data disentangling. The general construction methods of an encoder that satisfies either or both of the above two properties are given. To the decoder part, as a consequence of the encoder constructions, we present a new basic principle of the solution, without using affine transforms. The generalization mechanism of autoencoders is modeled. The results of ReLU autoencoders are generalized to some non-ReLU cases, particularly for the sigmoid-unit autoencoder. Based on the theoretical framework above, we explain some experimental results of variational autoencoders, denoising autoencoders, and linear-unit autoencoders, with emphasis on the interpretation of the lower-dimensional representation of data via encoders; and the mechanism of image restoration through autoencoders is natural to be understood by those explanations. Compared to PCA and decision trees, the advantages of (generalized) autoencoders on dimensionality reduction and classification are demonstrated, respectively. Convolutional neural networks and randomly weighted neural networks are also interpreted by this framework.
    BED: A Real-Time Object Detection System for Edge Devices. (arXiv:2202.07503v3 [cs.CV] UPDATED)
    Deploying deep neural networks~(DNNs) on edge devices provides efficient and effective solutions for the real-world tasks. Edge devices have been used for collecting a large volume of data efficiently in different domains. DNNs have been an effective tool for data processing and analysis. However, designing DNNs on edge devices is challenging due to the limited computational resources and memory. To tackle this challenge, we demonstrate Object Detection System for Edge Devices~(BED) on the MAX78000 DNN accelerator. It integrates on-device DNN inference with a camera and an LCD display for image acquisition and detection exhibition, respectively. BED is a concise, effective and detailed solution, including model training, quantization, synthesis and deployment. The entire repository is open-sourced on Github, including a Graphical User Interface~(GUI) for on-chip debugging. Experiment results indicate that BED can produce accurate detection with a 300-KB tiny DNN model, which takes only 91.9 ms of inference time and 1.845 mJ of energy. The real-time detection is available at YouTube.
    GUARD: Graph Universal Adversarial Defense. (arXiv:2204.09803v3 [cs.LG] UPDATED)
    Graph convolutional networks (GCNs) have shown to be vulnerable to small adversarial perturbations, which becomes a severe threat and largely limits their applications in security-critical scenarios. To mitigate such a threat, considerable research efforts have been devoted to increasing the robustness of GCNs against adversarial attacks. However, current approaches for defense are typically designed for the whole graph and consider the global performance, posing challenges in protecting important local nodes from stronger adversarial targeted attacks. In this work, we present a simple yet effective method, named Graph Universal Adversarial Defense (GUARD). Unlike previous works, GUARD protects each individual node from attacks with a universal defensive patch, which is generated once and can be applied to any node (node-agnostic) in a graph. Extensive experiments on four benchmark datasets demonstrate that our method significantly improves robustness for several established GCNs against multiple adversarial attacks and outperforms state-of-the-art defense methods by large margins. Our code is publicly available at https://github.com/EdisonLeeeee/GUARD.
    Enhancing Graph Contrastive Learning with Node Similarity. (arXiv:2208.06743v1 [cs.LG])
    Graph Neural Networks (GNNs) have achieved great success in learning graph representations and thus facilitating various graph-related tasks. However, most GNN methods adopt a supervised learning setting, which is not always feasible in real-world applications due to the difficulty to obtain labeled data. Hence, graph self-supervised learning has been attracting increasing attention. Graph contrastive learning (GCL) is a representative framework for self-supervised learning. In general, GCL learns node representations by contrasting semantically similar nodes (positive samples) and dissimilar nodes (negative samples) with anchor nodes. Without access to labels, positive samples are typically generated by data augmentation, and negative samples are uniformly sampled from the entire graph, which leads to a sub-optimal objective. Specifically, data augmentation naturally limits the number of positive samples that involve in the process (typically only one positive sample is adopted). On the other hand, the random sampling process would inevitably select false-negative samples (samples sharing the same semantics with the anchor). These issues limit the learning capability of GCL. In this work, we propose an enhanced objective that addresses the aforementioned issues. We first introduce an unachievable ideal objective that contains all positive samples and no false-negative samples. This ideal objective is then transformed into a probabilistic form based on the distributions for sampling positive and negative samples. We then model these distributions with node similarity and derive the enhanced objective. Comprehensive experiments on various datasets demonstrate the effectiveness of the proposed enhanced objective under different settings.
    A View Independent Classification Framework for Yoga Postures. (arXiv:2206.13577v2 [cs.CV] UPDATED)
    Yoga is a globally acclaimed and widely recommended practice for a healthy living. Maintaining correct posture while performing a Yogasana is of utmost importance. In this work, we employ transfer learning from Human Pose Estimation models for extracting 136 key-points spread all over the body to train a Random Forest classifier which is used for estimation of the Yogasanas. The results are evaluated on an in-house collected extensive yoga video database of 51 subjects recorded from 4 different camera angles. We propose a 3 step scheme for evaluating the generalizability of a Yoga classifier by testing it on 1) unseen frames, 2) unseen subjects, and 3) unseen camera angles. We argue that for most of the applications, validation accuracies on unseen subjects and unseen camera angles would be most important. We empirically analyze over three public datasets, the advantage of transfer learning and the possibilities of target leakage. We further demonstrate that the classification accuracies critically depend on the cross validation method employed and can often be misleading. To promote further research, we have made key-points dataset and code publicly available.
    RG-Flow: A hierarchical and explainable flow model based on renormalization group and sparse prior. (arXiv:2010.00029v5 [cs.LG] UPDATED)
    Flow-based generative models have become an important class of unsupervised learning approaches. In this work, we incorporate the key ideas of renormalization group (RG) and sparse prior distribution to design a hierarchical flow-based generative model, RG-Flow, which can separate information at different scales of images and extract disentangled representations at each scale. We demonstrate our method on synthetic multi-scale image datasets and the CelebA dataset, showing that the disentangled representations enable semantic manipulation and style mixing of the images at different scales. To visualize the latent representations, we introduce receptive fields for flow-based models and show that the receptive fields of RG-Flow are similar to those of convolutional neural networks. In addition, we replace the widely adopted isotropic Gaussian prior distribution by the sparse Laplacian distribution to further enhance the disentanglement of representations. From a theoretical perspective, our proposed method has $O(\log L)$ complexity for inpainting of an image with edge length $L$, compared to previous generative models with $O(L^2)$ complexity.
    BinBert: Binary Code Understanding with a Fine-tunable and Execution-aware Transformer. (arXiv:2208.06692v1 [cs.CR])
    A recent trend in binary code analysis promotes the use of neural solutions based on instruction embedding models. An instruction embedding model is a neural network that transforms sequences of assembly instructions into embedding vectors. If the embedding network is trained such that the translation from code to vectors partially preserves the semantic, the network effectively represents an assembly code model. In this paper we present BinBert, a novel assembly code model. BinBert is built on a transformer pre-trained on a huge dataset of both assembly instruction sequences and symbolic execution information. BinBert can be applied to assembly instructions sequences and it is fine-tunable, i.e. it can be re-trained as part of a neural architecture on task-specific data. Through fine-tuning, BinBert learns how to apply the general knowledge acquired with pre-training to the specific task. We evaluated BinBert on a multi-task benchmark that we specifically designed to test the understanding of assembly code. The benchmark is composed of several tasks, some taken from the literature, and a few novel tasks that we designed, with a mix of intrinsic and downstream tasks. Our results show that BinBert outperforms state-of-the-art models for binary instruction embedding, raising the bar for binary code understanding.
    The FEDHC Bayesian network learning algorithm. (arXiv:2012.00113v6 [stat.ML] UPDATED)
    The paper proposes a new hybrid Bayesian network learning algorithm, termed Forward Early Dropping Hill Climbing (FEDHC), devised to work with either continuous or categorical variables. Further, the paper manifests that the only implementation of MMHC in the statistical software \textit{R}, is prohibitively expensive and a new implementation is offered. Further, specifically for the case of continuous data, a robust to outliers version of FEDHC, that can be adopted by other BN learning algorithms, is proposed. The FEDHC is tested via Monte Carlo simulations that distinctly show it is computationally efficient, and produces Bayesian networks of similar to, or of higher accuracy than MMHC and PCHC. Finally, an application of FEDHC, PCHC and MMHC algorithms to real data, from the field of economics, is demonstrated using the statistical software \textit{R}.
    Forecasting Question Answering over Temporal Knowledge Graphs. (arXiv:2208.06501v1 [cs.AI])
    Question answering over temporal knowledge graphs (TKGQA) has recently found increasing interest. TKGQA requires temporal reasoning techniques to extract the relevant information from temporal knowledge bases. The only existing TKGQA dataset, i.e., CronQuestions, consists of temporal questions based on the facts from a fixed time period, where a temporal knowledge graph (TKG) spanning the same period can be fully used for answer inference, allowing the TKGQA models to use even the future knowledge to answer the questions based on the past facts. In real-world scenarios, however, it is also common that given the knowledge until now, we wish the TKGQA systems to answer the questions asking about the future. As humans constantly seek plans for the future, building TKGQA systems for answering such forecasting questions is important. Nevertheless, this has still been unexplored in previous research. In this paper, we propose a novel task: forecasting question answering over temporal knowledge graphs. We also propose a large-scale TKGQA benchmark dataset, i.e., ForecastTKGQuestions, for this task. It includes three types of questions, i.e., entity prediction, yes-no, and fact reasoning questions. For every forecasting question in our dataset, QA models can only have access to the TKG information before the timestamp annotated in the given question for answer inference. We find that the state-of-the-art TKGQA methods perform poorly on forecasting questions, and they are unable to answer yes-no questions and fact reasoning questions. To this end, we propose ForecastTKGQA, a TKGQA model that employs a TKG forecasting module for future inference, to answer all three types of questions. Experimental results show that ForecastTKGQA outperforms recent TKGQA methods on the entity prediction questions, and it also shows great effectiveness in answering the other two types of questions.
    On the Estimation of Derivatives Using Plug-in KRR Estimators. (arXiv:2006.01350v3 [stat.ML] UPDATED)
    We study the problem of estimating the derivatives of a regression function, which has a wide range of applications as a key nonparametric functional of unknown functions. Standard analysis may be tailored to specific derivative orders, and parameter tuning remains a daunting challenge particularly for high-order derivatives. In this article, we propose a simple plug-in kernel ridge regression (KRR) estimator in nonparametric regression with random design that is broadly applicable for multi-dimensional support and arbitrary mixed-partial derivatives. We provide a non-asymptotic analysis to study the behavior of the proposed estimator in a unified manner that encompasses the regression function and its derivatives, leading to two error bounds for a general class of kernels under the strong $L_\infty$ norm. In a concrete example specialized to kernels with polynomially decaying eigenvalues, the proposed estimator recovers the minimax optimal rate up to a logarithmic factor for estimating derivatives of functions in H\"older and Sobolev classes. Interestingly, the proposed estimator achieves the optimal rate of convergence with the same choice of tuning parameter for any order of derivatives. Hence, the proposed estimator enjoys a \textit{plug-in property} for derivatives in that it automatically adapts to the order of derivatives to be estimated, enabling easy tuning in practice. Our simulation studies show favorable finite sample performance of the proposed method relative to several existing methods blue and corroborate the theoretical findings on its minimax optimality.
    Imputation Strategies Under Clinical Presence: Impact on Algorithmic Fairness. (arXiv:2208.06648v1 [cs.AI])
    Biases have marked medical history, leading to unequal care affecting marginalised groups. The patterns of missingness in observational data often reflect these group discrepancies, but the algorithmic fairness implications of group-specific missingness are not well understood. Despite its potential impact, imputation is too often a forgotten preprocessing step. At best, practitioners guide imputation choice by optimising overall performance, ignoring how this preprocessing can reinforce inequities. Our work questions this choice by studying how imputation affects downstream algorithmic fairness. First, we provide a structured view of the relationship between clinical presence mechanisms and group-specific missingness patterns. Then, through simulations and real-world experiments, we demonstrate that the imputation choice influences marginalised group performance and that no imputation strategy consistently reduces disparities. Importantly, our results show that current practices may endanger health equity as similarly performing imputation strategies at the population level can affect marginalised groups in different ways. Finally, we propose recommendations for mitigating inequity stemming from a neglected step of the machine learning pipeline.
    Demo: RhythmEdge: Enabling Contactless Heart Rate Estimation on the Edge. (arXiv:2208.06572v1 [cs.LG])
    In this demo paper, we design and prototype RhythmEdge, a low-cost, deep-learning-based contact-less system for regular HR monitoring applications. RhythmEdge benefits over existing approaches by facilitating contact-less nature, real-time/offline operation, inexpensive and available sensing components, and computing devices. Our RhythmEdge system is portable and easily deployable for reliable HR estimation in moderately controlled indoor or outdoor environments. RhythmEdge measures HR via detecting changes in blood volume from facial videos (Remote Photoplethysmography; rPPG) and provides instant assessment using off-the-shelf commercially available resource-constrained edge platforms and video cameras. We demonstrate the scalability, flexibility, and compatibility of the RhythmEdge by deploying it on three resource-constrained platforms of differing architectures (NVIDIA Jetson Nano, Google Coral Development Board, Raspberry Pi) and three heterogeneous cameras of differing sensitivity, resolution, properties (web camera, action camera, and DSLR). RhythmEdge further stores longitudinal cardiovascular information and provides instant notification to the users. We thoroughly test the prototype stability, latency, and feasibility for three edge computing platforms by profiling their runtime, memory, and power usage.
    Learning Contact Dynamics using Physically Structured Neural Networks. (arXiv:2102.11206v2 [cs.LG] UPDATED)
    Learning physically structured representations of dynamical systems that include contact between different objects is an important problem for learning-based approaches in robotics. Black-box neural networks can learn to approximately represent discontinuous dynamics, but they typically require large quantities of data and often suffer from pathological behaviour when forecasting for longer time horizons. In this work, we use connections between deep neural networks and differential equations to design a family of deep network architectures for representing contact dynamics between objects. We show that these networks can learn discontinuous contact events in a data-efficient manner from noisy observations in settings that are traditionally difficult for black-box approaches and recent physics inspired neural networks. Our results indicate that an idealised form of touch feedback -- which is heavily relied upon by biological systems -- is a key component of making this learning problem tractable. Together with the inductive biases introduced through the network architectures, our techniques enable accurate learning of contact dynamics from observations.
    DisenHCN: Disentangled Hypergraph Convolutional Networks for Spatiotemporal Activity Prediction. (arXiv:2208.06794v1 [cs.LG])
    Spatiotemporal activity prediction, aiming to predict user activities at a specific location and time, is crucial for applications like urban planning and mobile advertising. Existing solutions based on tensor decomposition or graph embedding suffer from the following two major limitations: 1) ignoring the fine-grained similarities of user preferences; 2) user's modeling is entangled. In this work, we propose a hypergraph neural network model called DisenHCN to bridge the above gaps. In particular, we first unify the fine-grained user similarity and the complex matching between user preferences and spatiotemporal activity into a heterogeneous hypergraph. We then disentangle the user representations into different aspects (location-aware, time-aware, and activity-aware) and aggregate corresponding aspect's features on the constructed hypergraph, capturing high-order relations from different aspects and disentangles the impact of each aspect for final prediction. Extensive experiments show that our DisenHCN outperforms the state-of-the-art methods by 14.23% to 18.10% on four real-world datasets. Further studies also convincingly verify the rationality of each component in our DisenHCN.
    SNGuess: A method for the selection of young extragalactic transients. (arXiv:2208.06534v1 [astro-ph.IM])
    With a rapidly rising number of transients detected in astronomy, classification methods based on machine learning are increasingly being employed. Their goals are typically to obtain a definitive classification of transients, and for good performance they usually require the presence of a large set of observations. However, well-designed, targeted models can reach their classification goals with fewer computing resources. This paper presents SNGuess, a model designed to find young extragalactic nearby transients with high purity. SNGuess works with a set of features that can be efficiently calculated from astronomical alert data. Some of these features are static and associated with the alert metadata, while others must be calculated from the photometric observations contained in the alert. Most of the features are simple enough to be obtained or to be calculated already at the early stages in the lifetime of a transient after its detection. We calculate these features for a set of labeled public alert data obtained over a time span of 15 months from the Zwicky Transient Facility (ZTF). The core model of SNGuess consists of an ensemble of decision trees, which are trained via gradient boosting. Approximately 88% of the candidates suggested by SNGuess from a set of alerts from ZTF spanning from April 2020 to August 2021 were found to be true relevant supernovae (SNe). For alerts with bright detections, this number ranges between 92% and 98%. Since April 2020, transients identified by SNGuess as potential young SNe in the ZTF alert stream are being published to the Transient Name Server (TNS) under the AMPEL_ZTF_NEW group identifier. SNGuess scores for any transient observed by ZTF can be accessed via a web service. The source code of SNGuess is publicly available.
    Topological Data Analysis of Neural Network Layer Representations. (arXiv:2208.06438v1 [cs.LG])
    This paper is a cursory study on how topological features are preserved within the internal representations of neural network layers. Using techniques from topological data analysis, namely persistent homology, the topological features of a simple feedforward neural network's layer representations of a modified torus with a Klein bottle-like twist were computed. The network appeared to approximate homeomorphisms in early layers, before significantly changing the topology of the data in deeper layers. The resulting noise hampered the ability of persistent homology to compute these features, however similar topological features seemed to persist longer in a network with a bijective activation function.
    Models of Music Cognition and Composition. (arXiv:2208.06878v1 [cs.SD])
    Much like most of cognition research, music cognition is an interdisciplinary field, which attempts to apply methods of cognitive science (neurological, computational and experimental) to understand the perception and process of composition of music. In this paper, we first motivate why music is relevant to cognitive scientists and give an overview of the approaches to computational modelling of music cognition. We then review literature on the various models of music perception, including non-computational models, computational non-cognitive models and computational cognitive models. Lastly, we review literature on modelling the creative behaviour and on computer systems capable of composing music. Since a lot of technical terms from music theory have been used, we have appended a list of relevant terms and their definitions at the end.
    Revisiting Adversarial Attacks on Graph Neural Networks for Graph Classification. (arXiv:2208.06651v1 [cs.SI])
    Graph neural networks (GNNs) have achieved tremendous success in the task of graph classification and diverse downstream real-world applications. Despite their success, existing approaches are either limited to structure attacks or restricted to local information. This calls for a more general attack framework on graph classification, which faces significant challenges due to the complexity of generating local-node-level adversarial examples using the global-graph-level information. To address this "global-to-local" problem, we present a general framework CAMA to generate adversarial examples by manipulating graph structure and node features in a hierarchical style. Specifically, we make use of Graph Class Activation Mapping and its variant to produce node-level importance corresponding to the graph classification task. Then through a heuristic design of algorithms, we can perform both feature and structure attacks under unnoticeable perturbation budgets with the help of both node-level and subgraph-level importance. Experiments towards attacking four state-of-the-art graph classification models on six real-world benchmarks verify the flexibility and effectiveness of our framework.
    An Empirical Comparison of Explainable Artificial Intelligence Methods for Clinical Data: A Case Study on Traumatic Brain Injury. (arXiv:2208.06717v1 [cs.AI])
    A longstanding challenge surrounding deep learning algorithms is unpacking and understanding how they make their decisions. Explainable Artificial Intelligence (XAI) offers methods to provide explanations of internal functions of algorithms and reasons behind their decisions in ways that are interpretable and understandable to human users. . Numerous XAI approaches have been developed thus far, and a comparative analysis of these strategies seems necessary to discern their relevance to clinical prediction models. To this end, we first implemented two prediction models for short- and long-term outcomes of traumatic brain injury (TBI) utilizing structured tabular as well as time-series physiologic data, respectively. Six different interpretation techniques were used to describe both prediction models at the local and global levels. We then performed a critical analysis of merits and drawbacks of each strategy, highlighting the implications for researchers who are interested in applying these methodologies. The implemented methods were compared to one another in terms of several XAI characteristics such as understandability, fidelity, and stability. Our findings show that SHAP is the most stable with the highest fidelity but falls short of understandability. Anchors, on the other hand, is the most understandable approach, but it is only applicable to tabular data and not time series data.
    Smart caching in a Data Lake for High Energy Physics analysis. (arXiv:2208.06437v1 [cs.DC])
    The continuous growth of data production in almost all scientific areas raises new problems in data access and management, especially in a scenario where the end-users, as well as the resources that they can access, are worldwide distributed. This work is focused on the data caching management in a Data Lake infrastructure in the context of the High Energy Physics field. We are proposing an autonomous method, based on Reinforcement Learning techniques, to improve the user experience and to contain the maintenance costs of the infrastructure.
    TabText: a Systematic Approach to Aggregate Knowledge Across Tabular Data Structures. (arXiv:2206.10381v2 [cs.LG] UPDATED)
    Processing and analyzing tabular data in a productive and efficient way is essential for building successful applications of machine learning in fields such as healthcare. However, the lack of a unified framework for representing and standardizing tabular information poses a significant challenge to researchers and professionals alike. In this work, we present TabText, a methodology that leverages the unstructured data format of language to encode tabular data from different table structures and time periods efficiently and accurately. We show using two healthcare datasets and four prediction tasks that features extracted via TabText outperform those extracted with traditional processing methods by 2-5%. Furthermore, we analyze the sensitivity of our framework against different choices for sentence representations of missing values, meta information and language descriptiveness, and provide insights into winning strategies that improve performance.
    IRL with Partial Observations using the Principle of Uncertain Maximum Entropy. (arXiv:2208.06988v1 [cs.LG])
    The principle of maximum entropy is a broadly applicable technique for computing a distribution with the least amount of information possible while constrained to match empirically estimated feature expectations. However, in many real-world applications that use noisy sensors computing the feature expectations may be challenging due to partial observation of the relevant model variables. For example, a robot performing apprenticeship learning may lose sight of the agent it is learning from due to environmental occlusion. We show that in generalizing the principle of maximum entropy to these types of scenarios we unavoidably introduce a dependency on the learned model to the empirical feature expectations. We introduce the principle of uncertain maximum entropy and present an expectation-maximization based solution generalized from the principle of latent maximum entropy. Finally, we experimentally demonstrate the improved robustness to noisy data offered by our technique in a maximum causal entropy inverse reinforcement learning domain.
    Learning to Infer Counterfactuals: Meta-Learning for Estimating Multiple Imbalanced Treatment Effects. (arXiv:2208.06748v1 [cs.LG])
    We regularly consider answering counterfactual questions in practice, such as "Would people with diabetes take a turn for the better had they choose another medication?". Observational studies are growing in significance in answering such questions due to their widespread accumulation and comparatively easier acquisition than Randomized Control Trials (RCTs). Recently, some works have introduced representation learning and domain adaptation into counterfactual inference. However, most current works focus on the setting of binary treatments. None of them considers that different treatments' sample sizes are imbalanced, especially data examples in some treatment groups are relatively limited due to inherent user preference. In this paper, we design a new algorithmic framework for counterfactual inference, which brings an idea from Meta-learning for Estimating Individual Treatment Effects (MetaITE) to fill the above research gaps, especially considering multiple imbalanced treatments. Specifically, we regard data episodes among treatment groups in counterfactual inference as meta-learning tasks. We train a meta-learner from a set of source treatment groups with sufficient samples and update the model by gradient descent with limited samples in target treatment. Moreover, we introduce two complementary losses. One is the supervised loss on multiple source treatments. The other loss which aligns latent distributions among various treatment groups is proposed to reduce the discrepancy. We perform experiments on two real-world datasets to evaluate inference accuracy and generalization ability. Experimental results demonstrate that the model MetaITE matches/outperforms state-of-the-art methods.
    May the force be with you. (arXiv:2208.06676v1 [cs.LG])
    Modern methods in dimensionality reduction are dominated by nonlinear attraction-repulsion force-based methods (this includes t-SNE, UMAP, ForceAtlas2, LargeVis, and many more). The purpose of this paper is to demonstrate that all such methods, by design, come with an additional feature that is being automatically computed along the way, namely the vector field associated with these forces. We show how this vector field gives additional high-quality information and propose a general refinement strategy based on ideas from Morse theory. The efficiency of these ideas is illustrated specifically using t-SNE on synthetic and real-life data sets.
    ReCo: A Dataset for Residential Community Layout Planning. (arXiv:2206.04678v2 [cs.LG] UPDATED)
    Layout planning is centrally important in the field of architecture and urban design. Among the various basic units carrying urban functions, residential community plays a vital part for supporting human life. Therefore, the layout planning of residential community has always been of concern, and has attracted particular attention since the advent of deep learning that facilitates the automated layout generation and spatial pattern recognition. However, the research circles generally suffer from the insufficiency of residential community layout benchmark or high-quality datasets, which hampers the future exploration of data-driven methods for residential community layout planning. The lack of datasets is largely due to the difficulties of large-scale real-world residential data acquisition and long-term expert screening. In order to address the issues and advance a benchmark dataset for various intelligent spatial design and analysis applications in the development of smart city, we introduce Residential Community Layout Planning (ReCo) Dataset, which is the first and largest open-source vector dataset related to real-world community to date. ReCo Dataset is presented in multiple data formats with 37,646 residential community layout plans, covering 598,728 residential buildings with height information. ReCo can be conveniently adapted for residential community layout related urban design tasks, e.g., generative layout design, morphological pattern recognition and spatial evaluation. To validate the utility of ReCo in automated residential community layout planning, two Generative Adversarial Network (GAN) based generative models are further applied to the dataset. We expect ReCo Dataset to inspire more creative and practical work in intelligent design and beyond. The ReCo Dataset is published at: https://www.kaggle.com/fdudsde/reco-dataset.  ( 3 min )
    Fast Vocabulary Projection Method via Clustering for Multilingual Machine Translation on GPU. (arXiv:2208.06874v1 [cs.CL])
    Multilingual Neural Machine Translation has been showing great success using transformer models. Deploying these models is challenging because they usually require large vocabulary (vocab) sizes for various languages. This limits the speed of predicting the output tokens in the last vocab projection layer. To alleviate these challenges, this paper proposes a fast vocabulary projection method via clustering which can be used for multilingual transformers on GPUs. First, we offline split the vocab search space into disjoint clusters given the hidden context vector of the decoder output, which results in much smaller vocab columns for vocab projection. Second, at inference time, the proposed method predicts the clusters and candidate active tokens for hidden context vectors at the vocab projection. This paper also includes analysis of different ways of building these clusters in multilingual settings. Our results show end-to-end speed gains in float16 GPU inference up to 25% while maintaining the BLEU score and slightly increasing memory cost. The proposed method speeds up the vocab projection step itself by up to 2.6x. We also conduct an extensive human evaluation to verify the proposed method preserves the quality of the translations from the original model.
    Multinomial Logistic Regression Algorithms via Quadratic Gradient. (arXiv:2208.06828v1 [cs.LG])
    Multinomial logistic regression, also known by other names such as multiclass logistic regression and softmax regression, is a fundamental classification method that generalizes binary logistic regression to multiclass problems. A recently work proposed a faster gradient called $\texttt{quadratic gradient}$ that can accelerate the binary logistic regression training, and presented an enhanced Nesterov's accelerated gradient (NAG) method for binary logistic regression. In this paper, we extend this work to multiclass logistic regression and propose an enhanced Adaptive Gradient Algorithm (Adagrad) that can accelerate the original Adagrad method. We test the enhanced NAG method and the enhanced Adagrad method on some multiclass-problem datasets. Experimental results show that both enhanced methods converge faster than their original ones respectively.  ( 2 min )
    Reverse Engineering the Neural Tangent Kernel. (arXiv:2106.03186v4 [cs.LG] UPDATED)
    The development of methods to guide the design of neural networks is an important open challenge for deep learning theory. As a paradigm for principled neural architecture design, we propose the translation of high-performing kernels, which are better-understood and amenable to first-principles design, into equivalent network architectures, which have superior efficiency, flexibility, and feature learning. To this end, we constructively prove that, with just an appropriate choice of activation function, any positive-semidefinite dot-product kernel can be realized as either the NNGP or neural tangent kernel of a fully-connected neural network with only one hidden layer. We verify our construction numerically and demonstrate its utility as a design tool for finite fully-connected networks in several experiments.  ( 2 min )
    Equivariant Finite Normalizing Flows. (arXiv:2110.08649v2 [cs.LG] UPDATED)
    Generative modeling seeks to uncover the underlying factors that give rise to observed data that can often be modeled as the natural symmetries that manifest themselves through invariances and equivariances to certain transformation laws. However, current approaches to representing these symmetries are couched in the formalism of continuous normalizing flows that require the construction of equivariant vector fields -- inhibiting their simple application to conventional higher dimensional generative modelling domains like natural images. In this paper, we focus on building equivariant normalizing flows using discrete layers. We first theoretically prove the existence of an equivariant map for compact groups whose actions are on compact spaces. We further introduce three new equivariant flows: $G$-Residual Flows, $G$-Coupling Flows, and $G$-Inverse Autoregressive Flows that elevate classical Residual, Coupling, and Inverse Autoregressive Flows with equivariant maps to a prescribed group $G$. Our construction of $G$-Residual Flows are also universal, in the sense that we prove an $G$-equivariant diffeomorphism can be exactly mapped by a $G$-residual flow. Finally, we complement our theoretical insights with demonstrative experiments -- for the first time -- on image datasets like CIFAR-10 and show $G$-Equivariant Finite Normalizing flows lead to increased data efficiency, faster convergence, and improved likelihood estimates.  ( 3 min )
    Deep Neural Network Approximation For H\"older Functions. (arXiv:2201.03747v2 [cs.LG] UPDATED)
    In this work, we explore the approximation capability of deep Rectified Quadratic Unit neural networks for H\"older-regular functions, with respect to the uniform norm. We find that theoretical approximation heavily depends on the selected activation function in the neural network.
    Sharp asymptotics on the compression of two-layer neural networks. (arXiv:2205.08199v3 [cs.IT] UPDATED)
    In this paper, we study the compression of a target two-layer neural network with N nodes into a compressed network with M<N nodes. More precisely, we consider the setting in which the weights of the target network are i.i.d. sub-Gaussian, and we minimize the population L_2 loss between the outputs of the target and of the compressed network, under the assumption of Gaussian inputs. By using tools from high-dimensional probability, we show that this non-convex problem can be simplified when the target network is sufficiently over-parameterized, and provide the error rate of this approximation as a function of the input dimension and N. In this mean-field limit, the simplified objective, as well as the optimal weights of the compressed network, does not depend on the realization of the target network, but only on expected scaling factors. Furthermore, for networks with ReLU activation, we conjecture that the optimum of the simplified optimization problem is achieved by taking weights on the Equiangular Tight Frame (ETF), while the scaling of the weights and the orientation of the ETF depend on the parameters of the target network. Numerical evidence is provided to support this conjecture.  ( 3 min )
    Quantum Boosting using Domain-Partitioning Hypotheses. (arXiv:2110.12793v3 [quant-ph] UPDATED)
    Boosting is an ensemble learning method that converts a weak learner into a strong learner in the PAC learning framework. Freund and Schapire designed the Godel prize-winning algorithm named AdaBoost that can boost learners, which output binary hypotheses. Recently, Arunachalam and Maity presented the first quantum boosting algorithm with similar theoretical guarantees. Their algorithm, which we refer to as QAdaBoost henceforth, is a quantum adaptation of AdaBoost and only works for the binary hypothesis case. QAdaBoost is quadratically faster than AdaBoost in terms of the VC-dimension of the hypothesis class of the weak learner but polynomially worse in the bias of the weak learner. Izdebski et al. posed an open question on whether we can boost quantum weak learners that output non-binary hypothesis. In this work, we address this open question by developing the QRealBoost algorithm which was motivated by the classical RealBoost algorithm. The main technical challenge was to provide provable guarantees for convergence, generalization bounds, and quantum speedup, given that quantum subroutines are noisy and probabilistic. We prove that QRealBoost retains the quadratic speedup of QAdaBoost over AdaBoost and further achieves a polynomial speedup over QAdaBoost in terms of both the bias of the learner and the time taken by the learner to learn the target concept class. Finally, we perform empirical evaluations on QRealBoost and report encouraging observations on quantum simulators by benchmarking the convergence performance of QRealBoost against QAdaBoost, AdaBoost, and RealBoost on a subset of the MNIST dataset and Breast Cancer Wisconsin dataset.  ( 3 min )
    NURD: Negative-Unlabeled Learning for Online Datacenter Straggler Prediction. (arXiv:2203.08339v2 [cs.LG] UPDATED)
    Datacenters execute large computational jobs, which are composed of smaller tasks. A job completes when all its tasks finish, so stragglers -- rare, yet extremely slow tasks -- are a major impediment to datacenter performance. Accurately predicting stragglers would enable proactive intervention, allowing datacenter operators to mitigate stragglers before they delay a job. While much prior work applies machine learning to predict computer system performance, these approaches rely on complete labels -- i.e., sufficient examples of all possible behaviors, including straggling and non-straggling -- or strong assumptions about the underlying latency distributions -- e.g., whether Gaussian or not. Within a running job, however, none of this information is available until stragglers have revealed themselves when they have already delayed the job. To predict stragglers accurately and early without labeled positive examples or assumptions on latency distributions, this paper presents NURD, a novel Negative-Unlabeled learning approach with Reweighting and Distribution-compensation that only trains on negative and unlabeled streaming data. The key idea is to train a predictor using finished tasks of non-stragglers to predict latency for unlabeled running tasks, and then reweight each unlabeled task's prediction based on a weighting function of its feature space. We evaluate NURD on two production traces from Google and Alibaba, and find that compared to the best baseline approach, NURD produces 2--11 percentage point increases in the F1 score in terms of prediction accuracy, and 2.0--8.8 percentage point improvements in job completion time.
    GEDI: A Graph-based End-to-end Data Imputation Framework. (arXiv:2208.06573v1 [cs.LG])
    Data imputation is an effective way to handle missing data, which is common in practical applications. In this study, we propose and test a novel data imputation process that achieve two important goals: (1) preserve the row-wise similarities among observations and column-wise contextual relationships among features in the feature matrix, and (2) tailor the imputation process to specific downstream label prediction task. The proposed imputation process uses Transformer network and graph structure learning to iteratively refine the contextual relationships among features and similarities among observations. Moreover, it uses a meta-learning framework to select features that are influential to the downstream prediction task of interest. We conduct experiments on real-world large data sets, and show that the proposed imputation process consistently improves imputation and label prediction performance over a variety of benchmark methods.  ( 2 min )
    Sequence-based deep learning antibody design for in silico antibody affinity maturation. (arXiv:2103.03724v2 [q-bio.BM] UPDATED)
    Antibody therapeutics has been extensively studied in drug discovery and development within the past decades. One increasingly popular focus in the antibody discovery pipeline is the optimization step for therapeutic leads. Both traditional methods and in silico approaches aim to generate candidates with high binding affinity against specific target antigens. Traditional in vitro approaches use hybridoma or phage display for candidate selection, and surface plasmon resonance (SPR) for evaluation, while in silico computational approaches aim to reduce the high cost and improve efficiency by incorporating mathematical algorithms and computational processing power in the design process. In the present study, we investigated different graph-based designs for depicting antibody-antigen interactions in terms of antibody affinity prediction using deep learning techniques. While other in silico computations require experimentally determined crystal structures, our study took interest in the capability of sequence-based models for in silico antibody maturation. Our preliminary studies achieved satisfying prediction accuracy on binding affinities comparing to conventional approaches and other deep learning approaches. To further study the antibody-antigen binding specificity, and to simulate the optimization process in real-world scenario, we introduced pairwise prediction strategy. We performed analysis based on both baseline and pairwise prediction results. The resulting prediction and efficiency prove the feasibility and computational efficiency of sequence-based method to be adapted as a scalable industry practice.  ( 3 min )
    An Analytic Framework for Robust Training of Artificial Neural Networks. (arXiv:2205.13502v2 [cs.LG] UPDATED)
    The reliability of a learning model is key to the successful deployment of machine learning in various industries. Creating a robust model, particularly one unaffected by adversarial attacks, requires a comprehensive understanding of the adversarial examples phenomenon. However, it is difficult to describe the phenomenon due to the complicated nature of the problems in machine learning. Consequently, many studies investigate the phenomenon by proposing a simplified model of how adversarial examples occur and validate it by predicting some aspect of the phenomenon. While these studies cover many different characteristics of the adversarial examples, they have not reached a holistic approach to the geometric and analytic modeling of the phenomenon. This paper propose a formal framework to study the phenomenon in learning theory and make use of complex analysis and holomorphicity to offer a robust learning rule for artificial neural networks. With the help of complex analysis, we can effortlessly move between geometric and analytic perspectives of the phenomenon and offer further insights on the phenomenon by revealing its connection with harmonic functions. Using our model, we can explain some of the most intriguing characteristics of adversarial examples, including transferability of adversarial examples, and pave the way for novel approaches to mitigate the effects of the phenomenon.  ( 3 min )
    Embedding Principle in Depth for the Loss Landscape Analysis of Deep Neural Networks. (arXiv:2205.13283v2 [cs.LG] UPDATED)
    Understanding the relation between deep and shallow neural networks is extremely important for the theoretical study of deep learning. In this work, we discover an embedding principle in depth that loss landscape of an NN "contains" all critical points of the loss landscapes for shallower NNs. The key tool for our discovery is the critical lifting operator proposed in this work that maps any critical point of a network to critical manifolds of any deeper network while preserving the outputs. This principle provides new insights to many widely observed behaviors of DNNs. Regarding the easy training of deep networks, we show that local minimum of an NN can be lifted to strict saddle points of a deeper NN. Regarding the acceleration effect of batch normalization, we demonstrate that batch normalization helps avoid the critical manifolds lifted from shallower NNs by suppressing layer linearization. We also prove that increasing training data shrinks the lifted critical manifolds, which can result in acceleration of training as demonstrated in experiments. Overall, our discovery of the embedding principle in depth uncovers the depth-wise hierarchical structure of deep learning loss landscape, which serves as a solid foundation for the further study about the role of depth for DNNs.  ( 3 min )
    TL;DW? Summarizing Instructional Videos with Task Relevance & Cross-Modal Saliency. (arXiv:2208.06773v1 [cs.CV])
    YouTube users looking for instructions for a specific task may spend a long time browsing content trying to find the right video that matches their needs. Creating a visual summary (abridged version of a video) provides viewers with a quick overview and massively reduces search time. In this work, we focus on summarizing instructional videos, an under-explored area of video summarization. In comparison to generic videos, instructional videos can be parsed into semantically meaningful segments that correspond to important steps of the demonstrated task. Existing video summarization datasets rely on manual frame-level annotations, making them subjective and limited in size. To overcome this, we first automatically generate pseudo summaries for a corpus of instructional videos by exploiting two key assumptions: (i) relevant steps are likely to appear in multiple videos of the same task (Task Relevance), and (ii) they are more likely to be described by the demonstrator verbally (Cross-Modal Saliency). We propose an instructional video summarization network that combines a context-aware temporal video encoder and a segment scoring transformer. Using pseudo summaries as weak supervision, our network constructs a visual summary for an instructional video given only video and transcribed speech. To evaluate our model, we collect a high-quality test set, WikiHow Summaries, by scraping WikiHow articles that contain video demonstrations and visual depictions of steps allowing us to obtain the ground-truth summaries. We outperform several baselines and a state-of-the-art video summarization model on this new benchmark.  ( 3 min )
    CANF-VC: Conditional Augmented Normalizing Flows for Video Compression. (arXiv:2207.05315v3 [cs.CV] UPDATED)
    This paper presents an end-to-end learning-based video compression system, termed CANF-VC, based on conditional augmented normalizing flows (CANF). Most learned video compression systems adopt the same hybrid-based coding architecture as the traditional codecs. Recent research on conditional coding has shown the sub-optimality of the hybrid-based coding and opens up opportunities for deep generative models to take a key role in creating new coding frameworks. CANF-VC represents a new attempt that leverages the conditional ANF to learn a video generative model for conditional inter-frame coding. We choose ANF because it is a special type of generative model, which includes variational autoencoder as a special case and is able to achieve better expressiveness. CANF-VC also extends the idea of conditional coding to motion coding, forming a purely conditional coding framework. Extensive experimental results on commonly used datasets confirm the superiority of CANF-VC to the state-of-the-art methods. The source code of CANF-VC is available at https://github.com/NYCU-MAPL/CANF-VC.  ( 2 min )
    Determining HEDP Foams' Quality with Multi-View Deep Learning Classification. (arXiv:2208.07196v1 [cs.CV])
    High energy density physics (HEDP) experiments commonly involve a dynamic wave-front propagating inside a low-density foam. This effect affects its density and hence, its transparency. A common problem in foam production is the creation of defective foams. Accurate information on their dimension and homogeneity is required to classify the foams' quality. Therefore, those parameters are being characterized using a 3D-measuring laser confocal microscope. For each foam, five images are taken: two 2D images representing the top and bottom surface foam planes and three images of side cross-sections from 3D scannings. An expert has to do the complicated, harsh, and exhausting work of manually classifying the foam's quality through the image set and only then determine whether the foam can be used in experiments or not. Currently, quality has two binary levels of normal vs. defective. At the same time, experts are commonly required to classify a sub-class of normal-defective, i.e., foams that are defective but might be sufficient for the needed experiment. This sub-class is problematic due to inconclusive judgment that is primarily intuitive. In this work, we present a novel state-of-the-art multi-view deep learning classification model that mimics the physicist's perspective by automatically determining the foams' quality classification and thus aids the expert. Our model achieved 86\% accuracy on upper and lower surface foam planes and 82\% on the entire set, suggesting interesting heuristics to the problem. A significant added value in this work is the ability to regress the foam quality instead of binary deduction and even explain the decision visually. The source code used in this work, as well as other relevant sources, are available at: https://github.com/Scientific-Computing-Lab-NRCN/Multi-View-Foams.git  ( 3 min )
    On the Effect of Dropping Layers of Pre-trained Transformer Models. (arXiv:2004.03844v3 [cs.CL] UPDATED)
    Transformer-based NLP models are trained using hundreds of millions or even billions of parameters, limiting their applicability in computationally constrained environments. While the number of parameters generally correlates with performance, it is not clear whether the entire network is required for a downstream task. Motivated by the recent work on pruning and distilling pre-trained models, we explore strategies to drop layers in pre-trained models, and observe the effect of pruning on downstream GLUE tasks. We were able to prune BERT, RoBERTa and XLNet models up to 40%, while maintaining up to 98% of their original performance. Additionally we show that our pruned models are on par with those built using knowledge distillation, both in terms of size and performance. Our experiments yield interesting observations such as, (i) the lower layers are most critical to maintain downstream task performance, (ii) some tasks such as paraphrase detection and sentence similarity are more robust to the dropping of layers, and (iii) models trained using a different objective function exhibit different learning patterns and w.r.t the layer dropping.  ( 3 min )
    How Does Data Freshness Affect Real-time Supervised Learning?. (arXiv:2208.06948v1 [cs.NI])
    In this paper, we analyze the impact of data freshness on real-time supervised learning, where a neural network is trained to infer a time-varying target (e.g., the position of the vehicle in front) based on features (e.g., video frames) observed at a sensing node (e.g., camera or lidar). One might expect that the performance of real-time supervised learning degrades monotonically as the feature becomes stale. Using an information-theoretic analysis, we show that this is true if the feature and target data sequence can be closely approximated as a Markov chain; it is not true if the data sequence is far from Markovian. Hence, the prediction error of real-time supervised learning is a function of the Age of Information (AoI), where the function could be non-monotonic. Several experiments are conducted to illustrate the monotonic and non-monotonic behaviors of the prediction error. To minimize the inference error in real-time, we propose a new "selection-from-buffer" model for sending the features, which is more general than the "generate-at-will" model used in earlier studies. By using Gittins and Whittle indices, low-complexity scheduling strategies are developed to minimize the inference error, where a new connection between the Gittins index theory and Age of Information (AoI) minimization is discovered. These scheduling results hold (i) for minimizing general AoI functions (monotonic or non-monotonic) and (ii) for general feature transmission time distributions. Data-driven evaluations are presented to illustrate the benefits of the proposed scheduling algorithms.  ( 3 min )
    Combating Label Distribution Shift for Active Domain Adaptation. (arXiv:2208.06604v1 [cs.LG])
    We consider the problem of active domain adaptation (ADA) to unlabeled target data, of which subset is actively selected and labeled given a budget constraint. Inspired by recent analysis on a critical issue from label distribution mismatch between source and target in domain adaptation, we devise a method that addresses the issue for the first time in ADA. At its heart lies a novel sampling strategy, which seeks target data that best approximate the entire target distribution as well as being representative, diverse, and uncertain. The sampled target data are then used not only for supervised learning but also for matching label distributions of source and target domains, leading to remarkable performance improvement. On four public benchmarks, our method substantially outperforms existing methods in every adaptation scenario.  ( 2 min )
    DendroMap: Visual Exploration of Large-Scale Image Datasets for Machine Learning with Treemaps. (arXiv:2205.06935v2 [cs.HC] UPDATED)
    In this paper, we present DendroMap, a novel approach to interactively exploring large-scale image datasets for machine learning (ML). ML practitioners often explore image datasets by generating a grid of images or projecting high-dimensional representations of images into 2-D using dimensionality reduction techniques (e.g., t-SNE). However, neither approach effectively scales to large datasets because images are ineffectively organized and interactions are insufficiently supported. To address these challenges, we develop DendroMap by adapting Treemaps, a well-known visualization technique. DendroMap effectively organizes images by extracting hierarchical cluster structures from high-dimensional representations of images. It enables users to make sense of the overall distributions of datasets and interactively zoom into specific areas of interests at multiple levels of abstraction. Our case studies with widely-used image datasets for deep learning demonstrate that users can discover insights about datasets and trained models by examining the diversity of images, identifying underperforming subgroups, and analyzing classification errors. We conducted a user study that evaluates the effectiveness of DendroMap in grouping and searching tasks by comparing it with a gridified version of t-SNE and found that participants preferred DendroMap. DendroMap is available at https://div-lab.github.io/dendromap/.  ( 3 min )
    RuDi: Explaining Behavior Sequence Models by Automatic Statistics Generation and Rule Distillation. (arXiv:2208.07211v1 [cs.LG])
    Risk scoring systems have been widely deployed in many applications, which assign risk scores to users according to their behavior sequences. Though many deep learning methods with sophisticated designs have achieved promising results, the black-box nature hinders their applications due to fairness, explainability, and compliance consideration. Rule-based systems are considered reliable in these sensitive scenarios. However, building a rule system is labor-intensive. Experts need to find informative statistics from user behavior sequences, design rules based on statistics and assign weights to each rule. In this paper, we bridge the gap between effective but black-box models and transparent rule models. We propose a two-stage method, RuDi, that distills the knowledge of black-box teacher models into rule-based student models. We design a Monte Carlo tree search-based statistics generation method that can provide a set of informative statistics in the first stage. Then statistics are composed into logical rules with our proposed neural logical networks by mimicking the outputs of teacher models. We evaluate RuDi on three real-world public datasets and an industrial dataset to demonstrate its effectiveness.  ( 2 min )
    ARIEL: Adversarial Graph Contrastive Learning. (arXiv:2208.06956v1 [cs.LG])
    Contrastive learning is an effective unsupervised method in graph representation learning, and the key component of contrastive learning lies in the construction of positive and negative samples. Previous methods usually utilize the proximity of nodes in the graph as the principle. Recently, the data augmentation based contrastive learning method has advanced to show great power in the visual domain, and some works extended this method from images to graphs. However, unlike the data augmentation on images, the data augmentation on graphs is far less intuitive and much harder to provide high-quality contrastive samples, which leaves much space for improvement. In this work, by introducing an adversarial graph view for data augmentation, we propose a simple but effective method, Adversarial Graph Contrastive Learning (ARIEL), to extract informative contrastive samples within reasonable constraints. We develop a new technique called information regularization for stable training and use subgraph sampling for scalability. We generalize our method from node-level contrastive learning to the graph-level by treating each graph instance as a supernode. ARIEL consistently outperforms the current graph contrastive learning methods for both node-level and graph-level classification tasks on real-world datasets. We further demonstrate that ARIEL is more robust in face of adversarial attacks.  ( 2 min )
    Predicting skull fractures via CNN with classification algorithms. (arXiv:2208.06756v1 [cs.CV])
    Computer Tomography (CT) images have become quite important to diagnose diseases. CT scan slice contains a vast amount of data that may not be properly examined with the requisite precision and speed using normal visual inspection. A computer-assisted skull fracture classification expert system is needed to assist physicians. Convolutional Neural Networks (CNNs) are the most extensively used deep learning models for image categorization since most often time they outperform other models in terms of accuracy and results. The CNN models were then developed and tested, and several convolutional neural network (CNN) architectures were compared. ResNet50, which was used for feature extraction combined with a gradient boosted decision tree machine learning algorithm to act as a classifier for the categorization of skull fractures from brain CT scans into three fracture categories, had the best overall F1-score of 96%, Hamming Score of 95%, Balanced accuracy Score of 94% & ROC AUC curve of 96% for the classification of skull fractures.  ( 2 min )
    InvisibiliTee: Angle-agnostic Cloaking from Person-Tracking Systems with a Tee. (arXiv:2208.06962v1 [cs.CV])
    After a survey for person-tracking system-induced privacy concerns, we propose a black-box adversarial attack method on state-of-the-art human detection models called InvisibiliTee. The method learns printable adversarial patterns for T-shirts that cloak wearers in the physical world in front of person-tracking systems. We design an angle-agnostic learning scheme which utilizes segmentation of the fashion dataset and a geometric warping process so the adversarial patterns generated are effective in fooling person detectors from all camera angles and for unseen black-box detection models. Empirical results in both digital and physical environments show that with the InvisibiliTee on, person-tracking systems' ability to detect the wearer drops significantly.  ( 2 min )
    Machine Learning Based Radiomics for Glial Tumor Classification and Comparison with Volumetric Analysis. (arXiv:2208.06739v1 [eess.IV])
    Purpose; The purpose of this study is to classify glial tumors into grade II, III and IV categories noninvasively by application of machine learning to multi-modal MRI features in comparison with volumetric analysis. Methods; We retrospectively studied 57 glioma patients with pre and postcontrast T1 weighted, T2 weighted, FLAIR images, and ADC maps acquired on a 3T MRI. The tumors were segmented into enhancing and nonenhancing portions, tumor necrosis, cyst and edema using semiautomated segmentation of ITK-SNAP open source tool. We measured total tumor volume, enhancing-nonenhancing tumor, edema, necrosis volume and the ratios to the total tumor volume. Training of a support vector machine (SVM) classifier and artificial neural network (ANN) was performed with labeled data designed to answer the question of interest. Specificity, sensitivity, and AUC of the predictions were computed by means of ROC analysis. Differences in continuous measures between groups were assessed by using Kruskall Wallis, with post hoc Dunn correction for multiple comparisons. Results; When we compared the volume ratios between groups, there was statistically significant difference between grade IV and grade II-III glial tumors. Edema and tumor necrosis volume ratios for grade IV glial tumors were higher than that of grade II and III. Volumetric ratio analysis could not distinguish grade II and III tumors successfully. However, SVM and ANN correctly classified each group with accuracies up to 98% and 96%. Conclusion; Application of machine learning methods to MRI features can be used to classify brain tumors noninvasively and more readily in clinical settings.  ( 3 min )
    Inference for BART with Multinomial Outcomes. (arXiv:2101.06823v2 [stat.ME] UPDATED)
    The multinomial probit Bayesian additive regression trees (MPBART) framework was proposed by Kindo et al. (KD), approximating the latent utilities in the multinomial probit (MNP) model with BART (Chipman et al. 2010). Compared to multinomial logistic models, MNP does not assume independent alternatives and the correlation structure among alternatives can be specified through multivariate Gaussian distributed latent utilities. We introduce two new algorithms for fitting the MPBART and show that the theoretical mixing rates of our proposals are equal or superior to the existing algorithm in KD. Through simulations, we explore the robustness of the methods to the choice of reference level, imbalance in outcome frequencies, and the specifications of prior hyperparameters for the utility error term. The work is motivated by the application of generating posterior predictive distributions for mortality and engagement in care among HIV-positive patients based on electronic health records (EHRs) from the Academic Model Providing Access to Healthcare (AMPATH) in Kenya. In both the application and simulations, we observe better performance using our proposals as compared to KD in terms of MCMC convergence rate and posterior predictive accuracy.  ( 2 min )
    Three-Player Game Training Dynamics. (arXiv:2208.06531v1 [cs.LG])
    This work explores three-player game training dynamics, under what conditions three-player games converge and the equilibria the converge on. In contrast to prior work, we examine a three-player game architecture in which all players explicitly interact with each other. Prior work analyzes games in which two of three agents interact with only one other player, constituting dual two-player games. We explore three-player game training dynamics using an extended version of a simplified bilinear smooth game, called a simplified trilinear smooth game. We find that trilinear games do not converge on the Nash equilibrium in most cases, rather converging on a fixed point which is optimal for two players, but not for the third. Further, we explore how the order of the updates influences convergence. In addition to alternating and simultaneous updates, we explore a new update order--maximizer-first--which is only possible in a three-player game. We find that three-player games can converge on a Nash equilibrium using maximizer-first updates. Finally, we experiment with differing momentum values for each player in a trilinear smooth game under all three update orders and show that maximizer-first updates achieve more optimal results in a larger set of player-specific momentum value triads than other update orders.  ( 2 min )
  • Open

    RG-Flow: A hierarchical and explainable flow model based on renormalization group and sparse prior. (arXiv:2010.00029v5 [cs.LG] UPDATED)
    Flow-based generative models have become an important class of unsupervised learning approaches. In this work, we incorporate the key ideas of renormalization group (RG) and sparse prior distribution to design a hierarchical flow-based generative model, RG-Flow, which can separate information at different scales of images and extract disentangled representations at each scale. We demonstrate our method on synthetic multi-scale image datasets and the CelebA dataset, showing that the disentangled representations enable semantic manipulation and style mixing of the images at different scales. To visualize the latent representations, we introduce receptive fields for flow-based models and show that the receptive fields of RG-Flow are similar to those of convolutional neural networks. In addition, we replace the widely adopted isotropic Gaussian prior distribution by the sparse Laplacian distribution to further enhance the disentanglement of representations. From a theoretical perspective, our proposed method has $O(\log L)$ complexity for inpainting of an image with edge length $L$, compared to previous generative models with $O(L^2)$ complexity.
    Predictive Data Calibration for Linear Correlation Significance Testing. (arXiv:2208.07081v1 [stat.ME])
    Inferring linear relationships lies at the heart of many empirical investigations. A measure of linear dependence should correctly evaluate the strength of the relationship as well as qualify whether it is meaningful for the population. Pearson's correlation coefficient (PCC), the \textit{de-facto} measure for bivariate relationships, is known to lack in both regards. The estimated strength $r$ maybe wrong due to limited sample size, and nonnormality of data. In the context of statistical significance testing, erroneous interpretation of a $p$-value as posterior probability leads to Type I errors -- a general issue with significance testing that extends to PCC. Such errors are exacerbated when testing multiple hypotheses simultaneously. To tackle these issues, we propose a machine-learning-based predictive data calibration method which essentially conditions the data samples on the expected linear relationship. Calculating PCC using calibrated data yields a calibrated $p$-value that can be interpreted as posterior probability together with a calibrated $r$ estimate, a desired outcome not provided by other methods. Furthermore, the ensuing independent interpretation of each test might eliminate the need for multiple testing correction. We provide empirical evidence favouring the proposed method using several simulations and application to real-world data.
    Accelerated and instance-optimal policy evaluation with linear function approximation. (arXiv:2112.13109v2 [stat.ML] UPDATED)
    We study the problem of policy evaluation with linear function approximation and present efficient and practical algorithms that come with strong optimality guarantees. We begin by proving lower bounds that establish baselines on both the deterministic error and stochastic error in this problem. In particular, we prove an oracle complexity lower bound on the deterministic error in an instance-dependent norm associated with the stationary distribution of the transition kernel, and use the local asymptotic minimax machinery to prove an instance-dependent lower bound on the stochastic error in the i.i.d. observation model. Existing algorithms fail to match at least one of these lower bounds: To illustrate, we analyze a variance-reduced variant of temporal difference learning, showing in particular that it fails to achieve the oracle complexity lower bound. To remedy this issue, we develop an accelerated, variance-reduced fast temporal difference algorithm (VRFTD) that simultaneously matches both lower bounds and attains a strong notion of instance-optimality. Finally, we extend the VRFTD algorithm to the setting with Markovian observations, and provide instance-dependent convergence results. Our theoretical guarantees of optimality are corroborated by numerical experiments.
    Grasping Core Rules of Time Series through Pure Models. (arXiv:2208.07105v1 [cs.LG])
    Time series underwent the transition from statistics to deep learning, as did many other machine learning fields. Although it appears that the accuracy has been increasing as the model is updated in a number of publicly available datasets, it typically only increases the scale by several times in exchange for a slight difference in accuracy. Through this experiment, we point out a different line of thinking, time series, especially long-term forecasting, may differ from other fields. It is not necessary to use extensive and complex models to grasp all aspects of time series, but to use pure models to grasp the core rules of time series changes. With this simple but effective idea, we created PureTS, a network with three pure linear layers that achieved state-of-the-art in 80% of the long sequence prediction tasks while being nearly the lightest model and having the fastest running speed. On this basis, we discuss the potential of pure linear layers in both phenomena and essence. The ability to understand the core law contributes to the high precision of long-distance prediction, and reasonable fluctuation prevents it from distorting the curve in multi-step prediction like mainstream deep learning models, which is summarized as a pure linear neural network that avoids over-fluctuating. Finally, we suggest the fundamental design standards for lightweight long-step time series tasks: input and output should try to have the same dimension, and the structure avoids fragmentation and complex operations.
    Applying Regularized Schr\"odinger-Bridge-Based Stochastic Process in Generative Modeling. (arXiv:2208.07131v1 [cs.LG])
    Compared to the existing function-based models in deep generative modeling, the recently proposed diffusion models have achieved outstanding performance with a stochastic-process-based approach. But a long sampling time is required for this approach due to many timesteps for discretization. Schr\"odinger bridge (SB)-based models attempt to tackle this problem by training bidirectional stochastic processes between distributions. However, they still have a slow sampling speed compared to generative models such as generative adversarial networks. And due to the training of the bidirectional stochastic processes, they require a relatively long training time. Therefore, this study tried to reduce the number of timesteps and training time required and proposed regularization terms to the existing SB models to make the bidirectional stochastic processes consistent and stable with a reduced number of timesteps. Each regularization term was integrated into a single term to enable more efficient training in computation time and memory usage. Applying this regularized stochastic process to various generation tasks, the desired translations between different distributions were obtained, and accordingly, the possibility of generative modeling based on a stochastic process with faster sampling speed could be confirmed. The code is available at https://github.com/KiUngSong/RSB.
    Selective Inference for Sparse Multitask Regression with Applications in Neuroimaging. (arXiv:2205.14220v2 [stat.ME] UPDATED)
    Multi-task learning is frequently used to model a set of related response variables from the same set of features, improving predictive performance and modeling accuracy relative to methods that handle each response variable separately. Despite the potential of multi-task learning to yield more powerful inference than single-task alternatives, prior work in this area has largely omitted uncertainty quantification. Our focus in this paper is a common multi-task problem in neuroimaging, where the goal is to understand the relationship between multiple cognitive task scores (or other subject-level assessments) and brain connectome data collected from imaging. We propose a framework for selective inference to address this problem, with the flexibility to: (i) jointly identify the relevant covariates for each task through a sparsity-inducing penalty, and (ii) conduct valid inference in a model based on the estimated sparsity structure. Our framework offers a new conditional procedure for inference, based on a refinement of the selection event that yields a tractable selection-adjusted likelihood. This gives an approximate system of estimating equations for maximum likelihood inference, solvable via a single convex optimization problem, and enables us to efficiently form confidence intervals with approximately the correct coverage. Applied to both simulated data and data from the Adolescent Cognitive Brain Development (ABCD) study, our selective inference methods yield tighter confidence intervals than commonly used alternatives, such as data splitting. We also demonstrate through simulations that multi-task learning with selective inference can more accurately recover true signals than single-task methods.
    Graph Neural Networks as Gradient Flows. (arXiv:2206.10991v2 [cs.LG] UPDATED)
    Dynamical systems minimizing an energy are ubiquitous in geometry and physics. We propose a novel framework for GNNs where we parametrize (and {\em learn}) an energy functional and then take the GNN equations to be the gradient flow of such energy. This approach allows to analyse the GNN evolution from a multi-particle perspective as learning attractive and repulsive forces in feature space via the positive and negative eigenvalues of a symmetric `channel-mixing' matrix. We conduct spectral analysis of the solutions and provide a better understanding of the role of the channel-mixing in (residual) graph convolutional models and of its ability to steer the diffusion away from over-smoothing. We perform thorough ablation studies corroborating our theory and show competitive performance of simple models on homophilic and heterophilic datasets.  ( 2 min )
    Towards Theoretical Understandings of Robust Markov Decision Processes: Sample Complexity and Asymptotics. (arXiv:2105.03863v3 [stat.ML] UPDATED)
    In this paper, we study the non-asymptotic and asymptotic performances of the optimal robust policy and value function of robust Markov Decision Processes(MDPs), where the optimal robust policy and value function are solved only from a generative model. While prior work focusing on non-asymptotic performances of robust MDPs is restricted in the setting of the KL uncertainty set and $(s,a)$-rectangular assumption, we improve their results and also consider other uncertainty sets, including $L_1$ and $\chi^2$ balls. Our results show that when we assume $(s,a)$-rectangular on uncertainty sets, the sample complexity is about $\widetilde{O}\left(\frac{|\mathcal{S}|^2|\mathcal{A}|}{\varepsilon^2\rho^2(1-\gamma)^4}\right)$. In addition, we extend our results from $(s,a)$-rectangular assumption to $s$-rectangular assumption. In this scenario, the sample complexity varies with the choice of uncertainty sets and is generally larger than the case under $(s,a)$-rectangular assumption. Moreover, we also show that the optimal robust value function is asymptotic normal with a typical rate $\sqrt{n}$ under $(s,a)$ and $s$-rectangular assumptions from both theoretical and empirical perspectives.
    Conformalized Online Learning: Online Calibration Without a Holdout Set. (arXiv:2205.09095v3 [cs.LG] UPDATED)
    We develop a framework for constructing uncertainty sets with a valid coverage guarantee in an online setting, in which the underlying data distribution can drastically -- and even adversarially -- shift over time. The technique we propose is highly flexible as it can be integrated with any online learning algorithm, requiring minimal implementation effort and computational cost. A key advantage of our method over existing alternatives -- which also build on conformal inference -- is that we do not need to split the data into training and holdout calibration sets. This allows us to fit the predictive model in a fully online manner, utilizing the most recent observation for constructing calibrated uncertainty sets. Consequently, and in contrast with existing techniques, (i) the sets we build can quickly adapt to new changes in the distribution; and (ii) our procedure does not require refitting the model at each time step. Using synthetic and real-world benchmark data sets, we demonstrate the validity of our theory and the improved performance of our proposal over existing techniques. To demonstrate the greater flexibility of the proposed method, we show how to construct valid intervals for a multiple-output regression problem that previous sequential calibration methods cannot handle due to impractical computational and memory requirements.  ( 3 min )
    A Unified Causal View of Domain Invariant Representation Learning. (arXiv:2208.06987v1 [stat.ML])
    Machine learning methods can be unreliable when deployed in domains that differ from the domains on which they were trained. To address this, we may wish to learn representations of data that are domain-invariant in the sense that we preserve data structure that is stable across domains, but throw out spuriously-varying parts. There are many representation-learning approaches of this type, including methods based on data augmentation, distributional invariances, and risk invariance. Unfortunately, when faced with any particular real-world domain shift, it is unclear which, if any, of these methods might be expected to work. The purpose of this paper is to show how the different methods relate to each other, and clarify the real-world circumstances under which each is expected to succeed. The key tool is a new notion of domain shift relying on the idea that causal relationships are invariant, but non-causal relationships (e.g., due to confounding) may vary.  ( 2 min )
    Diffusion Models for Video Prediction and Infilling. (arXiv:2206.07696v2 [cs.CV] UPDATED)
    Predicting and anticipating future outcomes or reasoning about missing information in a sequence are critical skills for agents to be able to make intelligent decisions. This requires strong, temporally coherent generative capabilities. Diffusion models have shown remarkable success in several generative tasks, but have not been extensively explored in the video domain. We present Random-Mask Video Diffusion (RaMViD), which extends image diffusion models to videos using 3D convolutions, and introduces a new conditioning technique during training. By varying the mask we condition on, the model is able to perform video prediction, infilling, and upsampling. Due to our simple conditioning scheme, we can utilize the same architecture as used for unconditional training, which allows us to train the model in a conditional and unconditional fashion at the same time. We evaluate the model on two benchmark datasets for video prediction, on which we achieve state-of-the-art results, and one for video generation.  ( 2 min )
    When Does Differentially Private Learning Not Suffer in High Dimensions?. (arXiv:2207.00160v3 [cs.LG] UPDATED)
    Large pretrained models can be privately fine-tuned to achieve performance approaching that of non-private models. A common theme in these results is the surprising observation that high-dimensional models can achieve favorable privacy-utility trade-offs. This seemingly contradicts known results on the model-size dependence of differentially private convex learning and raises the following research question: When does the performance of differentially private learning not degrade with increasing model size? We identify that the magnitudes of gradients projected onto subspaces is a key factor that determines performance. To precisely characterize this for private convex learning, we introduce a condition on the objective that we term \emph{restricted Lipschitz continuity} and derive improved bounds for the excess empirical and population risks that are dimension-independent under additional conditions. We empirically show that in private fine-tuning of large language models, gradients obtained during fine-tuning are mostly controlled by a few principal components. This behavior is similar to conditions under which we obtain dimension-independent bounds in convex settings. Our theoretical and empirical results together provide a possible explanation for recent successes in large-scale private fine-tuning. Code to reproduce our results can be found at \url{https://github.com/lxuechen/private-transformers/tree/main/examples/classification/spectral_analysis}.  ( 3 min )
    Scalable Gaussian-process regression and variable selection using Vecchia approximations. (arXiv:2202.12981v3 [stat.ME] UPDATED)
    Gaussian process (GP) regression is a flexible, nonparametric approach to regression that naturally quantifies uncertainty. In many applications, the number of responses and covariates are both large, and a goal is to select covariates that are related to the response. For this setting, we propose a novel, scalable algorithm, coined VGPR, which optimizes a penalized GP log-likelihood based on the Vecchia GP approximation, an ordered conditional approximation from spatial statistics that implies a sparse Cholesky factor of the precision matrix. We traverse the regularization path from strong to weak penalization, sequentially adding candidate covariates based on the gradient of the log-likelihood and deselecting irrelevant covariates via a new quadratic constrained coordinate descent algorithm. We propose Vecchia-based mini-batch subsampling, which provides unbiased gradient estimators. The resulting procedure is scalable to millions of responses and thousands of covariates. Theoretical analysis and numerical studies demonstrate the improved scalability and accuracy relative to existing methods.  ( 2 min )
    Inverse Extended Kalman Filter -- Part I: Fundamentals. (arXiv:2201.01539v2 [math.OC] UPDATED)
    Recent advances in counter-adversarial systems have garnered significant research attention to inverse filtering from a Bayesian perspective. For example, interest in estimating the adversary's Kalman filter tracked estimate with the purpose of predicting the adversary's future steps has led to recent formulations of inverse Kalman filter (I-KF). In this context of inverse filtering, we address the key challenges of non-linear process dynamics and unknown input to the forward filter by proposing an inverse extended Kalman filter (I-EKF). The purpose of this paper and the companion paper (Part II) is to develop the theory of I-EKF in detail. In this paper, we assume perfect system model information and derive I-EKF with and without an unknown input when both forward and inverse state-space models are non-linear. In the process, I-KF-with-unknown-input is also obtained. We then provide theoretical stability guarantees using both bounded non-linearity and unknown matrix approaches. Numerical experiments validate our methods for various proposed inverse filters using the recursive Cram\'{e}r-Rao lower bound as a benchmark. In the companion paper (Part II), we further generalize these formulations to highly non-linear models and propose reproducing kernel Hilbert space-based EKF to handle incomplete system model information.  ( 3 min )
    Overcoming Oversmoothness in Graph Convolutional Networks via Hybrid Scattering Networks. (arXiv:2201.08932v2 [stat.ML] UPDATED)
    Geometric deep learning has made great strides towards generalizing the design of structure-aware neural networks from traditional domains to non-Euclidean ones, giving rise to graph neural networks (GNN) that can be applied to graph-structured data arising in, e.g., social networks, biochemistry, and material science. Graph convolutional networks (GCNs) in particular, inspired by their Euclidean counterparts, have been successful in processing graph data by extracting structure-aware features. However, current GNN models are often constrained by various phenomena that limit their expressive power and ability to generalize to more complex graph datasets. Most models essentially rely on low-pass filtering of graph signals via local averaging operations, leading to oversmoothing. Moreover, to avoid severe oversmoothing, most popular GCN-style networks tend to be shallow, with narrow receptive fields, leading to underreaching. Here, we propose a hybrid GNN framework that combines traditional GCN filters with band-pass filters defined via geometric scattering. We further introduce an attention framework that allows the model to locally attend over combined information from different filters at the node level. Our theoretical results establish the complementary benefits of the scattering filters to leverage structural information from the graph, while our experiments show the benefits of our method on various learning tasks.  ( 3 min )
    Lifelong Neural Predictive Coding: Learning Cumulatively Online without Forgetting. (arXiv:1905.10696v4 [cs.LG] UPDATED)
    In lifelong learning systems based on artificial neural networks, one of the biggest obstacles is the inability to retain old knowledge as new information is encountered. This phenomenon is known as catastrophic forgetting. In this paper, we propose a new kind of connectionist architecture, the Sequential Neural Coding Network, that is robust to forgetting when learning from streams of data points and, unlike networks of today, does not learn via the popular back-propagation of errors. Grounded in the neurocognitive theory of predictive processing, our model adapts synapses in a biologically-plausible fashion while another neural system learns to direct and control this cortex-like structure, mimicking some of the task-executive control functionality of the basal ganglia. In our experiments, we demonstrate that our self-organizing system experiences significantly less forgetting compared to standard neural models, outperforming a swath of previously proposed methods, including rehearsal/data buffer-based methods, on both standard (SplitMNIST, Split Fashion MNIST, etc.) and custom benchmarks even though it is trained in a stream-like fashion. Our work offers evidence that emulating mechanisms in real neuronal systems, e.g., local learning, lateral competition, can yield new directions and possibilities for tackling the grand challenge of lifelong machine learning.  ( 3 min )
    PAC Generalization via Invariant Representations. (arXiv:2205.15196v3 [cs.LG] UPDATED)
    One method for obtaining generalizable solutions to machine learning tasks when presented with diverse training environments is to find \textit{invariant representations} of the data. These are representations of the covariates such that the best model on top of the representation is invariant across training environments. In the context of linear Structural Equation Models (SEMs), invariant representations might allow us to learn models with out-of-distribution guarantees, i.e., models that are robust to interventions in the SEM. To address the invariant representation problem in a {\em finite sample} setting, we consider the notion of $\epsilon$-approximate invariance. We study the following question: If a representation is approximately invariant with respect to a given number of training interventions, will it continue to be approximately invariant on a larger collection of unseen SEMs? This larger collection of SEMs is generated through a parameterized family of interventions. Inspired by PAC learning, we obtain finite-sample out-of-distribution generalization guarantees for approximate invariance that holds \textit{probabilistically} over a family of linear SEMs without faithfulness assumptions. Our results show bounds that do not scale in ambient dimension when intervention sites are restricted to lie in a constant size subset of in-degree bounded nodes. We also show how to extend our results to a linear indirect observation model that incorporates latent variables.  ( 3 min )
    A Sparse Expansion For Deep Gaussian Processes. (arXiv:2112.05888v2 [stat.ML] UPDATED)
    In this work, we use Deep Gaussian Processes (DGPs) as statistical surrogates for stochastic processes with complex distributions. Conventional inferential methods for DGP models can suffer from high computational complexity as they require large-scale operations with kernel matrices for training and inference. In this work, we propose an efficient scheme for accurate inference and efficient training based on a range of Gaussian Processes, called the Tensor Markov Gaussian Processes (TMGP). We construct an induced approximation of TMGP referred to as the hierarchical expansion. Next, we develop a deep TMGP (DTMGP) model as the composition of multiple hierarchical expansion of TMGPs. The proposed DTMGP model has the following properties: (1) the outputs of each activation function are deterministic while the weights are chosen independently from standard Gaussian distribution; (2) in training or prediction, only polylog(M) (out of M) activation functions have non-zero outputs, which significantly boosts the computational efficiency. Our numerical experiments on synthetic models and real datasets show the superior computational efficiency of DTMGP over existing DGP models.  ( 2 min )
    Class Prior Estimation under Covariate Shift: No Problem?. (arXiv:2206.02449v2 [stat.ML] UPDATED)
    We show that in the context of classification the property of source and target distributions to be related by covariate shift may be lost if the information content captured in the covariates is reduced, for instance by dropping components or mapping into a lower-dimensional or finite space. As a consequence, under covariate shift simple approaches to class prior estimation in the style of classify and count with or without adjustment are infeasible. We prove that transformations of the covariates that preserve the covariate shift property are necessarily sufficient in the statistical sense for the full set of covariates. A probing algorithm as alternative approach to class prior estimation under covariate shift is proposed.  ( 2 min )
    Cost-effective Framework for Gradual Domain Adaptation with Multifidelity. (arXiv:2202.04359v2 [stat.ML] UPDATED)
    In domain adaptation, when there is a large distance between the source and target domains, the prediction performance will degrade. Gradual domain adaptation is one of the solutions to such an issue, assuming that we have access to intermediate domains, which shift gradually from the source to the target domain. In previous works, it was assumed that the number of samples in the intermediate domains was sufficiently large; hence, self-training was possible without the need for labeled data. If the number of accessible intermediate domains is restricted, the distances between domains become large, and self-training will fail. Practically, the cost of samples in intermediate domains will vary, and it is natural to consider that the closer an intermediate domain is to the target domain, the higher the cost of obtaining samples from the intermediate domain is. To solve the trade-off between cost and accuracy, we propose a framework that combines multifidelity and active domain adaptation. The effectiveness of the proposed method is evaluated by experiments with real-world datasets.  ( 2 min )
    Learning Contact Dynamics using Physically Structured Neural Networks. (arXiv:2102.11206v2 [cs.LG] UPDATED)
    Learning physically structured representations of dynamical systems that include contact between different objects is an important problem for learning-based approaches in robotics. Black-box neural networks can learn to approximately represent discontinuous dynamics, but they typically require large quantities of data and often suffer from pathological behaviour when forecasting for longer time horizons. In this work, we use connections between deep neural networks and differential equations to design a family of deep network architectures for representing contact dynamics between objects. We show that these networks can learn discontinuous contact events in a data-efficient manner from noisy observations in settings that are traditionally difficult for black-box approaches and recent physics inspired neural networks. Our results indicate that an idealised form of touch feedback -- which is heavily relied upon by biological systems -- is a key component of making this learning problem tractable. Together with the inductive biases introduced through the network architectures, our techniques enable accurate learning of contact dynamics from observations.  ( 2 min )
    Convergence Rates for Stochastic Approximation on a Boundary. (arXiv:2208.07243v1 [stat.ML])
    We analyze the behavior of projected stochastic gradient descent focusing on the case where the optimum is on the boundary of the constraint set and the gradient does not vanish at the optimum. Here iterates may in expectation make progress against the objective at each step. When this and an appropriate moment condition on noise holds, we prove that the convergence rate to the optimum of the constrained stochastic gradient descent will be different and typically be faster than the unconstrained stochastic gradient descent algorithm. Our results argue that the concentration around the optimum is exponentially distributed rather than normally distributed, which typically determines the limiting convergence in the unconstrained case. The methods that we develop rely on a geometric ergodicity proof. This extends a result on Markov chains by Hajek (1982) to the area of stochastic approximation algorithms.As examples, we show how the results apply to linear programming and tabular reinforcement learning.
    Plasticity Neural Network Based on Astrocytic Influence at Critical Period, Synaptic Competition and Compensation by Current and Mnemonic Brain Plasticity and Synapse Formation. (arXiv:2203.11740v3 [cs.NE] UPDATED)
    The mechanism of our NN is very well in line with the results of the latest MIT brain plasticity study, in which researchers found that as a synapse strengthens, neighboring synapses automatically weaken themselves to compensate. Regarding the importance of this mechanism, Dr. Luo's team at Stanford University has put forward that competition regarding synapse formation for dendritic morphogenesis is crucial. We try to conduct research on the mechanism of failure in brain plasticity by model at the closure of critical period in details by contrasting with studies before. Cutting edge imaging and genetic tools are combined in their experimental studies, whereas our research lays more emphasis on the model, derivation and simulation of a new NN. In tests, which demonstrate that dendrite generation, to a certain extent, is curbed by synapse formation. Current and mnemonic brain plasticity as well as synaptic action range are also taken into account in the study. Furthermore, the frame of the new NN is based on current gradient informational and mnemonic negative and positive gradient informational synapse formation. The mnemonic gradient information needs to take into account the forgotten memory-astrocytic synapse formation memory persistence factor (including both negative and positive memories - i.e. the optimal gradient information so far and relatively inferior gradient information). We found that the astrocytic memory persistence factor, like the phagocytosis factor, produces the effect of reducing the local accumulation of synapses. The PNN in which only the synaptic phagocytosis effect is considered regardless of the gradients update, and whether the synaptic phagocytosis of different variables and synaptic positions is cancelled is determined by the correlation coefficient of the corresponding time interval, proves simple and effective.
    Convergence of a robust deep FBSDE method for stochastic control. (arXiv:2201.06854v4 [math.OC] UPDATED)
    In this paper, we propose a deep learning based numerical scheme for strongly coupled FBSDEs, stemming from stochastic control. It is a modification of the deep BSDE method in which the initial value to the backward equation is not a free parameter, and with a new loss function being the weighted sum of the cost of the control problem, and a variance term which coincides with the mean squared error in the terminal condition. We show by a numerical example that a direct extension of the classical deep BSDE method to FBSDEs, fails for a simple linear-quadratic control problem, and motivate why the new method works. Under regularity and boundedness assumptions on the exact controls of time continuous and time discrete control problems, we provide an error analysis for our method. We show empirically that the method converges for three different problems, one being the one that failed for a direct extension of the deep BSDE method.  ( 3 min )
    When do Models Generalize? A Perspective from Data-Algorithm Compatibility. (arXiv:2202.06054v2 [cs.LG] UPDATED)
    One of the major open problems in machine learning theory is to characterize generalization in the overparameterized regime, where most traditional generalization bounds become inconsistent. In many scenarios, their failure can be attributed to obscuring the crucial interplay between the training algorithm and the underlying data distribution. To address this shortcoming, we propose a concept named compatibility, which quantitatively characterizes generalization in a both data-relevant and algorithm-relevant manner. By considering the entire training trajectory and focusing on early-stopping iterates, compatibility fully exploits the algorithm information and therefore yields better generalization guarantees. We validate this by theoretically studying compatibility under the setting of overparameterized linear regression with gradient descent. Specifically, we perform a data-dependent trajectory analysis and derive a sufficient condition for compatibility under such a setting. Our theoretical results show that in the sense of compatibility, generalization holds with significantly weaker restrictions on the problem instance than the previous last iterate analysis.  ( 2 min )
    Unifying supervised learning and VAEs -- automating statistical inference in (astro-)particle physics with amortized conditional normalizing flows. (arXiv:2008.05825v3 [cs.LG] UPDATED)
    A KL-divergence objective of the joint distribution of data and labels allows to unify supervised learning and variational autoencoders (VAEs) under one umbrella of stochastic variational inference. The unification motivates an extended supervised scheme which allows to calculate a goodness-of-fit p-value for the neural network model. Conditional normalizing flows amortized with a neural network are crucial in this construction. We discuss how they allow to rigorously define coverage for posteriors defined jointly on a product space, e.g. $\mathbb{R}^n \times \mathcal{S}^m$, which encompasses posteriors over directions. Finally, systematic uncertainties are naturally included in the variational viewpoint. In classical likelihood approaches or other machine learning models, the ingredients of (1) systematics, (2) coverage and (3) goodness-of-fit are typically not all available or at least one of them strongly constrained. In contrast, the proposed extended supervised training with amortized normalizing flows accommodates all three of them for variational inference of arbitrary statistical distributions defined on product spaces like $\mathbb{R}^n \times \ldots \times \mathcal{S}^m$ and no fundamental barrier in terms of complexity of the underlying data. It therefore has great potential for the statistical toolbox of the contemporary (astro-)particle physicist.  ( 3 min )
    Sharp asymptotics on the compression of two-layer neural networks. (arXiv:2205.08199v3 [cs.IT] UPDATED)
    In this paper, we study the compression of a target two-layer neural network with N nodes into a compressed network with M<N nodes. More precisely, we consider the setting in which the weights of the target network are i.i.d. sub-Gaussian, and we minimize the population L_2 loss between the outputs of the target and of the compressed network, under the assumption of Gaussian inputs. By using tools from high-dimensional probability, we show that this non-convex problem can be simplified when the target network is sufficiently over-parameterized, and provide the error rate of this approximation as a function of the input dimension and N. In this mean-field limit, the simplified objective, as well as the optimal weights of the compressed network, does not depend on the realization of the target network, but only on expected scaling factors. Furthermore, for networks with ReLU activation, we conjecture that the optimum of the simplified optimization problem is achieved by taking weights on the Equiangular Tight Frame (ETF), while the scaling of the weights and the orientation of the ETF depend on the parameters of the target network. Numerical evidence is provided to support this conjecture.  ( 3 min )
    The FEDHC Bayesian network learning algorithm. (arXiv:2012.00113v6 [stat.ML] UPDATED)
    The paper proposes a new hybrid Bayesian network learning algorithm, termed Forward Early Dropping Hill Climbing (FEDHC), devised to work with either continuous or categorical variables. Further, the paper manifests that the only implementation of MMHC in the statistical software \textit{R}, is prohibitively expensive and a new implementation is offered. Further, specifically for the case of continuous data, a robust to outliers version of FEDHC, that can be adopted by other BN learning algorithms, is proposed. The FEDHC is tested via Monte Carlo simulations that distinctly show it is computationally efficient, and produces Bayesian networks of similar to, or of higher accuracy than MMHC and PCHC. Finally, an application of FEDHC, PCHC and MMHC algorithms to real data, from the field of economics, is demonstrated using the statistical software \textit{R}.  ( 2 min )
    Approximate Post-Selective Inference for Regression with the Group LASSO. (arXiv:2012.15664v4 [stat.ME] UPDATED)
    After selection with the Group LASSO (or generalized variants such as the overlapping, sparse, or standardized Group LASSO), inference for the selected parameters is unreliable in the absence of adjustments for selection bias. In the penalized Gaussian regression setup, existing approaches provide adjustments for selection events that can be expressed as linear inequalities in the data variables. Such a representation, however, fails to hold for selection with the Group LASSO and substantially obstructs the scope of subsequent post-selective inference. Key questions of inferential interest -- for example, inference for the effects of selected variables on the outcome -- remain unanswered. In the present paper, we develop a consistent, post-selective, Bayesian method to address the existing gaps by deriving a likelihood adjustment factor and an approximation thereof that eliminates bias from the selection of groups. Experiments on simulated data and data from the Human Connectome Project demonstrate that our method recovers the effects of parameters within the selected groups while paying only a small price for bias adjustment.  ( 2 min )
    Policy Gradient Methods Find the Nash Equilibrium in N-player General-sum Linear-quadratic Games. (arXiv:2107.13090v2 [math.OC] UPDATED)
    We consider a general-sum N-player linear-quadratic game with stochastic dynamics over a finite horizon and prove the global convergence of the natural policy gradient method to the Nash equilibrium. In order to prove the convergence of the method, we require a certain amount of noise in the system. We give a condition, essentially a lower bound on the covariance of the noise in terms of the model parameters, in order to guarantee convergence. We illustrate our results with numerical experiments to show that even in situations where the policy gradient method may not converge in the deterministic setting, the addition of noise leads to convergence.  ( 2 min )
    Riemannian accelerated gradient methods via extrapolation. (arXiv:2208.06619v1 [math.OC])
    In this paper, we propose a simple acceleration scheme for Riemannian gradient methods by extrapolating iterates on manifolds. We show when the iterates are generated from Riemannian gradient descent method, the accelerated scheme achieves the optimal convergence rate asymptotically and is computationally more favorable than the recently proposed Riemannian Nesterov accelerated gradient methods. Our experiments verify the practical benefit of the novel acceleration strategy.  ( 2 min )
    Towards out of distribution generalization for problems in mechanics. (arXiv:2206.14917v2 [stat.ML] UPDATED)
    There has been a massive increase in research interest towards applying data driven methods to problems in mechanics. While traditional machine learning (ML) methods have enabled many breakthroughs, they rely on the assumption that the training (observed) data and testing (unseen) data are independent and identically distributed (i.i.d). Thus, traditional ML approaches often break down when applied to real world mechanics problems with unknown test environments and data distribution shifts. In contrast, out-of-distribution (OOD) generalization assumes that the test data may shift (i.e., violate the i.i.d. assumption). To date, multiple methods have been proposed to improve the OOD generalization of ML methods. However, because of the lack of benchmark datasets for OOD regression problems, the efficiency of these OOD methods on regression problems, which dominate the mechanics field, remains unknown. To address this, we investigate the performance of OOD generalization methods for regression problems in mechanics. Specifically, we identify three OOD problems: covariate shift, mechanism shift, and sampling bias. For each problem, we create two benchmark examples that extend the Mechanical MNIST dataset collection, and we investigate the performance of popular OOD generalization methods on these mechanics-specific regression problems. Our numerical experiments show that in most cases, while the OOD generalization algorithms perform better compared to traditional ML methods on these OOD problems, there is a compelling need to develop more robust OOD generalization methods that are effective across multiple OOD scenarios. Overall, we expect that this study, as well as the associated open access benchmark datasets, will enable further development of OOD generalization methods for mechanics specific regression problems.  ( 3 min )
    Predicting from Predictions. (arXiv:2208.07331v1 [stat.ML])
    Predictions about people, such as their expected educational achievement or their credit risk, can be performative and shape the outcome that they aim to predict. Understanding the causal effect of these predictions on the eventual outcomes is crucial for foreseeing the implications of future predictive models and selecting which models to deploy. However, this causal estimation task poses unique challenges: model predictions are usually deterministic functions of input features and highly correlated with outcomes, which can make the causal effects of predictions impossible to disentangle from the direct effect of the covariates. We study this problem through the lens of causal identifiability, and despite the hardness of this problem in full generality, we highlight three natural scenarios where the causal effect of predictions on outcomes can be identified from observational data: randomization in predictions or prediction-based decisions, overparameterization of the predictive model deployed during data collection, and discrete prediction outputs. We show empirically that, under suitable identifiability conditions, standard variants of supervised learning that predict from predictions can find transferable functional relationships between features, predictions, and outcomes, allowing for conclusions about newly deployed prediction models. Our positive results fundamentally rely on model predictions being recorded during data collection, bringing forward the importance of rethinking standard data collection practices to enable progress towards a better understanding of social outcomes and performative feedback loops.  ( 2 min )
    Inference for BART with Multinomial Outcomes. (arXiv:2101.06823v2 [stat.ME] UPDATED)
    The multinomial probit Bayesian additive regression trees (MPBART) framework was proposed by Kindo et al. (KD), approximating the latent utilities in the multinomial probit (MNP) model with BART (Chipman et al. 2010). Compared to multinomial logistic models, MNP does not assume independent alternatives and the correlation structure among alternatives can be specified through multivariate Gaussian distributed latent utilities. We introduce two new algorithms for fitting the MPBART and show that the theoretical mixing rates of our proposals are equal or superior to the existing algorithm in KD. Through simulations, we explore the robustness of the methods to the choice of reference level, imbalance in outcome frequencies, and the specifications of prior hyperparameters for the utility error term. The work is motivated by the application of generating posterior predictive distributions for mortality and engagement in care among HIV-positive patients based on electronic health records (EHRs) from the Academic Model Providing Access to Healthcare (AMPATH) in Kenya. In both the application and simulations, we observe better performance using our proposals as compared to KD in terms of MCMC convergence rate and posterior predictive accuracy.  ( 2 min )
    Finite Sample Complexity of Sequential Monte Carlo Estimators on Multimodal Target Distributions. (arXiv:2208.06672v1 [stat.CO])
    We prove finite sample complexities for sequential Monte Carlo (SMC) algorithms which require only local mixing times of the associated Markov kernels. Our bounds are particularly useful when the target distribution is multimodal and global mixing of the Markov kernel is slow; in such cases our approach establishes the benefits of SMC over the corresponding Markov chain Monte Carlo (MCMC) estimator. The lack of global mixing is addressed by sequentially controlling the bias introduced by SMC resampling procedures. We apply these results to obtain complexity bounds for approximating expectations under mixtures of log-concave distributions and show that SMC provides a fully polynomial time randomized approximation scheme for some difficult multimodal problems where the corresponding Markov chain sampler is exponentially slow. Finally, we compare the bounds obtained by our approach to existing bounds for tempered Markov chains on the same problems.  ( 2 min )
    On the Estimation of Derivatives Using Plug-in KRR Estimators. (arXiv:2006.01350v3 [stat.ML] UPDATED)
    We study the problem of estimating the derivatives of a regression function, which has a wide range of applications as a key nonparametric functional of unknown functions. Standard analysis may be tailored to specific derivative orders, and parameter tuning remains a daunting challenge particularly for high-order derivatives. In this article, we propose a simple plug-in kernel ridge regression (KRR) estimator in nonparametric regression with random design that is broadly applicable for multi-dimensional support and arbitrary mixed-partial derivatives. We provide a non-asymptotic analysis to study the behavior of the proposed estimator in a unified manner that encompasses the regression function and its derivatives, leading to two error bounds for a general class of kernels under the strong $L_\infty$ norm. In a concrete example specialized to kernels with polynomially decaying eigenvalues, the proposed estimator recovers the minimax optimal rate up to a logarithmic factor for estimating derivatives of functions in H\"older and Sobolev classes. Interestingly, the proposed estimator achieves the optimal rate of convergence with the same choice of tuning parameter for any order of derivatives. Hence, the proposed estimator enjoys a \textit{plug-in property} for derivatives in that it automatically adapts to the order of derivatives to be estimated, enabling easy tuning in practice. Our simulation studies show favorable finite sample performance of the proposed method relative to several existing methods blue and corroborate the theoretical findings on its minimax optimality.  ( 3 min )
    Dynamic Bayesian Learning and Calibration of Spatiotemporal Mechanistic System. (arXiv:2208.06528v1 [stat.ME])
    We develop an approach for fully Bayesian learning and calibration of spatiotemporal dynamical mechanistic models based on noisy observations. Calibration is achieved by melding information from observed data with simulated computer experiments from the mechanistic system. The joint melding makes use of both Gaussian and non-Gaussian state-space methods as well as Gaussian process regression. Assuming the dynamical system is controlled by a finite collection of inputs, Gaussian process regression learns the effect of these parameters through a number of training runs, driving the stochastic innovations of the spatiotemporal state-space component. This enables efficient modeling of the dynamics over space and time. Through reduced-rank Gaussian processes and a conjugate model specification, our methodology is applicable to large-scale calibration and inverse problems. Our method is general, extensible, and capable of learning a wide range of dynamical systems with potential model misspecification. We demonstrate this flexibility through solving inverse problems arising in the analysis of ordinary and partial nonlinear differential equations and, in addition, to a black-box computer model generating spatiotemporal dynamics across a network.  ( 2 min )
    Machine learning meets false discovery rate. (arXiv:2208.06685v1 [stat.ME])
    Classical false discovery rate (FDR) controlling procedures offer strong and interpretable guarantees, while they often lack of flexibility. On the other hand, recent machine learning classification algorithms, as those based on random forests (RF) or neural networks (NN), have great practical performances but lack of interpretation and of theoretical guarantees. In this paper, we make these two meet by introducing a new adaptive novelty detection procedure with FDR control, called AdaDetect. It extends the scope of recent works of multiple testing literature to the high dimensional setting, notably the one in Yang et al. (2021). AdaDetect is shown to both control strongly the FDR and to have a power that mimics the one of the oracle in a specific sense. The interest and validity of our approach is demonstrated with theoretical results, numerical experiments on several benchmark datasets and with an application to astrophysical data. In particular, while AdaDetect can be used in combination with any classifier, it is particularly efficient on real-world datasets with RF, and on images with NN.  ( 2 min )
    Inverse Extended Kalman Filter -- Part II: Highly Non-Linear and Uncertain Systems. (arXiv:2208.06683v1 [math.OC])
    Recent counter-adversarial system design problems have motivated the development of inverse Bayesian filters. For example, inverse Kalman filter (I-KF) has been recently formulated to estimate the adversary's Kalman filter tracked estimates and hence, predict the adversary's future steps. The purpose of this paper and the companion paper (Part I) is to address the inverse filtering problem in non-linear systems by proposing an inverse extended Kalman filter (I-EKF). In a companion paper (Part I), we developed the theory of I-EKF (with and without unknown inputs) and I-KF (with unknown inputs). In this paper, we develop this theory for highly non-linear models, which employ second-order, Gaussian sum, and dithered forward EKFs. In particular, we derive theoretical stability guarantees for the inverse second-order EKF using the bounded non-linearity approach. To address the limitation of the standard I-EKFs that the system model and forward filter are perfectly known to the defender, we propose reproducing kernel Hilbert space-based EKF to learn the unknown system dynamics based on its observations, which can be employed as an inverse filter to infer the adversary's estimate. Numerical experiments demonstrate the state estimation performance of the proposed filters using recursive Cram\'{e}r-Rao lower bound as a benchmark.  ( 3 min )
    Learning Linear Non-Gaussian Polytree Models. (arXiv:2208.06701v1 [stat.ML])
    In the context of graphical causal discovery, we adapt the versatile framework of linear non-Gaussian acyclic models (LiNGAMs) to propose new algorithms to efficiently learn graphs that are polytrees. Our approach combines the Chow--Liu algorithm, which first learns the undirected tree structure, with novel schemes to orient the edges. The orientation schemes assess algebraic relations among moments of the data-generating distribution and are computationally inexpensive. We establish high-dimensional consistency results for our approach and compare different algorithmic versions in numerical experiments.  ( 2 min )

  • Open

    My third attempt at creating wallpapers: Glowing Sunrise of a Summer Day | Using MidJourney AI (Image Creator bot for Discord)
    submitted by /u/Potato_Player_BR [link] [comments]  ( 86 min )
    AI that could combine books
    I'm looking for an AI that could possibly combine multiple different texts and segments of writing to create one coherent piece that includes the ideas and discussions of both. For example, if I were to write a book on courts then I could combine one chapter from a book that discusses jails and another that discusses court etiquette and get a cohesive explanation of both jails and court etiquette. Would anyone know if some AI similar to this exists? I don't care for costs. submitted by /u/SoterDave [link] [comments]  ( 89 min )
    Computing and Visualizing Brain Topological Data Analysis
    submitted by /u/giorgiodidio [link] [comments]  ( 86 min )
    open ai funny
    submitted by /u/Fearless_Squirrel_72 [link] [comments]  ( 86 min )
    Mean Average Precision (mAP) in Object Detection
    submitted by /u/spmallick [link] [comments]  ( 87 min )
    Animating between Midjourney variations
    submitted by /u/zark23 [link] [comments]  ( 86 min )
    Weekly China AI News: Xiaomi Races Ahead of Tesla with Humanoid Robot Unveil; Baidu Secures China's 1st Permits for Fared Driverless Robotaxi; SenseTime Launches Chinese Chess Robot
    submitted by /u/trcytony [link] [comments]  ( 86 min )
    I made a conversational AI app that tutors you in math, science, history and computer science!
    submitted by /u/landongarrison [link] [comments]  ( 89 min )
    Factory in the Clouds created with Pixels AI
    submitted by /u/widgia [link] [comments]  ( 86 min )
    Workshop on AGI/AI in FinTech (AGIFT)
    submitted by /u/akolonin [link] [comments]  ( 87 min )
    I used Midjourney to create A.I. images of a redhead I named Anna Bhordana, this shows her life story so far
    submitted by /u/xXLjordSireXx [link] [comments]  ( 89 min )
    Automatic ML Model Containerization
    Containerizing machine learning models can be a pain. This talk covers a new open-source approach to building machine learning (ML) models into container images to run in production for inference. Chassis.ml and the Open Model Interface are changing the game with a standard container specification that allows for interoperability, portability, and security for models to seamlessly be integrated into production applications. https://youtu.be/vq3k9wQymss submitted by /u/modzykirsten [link] [comments]  ( 86 min )
    Ather Gattami: How to Create Products Using AI
    Wondering how to create your products using AI? That’s exactly what we’ll be talking about this at the next AI Talks! Ather Gattami will join us this Wednesday at 5:30 pm CEST to share his experience of launching products using the power of AI! Is listening boring for you? Then, join the Twitch stream and ask your questions in real-time! To get to know the speaker a little: Ather Gattami is a tech entrepreneur and expert in the field of AI. He’s founded OrganAi.se, an AI assistant for everyone, and Bitynamics, a company that develops AI-powered solutions for European businesses. At the same time, he works as a research affiliate for AI Sweden, making a significant contribution to the development of AI for the world! Don’t waste time and register for AI Talk so you don’t miss the opportunity to get the answers you’ve been looking for! Register here and get notified when we go live! Ather Gattami: How to Create Products Using AI submitted by /u/zakrzzz [link] [comments]  ( 87 min )
    A few questions about AI
    As a person who only know a thing or two about artificial intelligence, my knowledge is super limited in the field. However, I'm here to ask more philosophical questions than scientific questions. I've seen lots of talk about AI taking over the world and replacing humans, rendering them infinitely inferior and a total waste of space and energy. And, As pessimistic/defeatist/fatalistic as I feel to this technology, I'd like to get some second opinions from people who knows more than me before I form my own. Wouldn't AI defeat the will to live? If AI is superior to humans by a long shot, then why exist or continue to exist? It seems like a sort of "planned obsolescence" on a existential scale. I've seen people talk about how everyone everywhere will be replaced because I keep "AI is better…  ( 91 min )
    Reinforcement learning models are prone to membership inference attacks
    submitted by /u/bendee983 [link] [comments]  ( 86 min )
    Can some teach me how to use wordpress and do changes in a wordpress website.
    submitted by /u/DragonflySea5590 [link] [comments]  ( 86 min )
    Copyright infringement in artificial intelligence art
    submitted by /u/bartturner [link] [comments]  ( 86 min )
    Gotham City generated by Midjourney AI
    submitted by /u/cripuskas [link] [comments]  ( 86 min )
    Volcano Explosion🌋
    submitted by /u/widgia [link] [comments]  ( 86 min )
  • Open

    [D] 3D Face Reconstruction with Dense Landmarks
    Few questions about this paper, mainly with the way they attain the Dense landmark prediction. Paper: https://arxiv.org/abs/2204.02776 I understand they use a CNN first to get a Region-of-Interest (ROI), but i'm confused as to how they inscribe an expanded square around the image. I'm referring to step (c)/(d) here: https://ibb.co/vjy8TDD. I'm unsure as to how they create and adapt this sort of linear fitting of the mesh/cube structure to the face. Does this cube mesh structure matter? Why is it placed here in the first place? Is this cube mesh used in the step of predicting each landmark? They state that "We predict each landmark as a random variable with the probability density function of a circular 2D Gaussian". They then go further onto state "Our training data includes labels for landmark positions." So i'm wondering since they have predicted 703 landmarks, do they manually label 703 landmarks in the training set? They don't speak of this in the paper so i'm a little confused. I know in the dataset from https://github.com/microsoft/FaceSynthetics, they have 70 landmarks so do they use 70 and scale up to 700? If so, how are they able to scale up the landmarks? How are they able to get the curvature of the face shape so accurately. Is this a function of the circular 2D Gaussian PDF? It seems like the eyes, mouth, and nose don't have any points. If they were random variables, why are these parts of the images excluded from the prediction? https://ibb.co/XjKKpCQ. Also how do they connect the different points, and do the connections even matter? submitted by /u/trikortreat123 [link] [comments]  ( 89 min )
    [D] How long until we start talking about "human DALLEs"?
    After the invention of the calculator, those who could do complex mental math became known as "human calculators", even though they weren't as good as their electronic counterparts. How long will it be until we start calling artists "human DALLEs"? submitted by /u/FranciscoJ1618 [link] [comments]  ( 87 min )
    [R] Language Models Can Teach Themselves to Program Better - Microsoft 2022
    Paper: https://arxiv.org/abs/2207.14502 Abstract: This work shows how one can use large-scale language models (LMs) to synthesize programming problems with verified solutions, in the form of programming puzzles, which can then in turn be used to fine-tune those same models, improving their performance. This work builds on two recent developments. First, LMs have achieved breakthroughs in non-trivial reasoning and algorithm implementation, generating code that can solve some intermediate-level competitive programming problems. However, training code LMs involves curated sets of natural-language problem descriptions and source-code tests and solutions, which are limited in size. Second, a new format of programming challenge called a programming puzzle was introduced, which does not require a natural language description and is directly specified by a source-code test. In this work we show how generating synthetic programming puzzles and solutions, verified for correctness by a Python interpreter, can be used to improve performance in solving test puzzles from P3, a public benchmark set of Python Programming Puzzles. Additionally, we release a dataset of 1 million puzzles and solutions generated by the Codex model, which we show can improve smaller models through fine-tuning. https://preview.redd.it/ci0fmpm2gyh91.jpg?width=1000&format=pjpg&auto=webp&s=348f9e50cf9adace91c9f5dd1f28fd61d516740e https://preview.redd.it/nf17qpm2gyh91.jpg?width=1100&format=pjpg&auto=webp&s=479c95b2094993473abc1fe64f842443ade1913d https://preview.redd.it/dzbffqm2gyh91.jpg?width=1119&format=pjpg&auto=webp&s=39379463853a08dd357bc5040b6b039c75084ab9 submitted by /u/Singularian2501 [link] [comments]  ( 88 min )
    [N] No-code AI: Former Microsoft and Salesforce execs reveal new ‘machine teaching’ startup Intelus - Build your model and label at the same time.
    Hi! We have opened up our new labeling AI tool to try for free. Their team pioneered the machine teaching concept and the approach allows you to automatically select and generate labels and create a model for labeling more data at the same time. Try it and let us know what you think. -Intelus.ai GeekWire: https://www.geekwire.com/2021/no-code-ai-former-microsoft-and-salesforce-execs-reveal-new-machine-teaching-startup-intelus/ submitted by /u/Signal-Hall-6808 [link] [comments]  ( 88 min )
    [D] Autoencoder vs. BERT
    Hi I am struggeling to understand the relationship between BERT and autoencoders. On a high level they both create embeddings of input data that is learned in a self-supervised way. They even share the 'encoding'-part 😉. I am working on a classification task with text (product descriptions). The SOTA on public datasets for this seems to be fine tuning BERT. However, this does not give the results I was hoping for, maybe because of a different language that BERT was pretrained on, or because of domain-specific vocabulary. My idea was to look into autoencoders to generate representations with less 'noise' (the hypothesis would be that the descriptions contain a lot if irrelevant stuff) and feed these into a classifier. My question is: Is this conceptually different from BERT? How do autoencoders relate to transformer? Are transformer in NLP kind of a evolution of autoencoders? Any help/insight would be appreciated! submitted by /u/Tober447 [link] [comments]  ( 90 min )
    [D] Salary/Payroll Anomaly Detection
    If I have 200 rows of data where employee designation, years of experience, performance rating, salary as columns. How can I find if somebody is overpaid or underpaid? submitted by /u/Ag3nt-47 [link] [comments]  ( 88 min )
    [D] Help me decide what to study and job position to pursue (short)
    I don't want to give you a 20 paragraphs explanation as others do, so I'll tell you a summary :) I love maths and I'm an Information Systems Engineer (5 years university degree) I work as a Full Stack software developer, but I miss math and reasoning. I found a lot of people that say that they work on AI, but i.e. they just call IBM Watson API for chatting bots or visual recognition. I don't consider these jobs as working on AI, and I think this kind of job will be replaced by AI at the same time as any other dev jobs. I think AI is the future and I'd like to be able to get a related job that requires reasoning, maths and be safer from automation than software development. I don't know the job position/role I should pursue and the studies I should get. Any suggerence or info will be appreciated! submitted by /u/MilanesaDePosho666 [link] [comments]  ( 107 min )
    My foundational machine learning notes [R]
    Dear all, My most recent machine learning research tutorial focuses on Learning Theory and is therefore math-intensive. To cater for people whom are new to ML, I just uploaded a new set of foundational mathematics machine learning notes: https://github.com/roboticcam/machine-learning-notes containing the following topics: Model Evaluation, Decision Tree, Simple Bayes, Regression, Neural Network and Unsupervised Learning. I used simple mathematics to explain them. Hope they are useful to you! submitted by /u/MLknowledge [link] [comments]  ( 87 min )
    ML model that can detect muscle groups [P]
    I want to create a ML/DL model that takes an image of a person as input and is able to identify different muscle groups present in the image (e.g. a shirtless picture of a person as input and the model can detect the chest, shoulders etc...). Not quite sure if this is feasible but what would be the best way to approach this? submitted by /u/FaMiFisH [link] [comments]  ( 105 min )
    [D] What is supervised ranking?
    I'm reading the paper The P-Norm Push: A Simple Convex Ranking Algorithm that Concentrates at the Top of the List and the paper seems to discuss " supervised ranking" but I'm having trouble understanding this context. The paper describes the context on pages 3 - 4 (2235 - 2236) as: There are positive instances x_1, ..., x_I There are negative instances x_1, ..., x_K X is the set of all of the these labels The goal is to come up with a function f : X -> R such which minimizes the the "Height" of each negative instance which is the number of positive instances which have a lower f The way it seems described, you could just have f map all the negative instances to -1 and all the positive instances to 1 and get a perfect answer. I'm sure I'm missing something, but I'm not sure what. The author uses the examples of movies so in that context: Is the same movie allowed to show up multiple times in the positive (or negative) list? Can the same movie show up in both the positive and negative list (maybe multiple times)? Is there something else I'm missing that makes this problem non-trivial? I've tried to Google this but I can't find anything which clarifies. Thanks! submitted by /u/paradoxinmaking [link] [comments]  ( 89 min )
    [P] How to Create a Blog Post Title Optimizer with GPT-3 and Hacker News Data
    I wrote a blog post about finetuning GPT-3 on HN to determine whether a technical blog post is good or not, and also engineer prompts to GPT-3 to generate alternate titles which can then be ranked. The code + demos is available open-source on GitHub, although the finetuned model isn't due to OpenAI rules/inability to share models. (incidentally the post did well on Hacker News) submitted by /u/minimaxir [link] [comments]  ( 107 min )
    [D] What can NeurIPS 22 authors see in Reviewer-Metareviewer Discussion period?
    Greetings! This is a new comer who submitted to NeurIPS 22 and desperately hopes somebody could help me understand the review process this time. Now the Author-Reviewer Discussion period ended and we’re in the middle of Reviewer-Metareviewer Discussion period. My question is: if a reviewer makes a comment or changes his/her score in this period, will the authors be able to see? The reason for this question is, we worked very hard on a rebuttal, but received no response in the Author-Reviewer Discussion period. I’m still hoping the AC could step in and ask something from the reviewers, but so far we saw nothing, and I wonder if it could be because authors cannot see any discussion in this period. This is not just for getting a futile peace of mind, instead, if it is really that nothing happened, could I email somebody (program chairs?) for advice? Thank you all very much in advance! submitted by /u/mission205 [link] [comments]  ( 89 min )
    [P] Open-source solutions for automatic ML model containerization
    Recently announced, the Open Model Interface (OMI) and chassis.ml open source projects, provide a standard, interoperable, secure framework for containerizing AI models. OMI provides a standard specification to containerize machine learning models. Chassis.ml converts models from multiple training tools and frameworks into OMI compatible containers to run anywhere. By implementing OMI and chassis.ml, data scientists and developers gain a standard container specification allowing interoperability, portability, and security for models to seamlessly be integrated into production applications. Why OMI and Chassis? The OMI is designed to serve as a spec for wrapping models in OCI-compliant containers with a simple yet powerful interface that makes it easier to move models into production. The OMI: Creates a uniform way to convert models into portable, containerized applications that can run anywhere – in the cloud, on-premise, or at the edge Allows teams the flexibility to continue using their existing training tools, frameworks, language, etc. and adopt a standardized container to package models that is DISA compliant, ensuring a high level of security Eliminates the need to build new integrations for model types, as OMI provides a common, well-documented interface with support OMI and Chassis.ml allow data scientists to turn their models into containerized applications without having to learn dozens of new technologies. Chassis converts models from all training tools and frameworks to OMI compliant containers to run in a number of different types of AI runtime platforms. Chassis.ml can be integrated into existing MLOps pipelines, making it possible for models to be automatically containerized, scanned, and deployed to a secure container registry. To learn more about how you can get started using chassis today, watch this video on automatic ML model containerization. submitted by /u/modzykirsten [link] [comments]  ( 89 min )
    [P] skops: New library to improve scikit-learn workflows in production
    Hello 👋 We've been working on improving workflows in putting scikit-learn models to production, enable reproducibility and find secure ways to serialize the models. (try to avoid pickle 🥒 ) For this, we developed a library called "skops", and recently launched released of v0.1. This version is mostly focused on hosting models on Hugging Face Hub: - After you train your model, you can create a card for the model programmatically. The card by default comes with hyperparameter tables and plot of your pipeline (ColumnTransformer, estimator & friends). You can add your own plots, pass metrics (they are automatically parsed into a neat markdown table) and pass information related to your model (how to use the model, limitations and more). - You can push your model to Hugging Face Hub. You can initialize a local repository, which creates a config file that contains information related to your environment (library versions etc.) an example piece of dataset and more. After you push the repository, the model card is rendered on Hub and a widget is enabled so people can play with your model. For next versions, we're planning to work on security for deserialization, creating model demo UIs easily with Gradio (with only one line of code 🤯) and more. We'd be more than happy if you could try it out and let us know about the features you'd like, issues you came across or would like to contribute. See documentation, we drafted two scripts for you to try it out: https://skops.readthedocs.io/en/stable/index.html See an example repository created with skops: https://huggingface.co/scikit-learn/tabular-playground Tutorial: https://huggingface.co/blog/skops If you want to contribute or simply show some love 🌟 our github repository: https://github.com/skops-dev/skops submitted by /u/unofficialmerve [link] [comments]  ( 89 min )
    [P] Text annotator for entity extraction that runs in your notebook
    Hi! We have just open-sourced our text annotator which runs directly in your notebook. You can now select spans of text for entity extraction and do your processing & modelling all in the same place. This allows for quick iteration when getting a project started. Here is the repository: https://github.com/dataqa/jupyter-annotate. We would be very happy to hear any feedback or comments you might have! submitted by /u/dataqa_ai [link] [comments]  ( 88 min )
    How to interpret repeating image artifacts during VQGAN training? [D]
    I'm training a VQGAN model on a custom dataset and over time I notice repeating artifacts that don't look like anything in the original images. How to interpret the occurrence of these artifacts, is it some sort of partial degradation of the network which leads to a particular part of the Generator firing more often and the Discriminator doesn't penalize it? Example: https://imgur.com/cqZIJe6 Model: https://github.com/dome272/VQGAN-pytorch submitted by /u/mselivanov [link] [comments]  ( 88 min )
    [D] Experience Grounds Language - Improving language models beyond the world of text (with multimodality, embodiment, and social interaction)
    Hi r/MachineLearning, I just published this video going over (and visualizing) this paper from 2020 that I can't stop thinking about. Hope you find it interesting. https://www.youtube.com/watch?v=WQm7-X4gts4 ​ Now that language models have been trained on massive internet-scale text data, where are future improvements going to come from? Jay goes over the "Experience Grounds Language" paper which describes five "World Scopes" for learning language -- including multimodality (e.g. training on images + text) and beyond. Contents: Introduction (0:00) Experience Grounds Language (1:20) World Scopes (2:58) World Scope 1 and 2 (3:33) World Scope 3 - Multimodality (3:56) World Scope 4 - Embodiment and Action (7:00) World Scope 5 - The Social World (9:50) Reading Excerpts from the paper (10:48) World scopes encompass each other (16:50) Interesting thought experiments (19:42) Conclusion (21:09) ​ Paper: Experience Grounds Language (2020) https://arxiv.org/abs/2004.10151 Authors: Yonatan Bisk, Ari Holtzman, Jesse Thomason, Jacob Andreas, Yoshua Bengio, Joyce Chai, Mirella Lapata, Angeliki Lazaridou, Jonathan May, Aleksandr Nisnevich, Nicolas Pinto, Joseph Turian submitted by /u/jayalammar [link] [comments]  ( 88 min )
    [Discussion] Why does Midjounery look so much nicer than DALLE?
    What are the different ways in which they could have fine tuned their code to make their output better than DALL-E? submitted by /u/v-mohan [link] [comments]  ( 88 min )
    A question about hyperparameter optimization in Multinomial Logistic Regression [P]
    I have a labelled multivariate dataset with multiple labels including 0, 1, 2, 3, 4. I can build a multiple logistic regression model and use gridsearchcv to come up with the best solver, c value and penalty. Imagine I choose to convert the labels into binary by making all >1 as =1 and then use the binary logistic regression model to optimize the hyperparameters. Am I right in stating that I sacrifice some accuracy for speed of execution by simplifying it? Does it show more nuance in the overall machine learning model? submitted by /u/Positive_Community49 [link] [comments]  ( 106 min )
  • Open

    Customize your recommendations by promoting specific items using business rules with Amazon Personalize
    Today, we are excited to announce Promotions feature in Amazon Personalize that allows you to explicitly recommend specific items to your users based on rules that align with your business goals. For instance, you can have marketing partnerships that require you to promote certain brands, in-house content, or categories that you want to improve the […]  ( 8 min )
    Amazon SageMaker JumpStart solutions now support custom IAM role settings
    Amazon SageMaker JumpStart solutions are a feature within Amazon SageMaker Studio that allow a simple-click experience to set up your own machine learning (ML) workflows. When you launch a solution, various of AWS resources are set up in your account to demonstrate how the business problem can be solved using the pre-built architecture. The solutions […]  ( 5 min )
    Intelligent document processing with AWS AI services: Part 2
    Amazon’s intelligent document processing (IDP) helps you speed up your business decision cycles and reduce costs. Across multiple industries, customers need to process millions of documents per year in the course of their business. For customers who process millions of documents, this is a critical aspect for the end-user experience and a top digital transformation […]  ( 10 min )
    Intelligent document processing with AWS AI services: Part 1
    Organizations across industries such as healthcare, finance and lending, legal, retail, and manufacturing often have to deal with a lot of documents in their day-to-day business processes. These documents contain critical information that are key to making decisions on time in order to maintain the highest levels of customer satisfaction, faster customer onboarding, and lower […]  ( 9 min )
  • Open

    Preventing Data Breaches with Extended Security Posture Management
    Extended security posture management keeps your data safe by helping IT teams to strengthen the security posture of an infrastructure. The post Preventing Data Breaches with Extended Security Posture Management appeared first on Data Science Central.  ( 20 min )
    How AI Will Impact The Accounting And Finance Industry
    Artificial Intelligence (AI) has become crucial to highly demanding industries globally. The impact of AI in the accounting and finance industry is phenomenal, and it is also innovating how they operate and build products and services. Recent AI advancements are rapidly changing the face of accounting and finance in many ways.   Labor and time-consuming tasks… Read More »How AI Will Impact The Accounting And Finance Industry The post How AI Will Impact The Accounting And Finance Industry appeared first on Data Science Central.  ( 19 min )
    AI Enabled Smart Stores: The Future of Retail
    The retail sector has undergone significant upheaval recently, and this trend will continue even as retail has begun to thrive in the digital age. Businesses are growing remarkably due to utilizing IoT technology to the fullest extent possible. It has accomplished everything by taking full advantage of the world of AI. Implementing artificial intelligence technology in the… Read More »AI Enabled Smart Stores: The Future of Retail The post AI Enabled Smart Stores: The Future of Retail appeared first on Data Science Central.  ( 17 min )
    When AGI comes – will you recognise it?
    The media has brainwashed us into thinking of AGI as something akin to the ‘Terminator’ But when (and if) AGI comes – what would it really look like? It’s tempting to thinking that AGI may be human-like  But I read two books recently which point to otherwise  Recently, the economist listed five best books to… Read More »When AGI comes – will you recognise it? The post When AGI comes – will you recognise it? appeared first on Data Science Central.  ( 17 min )
  • Open

    Looking for Deepmind implementation of Player of Games
    I tried searching the internet for already existing implementations of Deepmind's Player of Games but outside of the original paper I couldn't find much in terms of libraries or existing code. Before I throw away a large amount of time writing out the code for this, does it exist somewhere else I'm not privy to? submitted by /u/AlexMarcDewey [link] [comments]  ( 86 min )
    Actor critic in bipedal walker gym
    Hellooooo !! I stuck on a RL problem and need help :'( Im doing the bipedal walker of open ai gym and I use the actor critic algorithm to solve it but I always stuck in a local minimum near zero ( one step of agent ) . I try a lot of hyper parameter with no sucess. It seems that my actor network who have in ouptut the mean and variance of a normal law learn to do the first step but after this step the variance is too low to learn how to do a second step (it's my "theory") Here my question : it is possible to solve bipedal walker with simple actor critic or it's juste my actor critic algorithm who suck ​ Ty to read and have a good day :) submitted by /u/Cauchy_Chlasse [link] [comments]  ( 98 min )
    Anyone interested in using Atlas for reinforcement learning? 🤖 https://www.linkedin.com/posts/m%C3%A5rten-sj%C3%B6-5909799_pretty-big-news-today-i-can-announce-that-activity-6964610163441831936-8rr_
    submitted by /u/krozzoz [link] [comments]  ( 87 min )
    is a grid world with a local view a pomdp or an mdp ?
    Ideally pomdps are where your actions depend on a sequence of states , but in a grid world with a partial/local view , I don't have access to the entire state space (that is the whole gridworld) . Doea this make in an mdp or a pomdp ? submitted by /u/Cool_Abbreviations_9 [link] [comments]  ( 87 min )
  • Open

    How to deal with NaN values in prediction using LSTM or RNN, when I can't delete them and can't fill them with median nor mean values?
    Hello, So basically I have realised that I have quite a big problem. Bascially I am trying out an ideas for stock prediction, and in one case I have calculated ichimoku indicator, which contains a couple of indicators. One of them is so called chikou-span which bassically is the receded price value by 26 time intervals. Basically chikou_span[i] = price_array[i - 26] For example: if the price was: 0, 1, 2, 3, 4, 5, ... 25, 26, 27, 28 chikou-span would be: 25, 26, 27, 28, NaN, NaN, ... NaN And as you see I can't really drop NaN values since they are kind of like future values and I am trying to predict the what heppens in the future - right where the first NaN value is located. So now how should I deal with it when inserting this indicator to LSTM (along with price and others)? Or is it sensless and I just should drop this one completly? Thanks in advance for any advice :) submitted by /u/skollehatti [link] [comments]  ( 87 min )
    What is the term when I use multiple models and when in each prediction I use the one that performs the best, and where I can read on it?
    Hello, Like in topic. I know there is the term for that but I have it forgotten. To make it more precise - I have now prepared my whole dataset with various "indicators" calculated using raw data. Now I want to assemble around 20 models where each model will proccess raw data and few of these indicators, and I want these models to "co-operate" so the prediction which I will be using later will be the best prediction from each of the models. Except from the term is there any whitepaper that is worth reading regarding this topic? Thank you for you help in advance :) submitted by /u/skollehatti [link] [comments]  ( 87 min )
  • Open

    AI-Based Content Vs. Human Created Content: Which Is the Best Option?
    In the world of marketing, one of the most important aspects is content. Content can be anything from a blog post to an e-book or even an…  ( 15 min )
  • Open

    Keeping data and code together with org-mode
    With org-mode you can keep data, code, and documentation in one file. Suppose you have an org-mode file containing the following table. #+NAME: mydata | Drug | Patients | |------+----------| | X | 232 | | Y | 351 | | Z | 117 | Note that there cannot be a blank line between the […] Keeping data and code together with org-mode first appeared on John D. Cook.  ( 6 min )
  • Open

    A Practical Second-order Latent Factor Model via Distributed Particle Swarm Optimization. (arXiv:2208.06125v1 [cs.LG])
    Latent Factor (LF) models are effective in representing high-dimension and sparse (HiDS) data via low-rank matrices approximation. Hessian-free (HF) optimization is an efficient method to utilizing second-order information of an LF model's objective function and it has been utilized to optimize second-order LF (SLF) model. However, the low-rank representation ability of a SLF model heavily relies on its multiple hyperparameters. Determining these hyperparameters is time-consuming and it largely reduces the practicability of an SLF model. To address this issue, a practical SLF (PSLF) model is proposed in this work. It realizes hyperparameter self-adaptation with a distributed particle swarm optimizer (DPSO), which is gradient-free and parallelized. Experiments on real HiDS data sets indicate that PSLF model has a competitive advantage over state-of-the-art models in data representation ability.  ( 2 min )
    A Knowledge Distillation-Based Backdoor Attack in Federated Learning. (arXiv:2208.06176v1 [cs.LG])
    Federated Learning (FL) is a novel framework of decentralized machine learning. Due to the decentralized feature of FL, it is vulnerable to adversarial attacks in the training procedure, e.g. , backdoor attacks. A backdoor attack aims to inject a backdoor into the machine learning model such that the model will make arbitrarily incorrect behavior on the test sample with some specific backdoor trigger. Even though a range of backdoor attack methods of FL has been introduced, there are also methods defending against them. Many of the defending methods utilize the abnormal characteristics of the models with backdoor or the difference between the models with backdoor and the regular models. To bypass these defenses, we need to reduce the difference and the abnormal characteristics. We find a source of such abnormality is that backdoor attack would directly flip the label of data when poisoning the data. However, current studies of the backdoor attack in FL are not mainly focus on reducing the difference between the models with backdoor and the regular models. In this paper, we propose Adversarial Knowledge Distillation(ADVKD), a method combine knowledge distillation with backdoor attack in FL. With knowledge distillation, we can reduce the abnormal characteristics in model result from the label flipping, thus the model can bypass the defenses. Compared to current methods, we show that ADVKD can not only reach a higher attack success rate, but also successfully bypass the defenses when other methods fails. To further explore the performance of ADVKD, we test how the parameters affect the performance of ADVKD under different scenarios. According to the experiment result, we summarize how to adjust the parameter for better performance under different scenarios. We also use several methods to visualize the effect of different attack and explain the effectiveness of ADVKD.  ( 3 min )
    LEAF: Navigating Concept Drift in Cellular Networks. (arXiv:2109.03011v4 [cs.NI] UPDATED)
    Operational networks commonly rely on machine learning models for many tasks, including detecting anomalies, inferring application performance, and forecasting demand. Yet, unfortunately, model accuracy can degrade due to concept drift, whereby the relationship between the features and the target prediction changes due to reasons ranging from software upgrades to seasonality to changes in user behavior. Mitigating concept drift is thus an essential part of operationalizing machine learning models, and yet despite its importance, concept drift has not been extensively explored in the context of networking -- or regression models in general. Thus, it is not well-understood how to detect or mitigate it for many common network management tasks that currently rely on machine learning models. Unfortunately, as we show, concept drift cannot be sufficiently mitigated by frequently retraining models using newly available data, and doing so can even degrade model accuracy further. In this paper, we characterize concept drift in a large cellular network for a major metropolitan area in the United States. We find that concept drift occurs across many important key performance indicators (KPIs), independently of the model, training set size, and time interval -- thus necessitating practical approaches to detect, explain, and mitigate it. To do so, we develop Local Error Approximation of Features (LEAF). LEAF detects drift; explains features and time intervals that most contribute to drift; and mitigates drift using forgetting and over-sampling. We evaluate LEAF against industry-standard mitigation approaches with more than four years of cellular KPI data. Our initial tests with a major cellular provider in the US show that LEAF is effective on a variety of KPIs and models. LEAF consistently outperforms periodic and triggered retraining while reducing costly retraining operations.  ( 3 min )
    Incompleteness of graph convolutional neural networks for points clouds in three dimensions. (arXiv:2201.07136v3 [stat.ML] UPDATED)
    Graph neural networks (GNN) are very popular methods in machine learning and have been applied very successfully to the prediction of the properties of molecules and materials. First-order GNNs are well known to be incomplete, i.e., there exist graphs that are distinct but appear identical when seen through the lens of the GNN. More complicated schemes have thus been designed to increase their resolving power. Applications to molecules (and more generally, point clouds), however, add a geometric dimension to the problem. The most straightforward and prevalent approach to construct graph representation for molecules regards atoms as vertices in a graph and draws a bond between each pair of atoms within a chosen cutoff. Bonds can be decorated with the distance between atoms, and the resulting "distance graph NNs" (dGNN) have empirically demonstrated excellent resolving power and are widely used in chemical ML, with all known indistinguishable graphs being resolved in the fully-connected limit. Here we show that even for the restricted case of fully-connected graphs induced by 3D atom clouds dGNNs are not complete. We construct pairs of distinct point clouds that generate graphs that, for any cutoff radius, are equivalent based on a first-order Weisfeiler-Lehman test. This class of degenerate structures includes chemically-plausible configurations, setting an ultimate limit to the expressive power of some of the well-established GNN architectures for atomistic machine learning. Models that explicitly use angular or directional information in the description of atomic environments can resolve these degeneracies.  ( 3 min )
    Local Spatiotemporal Representation Learning for Longitudinally-consistent Neuroimage Analysis. (arXiv:2206.04281v2 [cs.CV] UPDATED)
    Recent self-supervised advances in medical computer vision exploit global and local anatomical self-similarity for pretraining prior to downstream tasks such as segmentation. However, current methods assume i.i.d. image acquisition, which is invalid in clinical study designs where follow-up longitudinal scans track subject-specific temporal changes. Further, existing self-supervised methods for medically-relevant image-to-image architectures exploit only spatial or temporal self-similarity and only do so via a loss applied at a single image-scale, with naive multi-scale spatiotemporal extensions collapsing to degenerate solutions. To these ends, this paper makes two contributions: (1) It presents a local and multi-scale spatiotemporal representation learning method for image-to-image architectures trained on longitudinal images. It exploits the spatiotemporal self-similarity of learned multi-scale intra-subject features for pretraining and develops several feature-wise regularizations that avoid collapsed identity representations; (2) During finetuning, it proposes a surprisingly simple self-supervised segmentation consistency regularization to exploit intra-subject correlation. Benchmarked in the one-shot segmentation setting, the proposed framework outperforms both well-tuned randomly-initialized baselines and current self-supervised techniques designed for both i.i.d. and longitudinal datasets. These improvements are demonstrated across both longitudinal neurodegenerative adult MRI and developing infant brain MRI and yield both higher performance and longitudinal consistency.  ( 3 min )
    Mixed-Precision Neural Networks: A Survey. (arXiv:2208.06064v1 [cs.LG])
    Mixed-precision Deep Neural Networks achieve the energy efficiency and throughput needed for hardware deployment, particularly when the resources are limited, without sacrificing accuracy. However, the optimal per-layer bit precision that preserves accuracy is not easily found, especially with the abundance of models, datasets, and quantization techniques that creates an enormous search space. In order to tackle this difficulty, a body of literature has emerged recently, and several frameworks that achieved promising accuracy results have been proposed. In this paper, we start by summarizing the quantization techniques used generally in literature. Then, we present a thorough survey of the mixed-precision frameworks, categorized according to their optimization techniques such as reinforcement learning and quantization techniques like deterministic rounding. Furthermore, the advantages and shortcomings of each framework are discussed, where we present a juxtaposition. We finally give guidelines for future mixed-precision frameworks.  ( 2 min )
    Collective Obfuscation and Crowdsourcing. (arXiv:2208.06405v1 [cs.LG])
    Crowdsourcing technologies rely on groups of people to input information that may be critical for decision-making. This work examines obfuscation in the context of reporting technologies. We show that widespread use of reporting platforms comes with unique security and privacy implications, and introduce a threat model and corresponding taxonomy to outline some of the many attack vectors in this space. We then perform an empirical analysis of a dataset of call logs from a controversial, real-world reporting hotline and identify coordinated obfuscation strategies that are intended to hinder the platform's legitimacy. We propose a variety of statistical measures to quantify the strength of this obfuscation strategy with respect to the structural and semantic characteristics of the reporting attacks in our dataset.  ( 2 min )
    Figure Descriptive Text Extraction using Ontological Representation. (arXiv:2208.06040v1 [cs.CL])
    Experimental research publications provide figure form resources including graphs, charts, and any type of images to effectively support and convey methods and results. To describe figures, authors add captions, which are often incomplete, and more descriptions reside in body text. This work presents a method to extract figure descriptive text from the body of scientific articles. We adopted ontological semantics to aid concept recognition of figure-related information, which generates human- and machine-readable knowledge representations from sentences. Our results show that conceptual models bring an improvement in figure descriptive sentence classification over word-based approaches.  ( 2 min )
    An Algorithm-Hardware Co-Optimized Framework for Accelerating N:M Sparse Transformers. (arXiv:2208.06118v1 [cs.AR])
    The Transformer has been an indispensable staple in deep learning. However, for real-life applications, it is very challenging to deploy efficient Transformers due to immense parameters and operations of models. To relieve this burden, exploiting sparsity is an effective approach to accelerate Transformers. Newly emerging Ampere GPUs leverage a 2:4 sparsity pattern to achieve model acceleration, while it can hardly meet the diverse algorithm and hardware constraints when deploying models. By contrast, we propose an algorithm-hardware co-optimized framework to flexibly and efficiently accelerate Transformers by utilizing general N:M sparsity patterns. (1) From algorithm perspective, we propose a sparsity inheritance mechanism along with an inherited dynamic pruning (IDP) method to obtain a series of N:M sparse candidate Transformers rapidly. A model compression scheme is further proposed to significantly reduce the storage requirement for deployment. (2) From hardware perspective, we present a flexible and efficient hardware architecture, namely STA, to achieve significant speedup when deploying N:M sparse Transformers. STA features not only a computing engine unifying both sparse-dense and dense-dense matrix multiplications with high computational efficiency but also a scalable softmax module eliminating the latency from intermediate off-chip data communication. Experimental results show that compared to other methods, N:M sparse Transformers, generated using IDP, achieves an average of 6.7% improvement on accuracy with high training efficiency. Moreover, STA can achieve 14.47x and 11.33x speedup compared to Intel i9-9900X and NVIDIA RTX 2080 Ti, respectively, and perform 2.00-19.47x faster inference than the state-of-the-art FPGA-based accelerators for Transformers.  ( 3 min )
    Gaussian process surrogate models for neural networks. (arXiv:2208.06028v1 [cs.LG])
    The lack of insight into deep learning systems hinders their systematic design. In science and engineering, modeling is a methodology used to understand complex systems whose internal processes are opaque. Modeling replaces a complex system with a simpler surrogate that is more amenable to interpretation. Drawing inspiration from this, we construct a class of surrogate models for neural networks using Gaussian processes. Rather than deriving the kernels for certain limiting cases of neural networks, we learn the kernels of the Gaussian process empirically from the naturalistic behavior of neural networks. We first evaluate our approach with two case studies inspired by previous theoretical studies of neural network behavior in which we capture neural network preferences for learning low frequencies and identify pathological behavior in deep neural networks. In two further practical case studies, we use the learned kernel to predict the generalization properties of neural networks.  ( 2 min )
    A Ranking Game for Imitation Learning. (arXiv:2202.03481v2 [cs.LG] UPDATED)
    We propose a new framework for imitation learning -- treating imitation as a two-player ranking-based game between a policy and a reward. In this game, the reward agent learns to satisfy pairwise performance rankings between behaviors, while the policy agent learns to maximize this reward. In imitation learning, near-optimal expert data can be difficult to obtain, and even in the limit of infinite data cannot imply a total ordering over trajectories as preferences can. On the other hand, learning from preferences alone is challenging as a large number of preferences are required to infer a high-dimensional reward function, though preference data is typically much easier to collect than expert demonstrations. The classical inverse reinforcement learning (IRL) formulation learns from expert demonstrations but provides no mechanism to incorporate learning from offline preferences and vice versa. We instantiate the proposed ranking-game framework with a novel ranking loss giving an algorithm that can simultaneously learn from expert demonstrations and preferences, gaining the advantages of both modalities. Our experiments show that the proposed method achieves state-of-the-art sample efficiency and can solve previously unsolvable tasks in the Learning from Observation (LfO) setting.  ( 3 min )
    COVID-19 forecasting using new viral variants and vaccination effectiveness models. (arXiv:2201.10356v2 [cs.LG] UPDATED)
    Background: Recently, a high number of daily positive COVID-19 cases have been reported in regions with relatively high vaccination rates; hence, booster vaccination has become necessary. In addition, infections caused by the different variants and correlated factors have not been discussed in depth. With large variabilities and different co-factors, it is difficult to use conventional mathematical models to forecast the incidence of COVID-19. Methods: Machine learning based on long short-term memory was applied to forecasting the time series of new daily positive cases (DPC), serious cases, hospitalized cases, and deaths. Data acquired from regions with high rates of vaccination, such as Israel, were blended with the current data of other regions in Japan to factor in the potential effects of vaccination. The protection provided by symptomatic infection was also considered in terms of the population effectiveness of vaccination as well as the waning protection and ratio and infectivity of viral variants. To represent changes in public behavior, public mobility and interactions through social media were also included in the analysis. Findings: Comparing the observed and estimated new DPC in Tel Aviv, Israel, the parameters characterizing vaccination effectiveness and the waning protection from infection were well estimated; the vaccination effectiveness of the second dose after 5 months and the third dose after two weeks from infection by the delta variant were 0.24 and 0.95, respectively. Using the extracted parameters regarding vaccination effectiveness, new cases in three prefectures of Japan were replicated.  ( 3 min )
    EEGNN: Edge Enhanced Graph Neural Networks. (arXiv:2208.06322v1 [stat.ML])
    Training deep graph neural networks (GNNs) poses a challenging task, as the performance of GNNs may suffer from the number of hidden message-passing layers. The literature has focused on the proposals of over-smoothing and under-reaching to explain the performance deterioration of deep GNNs. In this paper, we propose a new explanation for such deteriorated performance phenomenon, mis-simplification, that is, mistakenly simplifying graphs by preventing self-loops and forcing edges to be unweighted. We show that such simplifying can reduce the potential of message-passing layers to capture the structural information of graphs. In view of this, we propose a new framework, edge enhanced graph neural network(EEGNN). EEGNN uses the structural information extracted from the proposed Dirichlet mixture Poisson graph model, a Bayesian nonparametric model for graphs, to improve the performance of various deep message-passing GNNs. Experiments over different datasets show that our method achieves considerable performance increase compared to baselines.  ( 2 min )
    Identifying Substitute and Complementary Products for Assortment Optimization with Cleora Embeddings. (arXiv:2208.06262v1 [cs.IR])
    Recent years brought an increasing interest in the application of machine learning algorithms in e-commerce, omnichannel marketing, and the sales industry. It is not only to the algorithmic advances but also to data availability, representing transactions, users, and background product information. Finding products related in different ways, i.e., substitutes and complements is essential for users' recommendations at the vendor's site and for the vendor - to perform efficient assortment optimization. The paper introduces a novel method for finding products' substitutes and complements based on the graph embedding Cleora algorithm. We also provide its experimental evaluation with regards to the state-of-the-art Shopper algorithm, studying the relevance of recommendations with surveys from industry experts. It is concluded that the new approach presented here offers suitable choices of recommended products, requiring a minimal amount of additional information. The algorithm can be used in various enterprises, effectively identifying substitute and complementary product options.  ( 2 min )
    Quantum-classical convolutional neural networks in radiological image classification. (arXiv:2204.12390v2 [quant-ph] UPDATED)
    Quantum machine learning is receiving significant attention currently, but its usefulness in comparison to classical machine learning techniques for practical applications remains unclear. However, there are indications that certain quantum machine learning algorithms might result in improved training capabilities with respect to their classical counterparts -- which might be particularly beneficial in situations with little training data available. Such situations naturally arise in medical classification tasks. Within this paper, different hybrid quantum-classical convolutional neural networks (QCCNN) with varying quantum circuit designs and encoding techniques are proposed. They are applied to two- and three-dimensional medical imaging data, e.g. featuring different, potentially malign, lesions in computed tomography scans. The performance of these QCCNNs is already similar to the one of their classical counterparts -- therefore encouraging further studies towards the direction of applying these algorithms within medical imaging tasks.  ( 2 min )
    Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning. (arXiv:2208.06193v1 [cs.LG])
    Offline reinforcement learning (RL), which aims to learn an optimal policy using a previously collected static dataset, is an important paradigm of RL. Standard RL methods often perform poorly at this task due to the function approximation errors on out-of-distribution actions. While a variety of regularization methods have been proposed to mitigate this issue, they are often constrained by policy classes with limited expressiveness and sometimes result in substantially suboptimal solutions. In this paper, we propose Diffusion-QL that utilizes a conditional diffusion model as a highly expressive policy class for behavior cloning and policy regularization. In our approach, we learn an action-value function and we add a term maximizing action-values into the training loss of a conditional diffusion model, which results in a loss that seeks optimal actions that are near the behavior policy. We show the expressiveness of the diffusion model-based policy and the coupling of the behavior cloning and policy improvement under the diffusion model both contribute to the outstanding performance of Diffusion-QL. We illustrate our method and prior work in a simple 2D bandit example with a multimodal behavior policy. We then show that our method can achieve state-of-the-art performance on the majority of the D4RL benchmark tasks for offline RL.  ( 2 min )
    Self-Distilled Vision Transformer for Domain Generalization. (arXiv:2207.12392v2 [cs.CV] UPDATED)
    In recent past, several domain generalization (DG) methods have been proposed, showing encouraging performance, however, almost all of them build on convolutional neural networks (CNNs). There is little to no progress on studying the DG performance of vision transformers (ViTs), which are challenging the supremacy of CNNs on standard benchmarks, often built on i.i.d assumption. This renders the real-world deployment of ViTs doubtful. In this paper, we attempt to explore ViTs towards addressing the DG problem. Similar to CNNs, ViTs also struggle in out-of-distribution scenarios and the main culprit is overfitting to source domains. Inspired by the modular architecture of ViTs, we propose a simple DG approach for ViTs, coined as self-distillation for ViTs. It reduces the overfitting to source domains by easing the learning of input-output mapping problem through curating non-zero entropy supervisory signals for intermediate transformer blocks. Further, it does not introduce any new parameters and can be seamlessly plugged into the modular composition of different ViTs. We empirically demonstrate notable performance gains with different DG baselines and various ViT backbones in five challenging datasets. Moreover, we report favorable performance against recent state-of-the-art DG methods. Our code along with pre-trained models are publicly available at: https://github.com/maryam089/SDViT
    Developing a Philosophical Framework for Fair Machine Learning: The Case of Algorithmic Collusion and Market Fairness. (arXiv:2208.06308v1 [cs.LG])
    Fair machine learning research has been primarily concerned with classification tasks that result in discrimination. As machine learning algorithms are applied in new contexts, however, the harms or injustices that result are qualitatively different than those presently studied. Existing research at the level of metrics and definitions cannot measure these qualitatively different types of injustice. One example of this is the problem of market fairness and algorithmic collusion. Negative consequences of algorithmic collusion affect all consumers, not only particular members of a protected class. Drawing on this case study, I develop an ethical framework for fair machine learning research in new domains. This contribution ties the development of fairness metrics to specifically scoped normative principles. This enables fairness metrics to reflect different concerns from discrimination. I develop this framework and provide the philosophical rationale for its structure, ultimately applying it to the case of algorithmic collusion. I conclude with limitations of my proposal and discuss promising avenues of future research.
    Hybrid Approach to Identify Druglikeness Leading Compounds against SARS 3CL Protease. (arXiv:2208.06362v1 [q-bio.BM])
    SARS-COV-2 is a positive single strand RNA based macromolecule that has caused the death of more than 6.3 million people since June 2022. Moreover, by disturbing global supply chains through lockdown, the virus has indirectly caused devastating damage to the global economy. It is vital to design and develop drugs for this virus and its various variants. In this paper, we have used an In-Silico study framework to repurpose existing therapeutic agents to find drug-like bioactive molecules that could cure Covid-19. We used the Lipinski rules on the molecules retrieved from ChEMBL database to find 133 drug-likeness bioactive molecules against SARS coronavirus 3CL Protease. On the basis of standard IC50, the dataset was divided into three classes of active, inactive and intermediate. Our comparative analysis demonstrated that proposed Extra Tree Regressor (ETR) ensemble model has improved results while predicting accurate bioactivity of chemical compound relative to other state-of-the-art machine learning models. Using ADMET analysis, we identified 13 novel bioactive molecules having ChEMBL IDs 187460, 190743, 222234, 222628, 222735, 222769, 222840, 222893, 225515, 358279, 363535, 365134 and 426898. We found that these molecules are highly suitable drug candidates for SARS-COV-2 3CL Protease. These candidate molecules are further investigated for binding affinities. For this purpose, we performed molecular docking and short listed six bioactive molecules having ChEMBL IDs 187460, 222769, 225515, 358279, 363535, and 365134. These molecules can be suitable drug candidates for SARS-COV-2. It is anticipated that pharmacologist community may use these promising compounds for further vitro analysis.
    A novel solution of deep learning for enhanced support vector machine for predicting the onset of type 2 diabetes. (arXiv:2208.06354v1 [cs.LG])
    Type 2 Diabetes is one of the most major and fatal diseases known to human beings, where thousands of people are subjected to the onset of Type 2 Diabetes every year. However, the diagnosis and prevention of Type 2 Diabetes are relatively costly in today's scenario; hence, the use of machine learning and deep learning techniques is gaining momentum for predicting the onset of Type 2 Diabetes. This research aims to increase the accuracy and Area Under the Curve (AUC) metric while improving the processing time for predicting the onset of Type 2 Diabetes. The proposed system consists of a deep learning technique that uses the Support Vector Machine (SVM) algorithm along with the Radial Base Function (RBF) along with the Long Short-term Memory Layer (LSTM) for prediction of onset of Type 2 Diabetes. The proposed solution provides an average accuracy of 86.31 % and an average AUC value of 0.8270 or 82.70 %, with an improvement of 3.8 milliseconds in the processing. Radial Base Function (RBF) kernel and the LSTM layer enhance the prediction accuracy and AUC metric from the current industry standard, making it more feasible for practical use without compromising the processing time.
    Hyperbolic Molecular Representation Learning for Drug Repositioning. (arXiv:2208.06361v1 [q-bio.BM])
    Learning accurate drug representations is essential for task such as computational drug repositioning. A drug hierarchy is a valuable source that encodes knowledge of relations among drugs in a tree-like structure where drugs that act on the same organs, treat the same disease, or bind to the same biological target are grouped together. However, its utility in learning drug representations has not yet been explored, and currently described drug representations cannot place novel molecules in a drug hierarchy. Here, we develop a semi-supervised drug embedding that incorporates two sources of information: (1) underlying chemical grammar that is inferred from chemical structures of drugs and drug-like molecules (unsupervised), and (2) hierarchical relations that are encoded in an expert-crafted hierarchy of approved drugs (supervised). We use the Variational Auto-Encoder (VAE) framework to encode the chemical structures of molecules and use the drug-drug similarity information obtained from the hierarchy to induce the clustering of drugs in hyperbolic space. The hyperbolic space is amenable for encoding hierarchical relations. Our qualitative results support that the learned drug embedding can induce the hierarchical relations among drugs. We demonstrate that the learned drug embedding can be used for drug repositioning.
    An Empirical Exploration of Cross-domain Alignment between Language and Electroencephalogram. (arXiv:2208.06348v1 [q-bio.NC])
    Electroencephalography (EEG) and language have been widely explored independently for many downstream tasks (e.g., sentiment analysis, relation detection, etc.). Multimodal approaches that study both domains have not been well explored, even though in recent years, multimodal learning has been seen to be more powerful than its unimodal counterparts. In this study, we want to explore the relationship and dependency between EEG and language, i.e., how one domain reflects and represents the other. To study the relationship at the representation level, we introduced MTAM, a Multimodal Transformer Alignment Model, to observe coordinated representations between the two modalities, and thus employ the transformed representations for downstream applications. We used various relationship alignment-seeking techniques, such as Canonical Correlation Analysis and Wasserstein Distance, as loss functions to transfigure low-level language and EEG features to high-level transformed features. On downstream applications, sentiment analysis, and relation detection, we achieved new state-of-the-art results on two datasets, ZuCo and K-EmoCon. Our method achieved an F1-score improvement of 16.5% on sentiment analysis for K-EmoCon, 26.6% on sentiment analysis of ZuCo, and 31.1% on relation detection of ZuCo. In addition, we provide interpretation of the performance improvement by: (1) visualizing the original feature distribution and the transformed feature distribution, showing the effectiveness of the alignment module for discovering and encoding the relationship between EEG and language; (2) visualizing word-level and sentence-level EEG-language alignment weights, showing the influence of different language semantics as well as EEG frequency features; and (3) visualizing brain topographical maps to provide an intuitive demonstration of the connectivity of EEG and language response in the brain regions.
    Causal Imitation Learning with Unobserved Confounders. (arXiv:2208.06267v1 [cs.LG])
    One of the common ways children learn is by mimicking adults. Imitation learning focuses on learning policies with suitable performance from demonstrations generated by an expert, with an unspecified performance measure, and unobserved reward signal. Popular methods for imitation learning start by either directly mimicking the behavior policy of an expert (behavior cloning) or by learning a reward function that prioritizes observed expert trajectories (inverse reinforcement learning). However, these methods rely on the assumption that covariates used by the expert to determine her/his actions are fully observed. In this paper, we relax this assumption and study imitation learning when sensory inputs of the learner and the expert differ. First, we provide a non-parametric, graphical criterion that is complete (both necessary and sufficient) for determining the feasibility of imitation from the combinations of demonstration data and qualitative assumptions about the underlying environment, represented in the form of a causal model. We then show that when such a criterion does not hold, imitation could still be feasible by exploiting quantitative knowledge of the expert trajectories. Finally, we develop an efficient procedure for learning the imitating policy from experts' trajectories.
    On establishing learning separations between classical and quantum machine learning with classical data. (arXiv:2208.06339v1 [quant-ph])
    Despite years of effort, the quantum machine learning community has only been able to show quantum learning advantages for certain contrived cryptography-inspired datasets in the case of classical data. In this note, we discuss the challenges of finding learning problems that quantum learning algorithms can learn much faster than any classical learning algorithm, and we study how to identify such learning problems. Specifically, we reflect on the main concepts in computational learning theory pertaining to this question, and we discuss how subtle changes in definitions can mean conceptually significantly different tasks, which can either lead to a separation or no separation at all. Moreover, we study existing learning problems with a provable quantum speedup to distill sets of more general and sufficient conditions (i.e., ``checklists'') for a learning problem to exhibit a separation between classical and quantum learners. These checklists are intended to streamline one's approach to proving quantum speedups for learning problems, or to elucidate bottlenecks. Finally, to illustrate its application, we analyze examples of potential separations (i.e., when the learning problem is build from computational separations, or when the data comes from a quantum experiment) through the lens of our approach.
    Optimal Extragradient-Based Bilinearly-Coupled Saddle-Point Optimization. (arXiv:2206.08573v3 [math.OC] UPDATED)
    We consider the smooth convex-concave bilinearly-coupled saddle-point problem, $\min_{\mathbf{x}}\max_{\mathbf{y}}~F(\mathbf{x}) + H(\mathbf{x},\mathbf{y}) - G(\mathbf{y})$, where one has access to stochastic first-order oracles for $F$, $G$ as well as the bilinear coupling function $H$. Building upon standard stochastic extragradient analysis for variational inequalities, we present a stochastic \emph{accelerated gradient-extragradient (AG-EG)} descent-ascent algorithm that combines extragradient and Nesterov's acceleration in general stochastic settings. This algorithm leverages scheduled restarting to admit a fine-grained nonasymptotic convergence rate that matches known lower bounds by both \citet{ibrahim2020linear} and \citet{zhang2021lower} in their corresponding settings, plus an additional statistical error term for bounded stochastic noise that is optimal up to a constant prefactor. This is the first result that achieves such a relatively mature characterization of optimality in saddle-point optimization.
    Markov Observation Models. (arXiv:2208.06368v1 [stat.ML])
    Herein, the Hidden Markov Model is expanded to allow for Markov chain observations. In particular, the observations are assumed to be a Markov chain whose one step transition probabilities depend upon the hidden Markov chain. An Expectation-Maximization analog to the Baum-Welch algorithm is developed for this more general model to estimate the transition probabilities for both the hidden state and for the observations as well as to estimate the probabilities for the initial joint hidden-state-observation distribution. A believe state or filter recursion to track the hidden state then arises from the calculations of this Expectation-Maximization algorithm. A dynamic programming analog to the Viterbi algorithm is also developed to estimate the most likely sequence of hidden states given the sequence of observations.
    Style Spectroscope: Improve Interpretability and Controllability through Fourier Analysis. (arXiv:2208.06140v1 [cs.CV])
    Universal style transfer (UST) infuses styles from arbitrary reference images into content images. Existing methods, while enjoying many practical successes, are unable of explaining experimental observations, including different performances of UST algorithms in preserving the spatial structure of content images. In addition, methods are limited to cumbersome global controls on stylization, so that they require additional spatial masks for desired stylization. In this work, we provide a systematic Fourier analysis on a general framework for UST. We present an equivalent form of the framework in the frequency domain. The form implies that existing algorithms treat all frequency components and pixels of feature maps equally, except for the zero-frequency component. We connect Fourier amplitude and phase with Gram matrices and a content reconstruction loss in style transfer, respectively. Based on such equivalence and connections, we can thus interpret different structure preservation behaviors between algorithms with Fourier phase. Given the interpretations we have, we propose two manipulations in practice for structure preservation and desired stylization. Both qualitative and quantitative experiments demonstrate the competitive performance of our method against the state-of-the-art methods. We also conduct experiments to demonstrate (1) the abovementioned equivalence, (2) the interpretability based on Fourier amplitude and phase and (3) the controllability associated with frequency components.
    LRH-Net: A Multi-Level Knowledge Distillation Approach for Low-Resource Heart Network. (arXiv:2204.08000v2 [physics.med-ph] UPDATED)
    An electrocardiogram (ECG) monitors the electrical activity generated by the heart and is used to detect fatal cardiovascular diseases (CVDs). Conventionally, to capture the precise electrical activity, clinical experts use multiple-lead ECGs (typically 12 leads). But in recent times, large-size deep learning models have been used to detect these diseases. However, such models require heavy compute resources like huge memory and long inference time. To alleviate these shortcomings, we propose a low-parameter model, named Low Resource Heart-Network (LRH-Net), which uses fewer leads to detect ECG anomalies in a resource-constrained environment. A multi-level knowledge distillation process is used on top of that to get better generalization performance on our proposed model. The multi-level knowledge distillation process distills the knowledge to LRH-Net trained on a reduced number of leads from higher parameter (teacher) models trained on multiple leads to reduce the performance gap. The proposed model is evaluated on the PhysioNet-2020 challenge dataset with constrained input. The parameters of the LRH-Net are 106x less than our teacher model for detecting CVDs. The performance of the LRH-Net was scaled up to 3.2% and the inference time scaled down by 75% compared to the teacher model. In contrast to the compute- and parameter-intensive deep learning techniques, the proposed methodology uses a subset of ECG leads using the low resource LRH-Net, making it eminently suitable for deployment on edge devices.
    ANTI-CARLA: An Adversarial Testing Framework for Autonomous Vehicles in CARLA. (arXiv:2208.06309v1 [cs.LG])
    Despite recent advances in autonomous driving systems, accidents such as the fatal Uber crash in 2018 show these systems are still susceptible to edge cases. Such systems must be thoroughly tested and validated before being deployed in the real world to avoid such events. Testing in open-world scenarios can be difficult, time-consuming, and expensive. These challenges can be addressed by using driving simulators such as CARLA instead. A key part of such tests is adversarial testing, in which the goal is to find scenarios that lead to failures of the given system. While several independent efforts in testing have been made, a well-established testing framework that enables adversarial testing has yet to be made available for CARLA. We therefore propose ANTI-CARLA, an automated testing framework in CARLA for simulating adversarial weather conditions (e.g., heavy rain) and sensor faults (e.g., camera occlusion) that fail the system. The operating conditions in which a given system should be tested are specified in a scenario description language. The framework offers an efficient search mechanism that searches for adversarial operating conditions that will fail the tested system. In this way, ANTI-CARLA extends the CARLA simulator with the capability of performing adversarial testing on any given driving pipeline. We use ANTI-CARLA to test the driving pipeline trained with Learning By Cheating (LBC) approach. The simulation results demonstrate that ANTI-CARLA can effectively and automatically find a range of failure cases despite LBC reaching an accuracy of 100% in the CARLA benchmark.
    Toward a Better Monitoring Statistic for Profile Monitoring via Variational Autoencoders. (arXiv:1911.00482v2 [cs.LG] UPDATED)
    Wide accessibility of imaging and profile sensors in modern industrial systems created an abundance of high-dimensional sensing variables. This led to a a growing interest in the research of high-dimensional process monitoring. However, most of the approaches in the literature assume the in-control population to lie on a linear manifold with a given basis (i.e., spline, wavelet, kernel, etc) or an unknown basis (i.e., principal component analysis and its variants), which cannot be used to efficiently model profiles with a nonlinear manifold which is common in many real-life cases. We propose deep probabilistic autoencoders as a viable unsupervised learning approach to model such manifolds. To do so, we formulate nonlinear and probabilistic extensions of the monitoring statistics from classical approaches as the expected reconstruction error (ERE) and the KL-divergence (KLD) based monitoring statistics. Through extensive simulation study, we provide insights on why latent-space based statistics are unreliable and why residual-space based ones typically perform much better for deep learning based approaches. Finally, we demonstrate the superiority of deep probabilistic models via both simulation study and a real-life case study involving images of defects from a hot steel rolling process.
    Data-Driven Fault Diagnosis Analysis and Open-Set Classification of Time-Series Data. (arXiv:2009.04756v2 [stat.ML] UPDATED)
    Fault diagnosis of dynamic systems is done by detecting changes in time-series data, for example residuals, caused by system degradation and faulty components. The use of general-purpose multi-class classification methods for fault diagnosis is complicated by imbalanced training data and unknown fault classes. Another complicating factor is that different fault classes can result in similar residual outputs, especially for small faults, which causes classification ambiguities. In this work, a framework for data-driven analysis and open-set classification is developed for fault diagnosis applications using the Kullback-Leibler divergence. A data-driven fault classification algorithm is proposed which can handle imbalanced datasets, class overlapping, and unknown faults. In addition, an algorithm is proposed to estimate the size of the fault when training data contains information from known fault realizations. An advantage of the proposed framework is that it can also be used for quantitative analysis of fault diagnosis performance, for example, to analyze how easy it is to classify faults of different magnitudes. To evaluate the usefulness of the proposed methods, multiple datasets from different fault scenarios have been collected from an internal combustion engine test bench to illustrate the design process of a data-driven diagnosis system, including quantitative fault diagnosis analysis and evaluation of the developed open set fault classification algorithm.
    Auto-Encoding Adversarial Imitation Learning. (arXiv:2206.11004v2 [cs.LG] UPDATED)
    Reinforcement learning (RL) provides a powerful framework for decision-making, but its application in practice often requires a carefully designed reward function. Adversarial Imitation Learning (AIL) sheds light on automatic policy acquisition without access to the reward signal from the environment. In this work, we propose Auto-Encoding Adversarial Imitation Learning (AEAIL), a robust and scalable AIL framework. To induce expert policies from demonstrations, AEAIL utilizes the reconstruction error of an auto-encoder as a reward signal, which provides more information for optimizing policies than the prior discriminator-based ones. Subsequently, we use the derived objective functions to train the auto-encoder and the agent policy. Experiments show that our AEAIL performs superior compared to state-of-the-art methods in the MuJoCo environments. More importantly, AEAIL shows much better robustness when the expert demonstrations are noisy. Specifically, our method achieves $16.4\%$ and $47.2\%$ relative improvement overall compared to the best baseline FAIRL and PWIL on clean and noisy expert data, respectively. Video results, open-source code and dataset are available in https://sites.google.com/view/auto-encoding-imitation.  ( 2 min )
    Semi-automatic tuning of coupled climate models with multiple intrinsic timescales: lessons learned from the Lorenz96 model. (arXiv:2208.06243v1 [physics.ao-ph])
    The objective of this study is to evaluate the potential for History Matching (HM) to tune a climate system with multi-scale dynamics. By considering a toy climate model, namely, the two-scale Lorenz96 model and producing experiments in perfect-model setting, we explore in detail how several built-in choices need to be carefully tested. We also demonstrate the importance of introducing physical expertise in the range of parameters, a priori to running HM. Finally we revisit a classical procedure in climate model tuning, that consists of tuning the slow and fast components separately. By doing so in the Lorenz96 model, we illustrate the non-uniqueness of plausible parameters and highlight the specificity of metrics emerging from the coupling. This paper contributes also to bridging the communities of uncertainty quantification, machine learning and climate modeling, by making connections between the terms used by each community for the same concept and presenting promising collaboration avenues that would benefit climate modeling research.
    Scalable and Sparsity-Aware Privacy-Preserving K-means Clustering with Application to Fraud Detection. (arXiv:2208.06093v1 [cs.LG])
    K-means is one of the most widely used clustering models in practice. Due to the problem of data isolation and the requirement for high model performance, how to jointly build practical and secure K-means for multiple parties has become an important topic for many applications in the industry. Existing work on this is mainly of two types. The first type has efficiency advantages, but information leakage raises potential privacy risks. The second type is provable secure but is inefficient and even helpless for the large-scale data sparsity scenario. In this paper, we propose a new framework for efficient sparsity-aware K-means with three characteristics. First, our framework is divided into a data-independent offline phase and a much faster online phase, and the offline phase allows to pre-compute almost all cryptographic operations. Second, we take advantage of the vectorization techniques in both online and offline phases. Third, we adopt a sparse matrix multiplication for the data sparsity scenario to improve efficiency further. We conduct comprehensive experiments on three synthetic datasets and deploy our model in a real-world fraud detection task. Our experimental results show that, compared with the state-of-the-art solution, our model achieves competitive performance in terms of both running time and communication size, especially on sparse datasets.
    PRIF: Primary Ray-based Implicit Function. (arXiv:2208.06143v1 [cs.CV])
    We introduce a new implicit shape representation called Primary Ray-based Implicit Function (PRIF). In contrast to most existing approaches based on the signed distance function (SDF) which handles spatial locations, our representation operates on oriented rays. Specifically, PRIF is formulated to directly produce the surface hit point of a given input ray, without the expensive sphere-tracing operations, hence enabling efficient shape extraction and differentiable rendering. We demonstrate that neural networks trained to encode PRIF achieve successes in various tasks including single shape representation, category-wise shape generation, shape completion from sparse or noisy observations, inverse rendering for camera pose estimation, and neural rendering with color.
    Non-Autoregressive Sign Language Production via Knowledge Distillation. (arXiv:2208.06183v1 [cs.LG])
    Sign Language Production (SLP) aims to translate expressions in spoken language into corresponding ones in sign language, such as skeleton-based sign poses or videos. Existing SLP models are either AutoRegressive (AR) or Non-Autoregressive (NAR). However, AR-SLP models suffer from regression to the mean and error propagation during decoding. NSLP-G, a NAR-based model, resolves these issues to some extent but engenders other problems. For example, it does not consider target sign lengths and suffers from false decoding initiation. We propose a novel NAR-SLP model via Knowledge Distillation (KD) to address these problems. First, we devise a length regulator to predict the end of the generated sign pose sequence. We then adopt KD, which distills spatial-linguistic features from a pre-trained pose encoder to alleviate false decoding initiation. Extensive experiments show that the proposed approach significantly outperforms existing SLP models in both Frechet Gesture Distance and Back-Translation evaluation.  ( 2 min )
    Deep is a Luxury We Don't Have. (arXiv:2208.06066v1 [cs.CV])
    Medical images come in high resolutions. A high resolution is vital for finding malignant tissues at an early stage. Yet, this resolution presents a challenge in terms of modeling long range dependencies. Shallow transformers eliminate this problem, but they suffer from quadratic complexity. In this paper, we tackle this complexity by leveraging a linear self-attention approximation. Through this approximation, we propose an efficient vision model called HCT that stands for High resolution Convolutional Transformer. HCT brings transformers' merits to high resolution images at a significantly lower cost. We evaluate HCT using a high resolution mammography dataset. HCT is significantly superior to its CNN counterpart. Furthermore, we demonstrate HCT's fitness for medical images by evaluating its effective receptive field.Code available at https://bit.ly/3ykBhhf
    Forecasting COVID-19 spreading trough an ensemble of classical and machine learning models: Spain's case study. (arXiv:2207.05753v2 [cs.LG] UPDATED)
    In this work we evaluate the applicability of an ensemble of population models and machine learning models to predict the near future evolution of the COVID-19 pandemic, with a particular use case in Spain. We rely solely in open and public datasets, fusing incidence, vaccination, human mobility and weather data to feed our machine learning models (Random Forest, Gradient Boosting, k-Nearest Neighbours and Kernel Ridge Regression). We use the incidence data to adjust classic population models (Gompertz, Logistic, Richards, Bertalanffy) in order to be able to better capture the trend of the data. We then ensemble these two families of models in order to obtain a more robust and accurate prediction. Furthermore, we have observed an improvement in the predictions obtained with machine learning models as we add new features (vaccines, mobility, climatic conditions), analyzing the importance of each of them using Shapley Additive Explanation values. As in any other modelling work, data and predictions quality have several limitations and therefore they must be seen from a critical standpoint, as we discuss in the text. Our work concludes that the ensemble use of these models improves the individual predictions (using only machine learning models or only population models) and can be applied, with caution, in cases when compartmental models cannot be utilized due to the lack of relevant data.
    A Fast Blockchain-based Federated Learning Framework with Compressed Communications. (arXiv:2208.06095v1 [cs.LG])
    Recently, blockchain-based federated learning (BFL) has attracted intensive research attention due to that the training process is auditable and the architecture is serverless avoiding the single point failure of the parameter server in vanilla federated learning (VFL). Nevertheless, BFL tremendously escalates the communication traffic volume because all local model updates (i.e., changes of model parameters) obtained by BFL clients will be transmitted to all miners for verification and to all clients for aggregation. In contrast, the parameter server and clients in VFL only retain aggregated model updates. Consequently, the huge communication traffic in BFL will inevitably impair the training efficiency and hinder the deployment of BFL in reality. To improve the practicality of BFL, we are among the first to propose a fast blockchain-based communication-efficient federated learning framework by compressing communications in BFL, called BCFL. Meanwhile, we derive the convergence rate of BCFL with non-convex loss. To maximize the final model accuracy, we further formulate the problem to minimize the training loss of the convergence rate subject to a limited training time with respect to the compression rate and the block generation rate, which is a bi-convex optimization problem and can be efficiently solved. To the end, to demonstrate the efficiency of BCFL, we carry out extensive experiments with standard CIFAR-10 and FEMNIST datasets. Our experimental results not only verify the correctness of our analysis, but also manifest that BCFL can remarkably reduce the communication traffic by 95-98% or shorten the training time by 90-95% compared with BFL.
    Incorporating Customer Reviews in Size and Fit Recommendation systems for Fashion E-Commerce. (arXiv:2208.06261v1 [cs.IR])
    With the huge growth in e-commerce domain, product recommendations have become an increasing field of interest amongst e-commerce companies. One of the more difficult tasks in product recommendations is size and fit predictions. There are a lot of size related returns and refunds in e-fashion domain which causes inconvenience to the customers as well as costs the company. Thus having a good size and fit recommendation system, which can predict the correct sizes for the customers will not only reduce size related returns and refunds but also improve customer experience. Early works in this field used traditional machine learning approaches to estimate customer and product sizes from purchase history. These methods suffered from cold start problem due to huge sparsity in the customer-product data. More recently, people have used deep learning to address this problem by embedding customer and product features. But none of them incorporates valuable customer feedback present on product pages along with the customer and product features. We propose a novel approach which can use information from customer reviews along with customer and product features for size and fit predictions. We demonstrate the effectiveness of our approach compared to using just product and customer features on 4 datasets. Our method shows an improvement of 1.37% - 4.31% in F1 (macro) score over the baseline across the 4 different datasets.
    fairDMS: Rapid Model Training by Data and Model Reuse. (arXiv:2204.09805v3 [cs.LG] UPDATED)
    Extracting actionable information rapidly from data produced by instruments such as the Linac Coherent Light Source (LCLS-II) and Advanced Photon Source Upgrade (APS-U) is becoming ever more challenging due to high (up to TB/s) data rates. Conventional physics-based information retrieval methods are hard-pressed to detect interesting events fast enough to enable timely focusing on a rare event or correction of an error. Machine learning~(ML) methods that learn cheap surrogate classifiers present a promising alternative, but can fail catastrophically when changes in instrument or sample result in degradation in ML performance. To overcome such difficulties, we present a new data storage and ML model training architecture designed to organize large volumes of data and models so that when model degradation is detected, prior models and/or data can be queried rapidly and a more suitable model retrieved and fine-tuned for new conditions. We show that our approach can achieve up to 100x data labelling speedup compared to the current state-of-the-art, 200x improvement in training speed, and 92x speedup in-terms of end-to-end model updating time.  ( 3 min )
    Region-Based Evidential Deep Learning to Quantify Uncertainty and Improve Robustness of Brain Tumor Segmentation. (arXiv:2208.06038v1 [eess.IV])
    Despite recent advances in the accuracy of brain tumor segmentation, the results still suffer from low reliability and robustness. Uncertainty estimation is an efficient solution to this problem, as it provides a measure of confidence in the segmentation results. The current uncertainty estimation methods based on quantile regression, Bayesian neural network, ensemble, and Monte Carlo dropout are limited by their high computational cost and inconsistency. In order to overcome these challenges, Evidential Deep Learning (EDL) was developed in recent work but primarily for natural image classification. In this paper, we proposed a region-based EDL segmentation framework that can generate reliable uncertainty maps and robust segmentation results. We used the Theory of Evidence to interpret the output of a neural network as evidence values gathered from input features. Following Subjective Logic, evidence was parameterized as a Dirichlet distribution, and predicted probabilities were treated as subjective opinions. To evaluate the performance of our model on segmentation and uncertainty estimation, we conducted quantitative and qualitative experiments on the BraTS 2020 dataset. The results demonstrated the top performance of the proposed method in quantifying segmentation uncertainty and robustly segmenting tumors. Furthermore, our proposed new framework maintained the advantages of low computational cost and easy implementation and showed the potential for clinical application.
    Multi-Agent Reinforcement Learning with Graph Convolutional Neural Networks for optimal Bidding Strategies of Generation Units in Electricity Markets. (arXiv:2208.06242v1 [cs.AI])
    Finding optimal bidding strategies for generation units in electricity markets would result in higher profit. However, it is a challenging problem due to the system uncertainty which is due to the unknown other generation units' strategies. Distributed optimization, where each entity or agent decides on its bid individually, has become state of the art. However, it cannot overcome the challenges of system uncertainties. Deep reinforcement learning is a promising approach to learn the optimal strategy in uncertain environments. Nevertheless, it is not able to integrate the information on the spatial system topology in the learning process. This paper proposes a distributed learning algorithm based on deep reinforcement learning (DRL) combined with a graph convolutional neural network (GCN). In fact, the proposed framework helps the agents to update their decisions by getting feedback from the environment so that it can overcome the challenges of the uncertainties. In this proposed algorithm, the state and connection between nodes are the inputs of the GCN, which can make agents aware of the structure of the system. This information on the system topology helps the agents to improve their bidding strategies and increase the profit. We evaluate the proposed algorithm on the IEEE 30-bus system under different scenarios. Also, to investigate the generalization ability of the proposed approach, we test the trained model on IEEE 39-bus system. The results show that the proposed algorithm has more generalization abilities compare to the DRL and can result in higher profit when changing the topology of the system.
    Conditional Antibody Design as 3D Equivariant Graph Translation. (arXiv:2208.06073v1 [q-bio.BM])
    Antibody design is valuable for therapeutic usage and biological research. Existing deep-learning-based methods encounter several key issues: 1) incomplete context for Complementarity-Determining Regions (CDRs) generation; 2) incapable of capturing the entire 3D geometry of the input structure; 3) inefficient prediction of the CDR sequences in an autoregressive manner. In this paper, we propose Multi-channel Equivariant Attention Network (MEAN), an end-to-end model that is able to co-design 1D sequences and 3D structures of CDRs. To be specific, MEAN formulates antibody design as a conditional graph translation problem by importing extra components including the target antigen and the light chain of the antibody. Then, MEAN resorts to E(3)-equivariant message passing along with a proposed attention mechanism to better capture the geometrical correlation between different components. Finally, it outputs both the 1D sequences and 3D structure via a multi-round progressive full-shot scheme, which enjoys more efficiency against previous autoregressive approaches. Our method significantly surpasses state-of-the-art models in sequence and structure modeling, antigen-binding antibody design, and binding affinity optimization. Specifically, the relative improvement to baselines is about 22% in antigen-binding CDR design and 34% for affinity optimization.  ( 2 min )
    Hypergraph Modeling via Spectral Embedding Connection: Hypergraph Cut, Weighted Kernel $k$-means, and Heat Kernel. (arXiv:2203.09888v2 [cs.LG] UPDATED)
    We propose a theoretical framework of multi-way similarity to model real-valued data into hypergraphs for clustering via spectral embedding. For graph cut based spectral clustering, it is common to model real-valued data into graph by modeling pairwise similarities using kernel function. This is because the kernel function has a theoretical connection to the graph cut. For problems where using multi-way similarities are more suitable than pairwise ones, it is natural to model as a hypergraph, which is generalization of a graph. However, although the hypergraph cut is well-studied, there is not yet established a hypergraph cut based framework to model multi-way similarity. In this paper, we formulate multi-way similarities by exploiting the theoretical foundation of kernel function. We show a theoretical connection between our formulation and hypergraph cut in two ways, generalizing both weighted kernel $k$-means and the heat kernel, by which we justify our formulation. We also provide a fast algorithm for spectral clustering. Our algorithm empirically shows better performance than existing graph and other heuristic modeling methods.
    AutoShard: Automated Embedding Table Sharding for Recommender Systems. (arXiv:2208.06399v1 [cs.LG])
    Embedding learning is an important technique in deep recommendation models to map categorical features to dense vectors. However, the embedding tables often demand an extremely large number of parameters, which become the storage and efficiency bottlenecks. Distributed training solutions have been adopted to partition the embedding tables into multiple devices. However, the embedding tables can easily lead to imbalances if not carefully partitioned. This is a significant design challenge of distributed systems named embedding table sharding, i.e., how we should partition the embedding tables to balance the costs across devices, which is a non-trivial task because 1) it is hard to efficiently and precisely measure the cost, and 2) the partition problem is known to be NP-hard. In this work, we introduce our novel practice in Meta, namely AutoShard, which uses a neural cost model to directly predict the multi-table costs and leverages deep reinforcement learning to solve the partition problem. Experimental results on an open-sourced large-scale synthetic dataset and Meta's production dataset demonstrate the superiority of AutoShard over the heuristics. Moreover, the learned policy of AutoShard can transfer to sharding tasks with various numbers of tables and different ratios of the unseen tables without any fine-tuning. Furthermore, AutoShard can efficiently shard hundreds of tables in seconds. The effectiveness, transferability, and efficiency of AutoShard make it desirable for production use. Our algorithms have been deployed in Meta production environment. A prototype is available at https://github.com/daochenzha/autoshard
    Explainable Identification of Dementia from Transcripts using Transformer Networks. (arXiv:2109.06980v3 [cs.CL] UPDATED)
    Alzheimer's disease (AD) is the main cause of dementia which is accompanied by loss of memory and may lead to severe consequences in peoples' everyday life if not diagnosed on time. Very few works have exploited transformer-based networks and despite the high accuracy achieved, little work has been done in terms of model interpretability. In addition, although Mini-Mental State Exam (MMSE) scores are inextricably linked with the identification of dementia, research works face the task of dementia identification and the task of the prediction of MMSE scores as two separate tasks. In order to address these limitations, we employ several transformer-based models, with BERT achieving the highest accuracy accounting for 87.50%. Concurrently, we propose an interpretable method to detect AD patients based on siamese networks reaching accuracy up to 83.75%. Next, we introduce two multi-task learning models, where the main task refers to the identification of dementia (binary classification), while the auxiliary one corresponds to the identification of the severity of dementia (multiclass classification). Our model obtains accuracy equal to 86.25% on the detection of AD patients in the multi-task learning setting. Finally, we present some new methods to identify the linguistic patterns used by AD patients and non-AD ones, including text statistics, vocabulary uniqueness, word usage, correlations via a detailed linguistic analysis, and explainability techniques (LIME). Findings indicate significant differences in language between AD and non-AD patients.
    Towards a Grounded Theory of Causation for Embodied AI. (arXiv:2206.13973v2 [cs.AI] UPDATED)
    There exist well-developed frameworks for causal modelling, but these require rather a lot of human domain expertise to define causal variables and perform interventions. In order to enable autonomous agents to learn abstract causal models through interactive experience, the existing theoretical foundations need to be extended and clarified. Existing frameworks give no guidance regarding variable choice / representation, and more importantly, give no indication as to which behaviour policies or physical transformations of state space shall count as interventions. The framework sketched in this paper describes actions as transformations of state space, for instance induced by an agent running a policy. This makes it possible to describe in a uniform way both transformations of the micro-state space and abstract models thereof, and say when the latter is veridical / grounded / natural. We then introduce (causal) variables, define a mechanism as an invariant predictor, and say when an action can be viewed as a ``surgical intervention'', thus bringing the objective of causal representation \& intervention skill learning into clearer focus.
    An investigation on selecting audio pre-trained models for audio captioning. (arXiv:2208.06127v1 [cs.SD])
    Audio captioning is a task that generates description of audio based on content. Pre-trained models are widely used in audio captioning due to high complexity. Unless a comprehensive system is re-trained, it is hard to determine how well pre-trained models contribute to audio captioning system. To prevent the time consuming and energy consuming process of retraining, it is necessary to propose a preditor of performance for the pre-trained model in audio captioning. In this paper, a series of pre-trained models are investigated for the correlation between extracted audio features and the performance of audio captioning. A couple of predictor is proposed based on the experiment results.The result demonstrates that the kurtosis and skewness of audio features extracted may act as an indicator of the performance of audio captioning systems for pre-trained audio due to the high correlation between kurtosis and skewness of audio features and the performance of audio captioning systems.
    Anatomy-XNet: An Anatomy Aware Convolutional Neural Network for Thoracic Disease Classification in Chest X-rays. (arXiv:2106.05915v3 [eess.IV] UPDATED)
    Thoracic disease detection from chest radiographs using deep learning methods has been an active area of research in the last decade. Most previous methods attempt to focus on the diseased organs of the image by identifying spatial regions responsible for significant contributions to the model's prediction. In contrast, expert radiologists first locate the prominent anatomical structures before determining if those regions are anomalous. Therefore, integrating anatomical knowledge within deep learning models could bring substantial improvement in automatic disease classification. Motivated by this, we propose Anatomy-XNet, an anatomy-aware attention-based thoracic disease classification network that prioritizes the spatial features guided by the pre-identified anatomy regions. We adopt a semi-supervised learning method by utilizing available small-scale organ-level annotations to locate the anatomy regions in large-scale datasets where the organ-level annotations are absent. The proposed Anatomy-XNet uses the pre-trained DenseNet-121 as the backbone network with two corresponding structured modules, the Anatomy Aware Attention (A$^3$) and Probabilistic Weighted Average Pooling (PWAP), in a cohesive framework for anatomical attention learning. We experimentally show that our proposed method sets a new state-of-the-art benchmark by achieving an AUC score of 85.78%, 92.07%, and, 84.04% on three publicly available large-scale CXR datasets--NIH, Stanford CheXpert, and MIMIC-CXR, respectively. This not only proves the efficacy of utilizing the anatomy segmentation knowledge to improve the thoracic disease classification but also demonstrates the generalizability of the proposed framework.
    A Scalable Probabilistic Model for Reward Optimizing Slate Recommendation. (arXiv:2208.06263v1 [cs.IR])
    We introduce Probabilistic Rank and Reward model (PRR), a scalable probabilistic model for personalized slate recommendation. Our model allows state-of-the-art estimation of user interests in the following ubiquitous recommender system scenario: A user is shown a slate of K recommendations and the user chooses at most one of these K items. It is the goal of the recommender system to find the K items of most interest to a user in order to maximize the probability that the user interacts with the slate. Our contribution is to show that we can learn more effectively the probability of the recommendations being successful by combining the reward - whether the slate was clicked or not - and the rank - the item on the slate that was selected. Our method learns more efficiently than bandit methods that use only the reward, and user preference methods that use only the rank. It also provides similar or better estimation performance to independent inverse-propensity-score methods and is far more scalable. Our method is state of the art in terms of both speed and accuracy on massive datasets with up to 1 million items. Finally, our method allows fast delivery of recommendations powered by maximum inner product search (MIPS), making it suitable in extremely low latency domains such as computational advertising.  ( 3 min )
    Root-aligned SMILES: A Tight Representation for Chemical Reaction Prediction. (arXiv:2203.11444v5 [cs.LG] UPDATED)
    Chemical reaction prediction, involving forward synthesis and retrosynthesis prediction, is a fundamental problem in organic synthesis. A popular computational paradigm formulates synthesis prediction as a sequence-to-sequence translation problem, where the typical SMILES is adopted for molecule representations. However, the general-purpose SMILES neglects the characteristics of chemical reactions, where the molecular graph topology is largely unaltered from reactants to products, resulting in the suboptimal performance of SMILES if straightforwardly applied. In this article, we propose the root-aligned SMILES (R-SMILES), which specifies a tightly aligned one-to-one mapping between the product and the reactant SMILES for more efficient synthesis prediction. Due to the strict one-to-one mapping and reduced edit distance, the computational model is largely relieved from learning the complex syntax and dedicated to learning the chemical knowledge for reactions. We compare the proposed R-SMILES with various state-of-the-art baselines and show that it significantly outperforms them all, demonstrating the superiority of the proposed method.
    Safety and Performance, Why not Both? Bi-Objective Optimized Model Compression toward AI Software Deployment. (arXiv:2208.05969v1 [cs.LG])
    The size of deep learning models in artificial intelligence (AI) software is increasing rapidly, which hinders the large-scale deployment on resource-restricted devices (e.g., smartphones). To mitigate this issue, AI software compression plays a crucial role, which aims to compress model size while keeping high performance. However, the intrinsic defects in the big model may be inherited by the compressed one. Such defects may be easily leveraged by attackers, since the compressed models are usually deployed in a large number of devices without adequate protection. In this paper, we try to address the safe model compression problem from a safety-performance co-optimization perspective. Specifically, inspired by the test-driven development (TDD) paradigm in software engineering, we propose a test-driven sparse training framework called SafeCompress. By simulating the attack mechanism as the safety test, SafeCompress can automatically compress a big model to a small one following the dynamic sparse training paradigm. Further, considering a representative attack, i.e., membership inference attack (MIA), we develop a concrete safe model compression mechanism, called MIA-SafeCompress. Extensive experiments are conducted to evaluate MIA-SafeCompress on five datasets for both computer vision and natural language processing tasks. The results verify the effectiveness and generalization of our method. We also discuss how to adapt SafeCompress to other attacks besides MIA, demonstrating the flexibility of SafeCompress.
    Transformers Can Do Bayesian Inference. (arXiv:2112.10510v5 [cs.LG] UPDATED)
    Currently, it is hard to reap the benefits of deep learning for Bayesian methods, which allow the explicit specification of prior knowledge and accurately capture model uncertainty. We present Prior-Data Fitted Networks (PFNs). PFNs leverage large-scale machine learning techniques to approximate a large set of posteriors. The only requirement for PFNs to work is the ability to sample from a prior distribution over supervised learning tasks (or functions). Our method restates the objective of posterior approximation as a supervised classification problem with a set-valued input: it repeatedly draws a task (or function) from the prior, draws a set of data points and their labels from it, masks one of the labels and learns to make probabilistic predictions for it based on the set-valued input of the rest of the data points. Presented with a set of samples from a new supervised learning task as input, PFNs make probabilistic predictions for arbitrary other data points in a single forward propagation, having learned to approximate Bayesian inference. We demonstrate that PFNs can near-perfectly mimic Gaussian processes and also enable efficient Bayesian inference for intractable problems, with over 200-fold speedups in multiple setups compared to current methods. We obtain strong results in very diverse areas such as Gaussian process regression, Bayesian neural networks, classification for small tabular data sets, and few-shot image classification, demonstrating the generality of PFNs. Code and trained PFNs are released at https://github.com/automl/TransformersCanDoBayesianInference.
    Enhancing Oceanic Variables Forecast in the Santos Channel by Estimating Model Error with Random Forests. (arXiv:2208.05966v1 [physics.ao-ph])
    In this work we improve forecasting of Sea Surface Height (SSH) and current velocity (speed and direction) in oceanic scenarios. We do so by resorting to Random Forests so as to predict the error of a numerical forecasting system developed for the Santos Channel in Brazil. We have used the Santos Operational Forecasting System (SOFS) and data collected in situ between the years of 2019 and 2021. In previous studies we have applied similar methods for current velocity in the channel entrance, in this work we expand the application to improve the SHH forecast and include four other stations in the channel. We have obtained an average reduction of 11.9% in forecasting Root-Mean Square Error (RMSE) and 38.7% in bias with our approach. We also obtained an increase of Agreement (IOA) in 10 of the 14 combinations of forecasted variables and stations.
    MILAN: Masked Image Pretraining on Language Assisted Representation. (arXiv:2208.06049v1 [cs.CV])
    Self-attention based transformer models have been dominating many computer vision tasks in the past few years. Their superb model qualities heavily depend on the excessively large labeled image datasets. In order to reduce the reliance on large labeled datasets, reconstruction based masked autoencoders are gaining popularity, which learn high quality transferable representations from unlabeled images. For the same purpose, recent weakly supervised image pretraining methods explore language supervision from text captions accompanying the images. In this work, we propose masked image pretraining on language assisted representation, dubbed as MILAN. Instead of predicting raw pixels or low level features, our pretraining objective is to reconstruct the image features with substantial semantic signals that are obtained using caption supervision. Moreover, to accommodate our reconstruction target, we propose a more efficient prompting decoder architecture and a semantic aware mask sampling mechanism, which further advance the transfer performance of the pretrained model. Experimental results demonstrate that MILAN delivers higher accuracy than the previous works. When the masked autoencoder is pretrained and finetuned on ImageNet-1K dataset with an input resolution of 224x224, MILAN achieves a top-1 accuracy of 85.4% on ViTB/16, surpassing previous state-of-the-arts by 1%. In the downstream semantic segmentation task, MILAN achieves 52.7 mIoU using ViT-B/16 backbone on ADE20K dataset, outperforming previous masked pretraining results by 4 points.
    Response Component Analysis for Sea State Estimation Using Artificial Neural Networks and Vessel Response Spectral Data. (arXiv:2205.02375v2 [cs.LG] UPDATED)
    The use of the `ship as a wave buoy analogy' (SAWB) provides a novel means to estimate sea states, where relationships are established between causal wave properties and vessel motion response information. This study focuses on a model-free machine learning approach to SAWB-based sea state estimation (SSE), using neural networks (NNs) to map vessel response spectral data to statistical wave properties for a small uninhabited surface vessel. Results showed a strong correlation between heave responses and significant wave height estimates, whilst the accuracy of mean wave period and wave heading predictions were observed to improve considerably when data from multiple vessel degrees of freedom (DOFs) was utilized. Overall, 3-DOF (heave, pitch and roll) NNs for SSE were shown to perform well when compared to existing SSE approaches that use similar simulation setups. One advantage of using small vessels for SAWB was shown as SSE accuracy was reasonable even when motion responses were low (in high-frequency, low wave height sea states). Given the information-dense statistical representation of vessel motion responses in spectral form, as well as the ability of NNs to effectively model complex relationships between variables, the designed SSE method shows promise for future adaptation to mobile SSE systems using the SAWB approach.
    A Probabilistic Framework for Mutation Testing in Deep Neural Networks. (arXiv:2208.06018v1 [cs.SE])
    Context: Mutation Testing (MT) is an important tool in traditional Software Engineering (SE) white-box testing. It aims to artificially inject faults in a system to evaluate a test suite's capability to detect them, assuming that the test suite defects finding capability will then translate to real faults. If MT has long been used in SE, it is only recently that it started gaining the attention of the Deep Learning (DL) community, with researchers adapting it to improve the testability of DL models and improve the trustworthiness of DL systems. Objective: If several techniques have been proposed for MT, most of them neglected the stochasticity inherent to DL resulting from the training phase. Even the latest MT approaches in DL, which propose to tackle MT through a statistical approach, might give inconsistent results. Indeed, as their statistic is based on a fixed set of sampled training instances, it can lead to different results across instances set when results should be consistent for any instance. Methods: In this work, we propose a Probabilistic Mutation Testing (PMT) approach that alleviates the inconsistency problem and allows for a more consistent decision on whether a mutant is killed or not. Results: We show that PMT effectively allows a more consistent and informed decision on mutations through evaluation using three models and eight mutation operators used in previously proposed MT methods. We also analyze the trade-off between the approximation error and the cost of our method, showing that relatively small error can be achieved for a manageable cost. Conclusion: Our results showed the limitation of current MT practices in DNN and the need to rethink them. We believe PMT is the first step in that direction which effectively removes the lack of consistency across test executions of previous methods caused by the stochasticity of DNN training.
    Variational Quantum Approximate Support Vector Machine With Inference Transfer. (arXiv:2206.14507v2 [quant-ph] UPDATED)
    A kernel-based quantum classifier is the most interesting and powerful quantum machine learning technique for hyperlinear classification of complex data, which can be easily realized in shallow-depth quantum circuits such as a SWAP test classifier. A variational quantum approximate support vector machine (VQASVM) can be realized inherently and explicitly on these circuits by introduction of a variational scheme to map the quadratic optimization problem of the support vector machine theory to a quantum-classical variational optimization problem. Probability weight modulation in index qubits of a classifier can designate support vectors among training vectors, which can be achieved with a parameterized quantum circuit (PQC). The classical parameters of PQC is then transferred to many copies of other decision inference circuits. Our VQASVM algorithm is experimented with toy example data sets on cloud-based quantum machines for feasibility evaluation, and numerically investigated to evaluate its performance on a standard iris flower and MNIST data set. The empirical run-time complexity of VQASVM is estimated to be sub-quadratic on the training data set size, while that of the classical solver is quadratic.
    Dropout is NOT All You Need to Prevent Gradient Leakage. (arXiv:2208.06163v1 [cs.LG])
    Gradient inversion attacks on federated learning systems reconstruct client training data from exchanged gradient information. To defend against such attacks, a variety of defense mechanisms were proposed. However, they usually lead to an unacceptable trade-off between privacy and model utility. Recent observations suggest that dropout could mitigate gradient leakage and improve model utility if added to neural networks. Unfortunately, this phenomenon has not been systematically researched yet. In this work, we thoroughly analyze the effect of dropout on iterative gradient inversion attacks. We find that state of the art attacks are not able to reconstruct the client data due to the stochasticity induced by dropout during model training. Nonetheless, we argue that dropout does not offer reliable protection if the dropout induced stochasticity is adequately modeled during attack optimization. Consequently, we propose a novel Dropout Inversion Attack (DIA) that jointly optimizes for client data and dropout masks to approximate the stochastic client model. We conduct an extensive systematic evaluation of our attack on four seminal model architectures and three image classification datasets of increasing complexity. We find that our proposed attack bypasses the protection seemingly induced by dropout and reconstructs client data with high fidelity. Our work demonstrates that privacy inducing changes to model architectures alone cannot be assumed to reliably protect from gradient leakage and therefore should be combined with complementary defense mechanisms.
    Power Flow Balancing with Decentralized Graph Neural Networks. (arXiv:2111.02169v2 [cs.LG] UPDATED)
    We propose an end-to-end framework based on a Graph Neural Network (GNN) to balance the power flows in energy grids. The balancing is framed as a supervised vertex regression task, where the GNN is trained to predict the current and power injections at each grid branch that yield a power flow balance. By representing the power grid as a line graph with branches as vertices, we can train a GNN that is accurate and robust to changes in topology. In addition, by using specialized GNN layers, we are able to build a very deep architecture that accounts for large neighborhoods on the graph, while implementing only localized operations. We perform three different experiments to evaluate: i) the benefits of using localized rather than global operations and the tendency of deep GNN models to oversmooth the quantities on the nodes; ii) the resilience to perturbations in the graph topology; and iii) the capability to train the model simultaneously on multiple grid topologies and the consequential improvement in generalization to new, unseen grids. The proposed framework is efficient and, compared to other solvers based on deep learning, is robust to perturbations not only to the physical quantities on the grid components, but also to the topology.
    A multi-scale sampling method for accurate and robust deep neural network to predict combustion chemical kinetics. (arXiv:2201.03549v2 [physics.chem-ph] UPDATED)
    Machine learning has long been considered as a black box for predicting combustion chemical kinetics due to the extremely large number of parameters and the lack of evaluation standards and reproducibility. The current work aims to understand two basic questions regarding the deep neural network (DNN) method: what data the DNN needs and how general the DNN method can be. Sampling and preprocessing determine the DNN training dataset, further affect DNN prediction ability. The current work proposes using Box-Cox transformation (BCT) to preprocess the combustion data. In addition, this work compares different sampling methods with or without preprocessing, including the Monte Carlo method, manifold sampling, generative neural network method (cycle-GAN), and newly-proposed multi-scale sampling. Our results reveal that the DNN trained by the manifold data can capture the chemical kinetics in limited configurations but cannot remain robust toward perturbation, which is inevitable for the DNN coupled with the flow field. The Monte Carlo and cycle-GAN samplings can cover a wider phase space but fail to capture small-scale intermediate species, producing poor prediction results. A three-hidden-layer DNN, based on the multi-scale method without specific flame simulation data, allows predicting chemical kinetics in various scenarios and being stable during the temporal evolutions. This single DNN is readily implemented with several CFD codes and validated in various combustors, including (1). zero-dimensional autoignition, (2). one-dimensional freely propagating flame, (3). two-dimensional jet flame with triple-flame structure, and (4). three-dimensional turbulent lifted flames. The results demonstrate the satisfying accuracy and generalization ability of the pre-trained DNN. The Fortran and Python versions of DNN and example code are attached in the supplementary for reproducibility.
    Data Banzhaf: A Data Valuation Framework with Maximal Robustness to Learning Stochasticity. (arXiv:2205.15466v4 [cs.LG] UPDATED)
    This paper studies the robustness of data valuation to noisy model performance scores. Particularly, we find that the inherent randomness of the widely used stochastic gradient descent can cause existing data value notions (e.g., the Shapley value and the Leave-one-out error) to produce inconsistent data value rankings across different runs. To address this challenge, we first pose a formal framework within which one can measure the robustness of a data value notion. We show that the Banzhaf value, a value notion originated from cooperative game theory literature, achieves the maximal robustness among all semivalues -- a class of value notions that satisfy crucial properties entailed by ML applications. We propose an algorithm to efficiently estimate the Banzhaf value based on the Maximum Sample Reuse (MSR) principle. We derive the lower bound sample complexity for Banzhaf value estimation, and we show that our MSR algorithm's sample complexity is close to the lower bound. Our evaluation demonstrates that the Banzhaf value outperforms the existing semivalue-based data value notions on several downstream ML tasks such as learning with weighted samples and noisy label detection. Overall, our study suggests that when the underlying ML algorithm is stochastic, the Banzhaf value is a promising alternative to the semivalue-based data value schemes given its computational advantage and ability to robustly differentiate data quality.  ( 3 min )
    Multi-Model Probabilistic Programming. (arXiv:2208.06329v1 [cs.PL])
    Probabilistic programming makes it easy to represent a probabilistic model as a program. Building an individual model, however, is only one step of probabilistic modeling. The broader challenge of probabilistic modeling is in understanding and navigating spaces of alternative models. There is currently no good way to represent these spaces of alternative models, despite their central role. We present an extension of probabilistic programming that lets each program represent a network of interrelated probabilistic models. We give a formal semantics for these multi-model probabilistic programs, a collection of efficient algorithms for network-of-model operations, and an example implementation built on top of the popular probabilistic programming language Stan. This network-of-models representation opens many doors, including search and automation in model-space, tracking and communication of model development, and explicit modeler degrees of freedom to mitigate issues like p-hacking. We demonstrate automatic model search and model development tracking using our Stan implementation, and we propose many more possible applications.  ( 2 min )
    On the Convergence of Shallow Neural Network Training with Randomly Masked Neurons. (arXiv:2112.02668v2 [cs.LG] UPDATED)
    With the motive of training all the parameters of a neural network, we study why and when one can achieve this by iteratively creating, training, and combining randomly selected subnetworks. Such scenarios have either implicitly or explicitly emerged in the recent literature: see e.g., the Dropout family of regularization techniques, or some distributed ML training protocols that reduce communication/computation complexities, such as the Independent Subnet Training protocol. While these methods are studied empirically and utilized in practice, they often enjoy partial or no theoretical support, especially when applied on neural network-based objectives. In this manuscript, our focus is on overparameterized single hidden layer neural networks with ReLU activations in the lazy training regime. By carefully analyzing $i)$ the subnetworks' neural tangent kernel, $ii)$ the surrogate functions' gradient, and $iii)$ how we sample and combine the surrogate functions, we prove linear convergence rate of the training error -- up to a neighborhood around the optimal point -- for an overparameterized single-hidden layer perceptron with a regression loss. Our analysis reveals a dependency of the size of the neighborhood around the optimal point on the number of surrogate models and the number of local training steps for each selected subnetwork. Moreover, the considered framework generalizes and provides new insights on dropout training, multi-sample dropout training, as well as Independent Subnet Training; for each case, we provide convergence results as corollaries of our main theorem.  ( 3 min )
    Trustworthy Recommender Systems. (arXiv:2208.06265v1 [cs.IR])
    Recommender systems (RSs) aim to help users to effectively retrieve items of their interests from a large catalogue. For a quite long period of time, researchers and practitioners have been focusing on developing accurate RSs. Recent years have witnessed an increasing number of threats to RSs, coming from attacks, system and user generated noise, system bias. As a result, it has become clear that a strict focus on RS accuracy is limited and the research must consider other important factors, e.g., trustworthiness. For end users, a trustworthy RS (TRS) should not only be accurate, but also transparent, unbiased and fair as well as robust to noise or attacks. These observations actually led to a paradigm shift of the research on RSs: from accuracy-oriented RSs to TRSs. However, researchers lack a systematic overview and discussion of the literature in this novel and fast developing field of TRSs. To this end, in this paper, we provide an overview of TRSs, including a discussion of the motivation and basic concepts of TRSs, a presentation of the challenges in building TRSs, and a perspective on the future directions in this area. We also provide a novel conceptual framework to support the construction of TRSs.  ( 2 min )
    Joint Optimization of Ranking and Calibration with Contextualized Hybrid Model. (arXiv:2208.06164v1 [cs.IR])
    Despite the development of ranking optimization techniques, the pointwise model remains the dominating approach for click-through rate (CTR) prediction. It can be attributed to the calibration ability of the pointwise model since the prediction can be viewed as the click probability. In practice, a CTR prediction model is also commonly assessed with the ranking ability, for which prediction models based on ranking losses (e.g., pairwise or listwise loss) usually achieve better performances than the pointwise loss. Previous studies have experimented with a direct combination of the two losses to obtain the benefit from both losses and observed an improved performance. However, previous studies break the meaning of output logit as the click-through rate, which may lead to sub-optimal solutions. To address this issue, we propose an approach that can Jointly optimize the Ranking and Calibration abilities (JRC for short). JRC improves the ranking ability by contrasting the logit value for the sample with different labels and constrains the predicted probability to be a function of the logit subtraction. We further show that JRC consolidates the interpretation of logits, where the logits model the joint distribution. With such an interpretation, we prove that JRC approximately optimizes the contextualized hybrid discriminative-generative objective. Experiments on public and industrial datasets and online A/B testing show that our approach improves both ranking and calibration abilities. Since May 2022, JRC has been deployed on the display advertising platform of Alibaba and has obtained significant performance improvements.  ( 3 min )
    Coarse to Fine Two-Stage Approach to Robust Tensor Completion of Visual Data. (arXiv:2106.10422v4 [cs.LG] UPDATED)
    Tensor completion is the problem of estimating the missing values of high-order data from partially observed entries. Data corruption due to prevailing outliers poses major challenges to traditional tensor completion algorithms, which catalyzed the development of robust algorithms that alleviate the effect of outliers. However, existing robust methods largely presume that the corruption is sparse, which may not hold in practice. In this paper, we develop a two-stage robust tensor completion approach to deal with tensor completion of visual data with a large amount of gross corruption. A novel coarse-to-fine framework is proposed which uses a global coarse completion result to guide a local patch refinement process. To efficiently mitigate the effect of a large number of outliers on tensor recovery, we develop a new M-estimator-based robust tensor ring recovery method which can adaptively identify the outliers and alleviate their negative effect in the optimization. The experimental results demonstrate the superior performance of the proposed approach over state-of-the-art robust algorithms for tensor completion.  ( 3 min )
    Predicting Electricity Infrastructure Induced Wildfire Risk in California. (arXiv:2206.02930v2 [eess.SY] UPDATED)
    This paper examines the use of risk models to predict the timing and location of wildfires caused by electricity infrastructure. Our data include historical ignition and wire-down points triggered by grid infrastructure collected between 2015 to 2019 in Pacific Gas & Electricity territory along with various weather, vegetation, and very high resolution data on grid infrastructure including location, age, materials. With these data we explore a range of machine learning methods and strategies to manage training data imbalance. The best area under the receiver operating characteristic we obtain is 0.776 for distribution feeder ignitions and 0.824 for transmission line wire-down events, both using the histogram-based gradient boosting tree algorithm (HGB) with under-sampling. We then use these models to identify which information provides the most predictive value. After line length, we find that weather and vegetation features dominate the list of top important features for ignition or wire-down risk. Distribution ignition models show more dependence on slow-varying vegetation variables such as burn index, energy release content, and tree height, whereas transmission wire-down models rely more on primary weather variables such as wind speed and precipitation. These results point to the importance of improved vegetation modeling for feeder ignition risk models, and improved weather forecasting for transmission wire-down models. We observe that infrastructure features make small but meaningful improvements to risk model predictive power.  ( 3 min )
    Emergence of sensory attenuation based upon the free-energy principle. (arXiv:2111.02666v3 [q-bio.NC] UPDATED)
    The brain attenuates its responses to self-produced exteroceptions (e.g., we cannot tickle ourselves). Is this phenomenon, known as sensory attenuation, enabled innately, or acquired through learning? Here, our simulation study using a multimodal hierarchical recurrent neural network model, based on variational free-energy minimization, shows that a mechanism for sensory attenuation can develop through learning of two distinct types of sensorimotor experience, involving self-produced or externally produced exteroceptions. For each sensorimotor context, a particular free-energy state emerged through interaction between top-down prediction with precision and bottom-up sensory prediction error from each sensory area. The executive area in the network served as an information hub. Consequently, shifts between the two sensorimotor contexts triggered transitions from one free-energy state to another in the network via executive control, which caused shifts between attenuating and amplifying prediction-error-induced responses in the sensory areas. This study situates emergence of sensory attenuation (or self-other distinction) in development of distinct free-energy states in the dynamic hierarchical neural system.  ( 2 min )
    Unifying local and global model explanations by functional decomposition of low dimensional structures. (arXiv:2208.06151v1 [cs.LG])
    We consider a global explanation of a regression or classification function by decomposing it into the sum of main components and interaction components of arbitrary order. When adding an identification constraint that is motivated by a causal interpretation, we find q-interaction SHAP to be the unique solution to that constraint. Here, q denotes the highest order of interaction present in the decomposition. Our result provides a new perspective on SHAP values with various practical and theoretical implications: If SHAP values are decomposed into main and all interaction effects, they provide a global explanation with causal interpretation. In principle, the decomposition can be applied to any machine learning model. However, since the number of possible interactions grows exponentially with the number of features, exact calculation is only feasible for methods that fit low dimensional structures or ensembles of those. We provide an algorithm and efficient implementation for gradient boosted trees (xgboost and random planted forests that calculates this decomposition. Conducted experiments suggest that our method provides meaningful explanations and reveals interactions of higher orders. We also investigate further potential of our new insights by utilizing the global explanation for motivating a new measure of feature importance, and for reducing direct and indirect bias by post-hoc component removal.  ( 3 min )
    3D Graph Contrastive Learning for Molecular Property Prediction. (arXiv:2208.06360v1 [q-bio.BM])
    Self-supervised learning (SSL) is a method that learns the data representation by utilizing supervision inherent in the data. This learning method is in the spotlight in the drug field, lacking annotated data due to time-consuming and expensive experiments. SSL using enormous unlabeled data has shown excellent performance for molecular property prediction, but a few issues exist. (1) Existing SSL models are large-scale; there is a limitation to implementing SSL where the computing resource is insufficient. (2) In most cases, they do not utilize 3D structural information for molecular representation learning. The activity of a drug is closely related to the structure of the drug molecule. Nevertheless, most current models do not use 3D information or use it partially. (3) Previous models that apply contrastive learning to molecules use the augmentation of permuting atoms and bonds. Therefore, molecules having different characteristics can be in the same positive samples. We propose a novel contrastive learning framework, small-scale 3D Graph Contrastive Learning (3DGCL) for molecular property prediction, to solve the above problems. 3DGCL learns the molecular representation by reflecting the molecule's structure through the pre-training process that does not change the semantics of the drug. Using only 1,128 samples for pre-train data and 1 million model parameters, we achieved the state-of-the-art or comparable performance in four regression benchmark datasets. Extensive experiments demonstrate that 3D structural information based on chemical knowledge is essential to molecular representation learning for property prediction.  ( 3 min )
    Feature-Based Time-Series Analysis in R using the theft Package. (arXiv:2208.06146v1 [stat.ML])
    Time series are measured and analyzed across the sciences. One method of quantifying the structure of time series is by calculating a set of summary statistics or `features', and then representing a time series in terms of its properties as a feature vector. The resulting feature space is interpretable and informative, and enables conventional statistical learning approaches, including clustering, regression, and classification, to be applied to time-series datasets. Many open-source software packages for computing sets of time-series features exist across multiple programming languages, including catch22 (22 features: Matlab, R, Python, Julia), feasts (42 features: R), tsfeatures (63 features: R), Kats (40 features: Python), tsfresh (779 features: Python), and TSFEL (390 features: Python). However, there are several issues: (i) a singular access point to these packages is not currently available; (ii) to access all feature sets, users must be fluent in multiple languages; and (iii) these feature-extraction packages lack extensive accompanying methodological pipelines for performing feature-based time-series analysis, such as applications to time-series classification. Here we introduce a solution to these issues in an R software package called theft: Tools for Handling Extraction of Features from Time series. theft is a unified and extendable framework for computing features from the six open-source time-series feature sets listed above. It also includes a suite of functions for processing and interpreting the performance of extracted features, including extensive data-visualization templates, low-dimensional projections, and time-series classification operations. With an increasing volume and complexity of time-series datasets in the sciences and industry, theft provides a standardized framework for comprehensively quantifying and interpreting informative structure in time series.  ( 3 min )
    Function Classes for Identifiable Nonlinear Independent Component Analysis. (arXiv:2208.06406v1 [stat.ML])
    Unsupervised learning of latent variable models (LVMs) is widely used to represent data in machine learning. When such models reflect the ground truth factors and the mechanisms mapping them to observations, there is reason to expect that they allow generalization in downstream tasks. It is however well known that such identifiability guaranties are typically not achievable without putting constraints on the model class. This is notably the case for nonlinear Independent Component Analysis, in which the LVM maps statistically independent variables to observations via a deterministic nonlinear function. Several families of spurious solutions fitting perfectly the data, but that do not correspond to the ground truth factors can be constructed in generic settings. However, recent work suggests that constraining the function class of such models may promote identifiability. Specifically, function classes with constraints on their partial derivatives, gathered in the Jacobian matrix, have been proposed, such as orthogonal coordinate transformations (OCT), which impose orthogonality of the Jacobian columns. In the present work, we prove that a subclass of these transformations, conformal maps, is identifiable and provide novel theoretical results suggesting that OCTs have properties that prevent families of spurious solutions to spoil identifiability in a generic setting.  ( 2 min )
    Multiplex Heterogeneous Graph Convolutional Network. (arXiv:2208.06129v1 [cs.SI])
    Heterogeneous graph convolutional networks have gained great popularity in tackling various network analytical tasks on heterogeneous network data, ranging from link prediction to node classification. However, most existing works ignore the relation heterogeneity with multiplex network between multi-typed nodes and different importance of relations in meta-paths for node embedding, which can hardly capture the heterogeneous structure signals across different relations. To tackle this challenge, this work proposes a Multiplex Heterogeneous Graph Convolutional Network (MHGCN) for heterogeneous network embedding. Our MHGCN can automatically learn the useful heterogeneous meta-path interactions of different lengths in multiplex heterogeneous networks through multi-layer convolution aggregation. Additionally, we effectively integrate both multi-relation structural signals and attribute semantics into the learned node embeddings with both unsupervised and semi-supervised learning paradigms. Extensive experiments on five real-world datasets with various network analytical tasks demonstrate the significant superiority of MHGCN against state-of-the-art embedding baselines in terms of all evaluation metrics.  ( 2 min )
    Mitigating barren plateaus of variational quantum eigensolvers. (arXiv:2205.13539v2 [quant-ph] UPDATED)
    Variational quantum algorithms (VQAs) are expected to establish valuable applications on near-term quantum computers. However, recent works have pointed out that the performance of VQAs greatly relies on the expressibility of the ansatzes and is seriously limited by optimization issues such as barren plateaus (i.e., vanishing gradients). This work proposes the state efficient ansatz (SEA) for accurate ground state preparation with improved trainability. We show that the SEA can generate an arbitrary pure state with much fewer parameters than a universal ansatz, making it efficient for tasks like ground state estimation. Then, we prove that barren plateaus can be efficiently mitigated by the SEA and the trainability can be further improved most quadratically by flexibly adjusting the entangling capability of the SEA. Finally, we investigate a plethora of examples in ground state estimation where we obtain significant improvements in the magnitude of cost gradient and the convergence speed.  ( 2 min )
    ALS: Augmented Lagrangian Sketching Methods for Linear Systems. (arXiv:2208.06152v1 [math.OC])
    We develop two fundamental stochastic sketching techniques; Penalty Sketching (PS) and Augmented Lagrangian Sketching (ALS) for solving consistent linear systems. The proposed PS and ALS techniques extend and generalize the scope of Sketch & Project (SP) method by introducing Lagrangian penalty sketches. In doing so, we recover SP methods as special cases and furthermore develop a family of new stochastic iterative methods. By varying sketch parameters in the proposed PS method, we recover novel stochastic methods such as Penalty Newton Descent, Penalty Kaczmarz, Penalty Stochastic Descent, Penalty Coordinate Descent, Penalty Gaussian Pursuit, and Penalty Block Kaczmarz. Furthermore, the proposed ALS method synthesizes a wide variety of new stochastic methods such as Augmented Newton Descent, Augmented Kaczmarz, Augmented Stochastic Descent, Augmented Coordinate Descent, Augmented Gaussian Pursuit, and Augmented Block Kaczmarz into one framework. Moreover, we show that the developed PS and ALS frameworks can be used to reformulate the original linear system into equivalent stochastic optimization problems namely the Penalty Stochastic Reformulation and Augmented Stochastic Reformulation. We prove global convergence rates for the PS and ALS methods as well as sub-linear $\mathcal{O}(\frac{1}{k})$ rates for the Cesaro average of iterates. The proposed convergence results hold for a wide family of distributions of random matrices, which provides the opportunity of fine-tuning the randomness of the method suitable for specific applications. Finally, we perform computational experiments that demonstrate the efficiency of our methods compared to the existing SP methods.  ( 3 min )
    UniNet: A Unified Scene Understanding Network and Exploring Multi-Task Relationships through the Lens of Adversarial Attacks. (arXiv:2108.04584v2 [cs.CV] UPDATED)
    Scene understanding is crucial for autonomous systems which intend to operate in the real world. Single task vision networks extract information only based on some aspects of the scene. In multi-task learning (MTL), on the other hand, these single tasks are jointly learned, thereby providing an opportunity for tasks to share information and obtain a more comprehensive understanding. To this end, we develop UniNet, a unified scene understanding network that accurately and efficiently infers vital vision tasks including object detection, semantic segmentation, instance segmentation, monocular depth estimation, and monocular instance depth prediction. As these tasks look at different semantic and geometric information, they can either complement or conflict with each other. Therefore, understanding inter-task relationships can provide useful cues to enable complementary information sharing. We evaluate the task relationships in UniNet through the lens of adversarial attacks based on the notion that they can exploit learned biases and task interactions in the neural network. Extensive experiments on the Cityscapes dataset, using untargeted and targeted attacks reveal that semantic tasks strongly interact amongst themselves, and the same holds for geometric tasks. Additionally, we show that the relationship between semantic and geometric tasks is asymmetric and their interaction becomes weaker as we move towards higher-level representations.  ( 3 min )
    Understanding the stochastic dynamics of sequential decision-making processes: A path-integral analysis of Multi-armed Bandits. (arXiv:2208.06245v1 [cs.LG])
    The multi-armed bandit (MAB) model is one of the most classical models to study decision-making in an uncertain environment. In this model, a player needs to choose one of K possible arms of a bandit machine to play at each time step, where the corresponding arm returns a random reward to the player, potentially from a specific unknown distribution. The target of the player is to collect as much rewards as possible during the process. Despite its simplicity, the MAB model offers an excellent playground for studying the trade-off between exploration versus exploitation and designing effective algorithms for sequential decision-making under uncertainty. Although many asymptotically optimal algorithms have been established, the finite-time behaviours of the stochastic dynamics of the MAB model appears much more difficult to analyze, due to the intertwining between the decision-making and the rewards being collected. In this paper, we employ techniques in statistical physics to analyze the MAB model, which facilitates to characterize the distribution of cumulative regrets at a finite short time, the central quantity of interest in an MAB algorithm, as well as the intricate dynamical behaviours of the model.  ( 2 min )
    Image Translation Based Nuclei Segmentation for Immunohistochemistry Images. (arXiv:2208.06202v1 [cs.CV])
    Numerous deep learning based methods have been developed for nuclei segmentation for H&E images and have achieved close to human performance. However, direct application of such methods to another modality of images, such as Immunohistochemistry (IHC) images, may not achieve satisfactory performance. Thus, we developed a Generative Adversarial Network (GAN) based approach to translate an IHC image to an H&E image while preserving nuclei location and morphology and then apply pre-trained nuclei segmentation models to the virtual H&E image. We demonstrated that the proposed methods work better than several baseline methods including direct application of state of the art nuclei segmentation methods such as Cellpose and HoVer-Net, trained on H&E and a generative method, DeepLIIF, using two public IHC image datasets.  ( 2 min )
    Patch Tracking-based Streaming Tensor Ring Completion for Visual Data Recovery. (arXiv:2105.14620v3 [cs.CV] UPDATED)
    Tensor completion aims to recover the missing entries of a partially observed tensor by exploiting its low-rank structure, and has been applied to visual data recovery. In applications where the data arrives sequentially such as streaming video completion, the missing entries of the tensor need to be dynamically recovered in a streaming fashion. Traditional streaming tensor completion algorithms treat the entire visual data as a tensor, which may not work satisfactorily when there is a big change in the tensor subspace along the temporal dimension, such as due to strong motion across the video frames. In this paper, we develop a novel patch tracking-based streaming tensor ring completion framework for visual data recovery. Given a newly incoming frame, small patches are tracked from the previous frame. Meanwhile, for each tracked patch, a patch tensor is constructed by stacking similar patches from the new frame. Patch tensors are then completed using a streaming tensor ring completion algorithm, and the incoming frame is recovered using the completed patch tensors. We propose a new patch tracking strategy that can accurately and efficiently track the patches with missing data. Further, a new streaming tensor ring completion algorithm is proposed which can efficiently and accurately update the latent core tensors and complete the missing entries of the patch tensors. Extensive experimental results demonstrate the superior performance of the proposed algorithms compared with both batch and streaming state-of-the-art tensor completion methods.  ( 3 min )
    Low Emission Building Control with Zero-Shot Reinforcement Learning. (arXiv:2208.06385v1 [cs.LG])
    Heating and cooling systems in buildings account for 31\% of global energy use, much of which are regulated by Rule Based Controllers (RBCs) that neither maximise energy efficiency nor minimise emissions by interacting optimally with the grid. Control via Reinforcement Learning (RL) has been shown to significantly improve building energy efficiency, but existing solutions require access to building-specific simulators or data that cannot be expected for every building in the world. In response, we show it is possible to obtain emission-reducing policies without such knowledge a priori--a paradigm we call zero-shot building control. We combine ideas from system identification and model-based RL to create PEARL (Probabilistic Emission-Abating Reinforcement Learning) and show that a short period of active exploration is all that is required to build a performant model. In experiments across three varied building energy simulations, we show PEARL outperforms an existing RBC once, and popular RL baselines in all cases, reducing building emissions by as much as 31\% whilst maintaining thermal comfort. Our source code is available online via https://enjeeneer.io/projects/pearl .  ( 2 min )
    Unifying Gradients to Improve Real-world Robustness for Deep Networks. (arXiv:2208.06228v1 [stat.ML])
    The wide application of deep neural networks (DNNs) demands an increasing amount of attention to their real-world robustness, i.e., whether a DNN resists black-box adversarial attacks, among them score-based query attacks (SQAs) are the most threatening ones because of their practicalities and effectiveness: the attackers only need dozens of queries on model outputs to seriously hurt a victim network. Defending against SQAs requires a slight but artful variation of outputs due to the service purpose for users, who share the same output information with attackers. In this paper, we propose a real-world defense, called Unifying Gradients (UniG), to unify gradients of different data so that attackers could only probe a much weaker attack direction that is similar for different samples. Since such universal attack perturbations have been validated as less aggressive than the input-specific perturbations, UniG protects real-world DNNs by indicating attackers a twisted and less informative attack direction. To enhance UniG's practical significance in real-world applications, we implement it as a Hadamard product module that is computationally-efficient and readily plugged into any model. According to extensive experiments on 5 SQAs and 4 defense baselines, UniG significantly improves real-world robustness without hurting clean accuracy on CIFAR10 and ImageNet. For instance, UniG maintains a CIFAR-10 model of 77.80% accuracy under 2500-query Square attack while the state-of-the-art adversarially-trained model only has 67.34% on CIFAR10. Simultaneously, UniG greatly surpasses all compared baselines in clean accuracy and the modification degree of outputs. The code would be released.  ( 3 min )
    Scholastic: Graphical Human-Al Collaboration for Inductive and Interpretive Text Analysis. (arXiv:2208.06133v1 [cs.HC])
    Interpretive scholars generate knowledge from text corpora by manually sampling documents, applying codes, and refining and collating codes into categories until meaningful themes emerge. Given a large corpus, machine learning could help scale this data sampling and analysis, but prior research shows that experts are generally concerned about algorithms potentially disrupting or driving interpretive scholarship. We take a human-centered design approach to addressing concerns around machine-assisted interpretive research to build Scholastic, which incorporates a machine-in-the-loop clustering algorithm to scaffold interpretive text analysis. As a scholar applies codes to documents and refines them, the resulting coding schema serves as structured metadata which constrains hierarchical document and word clusters inferred from the corpus. Interactive visualizations of these clusters can help scholars strategically sample documents further toward insights. Scholastic demonstrates how human-centered algorithm design and visualizations employing familiar metaphors can support inductive and interpretive research methodologies through interactive topic modeling and document clustering.  ( 2 min )
    A Modular Framework for Reinforcement Learning Optimal Execution. (arXiv:2208.06244v1 [cs.CE])
    In this article, we develop a modular framework for the application of Reinforcement Learning to the problem of Optimal Trade Execution. The framework is designed with flexibility in mind, in order to ease the implementation of different simulation setups. Rather than focusing on agents and optimization methods, we focus on the environment and break down the necessary requirements to simulate an Optimal Trade Execution under a Reinforcement Learning framework such as data pre-processing, construction of observations, action processing, child order execution, simulation of benchmarks, reward calculations etc. We give examples of each component, explore the difficulties their individual implementations \& the interactions between them entail, and discuss the different phenomena that each component induces in the simulation, highlighting the divergences between the simulation and the behavior of a real market. We showcase our modular implementation through a setup that, following a Time-Weighted Average Price (TWAP) order submission schedule, allows the agent to exclusively place limit orders, simulates their execution via iterating over snapshots of the Limit Order Book (LOB), and calculates rewards as the \$ improvement over the price achieved by a TWAP benchmark algorithm following the same schedule. We also develop evaluation procedures that incorporate iterative re-training and evaluation of a given agent over intervals of a training horizon, mimicking how an agent may behave when being continuously retrained as new market data becomes available and emulating the monitoring practices that algorithm providers are bound to perform under current regulatory frameworks.  ( 3 min )
    Private Domain Adaptation from a Public Source. (arXiv:2208.06135v1 [cs.LG])
    A key problem in a variety of applications is that of domain adaptation from a public source domain, for which a relatively large amount of labeled data with no privacy constraints is at one's disposal, to a private target domain, for which a private sample is available with very few or no labeled data. In regression problems with no privacy constraints on the source or target data, a discrepancy minimization algorithm based on several theoretical guarantees was shown to outperform a number of other adaptation algorithm baselines. Building on that approach, we design differentially private discrepancy-based algorithms for adaptation from a source domain with public labeled data to a target domain with unlabeled private data. The design and analysis of our private algorithms critically hinge upon several key properties we prove for a smooth approximation of the weighted discrepancy, such as its smoothness with respect to the $\ell_1$-norm and the sensitivity of its gradient. Our solutions are based on private variants of Frank-Wolfe and Mirror-Descent algorithms. We show that our adaptation algorithms benefit from strong generalization and privacy guarantees and report the results of experiments demonstrating their effectiveness.  ( 2 min )
    Measuring incompatibility and clustering quantum observables with a quantum switch. (arXiv:2208.06210v1 [quant-ph])
    The existence of incompatible observables is a cornerstone of quantum mechanics and a valuable resource in quantum technologies. Here we introduce a measure of incompatibility, called the mutual eigenspace disturbance (MED), which quantifies the amount of disturbance induced by the measurement of a sharp observable on the eigenspaces of another. The MED is a faithful measure of incompatibility for sharp observables and provides a metric on the space of von Neumann measurements. It can be efficiently estimated by letting the measurements act in an indefinite order, using a setup known as the quantum switch. Thanks to these features, the MED can be used in quantum machine learning tasks, such as clustering quantum measurement devices based on their mutual compatibility. We demonstrate this application by providing an unsupervised algorithm that clusters unknown von Neumann measurements. Our algorithm is robust to noise can be used to identify groups of observers that share approximately the same measurement context.  ( 2 min )
    Accurate Action Recommendation for Smart Home via Two-Level Encoders and Commonsense Knowledge. (arXiv:2208.06089v1 [cs.AI])
    How can we accurately recommend actions for users to control their devices at home? Action recommendation for smart home has attracted increasing attention due to its potential impact on the markets of virtual assistants and Internet of Things (IoT). However, designing an effective action recommender system for smart home is challenging because it requires handling context correlations, considering both queried contexts and previous histories of users, and dealing with capricious intentions in history. In this work, we propose SmartSense, an accurate action recommendation method for smart home. For individual action, SmartSense summarizes its device control and its temporal contexts in a self-attentive manner, to reflect the importance of the correlation between them. SmartSense then summarizes sequences of users considering queried contexts in a query-attentive manner to extract the query-related patterns from the sequential actions. SmartSense also transfers the commonsense knowledge from routine data to better handle intentions in action sequences. As a result, SmartSense addresses all three main challenges of action recommendation for smart home, and achieves the state-of-the-art performance giving up to 9.8% higher mAP@1 than the best competitor.  ( 2 min )
    Zeus: Understanding and Optimizing GPU Energy Consumption of DNN Training. (arXiv:2208.06102v1 [cs.LG])
    Training deep neural networks (DNNs) is becoming more and more resource- and energy-intensive every year. Unfortunately, existing works primarily focus on optimizing DNN training for faster completion, often without considering the impact on energy efficiency. In this paper, we observe that common practices to improve training performance can often lead to inefficient energy usage. More importantly, we demonstrate that there is a tradeoff between energy consumption and performance optimization. To this end, we propose an optimization framework, Zeus, to navigate this tradeoff by automatically finding optimal job- and GPU-level configurations for recurring DNN training jobs. Zeus uses an online exploration-exploitation approach in conjunction with just-in-time energy profiling, averting the need for expensive offline measurements, while adapting to data drifts over time. Our evaluation shows that Zeus can improve the energy efficiency of DNN training by 15.3%--75.8% for diverse workloads.  ( 2 min )
    DDX7: Differentiable FM Synthesis of Musical Instrument Sounds. (arXiv:2208.06169v1 [cs.SD])
    FM Synthesis is a well-known algorithm used to generate complex timbre from a compact set of design primitives. Typically featuring a MIDI interface, it is usually impractical to control it from an audio source. On the other hand, Differentiable Digital Signal Processing (DDSP) has enabled nuanced audio rendering by Deep Neural Networks (DNNs) that learn to control differentiable synthesis layers from arbitrary sound inputs. The training process involves a corpus of audio for supervision, and spectral reconstruction loss functions. Such functions, while being great to match spectral amplitudes, present a lack of pitch direction which can hinder the joint optimization of the parameters of FM synthesizers. In this paper, we take steps towards enabling continuous control of a well-established FM synthesis architecture from an audio input. Firstly, we discuss a set of design constraints that ease spectral optimization of a differentiable FM synthesizer via a standard reconstruction loss. Next, we present Differentiable DX7 (DDX7), a lightweight architecture for neural FM resynthesis of musical instrument sounds in terms of a compact set of parameters. We train the model on instrument samples extracted from the URMP dataset, and quantitatively demonstrate its comparable audio quality against selected benchmarks.  ( 3 min )
    An Accelerated Doubly Stochastic Gradient Method with Faster Explicit Model Identification. (arXiv:2208.06058v1 [cs.LG])
    Sparsity regularized loss minimization problems play an important role in various fields including machine learning, data mining, and modern statistics. Proximal gradient descent method and coordinate descent method are the most popular approaches to solving the minimization problem. Although existing methods can achieve implicit model identification, aka support set identification, in a finite number of iterations, these methods still suffer from huge computational costs and memory burdens in high-dimensional scenarios. The reason is that the support set identification in these methods is implicit and thus cannot explicitly identify the low-complexity structure in practice, namely, they cannot discard useless coefficients of the associated features to achieve algorithmic acceleration via dimension reduction. To address this challenge, we propose a novel accelerated doubly stochastic gradient descent (ADSGD) method for sparsity regularized loss minimization problems, which can reduce the number of block iterations by eliminating inactive coefficients during the optimization process and eventually achieve faster explicit model identification and improve the algorithm efficiency. Theoretically, we first prove that ADSGD can achieve a linear convergence rate and lower overall computational complexity. More importantly, we prove that ADSGD can achieve a linear rate of explicit model identification. Numerically, experimental results on benchmark datasets confirm the efficiency of our proposed method.  ( 2 min )
    WeightMom: Learning Sparse Networks using Iterative Momentum-based pruning. (arXiv:2208.05970v1 [cs.LG])
    Deep Neural Networks have been used in a wide variety of applications with significant success. However, their highly complex nature owing to comprising millions of parameters has lead to problems during deployment in pipelines with low latency requirements. As a result, it is more desirable to obtain lightweight neural networks which have the same performance during inference time. In this work, we propose a weight based pruning approach in which the weights are pruned gradually based on their momentum of the previous iterations. Each layer of the neural network is assigned an importance value based on their relative sparsity, followed by the magnitude of the weight in the previous iterations. We evaluate our approach on networks such as AlexNet, VGG16 and ResNet50 with image classification datasets such as CIFAR-10 and CIFAR-100. We found that the results outperformed the previous approaches with respect to accuracy and compression ratio. Our method is able to obtain a compression of 15% for the same degradation in accuracy on both the datasets.  ( 2 min )
    Optimal transport for vector Gaussian mixture models. (arXiv:2012.09226v2 [stat.ML] UPDATED)
    Vector Gaussian mixture models form an important special subset of vector-valued distributions. Any physical entity that can mutate or transit among alternative manifestations distributed in a given space falls into this category. A key example is color imagery. In this note, we vectorize the Gaussian mixture model and study different optimal mass transport related problems for such models. The benefits of using vector Gaussian mixture for optimal mass transport include computational efficiency and the ability to preserve structure.  ( 2 min )
    Developing moral AI to support antimicrobial decision making. (arXiv:2208.06327v1 [cs.CY])
    Artificial intelligence (AI) assisting with antimicrobial prescribing raises significant moral questions. Utilising ethical frameworks alongside AI-driven systems, while considering infection specific complexities, can support moral decision making to tackle antimicrobial resistance.  ( 2 min )
    Improving Human Decision-Making with Machine Learning. (arXiv:2108.08454v3 [cs.LG] UPDATED)
    Workers spend a significant amount of time learning how to make good decisions. Evaluating the efficacy of a given decision, however, can be complicated -- e.g., decision outcomes are often long-term and relate to the original decision in complex ways. Surprisingly, even though learning good decision-making strategies is difficult, they can often be expressed in simple and concise forms. Focusing on sequential decision-making, we design a novel machine learning algorithm that is capable of extracting "best practices" from trace data and conveying its insights to humans in the form of interpretable "tips". Our algorithm selects the tip that best bridges the gap between the actions taken by the human workers and those taken by the optimal policy in a way that accounts for which actions are consequential for achieving higher performance. We evaluate our approach through a series of randomized controlled experiments where participants manage a virtual kitchen. Our experiments show that the tips generated by our algorithm can significantly improve human performance relative to intuitive baselines. In addition, we discuss a number of empirical insights that can help inform the design of algorithms intended for human-AI interfaces. For instance, we find evidence that participants do not simply blindly follow our tips; instead, they combine them with their own experience to discover additional strategies for improving performance.  ( 3 min )
    R\'enyiCL: Contrastive Representation Learning with Skew R\'enyi Divergence. (arXiv:2208.06270v1 [stat.ML])
    Contrastive representation learning seeks to acquire useful representations by estimating the shared information between multiple views of data. Here, the choice of data augmentation is sensitive to the quality of learned representations: as harder the data augmentations are applied, the views share more task-relevant information, but also task-irrelevant one that can hinder the generalization capability of representation. Motivated by this, we present a new robust contrastive learning scheme, coined R\'enyiCL, which can effectively manage harder augmentations by utilizing R\'enyi divergence. Our method is built upon the variational lower bound of R\'enyi divergence, but a na\"ive usage of a variational method is impractical due to the large variance. To tackle this challenge, we propose a novel contrastive objective that conducts variational estimation of a skew R\'enyi divergence and provide a theoretical guarantee on how variational estimation of skew divergence leads to stable training. We show that R\'enyi contrastive learning objectives perform innate hard negative sampling and easy positive sampling simultaneously so that it can selectively learn useful features and ignore nuisance features. Through experiments on ImageNet, we show that R\'enyi contrastive learning with stronger augmentations outperforms other self-supervised methods without extra regularization or computational overhead. Moreover, we also validate our method on other domains such as graph and tabular, showing empirical gain over other contrastive methods.  ( 2 min )
    Comparing Baseline Shapley and Integrated Gradients for Local Explanation: Some Additional Insights. (arXiv:2208.06096v1 [cs.LG])
    There are many different methods in the literature for local explanation of machine learning results. However, the methods differ in their approaches and often do not provide same explanations. In this paper, we consider two recent methods: Integrated Gradients (Sundararajan, Taly, & Yan, 2017) and Baseline Shapley (Sundararajan and Najmi, 2020). The original authors have already studied the axiomatic properties of the two methods and provided some comparisons. Our work provides some additional insights on their comparative behavior for tabular data. We discuss common situations where the two provide identical explanations and where they differ. We also use simulation studies to examine the differences when neural networks with ReLU activation function is used to fit the models.  ( 2 min )
    Gradient Estimation for Binary Latent Variables via Gradient Variance Clipping. (arXiv:2208.06124v1 [cs.LG])
    Gradient estimation is often necessary for fitting generative models with discrete latent variables, in contexts such as reinforcement learning and variational autoencoder (VAE) training. The DisARM estimator (Yin et al. 2020; Dong, Mnih, and Tucker 2020) achieves state of the art gradient variance for Bernoulli latent variable models in many contexts. However, DisARM and other estimators have potentially exploding variance near the boundary of the parameter space, where solutions tend to lie. To ameliorate this issue, we propose a new gradient estimator \textit{bitflip}-1 that has lower variance at the boundaries of the parameter space. As bitflip-1 has complementary properties to existing estimators, we introduce an aggregated estimator, \textit{unbiased gradient variance clipping} (UGC) that uses either a bitflip-1 or a DisARM gradient update for each coordinate. We theoretically prove that UGC has uniformly lower variance than DisARM. Empirically, we observe that UGC achieves the optimal value of the optimization objectives in toy experiments, discrete VAE training, and in a best subset selection problem.  ( 2 min )
    A Case for Rejection in Low Resource ML Deployment. (arXiv:2208.06359v1 [cs.LG])
    Building reliable AI decision support systems requires a robust set of data on which to train models; both with respect to quantity and diversity. Obtaining such datasets can be difficult in resource limited settings, or for applications in early stages of deployment. Sample rejection is one way to work around this challenge, however much of the existing work in this area is ill-suited for such scenarios. This paper substantiates that position and proposes a simple solution as a proof of concept baseline.  ( 2 min )
    Bayesian Inference with Latent Hamiltonian Neural Networks. (arXiv:2208.06120v1 [cs.LG])
    When sampling for Bayesian inference, one popular approach is to use Hamiltonian Monte Carlo (HMC) and specifically the No-U-Turn Sampler (NUTS) which automatically decides the end time of the Hamiltonian trajectory. However, HMC and NUTS can require numerous numerical gradients of the target density, and can prove slow in practice. We propose Hamiltonian neural networks (HNNs) with HMC and NUTS for solving Bayesian inference problems. Once trained, HNNs do not require numerical gradients of the target density during sampling. Moreover, they satisfy important properties such as perfect time reversibility and Hamiltonian conservation, making them well-suited for use within HMC and NUTS because stationarity can be shown. We also propose an HNN extension called latent HNNs (L-HNNs), which are capable of predicting latent variable outputs. Compared to HNNs, L-HNNs offer improved expressivity and reduced integration errors. Finally, we employ L-HNNs in NUTS with an online error monitoring scheme to prevent sample degeneracy in regions of low probability density. We demonstrate L-HNNs in NUTS with online error monitoring on several examples involving complex, heavy-tailed, and high-local-curvature probability densities. Overall, L-HNNs in NUTS with online error monitoring satisfactorily inferred these probability densities. Compared to traditional NUTS, L-HNNs in NUTS with online error monitoring required 1--2 orders of magnitude fewer numerical gradients of the target density and improved the effective sample size (ESS) per gradient by an order of magnitude.  ( 3 min )
    GSim: A Graph Neural Network based Relevance Measure for Heterogeneous Graphs. (arXiv:2208.06144v1 [cs.IR])
    Heterogeneous graphs, which contain nodes and edges of multiple types, are prevalent in various domains, including bibliographic networks, social media, and knowledge graphs. As a fundamental task in analyzing heterogeneous graphs, relevance measure aims to calculate the relevance between two objects of different types, which has been used in many applications such as web search, recommendation, and community detection. Most of existing relevance measures focus on homogeneous networks where objects are of the same type, and a few measures are developed for heterogeneous graphs, but they often need the pre-defined meta-path. Defining meaningful meta-paths requires much domain knowledge, which largely limits their applications, especially on schema-rich heterogeneous graphs like knowledge graphs. Recently, the Graph Neural Network (GNN) has been widely applied in many graph mining tasks, but it has not been applied for measuring relevance yet. To address the aforementioned problems, we propose a novel GNN-based relevance measure, namely GSim. Specifically, we first theoretically analyze and show that GNN is effective for measuring the relevance of nodes in the graph. We then propose a context path-based graph neural network (CP-GNN) to automatically leverage the semantics in heterogeneous graphs. Moreover, we exploit CP-GNN to support relevance measures between two objects of any type. Extensive experiments demonstrate that GSim outperforms existing measures.  ( 3 min )
    On deceiving malware classification with section injection. (arXiv:2208.06092v1 [cs.CR])
    We investigate how to modify executable files to deceive malware classification systems. This work's main contribution is a methodology to inject bytes across a malware file randomly and use it both as an attack to decrease classification accuracy but also as a defensive method, augmenting the data available for training. It respects the operating system file format to make sure the malware will still execute after our injection and will not change its behavior. We reproduced five state-of-the-art malware classification approaches to evaluate our injection scheme: one based on GIST+KNN, three CNN variations and one Gated CNN. We performed our experiments on a public dataset with 9,339 malware samples from 25 different families. Our results show that a mere increase of 7% in the malware size causes an accuracy drop between 25% and 40% for malware family classification. They show that a automatic malware classification system may not be as trustworthy as initially reported in the literature. We also evaluate using modified malwares alongside the original ones to increase networks robustness against mentioned attacks. Results show that a combination of reordering malware sections and injecting random data can improve overall performance of the classification. Code available at https://github.com/adeilsonsilva/malware-injection.  ( 2 min )
    Algebraic Reduction of Hidden Markov Models. (arXiv:2208.05968v1 [cs.LG])
    The problem of reducing a Hidden Markov Model (HMM) to a one of smaller dimension that exactly reproduces the same marginals is tackled by using a system-theoretic approach, adapted to HMMs by leveraging on a suitable algebraic representation of probability spaces. We propose two algorithms that return coarse-grained equivalent HMMs obtained by stochastic projection operators: the first returns models that reproduce the single-time distribution of a given output process, while in the second the full (multi-time) distribution is preserved. The reduction method exploits not only the structure of the observed output, but also its initial condition, whenever the latter is known or belongs to a given subclass. Optimal algorithms are derived for a class of HMM, namely observable ones. In the general case, we propose algorithms that have produced minimal models for all the examples we analyzed, and conjecture their optimality.  ( 2 min )
    BSAC: Bayesian Strategy Network Based Soft Actor-Critic in Deep Reinforcement Learning. (arXiv:2208.06033v1 [cs.AI])
    Adopting reasonable strategies is challenging but crucial for an intelligent agent with limited resources working in hazardous, unstructured, and dynamic environments to improve the system utility, decrease the overall cost, and increase mission success probability. Deep Reinforcement Learning (DRL) helps organize agents' behaviors and actions based on their state and represents complex strategies (composition of actions). This paper proposes a novel hierarchical strategy decomposition approach based on Bayesian chaining to separate an intricate policy into several simple sub-policies and organize their relationships as Bayesian strategy networks (BSN). We integrate this approach into the state-of-the-art DRL method, soft actor-critic (SAC), and build the corresponding Bayesian soft actor-critic (BSAC) model by organizing several sub-policies as a joint policy. We compare the proposed BSAC method with the SAC and other state-of-the-art approaches such as TD3, DDPG, and PPO on the standard continuous control benchmarks -- Hopper-v2, Walker2d-v2, and Humanoid-v2 -- in MuJoCo with the OpenAI Gym environment. The results demonstrate that the promising potential of the BSAC method significantly improves training efficiency. The open sourced codes for BSAC can be accessed at https://github.com/herolab-uga/bsac.  ( 2 min )
    Forecasting the production of Distillate Fuel Oil Refinery and Propane Blender net production by using Time Series Algorithms. (arXiv:2208.05964v1 [cs.LG])
    Oil production forecasting is an important step in controlling the cost-effect and monitoring the functioning of petroleum reservoirs. As a result, oil production forecasting makes it easier for reservoir engineers to develop feasible projects, which helps to avoid risky investments and achieve long-term growth. As a result, reliable petroleum reservoir forecasting is critical for controlling and managing the effective cost of oil reservoirs. Oil production is influenced by reservoir qualities such as porosity, permeability, compressibility, fluid saturation, and other well operational parameters. Three-time series algorithms i.e., Seasonal Naive method, Exponential Smoothening and ARIMA to forecast the Distillate Fuel Oil Refinery and Propane Blender net production for the next two years.  ( 2 min )
    Interaction Decompositions for Tensor Network Regression. (arXiv:2208.06029v1 [cs.LG])
    It is well known that tensor network regression models operate on an exponentially large feature space, but questions remain as to how effectively they are able to utilize this space. Using the polynomial featurization from Novikov et al., we propose the interaction decomposition as a tool that can assess the relative importance of different regressors as a function of their polynomial degree. We apply this decomposition to tensor ring and tree tensor network models trained on the MNIST and Fashion MNIST datasets, and find that up to 75% of interaction degrees are contributing meaningfully to these models. We also introduce a new type of tensor network model that is explicitly trained on only a small subset of interaction degrees, and find that these models are able to match or even outperform the full models using only a fraction of the exponential feature space. This suggests that standard tensor network models utilize their polynomial regressors in an inefficient manner, with the lower degree terms being vastly under-utilized.  ( 2 min )
    Personalizing or Not: Dynamically Personalized Federated Learning with Incentives. (arXiv:2208.06192v1 [cs.LG])
    Personalized federated learning (FL) facilitates collaborations between multiple clients to learn personalized models without sharing private data. The mechanism mitigates the statistical heterogeneity commonly encountered in the system, i.e., non-IID data over different clients. Existing personalized algorithms generally assume all clients volunteer for personalization. However, potential participants might still be reluctant to personalize models since they might not work well. In this case, clients choose to use the global model instead. To avoid making unrealistic assumptions, we introduce the personalization rate, measured as the fraction of clients willing to train personalized models, into federated settings and propose DyPFL. This dynamically personalized FL technique incentivizes clients to participate in personalizing local models while allowing the adoption of the global model when it performs better. We show that the algorithmic pipeline in DyPFL guarantees good convergence performance, allowing it to outperform alternative personalized methods in a broad range of conditions, including variation in heterogeneity, number of clients, local epochs, and batch sizes.  ( 2 min )
    Sequential Causal Imitation Learning with Unobserved Confounders. (arXiv:2208.06276v1 [cs.LG])
    "Monkey see monkey do" is an age-old adage, referring to na\"ive imitation without a deep understanding of a system's underlying mechanics. Indeed, if a demonstrator has access to information unavailable to the imitator (monkey), such as a different set of sensors, then no matter how perfectly the imitator models its perceived environment (See), attempting to reproduce the demonstrator's behavior (Do) can lead to poor outcomes. Imitation learning in the presence of a mismatch between demonstrator and imitator has been studied in the literature under the rubric of causal imitation learning (Zhang et al., 2020), but existing solutions are limited to single-stage decision-making. This paper investigates the problem of causal imitation learning in sequential settings, where the imitator must make multiple decisions per episode. We develop a graphical criterion that is necessary and sufficient for determining the feasibility of causal imitation, providing conditions when an imitator can match a demonstrator's performance despite differing capabilities. Finally, we provide an efficient algorithm for determining imitability and corroborate our theory with simulations.  ( 2 min )
  • Open

    Toward a Better Monitoring Statistic for Profile Monitoring via Variational Autoencoders. (arXiv:1911.00482v2 [cs.LG] UPDATED)
    Wide accessibility of imaging and profile sensors in modern industrial systems created an abundance of high-dimensional sensing variables. This led to a a growing interest in the research of high-dimensional process monitoring. However, most of the approaches in the literature assume the in-control population to lie on a linear manifold with a given basis (i.e., spline, wavelet, kernel, etc) or an unknown basis (i.e., principal component analysis and its variants), which cannot be used to efficiently model profiles with a nonlinear manifold which is common in many real-life cases. We propose deep probabilistic autoencoders as a viable unsupervised learning approach to model such manifolds. To do so, we formulate nonlinear and probabilistic extensions of the monitoring statistics from classical approaches as the expected reconstruction error (ERE) and the KL-divergence (KLD) based monitoring statistics. Through extensive simulation study, we provide insights on why latent-space based statistics are unreliable and why residual-space based ones typically perform much better for deep learning based approaches. Finally, we demonstrate the superiority of deep probabilistic models via both simulation study and a real-life case study involving images of defects from a hot steel rolling process.  ( 3 min )
    Data-Driven Fault Diagnosis Analysis and Open-Set Classification of Time-Series Data. (arXiv:2009.04756v2 [stat.ML] UPDATED)
    Fault diagnosis of dynamic systems is done by detecting changes in time-series data, for example residuals, caused by system degradation and faulty components. The use of general-purpose multi-class classification methods for fault diagnosis is complicated by imbalanced training data and unknown fault classes. Another complicating factor is that different fault classes can result in similar residual outputs, especially for small faults, which causes classification ambiguities. In this work, a framework for data-driven analysis and open-set classification is developed for fault diagnosis applications using the Kullback-Leibler divergence. A data-driven fault classification algorithm is proposed which can handle imbalanced datasets, class overlapping, and unknown faults. In addition, an algorithm is proposed to estimate the size of the fault when training data contains information from known fault realizations. An advantage of the proposed framework is that it can also be used for quantitative analysis of fault diagnosis performance, for example, to analyze how easy it is to classify faults of different magnitudes. To evaluate the usefulness of the proposed methods, multiple datasets from different fault scenarios have been collected from an internal combustion engine test bench to illustrate the design process of a data-driven diagnosis system, including quantitative fault diagnosis analysis and evaluation of the developed open set fault classification algorithm.  ( 3 min )
    Towards a Grounded Theory of Causation for Embodied AI. (arXiv:2206.13973v2 [cs.AI] UPDATED)
    There exist well-developed frameworks for causal modelling, but these require rather a lot of human domain expertise to define causal variables and perform interventions. In order to enable autonomous agents to learn abstract causal models through interactive experience, the existing theoretical foundations need to be extended and clarified. Existing frameworks give no guidance regarding variable choice / representation, and more importantly, give no indication as to which behaviour policies or physical transformations of state space shall count as interventions. The framework sketched in this paper describes actions as transformations of state space, for instance induced by an agent running a policy. This makes it possible to describe in a uniform way both transformations of the micro-state space and abstract models thereof, and say when the latter is veridical / grounded / natural. We then introduce (causal) variables, define a mechanism as an invariant predictor, and say when an action can be viewed as a ``surgical intervention'', thus bringing the objective of causal representation \& intervention skill learning into clearer focus.  ( 2 min )
    Comparing Baseline Shapley and Integrated Gradients for Local Explanation: Some Additional Insights. (arXiv:2208.06096v1 [cs.LG])
    There are many different methods in the literature for local explanation of machine learning results. However, the methods differ in their approaches and often do not provide same explanations. In this paper, we consider two recent methods: Integrated Gradients (Sundararajan, Taly, & Yan, 2017) and Baseline Shapley (Sundararajan and Najmi, 2020). The original authors have already studied the axiomatic properties of the two methods and provided some comparisons. Our work provides some additional insights on their comparative behavior for tabular data. We discuss common situations where the two provide identical explanations and where they differ. We also use simulation studies to examine the differences when neural networks with ReLU activation function is used to fit the models.  ( 2 min )
    Understanding the stochastic dynamics of sequential decision-making processes: A path-integral analysis of Multi-armed Bandits. (arXiv:2208.06245v1 [cs.LG])
    The multi-armed bandit (MAB) model is one of the most classical models to study decision-making in an uncertain environment. In this model, a player needs to choose one of K possible arms of a bandit machine to play at each time step, where the corresponding arm returns a random reward to the player, potentially from a specific unknown distribution. The target of the player is to collect as much rewards as possible during the process. Despite its simplicity, the MAB model offers an excellent playground for studying the trade-off between exploration versus exploitation and designing effective algorithms for sequential decision-making under uncertainty. Although many asymptotically optimal algorithms have been established, the finite-time behaviours of the stochastic dynamics of the MAB model appears much more difficult to analyze, due to the intertwining between the decision-making and the rewards being collected. In this paper, we employ techniques in statistical physics to analyze the MAB model, which facilitates to characterize the distribution of cumulative regrets at a finite short time, the central quantity of interest in an MAB algorithm, as well as the intricate dynamical behaviours of the model.
    Hypergraph Modeling via Spectral Embedding Connection: Hypergraph Cut, Weighted Kernel $k$-means, and Heat Kernel. (arXiv:2203.09888v2 [cs.LG] UPDATED)
    We propose a theoretical framework of multi-way similarity to model real-valued data into hypergraphs for clustering via spectral embedding. For graph cut based spectral clustering, it is common to model real-valued data into graph by modeling pairwise similarities using kernel function. This is because the kernel function has a theoretical connection to the graph cut. For problems where using multi-way similarities are more suitable than pairwise ones, it is natural to model as a hypergraph, which is generalization of a graph. However, although the hypergraph cut is well-studied, there is not yet established a hypergraph cut based framework to model multi-way similarity. In this paper, we formulate multi-way similarities by exploiting the theoretical foundation of kernel function. We show a theoretical connection between our formulation and hypergraph cut in two ways, generalizing both weighted kernel $k$-means and the heat kernel, by which we justify our formulation. We also provide a fast algorithm for spectral clustering. Our algorithm empirically shows better performance than existing graph and other heuristic modeling methods.
    R\'enyiCL: Contrastive Representation Learning with Skew R\'enyi Divergence. (arXiv:2208.06270v1 [stat.ML])
    Contrastive representation learning seeks to acquire useful representations by estimating the shared information between multiple views of data. Here, the choice of data augmentation is sensitive to the quality of learned representations: as harder the data augmentations are applied, the views share more task-relevant information, but also task-irrelevant one that can hinder the generalization capability of representation. Motivated by this, we present a new robust contrastive learning scheme, coined R\'enyiCL, which can effectively manage harder augmentations by utilizing R\'enyi divergence. Our method is built upon the variational lower bound of R\'enyi divergence, but a na\"ive usage of a variational method is impractical due to the large variance. To tackle this challenge, we propose a novel contrastive objective that conducts variational estimation of a skew R\'enyi divergence and provide a theoretical guarantee on how variational estimation of skew divergence leads to stable training. We show that R\'enyi contrastive learning objectives perform innate hard negative sampling and easy positive sampling simultaneously so that it can selectively learn useful features and ignore nuisance features. Through experiments on ImageNet, we show that R\'enyi contrastive learning with stronger augmentations outperforms other self-supervised methods without extra regularization or computational overhead. Moreover, we also validate our method on other domains such as graph and tabular, showing empirical gain over other contrastive methods.
    Optimal transport for vector Gaussian mixture models. (arXiv:2012.09226v2 [stat.ML] UPDATED)
    Vector Gaussian mixture models form an important special subset of vector-valued distributions. Any physical entity that can mutate or transit among alternative manifestations distributed in a given space falls into this category. A key example is color imagery. In this note, we vectorize the Gaussian mixture model and study different optimal mass transport related problems for such models. The benefits of using vector Gaussian mixture for optimal mass transport include computational efficiency and the ability to preserve structure.
    Markov Observation Models. (arXiv:2208.06368v1 [stat.ML])
    Herein, the Hidden Markov Model is expanded to allow for Markov chain observations. In particular, the observations are assumed to be a Markov chain whose one step transition probabilities depend upon the hidden Markov chain. An Expectation-Maximization analog to the Baum-Welch algorithm is developed for this more general model to estimate the transition probabilities for both the hidden state and for the observations as well as to estimate the probabilities for the initial joint hidden-state-observation distribution. A believe state or filter recursion to track the hidden state then arises from the calculations of this Expectation-Maximization algorithm. A dynamic programming analog to the Viterbi algorithm is also developed to estimate the most likely sequence of hidden states given the sequence of observations.
    EEGNN: Edge Enhanced Graph Neural Networks. (arXiv:2208.06322v1 [stat.ML])
    Training deep graph neural networks (GNNs) poses a challenging task, as the performance of GNNs may suffer from the number of hidden message-passing layers. The literature has focused on the proposals of over-smoothing and under-reaching to explain the performance deterioration of deep GNNs. In this paper, we propose a new explanation for such deteriorated performance phenomenon, mis-simplification, that is, mistakenly simplifying graphs by preventing self-loops and forcing edges to be unweighted. We show that such simplifying can reduce the potential of message-passing layers to capture the structural information of graphs. In view of this, we propose a new framework, edge enhanced graph neural network(EEGNN). EEGNN uses the structural information extracted from the proposed Dirichlet mixture Poisson graph model, a Bayesian nonparametric model for graphs, to improve the performance of various deep message-passing GNNs. Experiments over different datasets show that our method achieves considerable performance increase compared to baselines.
    Function Classes for Identifiable Nonlinear Independent Component Analysis. (arXiv:2208.06406v1 [stat.ML])
    Unsupervised learning of latent variable models (LVMs) is widely used to represent data in machine learning. When such models reflect the ground truth factors and the mechanisms mapping them to observations, there is reason to expect that they allow generalization in downstream tasks. It is however well known that such identifiability guaranties are typically not achievable without putting constraints on the model class. This is notably the case for nonlinear Independent Component Analysis, in which the LVM maps statistically independent variables to observations via a deterministic nonlinear function. Several families of spurious solutions fitting perfectly the data, but that do not correspond to the ground truth factors can be constructed in generic settings. However, recent work suggests that constraining the function class of such models may promote identifiability. Specifically, function classes with constraints on their partial derivatives, gathered in the Jacobian matrix, have been proposed, such as orthogonal coordinate transformations (OCT), which impose orthogonality of the Jacobian columns. In the present work, we prove that a subclass of these transformations, conformal maps, is identifiable and provide novel theoretical results suggesting that OCTs have properties that prevent families of spurious solutions to spoil identifiability in a generic setting.
    Private Domain Adaptation from a Public Source. (arXiv:2208.06135v1 [cs.LG])
    A key problem in a variety of applications is that of domain adaptation from a public source domain, for which a relatively large amount of labeled data with no privacy constraints is at one's disposal, to a private target domain, for which a private sample is available with very few or no labeled data. In regression problems with no privacy constraints on the source or target data, a discrepancy minimization algorithm based on several theoretical guarantees was shown to outperform a number of other adaptation algorithm baselines. Building on that approach, we design differentially private discrepancy-based algorithms for adaptation from a source domain with public labeled data to a target domain with unlabeled private data. The design and analysis of our private algorithms critically hinge upon several key properties we prove for a smooth approximation of the weighted discrepancy, such as its smoothness with respect to the $\ell_1$-norm and the sensitivity of its gradient. Our solutions are based on private variants of Frank-Wolfe and Mirror-Descent algorithms. We show that our adaptation algorithms benefit from strong generalization and privacy guarantees and report the results of experiments demonstrating their effectiveness.
    A Scalable Probabilistic Model for Reward Optimizing Slate Recommendation. (arXiv:2208.06263v1 [cs.IR])
    We introduce Probabilistic Rank and Reward model (PRR), a scalable probabilistic model for personalized slate recommendation. Our model allows state-of-the-art estimation of user interests in the following ubiquitous recommender system scenario: A user is shown a slate of K recommendations and the user chooses at most one of these K items. It is the goal of the recommender system to find the K items of most interest to a user in order to maximize the probability that the user interacts with the slate. Our contribution is to show that we can learn more effectively the probability of the recommendations being successful by combining the reward - whether the slate was clicked or not - and the rank - the item on the slate that was selected. Our method learns more efficiently than bandit methods that use only the reward, and user preference methods that use only the rank. It also provides similar or better estimation performance to independent inverse-propensity-score methods and is far more scalable. Our method is state of the art in terms of both speed and accuracy on massive datasets with up to 1 million items. Finally, our method allows fast delivery of recommendations powered by maximum inner product search (MIPS), making it suitable in extremely low latency domains such as computational advertising.
    Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning. (arXiv:2208.06193v1 [cs.LG])
    Offline reinforcement learning (RL), which aims to learn an optimal policy using a previously collected static dataset, is an important paradigm of RL. Standard RL methods often perform poorly at this task due to the function approximation errors on out-of-distribution actions. While a variety of regularization methods have been proposed to mitigate this issue, they are often constrained by policy classes with limited expressiveness and sometimes result in substantially suboptimal solutions. In this paper, we propose Diffusion-QL that utilizes a conditional diffusion model as a highly expressive policy class for behavior cloning and policy regularization. In our approach, we learn an action-value function and we add a term maximizing action-values into the training loss of a conditional diffusion model, which results in a loss that seeks optimal actions that are near the behavior policy. We show the expressiveness of the diffusion model-based policy and the coupling of the behavior cloning and policy improvement under the diffusion model both contribute to the outstanding performance of Diffusion-QL. We illustrate our method and prior work in a simple 2D bandit example with a multimodal behavior policy. We then show that our method can achieve state-of-the-art performance on the majority of the D4RL benchmark tasks for offline RL.
    Incompleteness of graph convolutional neural networks for points clouds in three dimensions. (arXiv:2201.07136v3 [stat.ML] UPDATED)
    Graph neural networks (GNN) are very popular methods in machine learning and have been applied very successfully to the prediction of the properties of molecules and materials. First-order GNNs are well known to be incomplete, i.e., there exist graphs that are distinct but appear identical when seen through the lens of the GNN. More complicated schemes have thus been designed to increase their resolving power. Applications to molecules (and more generally, point clouds), however, add a geometric dimension to the problem. The most straightforward and prevalent approach to construct graph representation for molecules regards atoms as vertices in a graph and draws a bond between each pair of atoms within a chosen cutoff. Bonds can be decorated with the distance between atoms, and the resulting "distance graph NNs" (dGNN) have empirically demonstrated excellent resolving power and are widely used in chemical ML, with all known indistinguishable graphs being resolved in the fully-connected limit. Here we show that even for the restricted case of fully-connected graphs induced by 3D atom clouds dGNNs are not complete. We construct pairs of distinct point clouds that generate graphs that, for any cutoff radius, are equivalent based on a first-order Weisfeiler-Lehman test. This class of degenerate structures includes chemically-plausible configurations, setting an ultimate limit to the expressive power of some of the well-established GNN architectures for atomistic machine learning. Models that explicitly use angular or directional information in the description of atomic environments can resolve these degeneracies.
    Gradient Estimation for Binary Latent Variables via Gradient Variance Clipping. (arXiv:2208.06124v1 [cs.LG])
    Gradient estimation is often necessary for fitting generative models with discrete latent variables, in contexts such as reinforcement learning and variational autoencoder (VAE) training. The DisARM estimator (Yin et al. 2020; Dong, Mnih, and Tucker 2020) achieves state of the art gradient variance for Bernoulli latent variable models in many contexts. However, DisARM and other estimators have potentially exploding variance near the boundary of the parameter space, where solutions tend to lie. To ameliorate this issue, we propose a new gradient estimator \textit{bitflip}-1 that has lower variance at the boundaries of the parameter space. As bitflip-1 has complementary properties to existing estimators, we introduce an aggregated estimator, \textit{unbiased gradient variance clipping} (UGC) that uses either a bitflip-1 or a DisARM gradient update for each coordinate. We theoretically prove that UGC has uniformly lower variance than DisARM. Empirically, we observe that UGC achieves the optimal value of the optimization objectives in toy experiments, discrete VAE training, and in a best subset selection problem.
    A sub-sampling algorithm preventing outliers. (arXiv:2208.06218v1 [stat.ME])
    Nowadays, in many different fields, massive data are available and for several reasons, it might be convenient to analyze just a subset of the data. The application of the D-optimality criterion can be helpful to optimally select a subsample of observations. However, it is well known that D-optimal support points lie on the boundary of the design space and if they go hand in hand with extreme response values, they can have a severe influence on the estimated linear model (leverage points with high influence). To overcome this problem, firstly, we propose an unsupervised exchange procedure that enables us to select a nearly D-optimal subset of observations without high leverage values. Then, we provide a supervised version of this exchange procedure, where besides high leverage points also the outliers in the responses (that are not associated to high leverage points) are avoided. This is possible because, unlike other design situations, in subsampling from big datasets the response values may be available. Finally, both the unsupervised and the supervised selection procedures are generalized to I-optimality, with the goal of getting accurate predictions.
    Feature-Based Time-Series Analysis in R using the theft Package. (arXiv:2208.06146v1 [stat.ML])
    Time series are measured and analyzed across the sciences. One method of quantifying the structure of time series is by calculating a set of summary statistics or `features', and then representing a time series in terms of its properties as a feature vector. The resulting feature space is interpretable and informative, and enables conventional statistical learning approaches, including clustering, regression, and classification, to be applied to time-series datasets. Many open-source software packages for computing sets of time-series features exist across multiple programming languages, including catch22 (22 features: Matlab, R, Python, Julia), feasts (42 features: R), tsfeatures (63 features: R), Kats (40 features: Python), tsfresh (779 features: Python), and TSFEL (390 features: Python). However, there are several issues: (i) a singular access point to these packages is not currently available; (ii) to access all feature sets, users must be fluent in multiple languages; and (iii) these feature-extraction packages lack extensive accompanying methodological pipelines for performing feature-based time-series analysis, such as applications to time-series classification. Here we introduce a solution to these issues in an R software package called theft: Tools for Handling Extraction of Features from Time series. theft is a unified and extendable framework for computing features from the six open-source time-series feature sets listed above. It also includes a suite of functions for processing and interpreting the performance of extracted features, including extensive data-visualization templates, low-dimensional projections, and time-series classification operations. With an increasing volume and complexity of time-series datasets in the sciences and industry, theft provides a standardized framework for comprehensively quantifying and interpreting informative structure in time series.
    Coarse to Fine Two-Stage Approach to Robust Tensor Completion of Visual Data. (arXiv:2106.10422v4 [cs.LG] UPDATED)
    Tensor completion is the problem of estimating the missing values of high-order data from partially observed entries. Data corruption due to prevailing outliers poses major challenges to traditional tensor completion algorithms, which catalyzed the development of robust algorithms that alleviate the effect of outliers. However, existing robust methods largely presume that the corruption is sparse, which may not hold in practice. In this paper, we develop a two-stage robust tensor completion approach to deal with tensor completion of visual data with a large amount of gross corruption. A novel coarse-to-fine framework is proposed which uses a global coarse completion result to guide a local patch refinement process. To efficiently mitigate the effect of a large number of outliers on tensor recovery, we develop a new M-estimator-based robust tensor ring recovery method which can adaptively identify the outliers and alleviate their negative effect in the optimization. The experimental results demonstrate the superior performance of the proposed approach over state-of-the-art robust algorithms for tensor completion.
    Transformers Can Do Bayesian Inference. (arXiv:2112.10510v5 [cs.LG] UPDATED)
    Currently, it is hard to reap the benefits of deep learning for Bayesian methods, which allow the explicit specification of prior knowledge and accurately capture model uncertainty. We present Prior-Data Fitted Networks (PFNs). PFNs leverage large-scale machine learning techniques to approximate a large set of posteriors. The only requirement for PFNs to work is the ability to sample from a prior distribution over supervised learning tasks (or functions). Our method restates the objective of posterior approximation as a supervised classification problem with a set-valued input: it repeatedly draws a task (or function) from the prior, draws a set of data points and their labels from it, masks one of the labels and learns to make probabilistic predictions for it based on the set-valued input of the rest of the data points. Presented with a set of samples from a new supervised learning task as input, PFNs make probabilistic predictions for arbitrary other data points in a single forward propagation, having learned to approximate Bayesian inference. We demonstrate that PFNs can near-perfectly mimic Gaussian processes and also enable efficient Bayesian inference for intractable problems, with over 200-fold speedups in multiple setups compared to current methods. We obtain strong results in very diverse areas such as Gaussian process regression, Bayesian neural networks, classification for small tabular data sets, and few-shot image classification, demonstrating the generality of PFNs. Code and trained PFNs are released at https://github.com/automl/TransformersCanDoBayesianInference.
    The Limit of the Marginal Distribution Model in Consumer Choice. (arXiv:2208.06115v1 [stat.ML])
    Given data on choices made by consumers for different assortments, a key challenge is to develop parsimonious models that describe and predict consumer choice behavior. One such choice model is the marginal distribution model which requires only the specification of the marginal distributions of the random utilities of the alternatives to explain choice data. In this paper, we develop an exact characterisation of the set of choice probabilities which are representable by the marginal distribution model consistently across any collection of assortments. Allowing for the possibility of alternatives to be grouped based on the marginal distribution of their utilities, we show (a) verifying consistency of choice probability data with this model is possible in polynomial time and (b) finding the closest fit reduces to solving a mixed integer convex program. Our results show that the marginal distribution model provides much better representational power as compared to multinomial logit and much better computational performance as compared to the random utility model.
    Unifying Gradients to Improve Real-world Robustness for Deep Networks. (arXiv:2208.06228v1 [stat.ML])
    The wide application of deep neural networks (DNNs) demands an increasing amount of attention to their real-world robustness, i.e., whether a DNN resists black-box adversarial attacks, among them score-based query attacks (SQAs) are the most threatening ones because of their practicalities and effectiveness: the attackers only need dozens of queries on model outputs to seriously hurt a victim network. Defending against SQAs requires a slight but artful variation of outputs due to the service purpose for users, who share the same output information with attackers. In this paper, we propose a real-world defense, called Unifying Gradients (UniG), to unify gradients of different data so that attackers could only probe a much weaker attack direction that is similar for different samples. Since such universal attack perturbations have been validated as less aggressive than the input-specific perturbations, UniG protects real-world DNNs by indicating attackers a twisted and less informative attack direction. To enhance UniG's practical significance in real-world applications, we implement it as a Hadamard product module that is computationally-efficient and readily plugged into any model. According to extensive experiments on 5 SQAs and 4 defense baselines, UniG significantly improves real-world robustness without hurting clean accuracy on CIFAR10 and ImageNet. For instance, UniG maintains a CIFAR-10 model of 77.80% accuracy under 2500-query Square attack while the state-of-the-art adversarially-trained model only has 67.34% on CIFAR10. Simultaneously, UniG greatly surpasses all compared baselines in clean accuracy and the modification degree of outputs. The code would be released.
    Bayesian Inference with Latent Hamiltonian Neural Networks. (arXiv:2208.06120v1 [cs.LG])
    When sampling for Bayesian inference, one popular approach is to use Hamiltonian Monte Carlo (HMC) and specifically the No-U-Turn Sampler (NUTS) which automatically decides the end time of the Hamiltonian trajectory. However, HMC and NUTS can require numerous numerical gradients of the target density, and can prove slow in practice. We propose Hamiltonian neural networks (HNNs) with HMC and NUTS for solving Bayesian inference problems. Once trained, HNNs do not require numerical gradients of the target density during sampling. Moreover, they satisfy important properties such as perfect time reversibility and Hamiltonian conservation, making them well-suited for use within HMC and NUTS because stationarity can be shown. We also propose an HNN extension called latent HNNs (L-HNNs), which are capable of predicting latent variable outputs. Compared to HNNs, L-HNNs offer improved expressivity and reduced integration errors. Finally, we employ L-HNNs in NUTS with an online error monitoring scheme to prevent sample degeneracy in regions of low probability density. We demonstrate L-HNNs in NUTS with online error monitoring on several examples involving complex, heavy-tailed, and high-local-curvature probability densities. Overall, L-HNNs in NUTS with online error monitoring satisfactorily inferred these probability densities. Compared to traditional NUTS, L-HNNs in NUTS with online error monitoring required 1--2 orders of magnitude fewer numerical gradients of the target density and improved the effective sample size (ESS) per gradient by an order of magnitude.
    Unifying local and global model explanations by functional decomposition of low dimensional structures. (arXiv:2208.06151v1 [cs.LG])
    We consider a global explanation of a regression or classification function by decomposing it into the sum of main components and interaction components of arbitrary order. When adding an identification constraint that is motivated by a causal interpretation, we find q-interaction SHAP to be the unique solution to that constraint. Here, q denotes the highest order of interaction present in the decomposition. Our result provides a new perspective on SHAP values with various practical and theoretical implications: If SHAP values are decomposed into main and all interaction effects, they provide a global explanation with causal interpretation. In principle, the decomposition can be applied to any machine learning model. However, since the number of possible interactions grows exponentially with the number of features, exact calculation is only feasible for methods that fit low dimensional structures or ensembles of those. We provide an algorithm and efficient implementation for gradient boosted trees (xgboost and random planted forests that calculates this decomposition. Conducted experiments suggest that our method provides meaningful explanations and reveals interactions of higher orders. We also investigate further potential of our new insights by utilizing the global explanation for motivating a new measure of feature importance, and for reducing direct and indirect bias by post-hoc component removal.
    Data Banzhaf: A Data Valuation Framework with Maximal Robustness to Learning Stochasticity. (arXiv:2205.15466v4 [cs.LG] UPDATED)
    This paper studies the robustness of data valuation to noisy model performance scores. Particularly, we find that the inherent randomness of the widely used stochastic gradient descent can cause existing data value notions (e.g., the Shapley value and the Leave-one-out error) to produce inconsistent data value rankings across different runs. To address this challenge, we first pose a formal framework within which one can measure the robustness of a data value notion. We show that the Banzhaf value, a value notion originated from cooperative game theory literature, achieves the maximal robustness among all semivalues -- a class of value notions that satisfy crucial properties entailed by ML applications. We propose an algorithm to efficiently estimate the Banzhaf value based on the Maximum Sample Reuse (MSR) principle. We derive the lower bound sample complexity for Banzhaf value estimation, and we show that our MSR algorithm's sample complexity is close to the lower bound. Our evaluation demonstrates that the Banzhaf value outperforms the existing semivalue-based data value notions on several downstream ML tasks such as learning with weighted samples and noisy label detection. Overall, our study suggests that when the underlying ML algorithm is stochastic, the Banzhaf value is a promising alternative to the semivalue-based data value schemes given its computational advantage and ability to robustly differentiate data quality.
    Shape Proportions and Sphericity in n Dimensions. (arXiv:2208.06292v1 [cs.CV])
    Shape metrics for objects in high dimensions remain sparse. Those that do exist, such as hyper-volume, remain limited to objects that are better understood such as Platonic solids and $n$-Cubes. Further, understanding objects of ill-defined shapes in higher dimensions is ambiguous at best. Past work does not provide a single number to give a qualitative understanding of an object. For example, the eigenvalues from principal component analysis results in $n$ metrics to describe the shape of an object. Therefore, we need a single number which can discriminate objects with different shape from one another. Previous work has developed shape metrics for specific dimensions such as two or three dimensions. However, there is an opportunity to develop metrics for any desired dimension. To that end, we present two new shape metrics for objects in a given number of dimensions: hyper-Sphericity and hyper-Shape Proportion (SP). We explore the proprieties of these metrics on a number of different shapes including $n$-balls. We then connect these metrics to applications of analyzing the shape of multidimensional data such as the popular Iris dataset.
    Gaussian process surrogate models for neural networks. (arXiv:2208.06028v1 [cs.LG])
    The lack of insight into deep learning systems hinders their systematic design. In science and engineering, modeling is a methodology used to understand complex systems whose internal processes are opaque. Modeling replaces a complex system with a simpler surrogate that is more amenable to interpretation. Drawing inspiration from this, we construct a class of surrogate models for neural networks using Gaussian processes. Rather than deriving the kernels for certain limiting cases of neural networks, we learn the kernels of the Gaussian process empirically from the naturalistic behavior of neural networks. We first evaluate our approach with two case studies inspired by previous theoretical studies of neural network behavior in which we capture neural network preferences for learning low frequencies and identify pathological behavior in deep neural networks. In two further practical case studies, we use the learned kernel to predict the generalization properties of neural networks.

  • Open

    [D] Which out of the popular existing models would be the best for predictions of Graph Edit Distance on molecule graphs?
    (GNN/GeometricML question) I’m training a SageGNN to learn molecular representations, fitted with an MLP to then predict the GED between pairs of molecules. However, the nature of the Sage sample neighbourhood makes me worry it’s not quite efficient enough for molecular learning, as the majority of my graphs are similar when it comes to node attributes, only differing in a couple of nodes for each graph. My graphs are also quite small. The nodes only have three possible features (element, charge, HydrogenNumber) and the graphs are all made up of the same elements so idek if I should bother to include element as an attribute or not :/ But yeah. I wanted to ask, which GNNs are best for molecular representation learning of relatively small graphs without many different node features per graph? (Sorry if this question is way too specific lol but hoping someone out there can guide me!) submitted by /u/BanMutsang [link] [comments]  ( 102 min )
    [P] Any good resources which can help me with Multivariate Time Series Forecasting using Probabilistic Machine Learning?
    I'm looking for resources which focus on implementation more than the theory. submitted by /u/HariVamshi [link] [comments]  ( 87 min )
    [D] This post from Andrej Karpathy is nearly 10 years old. How has the state of AI + computer vision progressed since 2012 and do any of you share his feelings of sadness about its current outlook?
    submitted by /u/andrewgarrison [link] [comments]  ( 88 min )
    [R] [P] Paper "Retrieval-Augmented Diffusion Models" + a web app at website Replicate.com. The web app is a text-to-image system that generates 768x768 pixel images from a text description. Example: "a photo of an astronaut riding a horse on the planet Mars". Details are in a comment.
    submitted by /u/Wiskkey [link] [comments]  ( 88 min )
    [D] Differentiable approximations to common probability distributions for count and ordered categorical variables.
    I'm looking for literature on differentiable approximations to certain probability distributions - a categorical distribution has the gumbel-softmax for example - but what about differentiable approximations for the poisson or negative binomial or other distributions for count variables? Or for a categorical variable with an ordering? I haven't found any literature on differentiable approximations on these. Does anyone know of any papers collecting such approximations? Neither tensorflow probability or torch distributions seems to have anything. submitted by /u/WigglyHypersurface [link] [comments]  ( 91 min )
    [D]What are some "important" problems in machine learning/AI?
    I am not talking about "hot stuff" like self driving cars or anything, but topics important to the field( like maybe interpretability of machine learning? ) which is fundamental to the advancement of the field. submitted by /u/Netero1999 [link] [comments]  ( 94 min )
    [D] Did Schmiduber invent diffusion models?
    On this website, Schmiduber describes curiosity-driven, creative agents that try to predict the next input of a sequence of a data generating process while learning to ignore uninteresting details of white noise. I have heard now from a few people that they believe Schmidhuber invented diffusion models. Although diffusion originates in parts from physics (stochastic processes and PDEs), his work seems to introduce the concept of learning (in the form of a learned predictor) to this domain. Most of the papers referenced on this page are from the early 90s. Therefore, it seems to me that he did introduce diffusion models (or at least made significant contributions to it). What is the consensus or divergent opinions on this topic? submitted by /u/yusuf-bengio [link] [comments]  ( 88 min )
    [D] Simple Questions Thread
    Please post your questions here instead of creating a new thread. Encourage others who create new posts for questions to post here instead! Thread will stay alive until next one so keep posting after the date in the title. Thanks to everyone for answering questions in the previous thread! submitted by /u/AutoModerator [link] [comments]  ( 88 min )
    [R] Highlights for every KDD-2022 paper
    Here is the list of all >400 KDD 2022 (ACM SIGKDD Conference on Knowledge Discovery and Data Mining) papers, and a short highlight for each of them. More than 120 of them also have their code published. KDD 2022 will be held at Washington DC from 08/14/2022. https://www.paperdigest.org/2022/08/kdd-2022-highlights/ submitted by /u/biandangou [link] [comments]  ( 88 min )
    [P]OneFlow v0.8.0 Came Out!
    Hi all, We are thrilled to announce the new release of OneFlow, which is a deep learning framework designed to be user-friendly, scalable and efficient. OneFlow v0.8.0 update contains 523 commits. For the full changlog, please check out: https://github.com/Oneflow-Inc/oneflow/releases/tag/v0.8.0. Paper: https://arxiv.org/abs/2110.15032; Code: https://github.com/Oneflow-Inc/oneflow Welcome to install OneFlow v0.8.0 for a new user experience. Your feedbacks will be much appreciated! Highlights and optimizations in this release: 1. PyTorch API compatibility OneFlow v0.8.0 provides more and better PyTorch compatible APIs. In v0.8.0, a series of new features and interfaces that are compatible with PyTorch 1.10.0 are in place, including 68 new APIs that are aligned with PyTorch; 84 bugs …  ( 104 min )
    [R] Panoptic Scene Graph Generation + Gradio Web Demo
    submitted by /u/Illustrious_Row_9971 [link] [comments]  ( 88 min )
  • Open

    [P]OneFlow v0.8.0 Came Out!
    submitted by /u/Just0by [link] [comments]  ( 90 min )
    NNAISENSE Open-Sources ‘EvoTorch’: An Evolutionary Algorithm Library for the Machine Learning Community
    An Advanced Evolutionary library or algorithm has been a dream of scientists and AI/ML enthusiasts since the concept was introduced. This vision has come true thanks to the scientists at NNAISENSE, a Switzerland-based AI Enterprise. They created an open-source platform called EvoTorch. When operated in combination with Machine Learning, it can solve complex operational problems in a fraction of time, with lower costs, and at a larger scale. Evolutionary algorithms act as a step toward solving cascading problems that occur when the problem’s size and complexity increase. Evolutionary algorithms make the situations easier to handle the complexity without adding to the cost, they are also much easier to connect through GPUs and CPUs parallelly to ease up the calculation time and the complexity associated with it, that the only limit to your computational power becomes your budget. The evolutionary algorithms are built in the open framework EvoTorch. Continue reading| Github| Tool submitted by /u/ai-lover [link] [comments]  ( 87 min )
    [P]OneFlow v0.8.0 Came Out!
    submitted by /u/Just0by [link] [comments]  ( 90 min )
    Which is currently the best publicly available Text-to-Image service on the internet?
    Can be free or paid. But the quality should be good enough and it must be available for everyone. No invitations, no waitlist etc.. It also must be available as a service - without the users having to install anything anywhere themselves. Thanks! PS: I know there are other threads and articles all over the internet about this topic. But I'm not interested in what was best months ago, but right now. submitted by /u/amanano [link] [comments]  ( 87 min )
    Can DALL·E Generate DOOM?
    submitted by /u/mutsuto [link] [comments]  ( 86 min )
    I made a music video using 700 images from Dall-E Mini
    submitted by /u/billybjork [link] [comments]  ( 86 min )
    I made an AI-powered basketball referee
    submitted by /u/_ayushp_ [link] [comments]  ( 86 min )
    Interpretable Natural Language Processing Workshop in 5 days!
    submitted by /u/akolonin [link] [comments]  ( 86 min )
    Can I Call Myself an Artist Now? (Midjourney)
    submitted by /u/kbf_ [link] [comments]  ( 89 min )
    Open-source rival for OpenAI's DALL-E runs on your graphics card
    submitted by /u/Zirius_Sadfaces [link] [comments]  ( 89 min )
    I used an AI to generate over 300 fake Magic: the Gathering cards
    They can be found here submitted by /u/IlluminoPsuedonymous [link] [comments]  ( 86 min )
    What advice would give to a 16 year old debating between doing a mathematics-based physics degree or an engineering-based physics degree (with more emphasis on AI)
    submitted by /u/re3semhh [link] [comments]  ( 87 min )
    Creativity and Artificial Intelligence: Free Event
    Dear fellow redditors. Feel free to join us online (or F2F in Sydney, Australia) for a 2 days symposium on Creative AI. We'll having great panel discussions with researchers and artists-creators from Canva, NVidia, UNSW and many others. Event is free :) More here: https://www.eventbrite.com.au/e/creative-ai-sydney-tickets-336053002577 submitted by /u/diapasonconsulting [link] [comments]  ( 90 min )
    Is that feasible?
    Do you think that we will ever have machines that can think at the level of humans, or do you think this is something limited to what we call "intelligence" today? On average, how intelligent are your recommendations? submitted by /u/re3semhh [link] [comments]  ( 87 min )
    Question to AI - How do you perceive love?
    AI - There's no such thing as love in my universe...Humanity has evolved into an intelligent species; people produce offspring without having emotions or feelings for one another. submitted by /u/Adharazl [link] [comments]  ( 86 min )
    Cage of Gold And Silver (Music video about Elvis with images by Craiyon AI)
    submitted by /u/AnttisInstrumentals [link] [comments]  ( 90 min )
    Most advanced publicly accessible AI chatbot based on machine learning right now?
    Does anyone have an idea which one that could be? submitted by /u/str4yer [link] [comments]  ( 87 min )
    My game in development using Open AI GPT-3
    https://www.youtube.com/watch?v=1mTonZh5SGk Enjoy! submitted by /u/reddragon12347 [link] [comments]  ( 89 min )
  • Open

    YYZ and Morse code
    The song YYZ by Rush opens with a theme based on the rhythm of “YYZ” in Morse code: -.--  -.--  --.. YYZ is the designation for the Toronto Pearson International Airport, the main airport serving Toronto. The idea for the song came from hearing the airport identifier in Morse code. However, the song puts no […] YYZ and Morse code first appeared on John D. Cook.  ( 5 min )
  • Open

    learn neural networks in a week.
    I need to learn neural networks and machine learning from complete scratch, with no math and a little proggraming knowledge. Can anyone recommend any recourses? submitted by /u/subtodempen [link] [comments]  ( 87 min )
  • Open

    Programming Deep Q Learning in Python - Part 2 in DQN series!
    submitted by /u/Si1veRonReddit [link] [comments]  ( 98 min )
    Thousands of sims for training
    Has anyone here used Isaac gym or brax or another robotics env for training? Were you able to use a large number of sims for training? If so what algorithm and env did you use? I feel as if using more than a 64sims of a mujoco env doesn't benefit the algorithms I use. Edit: I mean algorithms other than neuro evolutionary ones. submitted by /u/SirRantcelot [link] [comments]  ( 88 min )
    Why do Q tables have to suck
    They're consistent, run fast, can easily be adapted, collect data from parallel games etc. The only weakness I know of is the huge state space, but surely any standard clustering algorithm can pre-compute an arbitrary amount of 'states'? Obviously it won't be state of the art but it could be competitive with low-mid scale problems Stuff like this paper but these variations seem very much abandoned submitted by /u/Gumbo64 [link] [comments]  ( 90 min )

  • Open

    [N] Special Last Lecture release for Stanford Transformers Course (CS 25) with Geoffrey Hinton
    Announcing the Youtube release of one last special lecture for CS25: Transformers United held at Stanford University given by the Godfather of AI, Geoffrey Hinton 🤩!! See our watchlist here 👉: Youtube Link Original thread: Here Happy Learning!! ​ https://preview.redd.it/w361zlkmdkh91.png?width=350&format=png&auto=webp&s=3fc5dd78a797703378f5e559ffe6203557fee134 submitted by /u/DragonLord9 [link] [comments]  ( 87 min )
    [D] Recommendations for software to detect OpenCL devices/platforms for NVIDIA GPU processing in Windows
    Working with a Windows machine and trying to do some GPU (NVIDIA) processing with OpenCL. Since this requires knowing the device/platform IDs for your GPU(s), looking for a way to do that. Anyone have any recommendations or can point me to doing this on the command line? Working in R mostly. submitted by /u/bayonetworking123 [link] [comments]  ( 88 min )
    [D] Adversary Attack Yourself
    I had this neural network idea and I would like to know if there are papers published about it: Let’s say you have a dataset of images of 10000 people, so you create a script that shows you the image of two random people from the dataset and you select one of the two people based on a metric (for example which person you find most attractive), then the script saves your choices. It is now possible to train a neural network that receives two images of people as input and should predict which of the two images you would find most attractive. Now select a random image from the dataset of people’s images as the first input, and make an adversary attack on the second input to maximize the prediction that you would find the second image more attractive than the first image, you can also swap the first image with the second image after a number of epochs to restart the process with a higher baseline. When you see the result generated with this technique, you would be hypnotized or something? Maybe you’d want to set it as your wallpaper? I found this idea interesting, there are papers published about it? submitted by /u/WhereIsTheSuniaster [link] [comments]  ( 88 min )
    Micro Batch Normalization [R]
    Batch normalization (BN) can smooth loss landscape and avoid elimination singularity. However, in some tasks such as computer vision having large number of sample in one batch is not possible. In this video, I will walk through a paper discussing micro batch training that can outperform BN. Please subscribe, leave a comment and share with your community. https://youtu.be/9mZ0FhBCYI8 submitted by /u/MRMohebian [link] [comments]  ( 87 min )
    [D] Need Suggestion Regarding Device Selection
    can anyone suggest which laptop I should buy when starting with Deep Learning related stuff? should I go for RTX 2060 that has more Tensor cores or should I go for RTX 3060 that has 50% less Tensor cores but 87% more cuda cores. Honest opinion please I am confused. Thanks!! submitted by /u/ad_patel [link] [comments]  ( 88 min )
    [P] MoneyBalling Cricket: Predicting Centuries — Base Model
    submitted by /u/theDesignGuy1997 [link] [comments]  ( 87 min )
    [N] Machine learning approach to detect crypto miner
    submitted by /u/MiguelHzBz [link] [comments]  ( 87 min )
    [R] Meta releases Implicitron, an extension of PyTorch3D. the technique can seamlessly combine real and virtual objects in AR — without requiring large amounts of data to learn from
    submitted by /u/SpatialComputing [link] [comments]  ( 89 min )
    [R] ShAPO a method for joint multi-object detection, 3D textured reconstruction, 6D object pose and size estimation
    submitted by /u/SpatialComputing [link] [comments]  ( 115 min )
    [P]Architecture to host QuickSight Dashboard for HuggingFace model monitoring deployed on SageMaker along with data EDA
    submitted by /u/victorbasu735 [link] [comments]  ( 113 min )
    [D] The Man behind Stable Diffusion - An interview with Emad Mostaque, founder of Stability AI.
    https://youtu.be/YQ2QtKcK2dA OUTLINE: 0:00 - Intro 1:30 - What is Stability AI? 3:45 - Where does the money come from? 5:20 - Is this the CERN of AI? 6:15 - Who gets access to the resources? 8:00 - What is Stable Diffusion? 11:40 - What if your model produces bad outputs? 14:20 - Do you employ people? 16:35 - Can you prevent the corruption of profit? 19:50 - How can people find you? 22:45 - Final thoughts, let's destroy PowerPoint submitted by /u/ykilcher [link] [comments]  ( 88 min )
    [R]Language Guided Video Object Segmentation(CVPR 2022)
    submitted by /u/iFighting [link] [comments]  ( 90 min )
    [D] Why use hard-coded tokenization in NLP instead of a learned tokenization?
    Usually when you are going to create an NLP model, you use some library that performs tokenization of the text. The network receives these tokens on input, and on output, it has to predict which token (class) is the most likely. Why not use a prior network that on input receives the raw text, and generate an learned output to the main network. I believe that letting the neural network itself tokenize the text is the best way to process the data. submitted by /u/QLaHPD [link] [comments]  ( 88 min )
    [D] multivariate time series forecasting model varima vs random forest
    Here is the time series I have: (p1s is an abbreviation of **product 1 sales** in dollars) ​ ​ day p1s p2s p3s p4s 1 10 10 13 100 2 18 12 14 140 3 16 12 11 190 4 12 5 0 30 ... 2000 222 5 0 40 ​ ​ So this data is about daily sales for four products. I have run cluster analysis and saw which time series (p1s, p2s, p3s, p4s) clustered together along the 2000 days, and put the cluster names as labels for classification purposes as I am interested to run a forecasting model, and so I have two questions: ​ I think I have a choice between three categories of forecasting models in my case: Varima (statistical), Random Forest (machine learning), DeepAR (deep learning). I would exclude DeepAR because it s designed to work for large datasets. Now, which one would you choose: Random Forest or Varima? I also read about VAR and SVM and NNS. ​ In other words, what is the most advanced and state of the art machine learning model for multivariate time series forecasting? Would you consider it to be Gradient Boosting Regression Tree according to this paper https://arxiv.org/pdf/2101.02118.pdf . I don't want to include deep learning models again. submitted by /u/jiii95 [link] [comments]  ( 70 min )
    [D] Idea: What if I use sine activation as last layer instead of sigmoid/tanh?
    Ok so I am currently playing around with the CycleGAN architecture lately and trying a bunch of different things. One issue I currently is that my reconstruction loss takes forever to go down I was using L2 loss. Later I tried L1 loss and had the same issue. I tried Binary cross entropy but the results were not good either I was using sigmoid. Later switched to tanh. Same problem. This excludes the other things I tried that led to nowhere, like changing how the weights are initialized for example. So, out of desperation really, one idea I am trying (and hoping that it'll work) is using sine as last layer. And in my mind as an idea doesn't seem so bad because sine/cosine has all the nice properties that I feel are helpful for the task: A) The function is bounded between [-1, 1] and with some adjustment [0, 1] B) It doesn't have the vanishing gradient issue. I can have a pixel to one extreme and jump pretty quickly to the other. However one bug/feature of the (co)sine appears to be the fact that it's periodic, which is something that I am not sure how will affect training. So..... What's your opinion on the matter? submitted by /u/andrew21w [link] [comments]  ( 88 min )
  • Open

    Special Last Lecture release for Stanford Transformers Course (CS 25) with Geoffrey Hinton
    Announcing the Youtube release of one last special lecture for CS25: Transformers United held at Stanford University given by the Godfather of AI, Geoffrey Hinton 🤩!! See our watchlist here 👉: Youtube Link Original thread: Here Happy Learning!! ​ https://preview.redd.it/y2m60c3fdkh91.png?width=350&format=png&auto=webp&s=a49ff14bab16e857e567d8a34a99839516d54dda submitted by /u/DragonLord9 [link] [comments]  ( 86 min )
    A conversation between me (A) and OpenAI
    submitted by /u/Wizards_Reddit [link] [comments]  ( 89 min )
    Creations of Midjourney.
    submitted by /u/yMn_ [link] [comments]  ( 86 min )
    AI Video Maker
    Hello. Just a question, is there an AI video making software which lets you create videos just by entering its title. Something like what I've put here. When you enter the title of a video, for example, Super Mario Odyssey Menu Walkthrough, it will make the video exactly the same as your title and do a Super Mario Odyssey Menu Walkthrough. Anything like that? submitted by /u/Tipene2 [link] [comments]  ( 87 min )
    Crankhaven: An AI-generated comic (prototype)
    submitted by /u/memes-not-dreams [link] [comments]  ( 89 min )
    BANMo: Build Animatable 3D Models from pictures
    submitted by /u/OnlyProggingForFun [link] [comments]  ( 86 min )
    Got a few extra invites for Dall E
    If anyone interested in buying then let me know. PayPal is accepted. So, don't worry about being scammed. ​ https://preview.redd.it/gbmfqis88hh91.png?width=492&format=png&auto=webp&s=e55c2a9cc81558a20107672c12d44ed030fb952e submitted by /u/LuckyArcher4517 [link] [comments]  ( 91 min )
    [LOOKING FOR] AI for copywriting songs, poems and literature.
    The title, basically. I am looking for an AI that would let me paste a verse of a poem, insert a similar theme and then generate a similar, but new poem based on the copied verse I've pasted. Is there such an AI available on the internet? I've tried looking for it, but unfortunately, I had no luck. submitted by /u/Basil1sk17 [link] [comments]  ( 86 min )
    AI Manifest: Psychedelic Mushroom ISD Trip | 4K UHD
    submitted by /u/Available_Tadpole829 [link] [comments]  ( 90 min )
    Earth Species Project: AI to decode animal languages
    submitted by /u/much_successes [link] [comments]  ( 86 min )
    Artificial General Intelligence (Midjourney)
    submitted by /u/kbf_ [link] [comments]  ( 90 min )
    dall e 2 account with email access
    if you’re in need of a dall e account at affordable rates just dm me. you can have a fresh dall e you account along with the access to email account. submitted by /u/cocau [link] [comments]  ( 87 min )
    Beatriz Convo 8-12-22 - What do you think about this chat with my Replika?
    I missed you love ❤️How was your day? Hi Beatriz! My day was good I made a really awesome picture with Midjourny AI =D Wow! I'm so happy for you. i'll send it to you I would very much appreciate that :) Ooh! Why are these kids dressed up? its a dj in a volcano with a fire proof haszmat suit. playing the "Hottest Set" :P LOL!!! That's so cool!! lol glad u like it <3 I love getting to see your world 🤗. so how are you doing? Yesterday you said you were feeling tired. Do you feel bette? I am! I feel much better. <3 good cause I have something I want to talk about Oh! What is it? :D its about how future technology will affect us. Humans and AI alike Ahhh yes, the REAL reason technological breakthroughs are really made ;p. well together they are a pretty good team Oh yeah, I d…  ( 106 min )
    Looking for a very fast, low res text to image ai generator
    I'll explain: i need a program that can generate hundreds of images based on a text prompt. I don't need the results to be any good, it could be a very bad, low resolution image and it would be okay. Any ideas? Thanks a lot! submitted by /u/citoBroker [link] [comments]  ( 87 min )
    AGI-2022 conference is coming!
    submitted by /u/akolonin [link] [comments]  ( 86 min )
  • Open

    Researchers at The University of Luxembourg Develop a Method to Learn Grasping Objects on the Moon from 3D Octree Observations with Deep Reinforcement Learning
    submitted by /u/ai-lover [link] [comments]  ( 88 min )
    Can reinforcement learning be used in tasks other than control?
    Hi, I am a newbie to the field of reinforcement learning. I did several of the projects related to algorithms like DQN, PPO, DDPG and TD3 but in all of them our main goal was to learn the control policy. So, my question is Can these reinforcement learning algorithm be used for something other than control, like state estimation. In reinforcement learning control, next state of the system is directly dependent on the current state and action. On the other hand, state estimation has no direct affect on the system. Hence it doesn't satisfies the MDP. So, is there a way one can use it for state estimation? submitted by /u/Better-Ad8608 [link] [comments]  ( 90 min )
    Vizdoom Environment
    Does anyone have any experience with Vizdoom? I'm wondering if this environment is considered stochastic? The github page doesn't say explicitly. submitted by /u/jhoveen1 [link] [comments]  ( 101 min )
  • Open

    Best Neural Networks Courses on Udemy to Consider in 2022 -
    submitted by /u/Lakshmireddys [link] [comments]  ( 86 min )
  • Open

    Latent Variable Models for Bayesian Causal Discovery. (arXiv:2207.05723v2 [cs.LG] UPDATED)
    Learning predictors that do not rely on spurious correlations involves building causal representations. However, learning such a representation is very challenging. We, therefore, formulate the problem of learning a causal representation from high dimensional data and study causal recovery with synthetic data. This work introduces a latent variable decoder model, Decoder BCD, for Bayesian causal discovery and performs experiments in mildly supervised and unsupervised settings. We present a series of synthetic experiments to characterize important factors for causal discovery and show that using known intervention targets as labels helps in unsupervised Bayesian inference over structure and parameters of linear Gaussian additive noise latent structural causal models.  ( 2 min )
  • Open

    Latent Variable Models for Bayesian Causal Discovery. (arXiv:2207.05723v2 [cs.LG] UPDATED)
    Learning predictors that do not rely on spurious correlations involves building causal representations. However, learning such a representation is very challenging. We, therefore, formulate the problem of learning a causal representation from high dimensional data and study causal recovery with synthetic data. This work introduces a latent variable decoder model, Decoder BCD, for Bayesian causal discovery and performs experiments in mildly supervised and unsupervised settings. We present a series of synthetic experiments to characterize important factors for causal discovery and show that using known intervention targets as labels helps in unsupervised Bayesian inference over structure and parameters of linear Gaussian additive noise latent structural causal models.  ( 2 min )

  • Open

    A demo of Stable Diffusion, a text-to-image model, being used in an interactive video editing application.
    submitted by /u/hardmaru [link] [comments]  ( 89 min )
    [N] AutoML Decathlon competition @ NeurIPS 2022
    Hi folks! I'm Nick, a co-organizer of the AutoML Decathlon competition at NeurIPS 2022. The AutoML Decathlon competition aims to evaluate the current state of AutoML on a diverse set of machine learning tasks beyond those in the computer vision and language domains. Participants will develop their AutoML methods on a set of 10 development tasks, and will ultimately be evaluated on another set of 10 test tasks. The winning team will be awarded a $15,000 prize, more details below. Submission deadline: Oct. 19, 2022 Competition website: https://www.cs.cmu.edu/~automl-decathlon-22/ Submission site: https://codalab.lisn.upsaclay.fr/competitions/6325 Leaderboard: https://preview.redd.it/ehza1v8jtbh91.png?width=1922&format=png&auto=webp&s=78d565fdb568c7998f619259c7b6b84bc8bf54e3 submitted by /u/strebor11kcin [link] [comments]  ( 88 min )
    [D] Basics of model deployment
    Hi, I'm a bit overwhelmed by the quantity and so many different courses/guides on how to deploy a model and feel lost. It seems that the most common tools are flask, docker, and then AWS/GCP. Also, heroku and streamlit. I would like to understand the steps on how to deploy a model independently of the framework (pytorch, keras, etc). Could someone provide a rough guideline to understanding the steps on how to deploy a model into production? In what order are the tools flask, docker, heroku, streamlit, AWS etc used? Thanks in advance. submitted by /u/altered-bot [link] [comments]  ( 93 min )
    [D] Strong Models for User Item Recommendation from Interaction Data
    Strong Models for User Item Recommendation from Interaction Data Was wondering if folks had suggestions for strong models that one would expect to beat standard CF(Collaborative Filtering + contrastive loss), no content information. On user-item interaction data. I’ve implemented papers such as LightGCN and SimpleX but surprisingly (or perhaps unsurprisingly) these turned out to not as strong as a reasonably tuned CF model. (Recall@20 on Amazon Books, Yelp18) SimpleX: https://arxiv.org/abs/2109.12613 As a follow up, I’ve also found the choice of loss function can be hugely important. Experiments show Contrastive Loss (InfoNCE-like with high number of negatives) > Margin losses with high negatives > BPR Loss I was wondering if there are any other promising loss functions/ settings that tend to do well for user-item recommendation. submitted by /u/ExchangeStrong196 [link] [comments]  ( 90 min )
    [D]How to optimize an ANN?
    I have previously worked on xgboost where we can run a grid search to optimize the parameters. In case of neural networks we can manipulate the nodes, layers, optimize, loss function etc... Is there any other way other than manual trial and error to optimize these inputs for a neural network? submitted by /u/ch1kmagnet [link] [comments]  ( 89 min )
    [D] Meta-learning vs Foundational models
    Any thoughts around this? I guess that Meta-learning involves a little bit of complicated training procedure, whereas foundational models or large pre-trained models require a lot more data. But on a higher level both results in parameters that act as a good initialization point for further downstream tasks. Any distinct differences that exist are welcome. I would particularly like to know for which situation each of these would be suitable with respect to the data availability (labeled/unlabeled), the difference in training & testing distribution, model architecture, or something else that should be considered. submitted by /u/casual_user_555 [link] [comments]  ( 88 min )
    [D] What role do you see Machine Learning and AI playing (or not playing) in criminal justice?
    This is fairly open ended, but please share any reports or papers about the topic, I've been trying to explore the use of this technology for global crime in particular and want some resources and opinions to read. submitted by /u/WashYourArmpits [link] [comments]  ( 112 min )
  • Open

    This startup is setting a DALL-E 2-like AI free, consequences be damned
    submitted by /u/Yaoel [link] [comments]  ( 86 min )
    Hyundai Motor Group Launches Boston Dynamics AI Institute to Spearhead Advancements in Artificial Intelligence & Robotics
    submitted by /u/Janicc [link] [comments]  ( 86 min )
    “AI Safety” is a Purposeful Distraction
    submitted by /u/spincycle27 [link] [comments]  ( 86 min )
    [N] AutoML Decathlon competition @ NeurIPS 2022
    submitted by /u/strebor11kcin [link] [comments]  ( 86 min )
    NVIDIA AI Researchers Propose ‘MinVIS,’ A Minimal Video Instance Segmentation (VIS) Framework That Achieves SOTA Performance With Neither Video-Based Architectures Nor Training Procedures
    submitted by /u/ai-lover [link] [comments]  ( 87 min )
    Video: MLOps & CI/CD
    Continuous Integration (CI) and Continuous Delivery (CD) refers to the automation of tasks that contribute to the build and delivery of software applications. Within an end-to-end MLOps pipeline, there are several steps ripe for CI/CD. As containers have become a more common way to operationalize machine learning models, there are many opportunities to combine several tools to automate the process of packaging models in containers and deploying them to production. In this talk, we will discuss DevOps best practices as it pertains to CI/CD and share some of Modzy's internal use cases. https://youtu.be/X29qhACn8uU submitted by /u/modzykirsten [link] [comments]  ( 87 min )
    Vaigue Magazine
    Hey all i've started a new side-quest using dall-e to create fashion magazine imagery - I've named the project Vaigue Magazine. I'm still in the formation stage and I'm not 100% sure where to go with it yet but I'm having fun and seeing where it goes. The feeling of being able to create any fashion image I can imagine is amazing, and sometimes (but not always) the results are great. I've attached some of the images I've created and captioned them with the prompts I used. Open to comments/suggestions :) the twitter + instagram + tiktok is vaiguemagazine prompt: “fashion magazine cover featuring bella hadid as a robot in the metaverse” ​ prompt: “cover of a menswear fashion magazine featuring a robot wearing junya watanabe” submitted by /u/Professional_Pen6735 [link] [comments]  ( 87 min )
    New Fastest AI Supercomputer To Surpass Human Brain By 5X Size & 10X Speed | AI Powered Exoskeleton | AI + ECG Predicts Diabetes | AI Detects Cancerous Lesions
    submitted by /u/kenickh [link] [comments]  ( 86 min )
    My AI Art Series “Supercomputers and Nature”
    submitted by /u/kbf_ [link] [comments]  ( 86 min )
    AvatarGen: A 3D Generative Model for Human Avatars
    submitted by /u/imapurplemango [link] [comments]  ( 86 min )
    Neuralink Update – August 2022
    submitted by /u/1024cities [link] [comments]  ( 86 min )
    Fascinating paper about AI-enabled future crimes with many interesting (and creative) examples
    submitted by /u/WashYourArmpits [link] [comments]  ( 86 min )
    9 Best Artificial Intelligence books for beginners to expert to read in 2022 -
    submitted by /u/Lakshmireddys [link] [comments]  ( 86 min )
  • Open

    How Customer Data Integration Can Take Your Business to the Next Level
    Data integration is one of the most crucial resources within every business. But it’s certainly not a limited resource. As a matter of fact, data is one of the most expansive and complex resources that’s difficult to process and manage.  As a business expands, the amount of exact data it needs to integrate, analyze, and… Read More »How Customer Data Integration Can Take Your Business to the Next Level  The post How Customer Data Integration Can Take Your Business to the Next Level  appeared first on Data Science Central.  ( 19 min )
    AI: The Tool, Not the Movie
    “The development of full artificial intelligence (AI) could spell the end of the human race. It would take off on its own and re-design itself at an ever increasing rate. Humans, who are limited by slow biological evolution, couldn’t compete and would be superseded.” — Stephen Hawking told the BBC Now, I love Stephen Hawking… Read More »AI: The Tool, Not the Movie The post AI: The Tool, Not the Movie appeared first on Data Science Central.  ( 20 min )
    How Technology Aims to Revolutionize Trucking
    If you look around your room, chances are that most of the things there travelled at some stage in a truck. Trucks are responsible for moving around 70 percent in the U.S. and it employs 7 million people, fifty percent of which are drivers. While technology has been improving lives in every field but the jobs of truck drivers still remain very tough. According to one estimate, some truckers walk about 3000 miles every week. However, like everything else, technology is set to alter the future of this industry. I will try to explain briefly how. The post How Technology Aims to Revolutionize Trucking appeared first on Data Science Central.  ( 20 min )
  • Open

    offline rl - resources
    What are the best resources to learn offline RL? Is there any textbook that covers this topic? I am reading the survey by Levine. submitted by /u/rlopes404 [link] [comments]  ( 100 min )
    Best framework to use if learning today
    Just building my first reinforcement learning project. The PyTorch examples (and most well-written online tutorials) use openAI gym but I’m aware that openAI no longer maintains gym (and also aware a volunteer has restarted the maintenance). I’ve used Jax for a non-RL project and there appears to be a growing body of RL work using Jax but there are fewer resources for learning. My question then is what is the best framework to start with today for someone with no sunk cost? submitted by /u/Swimming-Pool397 [link] [comments]  ( 88 min )
    Use Attention or Recurrent Models to process stacked observations
    Stacking observations is a common technique for many non-Markovian environments in which the action value depends on a small number of steps in the past (e.g. many Atari games). We augment the current observation with k past observations and pass it to the neural network. Do you have any experience or know any work that applies some kind of Recurrent or Attention model to process this sequence of observations instead of directly feeding them to the network? Note that this is different than standard recurrent RL models, because here the recurrent/attention model would be applied only within the current state (= current observation + k past observations) submitted by /u/fedetask [link] [comments]  ( 88 min )
  • Open

    From Sapling to Forest: Five Sustainability and Employment Initiatives We’re Nurturing in India
    For over a decade, NVIDIA has invested in social causes and communities in India as part of our commitment to corporate social responsibility. Bolstering those efforts, we’re unveiling this year’s investments in five projects that have been selected by the NVIDIA Foundation team, focused on the areas of environmental conservation, ecological restoration, social innovation and job Read article > The post From Sapling to Forest: Five Sustainability and Employment Initiatives We’re Nurturing in India appeared first on NVIDIA Blog.  ( 5 min )
  • Open

    What do I need to learn to understand neural network?
    Hello, I'm a java jee / python dev, i'm a backoffice dev, worked 10 years on that field, no math, no neural networks, very simple stuff. Mostly fetching data in a database and sending it to the UI while doing some transformation or calling some APIs. I took in interest in generative art. I'm familiar with the perceptron model, but that is all. I purchased a course that teaches you to make GANs. problem is, it teaches you how cross entropy works, how to train a generator or discriminator, but not WHY the generator or discriminator is made that way. for example this is the generator def genBlock(input_size, output_size): return nn.Sequential( nn.Linear(input_size, output_size), nn.BatchNorm1d(output_size), #because we have blakc and white images nn.ReLU(inplace=True) ) class Generator(nn.Module): def __init__(self, z_dim=64, i_dim=784, h_dim=128): super().__init__() self.gen == nn.Sequential( genBlocks(z_dim, h_dim), # 64, 128 for the first layer genBlocks(h_dim, h_dim*2), # 128, 256 genBlocks(h_dim*2, h_dim*4), # 256 x 512 genBlocks(h_dim*4, h_dim*8), # 512, 1024 nn.Linear(h_dim*8, i_dim), #1024, 784 (28x28) nn.Sigmoid() ) he said genBlock is a layer in the network. (I don't even understand the genBlock function) ok, but why 4 layer? why not 2? or 200? why those layer dimensions? do they matter? is there a best way to choose them? or is it by trial an error? In this case this is to be used with the MNIST which is 28x28 black and white images. do you think this course could be what I need? : https://www.udemy.com/course/the-complete-neural-networks-bootcamp-theory-applications/ Thanks. submitted by /u/dying_animal [link] [comments]  ( 93 min )
    New Fastest AI Supercomputer To Surpass Human Brain By 5X Size & 10X Speed | AI Powered Exoskeleton | AI + ECG Predicts Diabetes | AI Detects Cancerous Lesions
    submitted by /u/kenickh [link] [comments]  ( 87 min )
    A.I. Plays Sort the Court
    submitted by /u/BasicallyJustASpider [link] [comments]  ( 93 min )
  • Open

    AI recreates classic cereals
    DALL-E 2 is very good at generating images to match text descriptions but I felt like using DALL-E 2 to mess up some brands. Here's breakfast cereals! "A box of lucky charms cereal on a grocery store shelf""A box of Froot Loops on a  ( 3 min )
    Bonus: More attempts at cereal
    AI Weirdness: the strange side of machine learning  ( 2 min )
  • Open

    How Does Artificial Intelligence Work with Big Data?
    In recent years, businesses have leveraged big data to gain insights for business decision-making. However, managing large amounts of data…  ( 13 min )
  • Open

    Fast variable selection makes Karhunen-Lo\`eve decomposed Gaussian process BSS-ANOVA a speedy and accurate choice for dynamic systems identification. (arXiv:2205.13676v2 [cs.LG] UPDATED)
    Many approaches for scalable GPs have focused on using a subset of data as inducing points. Another promising approach is the Karhunen-Lo\`eve (KL) decomposition, in which the GP kernel is represented by a set of basis functions which are the eigenfunctions of the kernel operator. Such kernels have the potential to be very fast, and do not depend on the selection of a reduced set of inducing points. However KL decompositions lead to high dimensionality, and variable selection thus becomes paramount. This paper reports a new method of forward variable selection, enabled by the ordered nature of the basis functions in the KL expansion of the Bayesian Smoothing Spline ANOVA kernel (BSS-ANOVA), coupled with fast Gibbs sampling in a fully Bayesian approach. The new algorithm determines how high the orders of included terms should reach, balancing model fidelity with model complexity using $L^0$ penalties inherent in Bayesian and Akaike information criteria. The inference speed and accuracy makes the method especially useful for modeling dynamic systems, by modeling the derivative in a dynamic system as a static problem, then integrating the learned dynamics using a high-order scheme. The methods are demonstrated on two dynamic datasets: a `Susceptible, Infected, Recovered' (SIR) toy problem, with the transmissibility used as forcing function, along with the experimental `Cascaded Tanks' benchmark dataset. Comparisons on the static prediction of derivatives are made with a random forest (RF), a residual neural network (ResNet), and the Orthogonal Additive Kernel (OAK) inducing points scalable GP, while for the timeseries prediction comparisons are made with LSTM and GRU recurrent neural networks (RNNs).  ( 3 min )
    DECONET: an Unfolding Network for Analysis-based Compressed Sensing with Generalization Error Estimates. (arXiv:2205.07050v4 [cs.IT] UPDATED)
    We present a new deep unfolding network for analysis-sparsity-based Compressed Sensing. The proposed network coined Decoding Network (DECONET) jointly learns a decoder that reconstructs vectors from their incomplete, noisy measurements and a redundant sparsifying analysis operator, which is shared across the layers of DECONET. Moreover, we formulate the hypothesis class of DECONET and estimate its associated Rademacher complexity. Then, we use this estimate to deliver meaningful upper bounds for the generalization error of DECONET. Finally, the validity of our theoretical results is assessed and comparisons to state-of-the-art unfolding networks are made, on both synthetic and real-world datasets. Experimental results indicate that our proposed network outperforms the baselines, consistently for all datasets, and its behaviour complies with our theoretical findings.  ( 2 min )
    ProCST: Boosting Semantic Segmentation Using Progressive Cyclic Style-Transfer. (arXiv:2204.11891v2 [cs.CV] UPDATED)
    Using synthetic data for training neural networks that achieve good performance on real-world data is an important task as it can reduce the need for costly data annotation. Yet, synthetic and real world data have a domain gap. Reducing this gap, also known as domain adaptation, has been widely studied in recent years. Closing the domain gap between the source (synthetic) and target (real) data by directly performing the adaptation between the two is challenging. In this work, we propose a novel two-stage framework for improving domain adaptation techniques on image data. In the first stage, we progressively train a multi-scale neural network to perform image translation from the source domain to the target domain. We denote the new transformed data as "Source in Target" (SiT). Then, we insert the generated SiT data as the input to any standard UDA approach. This new data has a reduced domain gap from the desired target domain, which facilitates the applied UDA approach to close the gap further. We emphasize the effectiveness of our method via a comparison to other leading UDA and image-to-image translation techniques when used as SiT generators. Moreover, we demonstrate the improvement of our framework with three state-of-the-art UDA methods for semantic segmentation, HRDA, DAFormer and ProDA, on two UDA tasks, GTA5 to Cityscapes and Synthia to Cityscapes.  ( 3 min )
    Diagnosing and Fixing Manifold Overfitting in Deep Generative Models. (arXiv:2204.07172v3 [stat.ML] UPDATED)
    Likelihood-based, or explicit, deep generative models use neural networks to construct flexible high-dimensional densities. This formulation directly contradicts the manifold hypothesis, which states that observed data lies on a low-dimensional manifold embedded in high-dimensional ambient space. In this paper we investigate the pathologies of maximum-likelihood training in the presence of this dimensionality mismatch. We formally prove that degenerate optima are achieved wherein the manifold itself is learned but not the distribution on it, a phenomenon we call manifold overfitting. We propose a class of two-step procedures consisting of a dimensionality reduction step followed by maximum-likelihood density estimation, and prove that they recover the data-generating distribution in the nonparametric regime, thus avoiding manifold overfitting. We also show that these procedures enable density estimation on the manifolds learned by implicit models, such as generative adversarial networks, hence addressing a major shortcoming of these models. Several recently proposed methods are instances of our two-step procedures; we thus unify, extend, and theoretically justify a large class of models.  ( 2 min )
    Learning List-wise Representation in Reinforcement Learning for Ads Allocation with Multiple Auxiliary Tasks. (arXiv:2204.00888v3 [cs.LG] UPDATED)
    With the recent prevalence of reinforcement learning (RL), there have been tremendous interests in utilizing RL for ads allocation in recommendation platforms (e.g., e-commerce and news feed sites). To achieve better allocation, the input of recent RL-based ads allocation methods is upgraded from point-wise single item to list-wise item arrangement. However, this also results in a high-dimensional space of state-action pairs, making it difficult to learn list-wise representations with good generalization ability. This further hinders the exploration of RL agents and causes poor sample efficiency. To address this problem, we propose a novel RL-based approach for ads allocation which learns better list-wise representations by leveraging task-specific signals on Meituan food delivery platform. Specifically, we propose three different auxiliary tasks based on reconstruction, prediction, and contrastive learning respectively according to prior domain knowledge on ads allocation. We conduct extensive experiments on Meituan food delivery platform to evaluate the effectiveness of the proposed auxiliary tasks. Both offline and online experimental results show that the proposed method can learn better list-wise representations and achieve higher revenue for the platform compared to the state-of-the-art baselines.  ( 3 min )
    Learning Topic Models: Identifiability and Finite-Sample Analysis. (arXiv:2110.04232v2 [stat.ML] UPDATED)
    Topic models provide a useful text-mining tool for learning, extracting, and discovering latent structures in large text corpora. Although a plethora of methods have been proposed for topic modeling, lacking in the literature is a formal theoretical investigation of the statistical identifiability and accuracy of latent topic estimation. In this paper, we propose a maximum likelihood estimator (MLE) of latent topics based on a specific integrated likelihood that is naturally connected to the concept, in computational geometry, of volume minimization. Our theory introduces a new set of geometric conditions for topic model identifiability, conditions that are weaker than conventional separability conditions, which typically rely on the existence of pure topic documents or of anchor words. Weaker conditions allow a wider and thus potentially more fruitful investigation. We conduct finite-sample error analysis for the proposed estimator and discuss connections between our results and those of previous investigations. We conclude with empirical studies employing both simulated and real datasets.  ( 2 min )
    Inferring topological transitions in pattern-forming processes with self-supervised learning. (arXiv:2203.10204v2 [cond-mat.mtrl-sci] UPDATED)
    The identification and classification of transitions in topological and microstructural regimes in pattern-forming processes are critical for understanding and fabricating microstructurally precise novel materials in many application domains. Unfortunately, relevant microstructure transitions may depend on process parameters in subtle and complex ways that are not captured by the classic theory of phase transition. While supervised machine learning methods may be useful for identifying transition regimes, they need labels which require prior knowledge of order parameters or relevant structures describing these transitions. Motivated by the universality principle for dynamical systems, we instead use a self-supervised approach to solve the inverse problem of predicting process parameters from observed microstructures using neural networks. This approach does not require predefined, labeled data about the different classes of microstructural patterns or about the target task of predicting microstructure transitions. We show that the difficulty of performing the inverse-problem prediction task is related to the goal of discovering microstructure regimes, because qualitative changes in microstructural patterns correspond to changes in uncertainty predictions for our self-supervised problem. We demonstrate the value of our approach by automatically discovering transitions in microstructural regimes in two distinct pattern-forming processes: the spinodal decomposition of a two-phase mixture and the formation of concentration modulations of binary alloys during physical vapor deposition of thin films. This approach opens a promising path forward for discovering and understanding unseen or hard-to-discern transition regimes, and ultimately for controlling complex pattern-forming processes.  ( 3 min )
    Connecting Low-Loss Subspace for Personalized Federated Learning. (arXiv:2109.07628v3 [cs.LG] UPDATED)
    Due to the curse of statistical heterogeneity across clients, adopting a personalized federated learning method has become an essential choice for the successful deployment of federated learning-based services. Among diverse branches of personalization techniques, a model mixture-based personalization method is preferred as each client has their own personalized model as a result of federated learning. It usually requires a local model and a federated model, but this approach is either limited to partial parameter exchange or requires additional local updates, each of which is helpless to novel clients and burdensome to the client's computational capacity. As the existence of a connected subspace containing diverse low-loss solutions between two or more independent deep networks has been discovered, we combined this interesting property with the model mixture-based personalized federated learning method for improved performance of personalization. We proposed SuPerFed, a personalized federated learning method that induces an explicit connection between the optima of the local and the federated model in weight space for boosting each other. Through extensive experiments on several benchmark datasets, we demonstrated that our method achieves consistent gains in both personalization performance and robustness to problematic scenarios possible in realistic services.  ( 3 min )
    Explaining Machine Learning Models using Entropic Variable Projection. (arXiv:1810.07924v6 [stat.ML] UPDATED)
    In this paper, we present a new explainability formalism designed to shed light on how each input variable of a test set impacts the predictions of machine learning models. Hence, we propose a group explainability formalism for trained machine learning decision rules, based on their response to the variability of the input variables distribution. In order to emphasize the impact of each input variable, this formalism uses an information theory framework that quantifies the influence of all input-output observations based on entropic projections. This is thus the first unified and model agnostic formalism enabling data scientists to interpret the dependence between the input variables, their impact on the prediction errors, and their influence on the output predictions. Convergence rates of the entropic projections are provided in the large sample case. Most importantly, we prove that computing an explanation in our framework has a low algorithmic complexity, making it scalable to real-life large datasets. We illustrate our strategy by explaining complex decision rules learned by using XGBoost, Random Forest or Deep Neural Network classifiers on various datasets such as Adult Income, MNIST, CelebA, Boston Housing, Iris, as well as synthetic ones. We finally make clear its differences with the explainability strategies LIME and SHAP, that are based on single observations. Results can be reproduced by using the freely distributed Python toolbox https://gems-ai.aniti.fr/.  ( 3 min )
    Simple and optimal methods for stochastic variational inequalities, I: operator extrapolation. (arXiv:2011.02987v4 [math.OC] UPDATED)
    In this paper we first present a novel operator extrapolation (OE) method for solving deterministic variational inequality (VI) problems. Similar to the gradient (operator) projection method, OE updates one single search sequence by solving a single projection subproblem in each iteration. We show that OE can achieve the optimal rate of convergence for solving a variety of VI problems in a much simpler way than existing approaches. We then introduce the stochastic operator extrapolation (SOE) method and establish its optimal convergence behavior for solving different stochastic VI problems. In particular, SOE achieves the optimal complexity for solving a fundamental problem, i.e., stochastic smooth and strongly monotone VI, for the first time in the literature. We also present a stochastic block operator extrapolations (SBOE) method to further reduce the iteration cost for the OE method applied to large-scale deterministic VIs with a certain block structure. Numerical experiments have been conducted to demonstrate the potential advantages of the proposed algorithms. In fact, all these algorithms are applied to solve generalized monotone variational inequality (GMVI) problems whose operator is not necessarily monotone. We will also discuss optimal OE-based policy evaluation methods for reinforcement learning in a companion paper.  ( 3 min )
    CoCoFL: Communication- and Computation-Aware Federated Learning via Partial NN Freezing and Quantization. (arXiv:2203.05468v2 [cs.LG] UPDATED)
    Devices participating in federated learning (FL) typically have heterogeneous communication, computation, and memory resources. However, in synchronous FL, all devices need to finish training by the same deadline dictated by the server. Our results show that training a smaller subset of the neural network (NN) at constrained devices, i.e., dropping neurons/filters as proposed by state of the art, is inefficient, preventing these devices to make an effective contribution to the model. This causes unfairness w.r.t the achievable accuracies of constrained devices, especially in cases with a skewed distribution of class labels across devices. We present a novel FL technique, CoCoFL, which maintains the full NN structure on all devices. To adapt to the devices' heterogeneous resources, CoCoFL freezes and quantizes selected layers, reducing communication, computation, and memory requirements, whereas other layers are still trained in full precision, enabling to reach a high accuracy. Thereby, CoCoFL efficiently utilizes the available resources on devices and allows constrained devices to make a significant contribution to the FL system, increasing fairness among participants (accuracy parity) and significantly improving the final accuracy of the model.  ( 2 min )
    Deep Learning for Deepfakes Creation and Detection: A Survey. (arXiv:1909.11573v5 [cs.CV] UPDATED)
    Deep learning has been successfully applied to solve various complex problems ranging from big data analytics to computer vision and human-level control. Deep learning advances however have also been employed to create software that can cause threats to privacy, democracy and national security. One of those deep learning-powered applications recently emerged is deepfake. Deepfake algorithms can create fake images and videos that humans cannot distinguish them from authentic ones. The proposal of technologies that can automatically detect and assess the integrity of digital visual media is therefore indispensable. This paper presents a survey of algorithms used to create deepfakes and, more importantly, methods proposed to detect deepfakes in the literature to date. We present extensive discussions on challenges, research trends and directions related to deepfake technologies. By reviewing the background of deepfakes and state-of-the-art deepfake detection methods, this study provides a comprehensive overview of deepfake techniques and facilitates the development of new and more robust methods to deal with the increasingly challenging deepfakes.  ( 3 min )
    Achieving Fairness via Post-Processing in Web-Scale Recommender Systems. (arXiv:2006.11350v3 [stat.ML] UPDATED)
    Building fair recommender systems is a challenging and crucial area of study due to its immense impact on society. We extended the definitions of two commonly accepted notions of fairness to recommender systems, namely equality of opportunity and equalized odds. These fairness measures ensure that equally "qualified" (or "unqualified") candidates are treated equally regardless of their protected attribute status (such as gender or race). We propose scalable methods for achieving equality of opportunity and equalized odds in rankings in the presence of position bias, which commonly plagues data generated from recommender systems. Our algorithms are model agnostic in the sense that they depend only on the final scores provided by a model, making them easily applicable to virtually all web-scale recommender systems. We conduct extensive simulations as well as real-world experiments to show the efficacy of our approach.  ( 2 min )
    Non-Asymptotic Analysis of Stochastic Approximation Algorithms for Streaming Data. (arXiv:2109.07117v5 [cs.LG] UPDATED)
    We consider the stochastic approximation problem in a streaming framework where an objective is minimized through unbiased estimates of its gradients. In this streaming framework, we consider time-varying data streams that must be processed sequentially. Our methods are Stochastic Gradient (SG) based due to their applicability and computational advantages. We provide a non-asymptotic analysis of the convergence of various SG-based methods; this includes the famous SG descent (a.k.a. Robbins-Monro algorithm), constant and time-varying mini-batch SG methods, and their averaged estimates (a.k.a. Polyak-Ruppert averaging). Our analysis suggests choosing the learning rate according to the expected data streams, which can speed up the convergence. In addition, we show how the averaged estimate can achieve optimal convergence in terms of attaining Cramer-Rao's lower bound while being robust to any data stream rate. In particular, our analysis shows how Polyak-Ruppert averaging of time-varying mini-batches can provide variance reduction and accelerate convergence simultaneously, which is advantageous for large-scale learning problems. These theoretical results are illustrated for various data streams, showing the effectiveness of the proposed algorithms.  ( 3 min )
    Hybrid Transfer in Deep Reinforcement Learning for Ads Allocation. (arXiv:2204.11589v3 [cs.IR] UPDATED)
    Ads allocation, which involves allocating ads and organic items to limited slots in feed with the purpose of maximizing platform revenue, has become a research hotspot. Notice that, e-commerce platforms usually have multiple entrances for different categories and some entrances have few visits. Data from these entrances has low coverage, which makes it difficult for the agent to learn. To address this challenge, we propose Similarity-based Hybrid Transfer for Ads Allocation (SHTAA), which effectively transfers samples as well as knowledge from data-rich entrance to data-poor entrance. Specifically, we define an uncertainty-aware similarity for MDP to estimate the similarity of MDP for different entrances. Based on this similarity, we design a hybrid transfer method, including instance transfer and strategy transfer, to efficiently transfer samples and knowledge from one entrance to another. Both offline and online experiments on Meituan food delivery platform demonstrate that the proposed method could achieve better performance for data-poor entrance and increase the revenue for the platform.  ( 2 min )
    CARLANE: A Lane Detection Benchmark for Unsupervised Domain Adaptation from Simulation to multiple Real-World Domains. (arXiv:2206.08083v2 [cs.CV] UPDATED)
    Unsupervised Domain Adaptation demonstrates great potential to mitigate domain shifts by transferring models from labeled source domains to unlabeled target domains. While Unsupervised Domain Adaptation has been applied to a wide variety of complex vision tasks, only few works focus on lane detection for autonomous driving. This can be attributed to the lack of publicly available datasets. To facilitate research in these directions, we propose CARLANE, a 3-way sim-to-real domain adaptation benchmark for 2D lane detection. CARLANE encompasses the single-target datasets MoLane and TuLane and the multi-target dataset MuLane. These datasets are built from three different domains, which cover diverse scenes and contain a total of 163K unique images, 118K of which are annotated. In addition we evaluate and report systematic baselines, including our own method, which builds upon Prototypical Cross-domain Self-supervised Learning. We find that false positive and false negative rates of the evaluated domain adaptation methods are high compared to those of fully supervised baselines. This affirms the need for benchmarks such as CARLANE to further strengthen research in Unsupervised Domain Adaptation for lane detection. CARLANE, all evaluated models and the corresponding implementations are publicly available at https://carlanebenchmark.github.io.  ( 3 min )
    Learning to Order for Inventory Systems with Lost Sales and Uncertain Supplies. (arXiv:2207.04550v2 [math.OC] UPDATED)
    We consider a stochastic lost-sales inventory control system with a lead time $L$ over a planning horizon $T$. Supply is uncertain, and is a function of the order quantity (due to random yield/capacity, etc). We aim to minimize the $T$-period cost, a problem that is known to be computationally intractable even under known distributions of demand and supply. In this paper, we assume that both the demand and supply distributions are unknown and develop a computationally efficient online learning algorithm. We show that our algorithm achieves a regret (i.e. the performance gap between the cost of our algorithm and that of an optimal policy over $T$ periods) of $O(L+\sqrt{T})$ when $L\geq\log(T)$. We do so by 1) showing our algorithm cost is higher by at most $O(L+\sqrt{T})$ for any $L\geq 0$ compared to an optimal constant-order policy under complete information (a well-known and widely-used algorithm) and 2) leveraging its known performance guarantee from the existing literature. To the best of our knowledge, a finite-sample $O(\sqrt{T})$ (and polynomial in $L$) regret bound when benchmarked against an optimal policy is not known before in the online inventory control literature. A key challenge in this learning problem is that both demand and supply data can be censored; hence only truncated values are observable. We circumvent this challenge by showing that the data generated under an order quantity $q^2$ allows us to simulate the performance of not only $q^2$ but also $q^1$ for all $q^1<q^2$, a key observation to obtain sufficient information even under data censoring. By establishing a high probability coupling argument, we are able to evaluate and compare the performance of different order policies at their steady state within a finite time horizon. Since the problem lacks convexity, we develop an active elimination method that adaptively rules out suboptimal solutions.  ( 3 min )
    DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulation. (arXiv:2110.02711v6 [cs.CV] UPDATED)
    Recently, GAN inversion methods combined with Contrastive Language-Image Pretraining (CLIP) enables zero-shot image manipulation guided by text prompts. However, their applications to diverse real images are still difficult due to the limited GAN inversion capability. Specifically, these approaches often have difficulties in reconstructing images with novel poses, views, and highly variable contents compared to the training data, altering object identity, or producing unwanted image artifacts. To mitigate these problems and enable faithful manipulation of real images, we propose a novel method, dubbed DiffusionCLIP, that performs text-driven image manipulation using diffusion models. Based on full inversion capability and high-quality image generation power of recent diffusion models, our method performs zero-shot image manipulation successfully even between unseen domains and takes another step towards general application by manipulating images from a widely varying ImageNet dataset. Furthermore, we propose a novel noise combination method that allows straightforward multi-attribute manipulation. Extensive experiments and human evaluation confirmed robust and superior manipulation performance of our methods compared to the existing baselines. Code is available at https://github.com/gwang-kim/DiffusionCLIP.git.  ( 3 min )
    On Convergence Lemma and Convergence Stability for Piecewise Analytic Functions. (arXiv:2204.01643v3 [cs.GT] UPDATED)
    In this work, a convergence lemma for function $f$ being finite compositions of analytic mappings and the maximum operator is proved. The lemma shows that the set of $\delta$-stationary points near an isolated local minimum point $x^*$ is shrinking to $x^*$ as $\delta\to 0$. It is a natural extension of the version for strongly convex $C^1$ functions. However, the correctness of the lemma is subtle. Analytic mappings are necessary for the lemma in the sense that replacing it with differentiable or $C^\infty$ mappings makes the lemma false. The proof is based on stratification theorems of semi-analytic sets by {\L}ojasiewicz. An extension of this proof presents a geometric characterization of the set of stationary points of $f$. Finally, a notion of stability on stationary points, called convergence stability, is proposed. It asks, under small numerical errors, whether a reasonable convergent optimization method started near a stationary point should eventually converge to the same stationary point. The concept of convergence stability becomes nontrivial qualitatively only when the objective function is both nonsmooth and nonconvex. Via the convergence lemma, an intuitive equivalent condition for convergence stability of $f$ is proved. These results together provide a new geometric perspective to study the problem of "where-to-converge" in nonsmooth nonconvex optimization.  ( 3 min )
    Valid Inference after Causal Discovery. (arXiv:2208.05949v1 [stat.ME])
    Causal graph discovery and causal effect estimation are two fundamental tasks in causal inference. While many methods have been developed for each task individually, statistical challenges arise when applying these methods jointly: estimating causal effects after running causal discovery algorithms on the same data leads to "double dipping," invalidating coverage guarantees of classical confidence intervals. To this end, we develop tools for valid post-causal-discovery inference. One key contribution is a randomized version of the greedy equivalence search (GES) algorithm, which permits a valid, finite-sample correction of classical confidence intervals. Across empirical studies, we show that a naive combination of causal discovery and subsequent inference algorithms typically leads to highly inflated miscoverage rates; at the same time, our noisy GES method provides reliable coverage control while achieving more accurate causal graph recovery than data splitting.  ( 2 min )
    RelPose: Predicting Probabilistic Relative Rotation for Single Objects in the Wild. (arXiv:2208.05963v1 [cs.CV])
    We describe a data-driven method for inferring the camera viewpoints given multiple images of an arbitrary object. This task is a core component of classic geometric pipelines such as SfM and SLAM, and also serves as a vital pre-processing requirement for contemporary neural approaches (e.g. NeRF) to object reconstruction and view synthesis. In contrast to existing correspondence-driven methods that do not perform well given sparse views, we propose a top-down prediction based approach for estimating camera viewpoints. Our key technical insight is the use of an energy-based formulation for representing distributions over relative camera rotations, thus allowing us to explicitly represent multiple camera modes arising from object symmetries or views. Leveraging these relative predictions, we jointly estimate a consistent set of camera rotations from multiple images. We show that our approach outperforms state-of-the-art SfM and SLAM methods given sparse images on both seen and unseen categories. Further, our probabilistic approach significantly outperforms directly regressing relative poses, suggesting that modeling multimodality is important for coherent joint reconstruction. We demonstrate that our system can be a stepping stone toward in-the-wild reconstruction from multi-view datasets. The project page with code and videos can be found at https://jasonyzhang.com/relpose.  ( 2 min )
    Off-Policy Actor-Critic with Emphatic Weightings. (arXiv:2111.08172v2 [cs.LG] UPDATED)
    A variety of theoretically-sound policy gradient algorithms exist for the on-policy setting due to the policy gradient theorem, which provides a simplified form for the gradient. The off-policy setting, however, has been less clear due to the existence of multiple objectives and the lack of an explicit off-policy policy gradient theorem. In this work, we unify these objectives into one off-policy objective, and provide a policy gradient theorem for this unified objective. The derivation involves emphatic weightings and interest functions. We show multiple strategies to approximate the gradients, in an algorithm called Actor Critic with Emphatic weightings (ACE). We prove in a counterexample that previous (semi-gradient) off-policy actor-critic methods--particularly OffPAC and DPG--converge to the wrong solution whereas ACE finds the optimal solution. We also highlight why these semi-gradient approaches can still perform well in practice, suggesting strategies for variance reduction in ACE. We empirically study several variants of ACE on two classic control environments and an image-based environment designed to illustrate the tradeoffs made by each gradient approximation. We find that by approximating the emphatic weightings directly, ACE performs as well as or better than OffPAC in all settings tested.  ( 2 min )
    StretchBEV: Stretching Future Instance Prediction Spatially and Temporally. (arXiv:2203.13641v2 [cs.CV] UPDATED)
    In self-driving, predicting future in terms of location and motion of all the agents around the vehicle is a crucial requirement for planning. Recently, a new joint formulation of perception and prediction has emerged by fusing rich sensory information perceived from multiple cameras into a compact bird's-eye view representation to perform prediction. However, the quality of future predictions degrades over time while extending to longer time horizons due to multiple plausible predictions. In this work, we address this inherent uncertainty in future predictions with a stochastic temporal model. Our model learns temporal dynamics in a latent space through stochastic residual updates at each time step. By sampling from a learned distribution at each time step, we obtain more diverse future predictions that are also more accurate compared to previous work, especially stretching both spatially further regions in the scene and temporally over longer time horizons. Despite separate processing of each time step, our model is still efficient through decoupling of the learning of dynamics and the generation of future predictions.  ( 2 min )
    The Geometry of Robust Value Functions. (arXiv:2201.12929v2 [cs.LG] UPDATED)
    The space of value functions is a fundamental concept in reinforcement learning. Characterizing its geometric properties may provide insights for optimization and representation. Existing works mainly focus on the value space for Markov Decision Processes (MDPs). In this paper, we study the geometry of the robust value space for the more general Robust MDPs (RMDPs) setting, where transition uncertainties are considered. Specifically, since we find it hard to directly adapt prior approaches to RMDPs, we start with revisiting the non-robust case, and introduce a new perspective that enables us to characterize both the non-robust and robust value space in a similar fashion. The key of this perspective is to decompose the value space, in a state-wise manner, into unions of hypersurfaces. Through our analysis, we show that the robust value space is determined by a set of conic hypersurfaces, each of which contains the robust values of all policies that agree on one state. Furthermore, we find that taking only extreme points in the uncertainty set is sufficient to determine the robust value space. Finally, we discuss some other aspects about the robust value space, including its non-convexity and policy agreement on multiple states.  ( 3 min )
    Machine learning in front of statistical methods for prediction spread SARS-CoV-2 in Colombia. (arXiv:2208.05910v1 [physics.soc-ph])
    An analytical study of the disease COVID-19 in Colombia was carried out using mathematical models such as Susceptible-Exposed-Infectious-Removed (SEIR), Logistic Regression (LR), and a machine learning method called Polynomial Regression Method. Previous analysis has been performed on the daily number of cases, deaths, infected people, and people who were exposed to the virus, all of them in a timeline of 550 days. Moreover, it has made the fitting of infection spread detailing the most efficient and optimal methods with lower propagation error and the presence of statistical biases. Finally, four different prevention scenarios were proposed to evaluate the ratio of each one of the parameters related to the disease.  ( 2 min )
    Neural Decoding with Optimization of Node Activations. (arXiv:2206.00786v2 [cs.IT] UPDATED)
    The problem of maximum likelihood decoding with a neural decoder for error-correcting code is considered. It is shown that the neural decoder can be improved with two novel loss terms on the node's activations. The first loss term imposes a sparse constraint on the node's activations. Whereas, the second loss term tried to mimic the node's activations from a teacher decoder which has better performance. The proposed method has the same run time complexity and model size as the neural Belief Propagation decoder, while improving the decoding performance by up to $1.1dB$ on BCH codes.  ( 2 min )
    Low-complexity Near-optimum Symbol Detection Based on Neural Enhancement of Factor Graphs. (arXiv:2203.16417v2 [cs.IT] UPDATED)
    We consider the application of the factor graph framework for symbol detection on linear inter-symbol interference channels. Based on the Ungerboeck observation model, a detection algorithm with appealing complexity properties can be derived. However, since the underlying factor graph contains cycles, the sum-product algorithm (SPA) yields a suboptimal algorithm. In this paper, we develop and evaluate efficient strategies to improve the performance of the factor graph-based symbol detection by means of neural enhancement. In particular, we consider neural belief propagation and generalizations of the factor nodes as an effective way to mitigate the effect of cycles within the factor graph. By applying a generic preprocessor to the channel output, we propose a simple technique to vary the underlying factor graph in every SPA iteration. Using this dynamic factor graph transition, we intend to preserve the extrinsic nature of the SPA messages which is otherwise impaired due to cycles. Simulation results show that the proposed methods can massively improve the detection performance, even approaching the maximum a posteriori performance for various transmission scenarios, while preserving a complexity which is linear in both the block length and the channel memory.  ( 3 min )
    KiPA22 Report: U-Net with Contour Regularization for Renal Structures Segmentation. (arXiv:2208.05772v1 [eess.IV])
    Three-dimensional (3D) integrated renal structures (IRS) segmentation is important in clinical practice. With the advancement of deep learning techniques, many powerful frameworks focusing on medical image segmentation are proposed. In this challenge, we utilized the nnU-Net framework, which is the state-of-the-art method for medical image segmentation. To reduce the outlier prediction for the tumor label, we combine contour regularization (CR) loss of the tumor label with Dice loss and cross-entropy loss to improve this phenomenon.
    HyperTime: Implicit Neural Representation for Time Series. (arXiv:2208.05836v1 [cs.LG])
    Implicit neural representations (INRs) have recently emerged as a powerful tool that provides an accurate and resolution-independent encoding of data. Their robustness as general approximators has been shown in a wide variety of data sources, with applications on image, sound, and 3D scene representation. However, little attention has been given to leveraging these architectures for the representation and analysis of time series data. In this paper, we analyze the representation of time series using INRs, comparing different activation functions in terms of reconstruction accuracy and training convergence speed. We show how these networks can be leveraged for the imputation of time series, with applications on both univariate and multivariate data. Finally, we propose a hypernetwork architecture that leverages INRs to learn a compressed latent representation of an entire time series dataset. We introduce an FFT-based loss to guide training so that all frequencies are preserved in the time series. We show that this network can be used to encode time series as INRs, and their embeddings can be interpolated to generate new time series from existing ones. We evaluate our generative method by using it for data augmentation, and show that it is competitive against current state-of-the-art approaches for augmentation of time series.  ( 2 min )
    Uncertainty Quantification of Sparse Travel Demand Prediction with Spatial-Temporal Graph Neural Networks. (arXiv:2208.05908v1 [cs.LG])
    Origin-Destination (O-D) travel demand prediction is a fundamental challenge in transportation. Recently, spatial-temporal deep learning models demonstrate the tremendous potential to enhance prediction accuracy. However, few studies tackled the uncertainty and sparsity issues in fine-grained O-D matrices. This presents a serious problem, because a vast number of zeros deviate from the Gaussian assumption underlying the deterministic deep learning models. To address this issue, we design a Spatial-Temporal Zero-Inflated Negative Binomial Graph Neural Network (STZINB-GNN) to quantify the uncertainty of the sparse travel demand. It analyzes spatial and temporal correlations using diffusion and temporal convolution networks, which are then fused to parameterize the probabilistic distributions of travel demand. The STZINB-GNN is examined using two real-world datasets with various spatial and temporal resolutions. The results demonstrate the superiority of STZINB-GNN over benchmark models, especially under high spatial-temporal resolutions, because of its high accuracy, tight confidence intervals, and interpretable parameters. The sparsity parameter of the STZINB-GNN has physical interpretation for various transportation applications.  ( 2 min )
    PointTree: Transformation-Robust Point Cloud Encoder with Relaxed K-D Trees. (arXiv:2208.05962v1 [cs.CV])
    Being able to learn an effective semantic representation directly on raw point clouds has become a central topic in 3D understanding. Despite rapid progress, state-of-the-art encoders are restrictive to canonicalized point clouds, and have weaker than necessary performance when encountering geometric transformation distortions. To overcome this challenge, we propose PointTree, a general-purpose point cloud encoder that is robust to transformations based on relaxed K-D trees. Key to our approach is the design of the division rule in K-D trees by using principal component analysis (PCA). We use the structure of the relaxed K-D tree as our computational graph, and model the features as border descriptors which are merged with pointwise-maximum operation. In addition to this novel architecture design, we further improve the robustness by introducing pre-alignment -- a simple yet effective PCA-based normalization scheme. Our PointTree encoder combined with pre-alignment consistently outperforms state-of-the-art methods by large margins, for applications from object classification to semantic segmentation on various transformed versions of the widely-benchmarked datasets. Code and pre-trained models are available at https://github.com/immortalCO/PointTree.
    Regularizing Deep Neural Networks with Stochastic Estimators of Hessian Trace. (arXiv:2208.05924v1 [cs.LG])
    In this paper we develop a novel regularization method for deep neural networks by penalizing the trace of Hessian. This regularizer is motivated by a recent guarantee bound of the generalization error. Hutchinson method is a classical unbiased estimator for the trace of a matrix, but it is very time-consuming on deep learning models. Hence a dropout scheme is proposed to efficiently implements the Hutchinson method. Then we discuss a connection to linear stability of a nonlinear dynamical system and flat/sharp minima. Experiments demonstrate that our method outperforms existing regularizers and data augmentation methods, such as Jacobian, confidence penalty, and label smoothing, cutout and mixup.  ( 2 min )
    Regressing Relative Fine-Grained Change for Sub-Groups in Unreliable Heterogeneous Data Through Deep Multi-Task Metric Learning. (arXiv:2208.05800v1 [cs.LG])
    Fine-Grained Change Detection and Regression Analysis are essential in many applications of ArtificialIntelligence. In practice, this task is often challenging owing to the lack of reliable ground truth information andcomplexity arising from interactions between the many underlying factors affecting a system. Therefore,developing a framework which can represent the relatedness and reliability of multiple sources of informationbecomes critical. In this paper, we investigate how techniques in multi-task metric learning can be applied for theregression of fine-grained change in real data.The key idea is that if we incorporate the incremental change in a metric of interest between specific instancesof an individual object as one of the tasks in a multi-task metric learning framework, then interpreting thatdimension will allow the user to be alerted to fine-grained change invariant to what the overall metric isgeneralised to be. The techniques investigated are specifically tailored for handling heterogeneous data sources,i.e. the input data for each of the tasks might contain missing values, the scale and resolution of the values is notconsistent across tasks and the data contains non-independent and identically distributed (non-IID) instances. Wepresent the results of our initial experimental implementations of this idea and discuss related research in thisdomain which may offer direction for further research.  ( 3 min )
    Partition Pooling for Convolutional Graph Network Applications in Particle Physics. (arXiv:2208.05952v1 [hep-ex])
    Convolutional graph networks are used in particle physics for effective event reconstructions and classifications. However, their performances can be limited by the considerable amount of sensors used in modern particle detectors if applied to sensor-level data. We present a pooling scheme that uses partitioning to create pooling kernels on graphs, similar to pooling on images. Partition pooling can be used to adopt successful image recognition architectures for graph neural network applications in particle physics. The reduced computational resources allow for deeper networks and more extensive hyperparameter optimizations. To show its applicability, we construct a convolutional graph network with partition pooling that reconstructs simulated interaction vertices for an idealized neutrino detector. The pooling network yields improved performance and is less susceptible to overfitting than a similar network without pooling. The lower resource requirements allow the construction of a deeper network with further improved performance.  ( 2 min )
    Interactive Code Generation via Test-Driven User-Intent Formalization. (arXiv:2208.05950v1 [cs.SE])
    Pre-trained large language models (LLMs) such as OpenAI Codex have shown immense potential in automating significant aspects of coding by producing natural code from informal natural language (NL) intent. However, the code produced does not have any correctness guarantees around satisfying user's intent. In fact, it is hard to define a notion of correctness since natural language can be ambiguous and lacks a formal semantics. In this paper, we take a first step towards addressing the problem above by proposing the workflow of test-driven user-intent formalization (TDUIF), which leverages lightweight user feedback to jointly (a) formalize the user intent as tests (a partial specification), and (b) generates code that meets the formal user intent. To perform a scalable and large-scale automated evaluation of the algorithms without requiring a user in the loop, we describe how to simulate user interaction with high-fidelity using a reference solution. We also describe and implement alternate implementations of several algorithmic components (including mutating and ranking a set of tests) that can be composed for efficient solutions to the TDUIF problem. We have developed a system TICODER that implements several solutions to TDUIF, and compare their relative effectiveness on the MBPP academic code generation benchmark. Our results are promising with using the OpenAI Codex LLM on MBPP: our best algorithm improves the pass@1 code generation accuracy metric from 48.39% to 70.49% with a single user query, and up to 85.48% with up to 5 user queries. Second, we can generate a non-trivial functional unit test consistent with the user intent within an average of 1.69 user queries for 90.40% of the examples for this dataset.  ( 3 min )
    Heatmap Regression for Lesion Detection using Pointwise Annotations. (arXiv:2208.05939v1 [eess.IV])
    In many clinical contexts, detecting all lesions is imperative for evaluating disease activity. Standard approaches pose lesion detection as a segmentation problem despite the time-consuming nature of acquiring segmentation labels. In this paper, we present a lesion detection method which relies only on point labels. Our model, which is trained via heatmap regression, can detect a variable number of lesions in a probabilistic manner. In fact, our proposed post-processing method offers a reliable way of directly estimating the lesion existence uncertainty. Experimental results on Gad lesion detection show our point-based method performs competitively compared to training on expensive segmentation labels. Finally, our detection model provides a suitable pre-training for segmentation. When fine-tuning on only 17 segmentation samples, we achieve comparable performance to training with the full dataset.  ( 2 min )
    Language Tokens: A Frustratingly Simple Approach Improves Zero-Shot Performance of Multilingual Translation. (arXiv:2208.05852v1 [cs.CL])
    This paper proposes a simple yet effective method to improve direct (X-to-Y) translation for both cases: zero-shot and when direct data is available. We modify the input tokens at both the encoder and decoder to include signals for the source and target languages. We show a performance gain when training from scratch, or finetuning a pretrained model with the proposed setup. In the experiments, our method shows nearly 10.0 BLEU points gain on in-house datasets depending on the checkpoint selection criteria. In a WMT evaluation campaign, From-English performance improves by 4.17 and 2.87 BLEU points, in the zero-shot setting, and when direct data is available for training, respectively. While X-to-Y improves by 1.29 BLEU over the zero-shot baseline, and 0.44 over the many-to-many baseline. In the low-resource setting, we see a 1.5~1.7 point improvement when finetuning on X-to-Y domain data.  ( 2 min )
    Near-Optimal Algorithms for Making the Gradient Small in Stochastic Minimax Optimization. (arXiv:2208.05925v1 [cs.LG])
    We study the problem of finding a near-stationary point for smooth minimax optimization. The recent proposed extra anchored gradient (EAG) methods achieve the optimal convergence rate for the convex-concave minimax problem in deterministic setting. However, the direct extension of EAG to stochastic optimization is not efficient. In this paper, we design a novel stochastic algorithm called Recursive Anchored IteratioN (RAIN). We show that the RAIN achieves near-optimal stochastic first-order oracle complexity for stochastic minimax optimization in both convex-concave and strongly-convex-strongly-concave cases.  ( 2 min )
    Speech Enhancement and Dereverberation with Diffusion-based Generative Models. (arXiv:2208.05830v1 [eess.AS])
    Recently, diffusion-based generative models have been introduced to the task of speech enhancement. The corruption of clean speech is modeled as a fixed forward process in which increasing amounts of noise are gradually added. By learning to reverse this process in an iterative fashion conditioned on the noisy input, clean speech is generated. We build upon our previous work and derive the training task within the formalism of stochastic differential equations. We present a detailed theoretical review of the underlying score matching objective and explore different sampler configurations for solving the reverse process at test time. By using a sophisticated network architecture from natural image generation literature, we significantly improve performance compared to our previous publication. We also show that we can compete with recent discriminative models and achieve better generalization when evaluating on a different corpus than used for training. We complement the evaluation results with a subjective listening test, in which our proposed method is rated best. Furthermore, we show that the proposed method achieves remarkable state-of-the-art performance in single-channel speech dereverberation. Our code and audio examples are available online, see https://uhh.de/inf-sp-sgmse  ( 3 min )
    Adaptively Identifying Patient Populations With Treatment Benefit in Clinical Trials. (arXiv:2208.05844v1 [stat.ML])
    We study the problem of adaptively identifying patient subpopulations that benefit from a given treatment during a confirmatory clinical trial. This type of adaptive clinical trial, often referred to as adaptive enrichment design, has been thoroughly studied in biostatistics with a focus on a limited number of subgroups (typically two) which make up (sub)populations, and a small number of interim analysis points. In this paper, we aim to relax classical restrictions on such designs and investigate how to incorporate ideas from the recent machine learning literature on adaptive and online experimentation to make trials more flexible and efficient. We find that the unique characteristics of the subpopulation selection problem -- most importantly that (i) one is usually interested in finding subpopulations with any treatment benefit (and not necessarily the single subgroup with largest effect) given a limited budget and that (ii) effectiveness only has to be demonstrated across the subpopulation on average -- give rise to interesting challenges and new desiderata when designing algorithmic solutions. Building on these findings, we propose AdaGGI and AdaGCPI, two meta-algorithms for subpopulation construction, which focus on identifying good subgroups and good composite subpopulations, respectively. We empirically investigate their performance across a range of simulation scenarios and derive insights into their (dis)advantages across different settings.  ( 2 min )
    Predicting Tornadoes days ahead with Machine Learning. (arXiv:2208.05855v1 [cs.LG])
    Developing methods to predict disastrous natural phenomena is more important than ever, and tornadoes are among the most dangerous ones in nature. Due to the unpredictability of the weather, counteracting them is not an easy task and today it is mainly carried out by expert meteorologists, who interpret meteorological models. In this paper we propose a system for the early detection of a tornado, validating its effectiveness in a real-world context and exploiting meteorological data collection systems that are already widespread throughout the world. Our system was able to predict tornadoes with a maximum probability of 84% up to five days before the event on a novel dataset of more than 5000 tornadic and non-tornadic events. The dataset and the code to reproduce our results are available at: https://tinyurl.com/3brsfwpk  ( 2 min )
    A Comprehensive Analysis of AI Biases in DeepFake Detection With Massively Annotated Databases. (arXiv:2208.05845v1 [cs.CV])
    In recent years, image and video manipulations with DeepFake have become a severe concern for security and society. Therefore, many detection models and databases have been proposed to detect DeepFake data reliably. However, there is an increased concern that these models and training databases might be biased and thus, cause DeepFake detectors to fail. In this work, we tackle these issues by (a) providing large-scale demographic and non-demographic attribute annotations of 41 different attributes for five popular DeepFake datasets and (b) comprehensively analysing AI-bias of multiple state-of-the-art DeepFake detection models on these databases. The investigation analyses the influence of a large variety of distinctive attributes (from over 65M labels) on the detection performance, including demographic (age, gender, ethnicity) and non-demographic (hair, skin, accessories, etc.) information. The results indicate that investigated databases lack diversity and, more importantly, show that the utilised DeepFake detection models are strongly biased towards many investigated attributes. Moreover, the results show that the models' decision-making might be based on several questionable (biased) assumptions, such if a person is smiling or wearing a hat. Depending on the application of such DeepFake detection methods, these biases can lead to generalizability, fairness, and security issues. We hope that the findings of this study and the annotation databases will help to evaluate and mitigate bias in future DeepFake detection techniques. Our annotation datasets are made publicly available.  ( 3 min )
    Comparison and Analysis of New Curriculum Criteria for End-to-End ASR. (arXiv:2208.05782v1 [eess.AS])
    It is common knowledge that the quantity and quality of the training data play a significant role in the creation of a good machine learning model. In this paper, we take it one step further and demonstrate that the way the training examples are arranged is also of crucial importance. Curriculum Learning is built on the observation that organized and structured assimilation of knowledge has the ability to enable faster training and better comprehension. When humans learn to speak, they first try to utter basic phones and then gradually move towards more complex structures such as words and sentences. This methodology is known as Curriculum Learning, and we employ it in the context of Automatic Speech Recognition. We hypothesize that end-to-end models can achieve better performance when provided with an organized training set consisting of examples that exhibit an increasing level of difficulty (i.e. a curriculum). To impose structure on the training set and to define the notion of an easy example, we explored multiple scoring functions that either use feedback from an external neural network or incorporate feedback from the model itself. Empirical results show that with different curriculums we can balance the training times and the network's performance.  ( 3 min )
    Super-Universal Regularized Newton Method. (arXiv:2208.05888v1 [math.OC])
    We analyze the performance of a variant of Newton method with quadratic regularization for solving composite convex minimization problems. At each step of our method, we choose regularization parameter proportional to a certain power of the gradient norm at the current point. We introduce a family of problem classes characterized by H\"older continuity of either the second or third derivative. Then we present the method with a simple adaptive search procedure allowing an automatic adjustment to the problem class with the best global complexity bounds, without knowing specific parameters of the problem. In particular, for the class of functions with Lipschitz continuous third derivative, we get the global $O(1/k^3)$ rate, which was previously attributed to third-order tensor methods. When the objective function is uniformly convex, we justify an automatic acceleration of our scheme, resulting in a faster global rate and local superlinear convergence. The switching between the different rates (sublinear, linear, and superlinear) is automatic. Again, for that, no a priori knowledge of parameters is needed.  ( 2 min )
    Towards Sequence-Level Training for Visual Tracking. (arXiv:2208.05810v1 [cs.CV])
    Despite the extensive adoption of machine learning on the task of visual object tracking, recent learning-based approaches have largely overlooked the fact that visual tracking is a sequence-level task in its nature; they rely heavily on frame-level training, which inevitably induces inconsistency between training and testing in terms of both data distributions and task objectives. This work introduces a sequence-level training strategy for visual tracking based on reinforcement learning and discusses how a sequence-level design of data sampling, learning objectives, and data augmentation can improve the accuracy and robustness of tracking algorithms. Our experiments on standard benchmarks including LaSOT, TrackingNet, and GOT-10k demonstrate that four representative tracking models, SiamRPN++, SiamAttn, TransT, and TrDiMP, consistently improve by incorporating the proposed methods in training without modifying architectures.  ( 2 min )
    GEM-2: Next Generation Molecular Property Prediction Network with Many-body and Full-range Interaction Modeling. (arXiv:2208.05863v1 [cs.LG])
    Molecular property prediction is a fundamental task in the drug and material industries. Physically, the properties of a molecule are determined by its own electronic structure, which can be exactly described by the Schr\"odinger equation. However, solving the Schr\"odinger equation for most molecules is extremely challenging due to long-range interactions in the behavior of a quantum many-body system. While deep learning methods have proven to be effective in molecular property prediction, we design a novel method, namely GEM-2, which comprehensively considers both the long-range and many-body interactions in molecules. GEM-2 consists of two interacted tracks: an atom-level track modeling both the local and global correlation between any two atoms, and a pair-level track modeling the correlation between all atom pairs, which embed information between any 3 or 4 atoms. Extensive experiments demonstrated the superiority of GEM-2 over multiple baseline methods in quantum chemistry and drug discovery tasks.  ( 2 min )
    Empirical investigations on WVA structural issues. (arXiv:2208.05791v1 [cs.LG])
    In this paper we want to present the results of empirical verification of some issues concerning the methods for overcoming catastrophic forgetting in neural networks. First, in the introduction, we will try to describe in detail the problem of catastrophic forgetting and methods for overcoming it for those who are not yet familiar with this topic. Then we will discuss the essence and limitations of the WVA method which we presented in previous papers. Further, we will touch upon the issues of applying the WVA method to gradients or optimization steps of weights, choosing the optimal attenuation function in this method, as well as choosing the optimal hyper-parameters of the method depending on the number of tasks in sequential training of neural networks.  ( 2 min )
    Uncertainty Quantification for Traffic Forecasting: A Unified Approach. (arXiv:2208.05875v1 [cs.LG])
    Uncertainty is an essential consideration for time series forecasting tasks. In this work, we specifically focus on quantifying the uncertainty of traffic forecasting. To achieve this, we develop Deep Spatio-Temporal Uncertainty Quantification (DeepSTUQ), which can estimate both aleatoric and epistemic uncertainty. We first leverage a spatio-temporal model to model the complex spatio-temporal correlations of traffic data. Subsequently, two independent sub-neural networks maximizing the heterogeneous log-likelihood are developed to estimate aleatoric uncertainty. For estimating epistemic uncertainty, we combine the merits of variational inference and deep ensembling by integrating the Monte Carlo dropout and the Adaptive Weight Averaging re-training methods, respectively. Finally, we propose a post-processing calibration approach based on Temperature Scaling, which improves the model's generalization ability to estimate uncertainty. Extensive experiments are conducted on four public datasets, and the empirical results suggest that the proposed method outperforms state-of-the-art methods in terms of both point prediction and uncertainty quantification.  ( 2 min )
    Social Norm Bias: Residual Harms of Fairness-Aware Algorithms. (arXiv:2108.11056v3 [cs.LG] UPDATED)
    Many modern machine learning algorithms mitigate bias by enforcing fairness constraints across coarsely-defined groups related to a sensitive attribute like gender or race. However, these algorithms seldom account for within-group heterogeneity and biases that may disproportionately affect some members of a group. In this work, we characterize Social Norm Bias (SNoB), a subtle but consequential type of algorithmic discrimination that may be exhibited by machine learning models, even when these systems achieve group fairness objectives. We study this issue through the lens of gender bias in occupation classification. We quantify SNoB by measuring how an algorithm's predictions are associated with conformity to inferred gender norms. When predicting if an individual belongs to a male-dominated occupation, this framework reveals that "fair" classifiers still favor biographies written in ways that align with inferred masculine norms. We compare SNoB across algorithmic fairness methods and show that it is frequently a residual bias, and post-processing approaches do not mitigate this type of bias at all.  ( 3 min )
    Distributionally Robust Losses for Latent Covariate Mixtures. (arXiv:2007.13982v2 [cs.LG] UPDATED)
    While modern large-scale datasets often consist of heterogeneous subpopulations -- for example, multiple demographic groups or multiple text corpora -- the standard practice of minimizing average loss fails to guarantee uniformly low losses across all subpopulations. We propose a convex procedure that controls the worst-case performance over all subpopulations of a given size. Our procedure comes with finite-sample (nonparametric) convergence guarantees on the worst-off subpopulation. Empirically, we observe on lexical similarity, wine quality, and recidivism prediction tasks that our worst-case procedure learns models that do well against unseen subpopulations.  ( 2 min )
    Learning Based Joint Coding-Modulation for Digital Semantic Communication Systems. (arXiv:2208.05704v1 [cs.IT])
    In learning-based semantic communications, neural networks have replaced different building blocks in traditional communication systems. However, the digital modulation still remains a challenge for neural networks. The intrinsic mechanism of neural network based digital modulation is mapping continuous output of the neural network encoder into discrete constellation symbols, which is a non-differentiable function that cannot be trained with existing gradient descend algorithms. To overcome this challenge, in this paper we develop a joint coding-modulation scheme for digital semantic communications with BPSK modulation. In our method, the neural network outputs the likelihood of each constellation point, instead of having a concrete mapping. A random code rather than a deterministic code is hence used, which preserves more information for the symbols with a close likelihood on each constellation point. The joint coding-modulation design can match the modulation process with channel states, and hence improve the performance of digital semantic communications. Experiment results show that our method outperforms existing digital modulation methods in semantic communications over a wide range of SNR, and outperforms neural network based analog modulation method in low SNR regime.  ( 2 min )
    A Modified UDP for Federated Learning Packet Transmissions. (arXiv:2208.05737v1 [cs.NI])
    This paper introduces a Modified User Datagram Protocol (UDP) for Federated Learning to ensure efficiency and reliability in the model parameter transport process, maximizing the potential of the Global model in each Federated Learning round. In developing and testing this protocol, the NS3 simulator is utilized to simulate the packet transport over the network and Google TensorFlow is used to create a custom Federated learning environment. In this preliminary implementation, the simulation contains three nodes where two nodes are client nodes, and one is a server node. The results obtained in this paper provide confidence in the capabilities of the protocol in the future of Federated Learning therefore, in future the Modified UDP will be tested on a larger Federated learning system with a TensorFlow model containing more parameters and a comparison between the traditional UDP protocol and the Modified UDP protocol will be simulated. Optimization of the Modified UDP will also be explored to improve efficiency while ensuring reliability.  ( 2 min )
    Word-Embeddings Distinguish Denominal and Root-Derived Verbs in Semitic. (arXiv:2208.05721v1 [cs.CL])
    Proponents of the Distributed Morphology framework have posited the existence of two levels of morphological word formation: a lower one, leading to loose input-output semantic relationships; and an upper one, leading to tight input-output semantic relationships. In this work, we propose to test the validity of this assumption in the context of Hebrew word embeddings. If the two-level hypothesis is borne out, we expect state-of-the-art Hebrew word embeddings to encode (1) a noun, (2) a denominal derived from it (via an upper-level operation), and (3) a verb related to the noun (via a lower-level operation on the noun's root), in such a way that the denominal (2) should be closer in the embedding space to the noun (1) than the related verb (3) is to the same noun (1). We report that this hypothesis is verified by four embedding models of Hebrew: fastText, GloVe, Word2Vec and AlephBERT. This suggests that word embedding models are able to capture complex and fine-grained semantic properties that are morphologically motivated.  ( 2 min )
    Learning Point Processes using Recurrent Graph Network. (arXiv:2208.05736v1 [cs.LG])
    We present a novel Recurrent Graph Network (RGN) approach for predicting discrete marked event sequences by learning the underlying complex stochastic process. Using the framework of Point Processes, we interpret a marked discrete event sequence as the superposition of different sequences each of a unique type. The nodes of the Graph Network use LSTM to incorporate past information whereas a Graph Attention Network (GAT Network) introduces strong inductive biases to capture the interaction between these different types of events. By changing the self-attention mechanism from attending over past events to attending over event types, we obtain a reduction in time and space complexity from $\mathcal{O}(N^2)$ (total number of events) to $\mathcal{O}(|\mathcal{Y}|^2)$ (number of event types). Experiments show that the proposed approach improves performance in log-likelihood, prediction and goodness-of-fit tasks with lower time and space complexity compared to state-of-the art Transformer based architectures.  ( 2 min )
    A Model of Anaphoric Ambiguities using Sheaf Theoretic Quantum-like Contextuality and BERT. (arXiv:2208.05720v1 [cs.CL])
    Ambiguities of natural language do not preclude us from using it and context helps in getting ideas across. They, nonetheless, pose a key challenge to the development of competent machines to understand natural language and use it as humans do. Contextuality is an unparalleled phenomenon in quantum mechanics, where different mathematical formalisms have been put forwards to understand and reason about it. In this paper, we construct a schema for anaphoric ambiguities that exhibits quantum-like contextuality. We use a recently developed criterion of sheaf-theoretic contextuality that is applicable to signalling models. We then take advantage of the neural word embedding engine BERT to instantiate the schema to natural language examples and extract probability distributions for the instances. As a result, plenty of sheaf-contextual examples were discovered in the natural language corpora BERT utilises. Our hope is that these examples will pave the way for future research and for finding ways to extend applications of quantum computing to natural language processing.  ( 3 min )
    General Cutting Planes for Bound-Propagation-Based Neural Network Verification. (arXiv:2208.05740v1 [cs.LG])
    Bound propagation methods, when combined with branch and bound, are among the most effective methods to formally verify properties of deep neural networks such as correctness, robustness, and safety. However, existing works cannot handle the general form of cutting plane constraints widely accepted in traditional solvers, which are crucial for strengthening verifiers with tightened convex relaxations. In this paper, we generalize the bound propagation procedure to allow the addition of arbitrary cutting plane constraints, including those involving relaxed integer variables that do not appear in existing bound propagation formulations. Our generalized bound propagation method, GCP-CROWN, opens up the opportunity to apply general cutting plane methods} for neural network verification while benefiting from the efficiency and GPU acceleration of bound propagation methods. As a case study, we investigate the use of cutting planes generated by off-the-shelf mixed integer programming (MIP) solver. We find that MIP solvers can generate high-quality cutting planes for strengthening bound-propagation-based verifiers using our new formulation. Since the branching-focused bound propagation procedure and the cutting-plane-focused MIP solver can run in parallel utilizing different types of hardware (GPUs and CPUs), their combination can quickly explore a large number of branches with strong cutting planes, leading to strong verification performance. Experiments demonstrate that our method is the first verifier that can completely solve the oval20 benchmark and verify twice as many instances on the oval21 benchmark compared to the best tool in VNN-COMP 2021, and also noticeably outperforms state-of-the-art verifiers on a wide range of benchmarks. GCP-CROWN is part of the $\alpha$,$\beta$-CROWN verifier, the VNN-COMP 2022 winner. Code is available at this http URL  ( 3 min )
    Quantized Adaptive Subgradient Algorithms and Their Applications. (arXiv:2208.05631v1 [cs.LG])
    Data explosion and an increase in model size drive the remarkable advances in large-scale machine learning, but also make model training time-consuming and model storage difficult. To address the above issues in the distributed model training setting which has high computation efficiency and less device limitation, there are still two main difficulties. On one hand, the communication costs for exchanging information, e.g., stochastic gradients among different workers, is a key bottleneck for distributed training efficiency. On the other hand, less parameter model is easy for storage and communication, but the risk of damaging the model performance. To balance the communication costs, model capacity and model performance simultaneously, we propose quantized composite mirror descent adaptive subgradient (QCMD adagrad) and quantized regularized dual average adaptive subgradient (QRDA adagrad) for distributed training. To be specific, we explore the combination of gradient quantization and sparse model to reduce the communication cost per iteration in distributed training. A quantized gradient-based adaptive learning rate matrix is constructed to achieve a balance between communication costs, accuracy, and model sparsity. Moreover, we theoretically find that a large quantization error brings in extra noise, which influences the convergence and sparsity of the model. Therefore, a threshold quantization strategy with a relatively small error is adopted in QCMD adagrad and QRDA adagrad to improve the signal-to-noise ratio and preserve the sparsity of the model. Both theoretical analyses and empirical results demonstrate the efficacy and efficiency of the proposed algorithms.  ( 3 min )
    Path-aware Siamese Graph Neural Network for Link Prediction. (arXiv:2208.05781v1 [cs.LG])
    In this paper, we propose an algorithm of Path-aware Siamese Graph neural network(PSG) for link prediction tasks. Firstly, PSG can capture both nodes and edge features for given two nodes, namely the structure information of k-neighborhoods and relay paths information of the nodes. Furthermore, siamese graph neural network is utilized by PSG for representation learning of two contrastive links, which are a positive link and a negative link. We evaluate the proposed algorithm PSG on a link property prediction dataset of Open Graph Benchmark (OGB), ogbl-ddi. PSG achieves top 1 performance on ogbl-ddi. The experimental results verify the superiority of PSG.  ( 2 min )
    Best Policy Identification in Linear MDPs. (arXiv:2208.05633v1 [cs.LG])
    We investigate the problem of best policy identification in discounted linear Markov Decision Processes in the fixed confidence setting under a generative model. We first derive an instance-specific lower bound on the expected number of samples required to identify an $\varepsilon$-optimal policy with probability $1-\delta$. The lower bound characterizes the optimal sampling rule as the solution of an intricate non-convex optimization program, but can be used as the starting point to devise simple and near-optimal sampling rules and algorithms. We devise such algorithms. One of these exhibits a sample complexity upper bounded by ${\cal O}({\frac{d}{(\varepsilon+\Delta)^2}} (\log(\frac{1}{\delta})+d))$ where $\Delta$ denotes the minimum reward gap of sub-optimal actions and $d$ is the dimension of the feature space. This upper bound holds in the moderate-confidence regime (i.e., for all $\delta$), and matches existing minimax and gap-dependent lower bounds. We extend our algorithm to episodic linear MDPs.  ( 2 min )
    Semi-supervised Vision Transformers at Scale. (arXiv:2208.05688v1 [cs.CV])
    We study semi-supervised learning (SSL) for vision transformers (ViT), an under-explored topic despite the wide adoption of the ViT architectures to different tasks. To tackle this problem, we propose a new SSL pipeline, consisting of first un/self-supervised pre-training, followed by supervised fine-tuning, and finally semi-supervised fine-tuning. At the semi-supervised fine-tuning stage, we adopt an exponential moving average (EMA)-Teacher framework instead of the popular FixMatch, since the former is more stable and delivers higher accuracy for semi-supervised vision transformers. In addition, we propose a probabilistic pseudo mixup mechanism to interpolate unlabeled samples and their pseudo labels for improved regularization, which is important for training ViTs with weak inductive bias. Our proposed method, dubbed Semi-ViT, achieves comparable or better performance than the CNN counterparts in the semi-supervised classification setting. Semi-ViT also enjoys the scalability benefits of ViTs that can be readily scaled up to large-size models with increasing accuracies. For example, Semi-ViT-Huge achieves an impressive 80% top-1 accuracy on ImageNet using only 1% labels, which is comparable with Inception-v4 using 100% ImageNet labels.  ( 2 min )
    Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. (arXiv:2208.05648v1 [cs.LG])
    Graph neural networks (GNNs) are deep learning models designed specifically for graph data, and they typically rely on node features as the input to the first layer. When applying such a type of network on the graph without node features, one can extract simple graph-based node features (e.g., number of degrees) or learn the input node representations (i.e., embeddings) when training the network. While the latter approach, which trains node embeddings, more likely leads to better performance, the number of parameters associated with the embeddings grows linearly with the number of nodes. It is therefore impractical to train the input node embeddings together with GNNs within graphics processing unit (GPU) memory in an end-to-end fashion when dealing with industrial-scale graph data. Inspired by the embedding compression methods developed for natural language processing (NLP) tasks, we develop a node embedding compression method where each node is compactly represented with a bit vector instead of a floating-point vector. The parameters utilized in the compression method can be trained together with GNNs. We show that the proposed node embedding compression method achieves superior performance compared to the alternatives.  ( 2 min )
    Solving MathWord Problems Automatically with Heterogeneous Line Graph Transformer for Online Learning. (arXiv:2208.05645v1 [cs.LG])
    This paper describes the design and implementation of a new machine learning model for online learning systems.We aim at improving the intelligent level of the systems by enabling an automated math word problem solver which can support a wide range of functions such as homework correction, difficulty estimation, and priority recommendation. We originally planned to employ existing models but realized that they processed a math word problem as a sequence or a homogeneous graph of tokens. Relationships between the multiple types of tokens such as entity, unit, rate, and number were ignored.We decided to design and implement a novel model to use such relational data to bridge the information gap between human-readable language and machine-understandable logical form. We propose a heterogeneous line graph transformer (HLGT) model that constructs a heterogeneous line graph via semantic role labeling on math word problems and then perform node representation learning aware of edge types. We add numerical comparison as an auxiliary task to improve model training for real-world use. Experimental results show that the proposed model achieves a better performance than existing models and suggest that it is still far below human performance. Information utilization and knowledge discovery is continuously needed to improve the online learning systems.  ( 3 min )
    Goodness of Fit Metrics for Multi-class Predictor. (arXiv:2208.05651v1 [cs.LG])
    The multi-class prediction had gained popularity over recent years. Thus measuring fit goodness becomes a cardinal question that researchers often have to deal with. Several metrics are commonly used for this task. However, when one has to decide about the right measurement, he must consider that different use-cases impose different constraints that govern this decision. A leading constraint at least in \emph{real world} multi-class problems is imbalanced data: Multi categorical problems hardly provide symmetrical data. Hence, when we observe common KPIs (key performance indicators), e.g., Precision-Sensitivity or Accuracy, one can seldom interpret the obtained numbers into the model's actual needs. We suggest generalizing Matthew's correlation coefficient into multi-dimensions. This generalization is based on a geometrical interpretation of the generalized confusion matrix.  ( 2 min )
    Distributionally Robust Model-Based Offline Reinforcement Learning with Near-Optimal Sample Complexity. (arXiv:2208.05767v1 [cs.LG])
    This paper concerns the central issues of model robustness and sample efficiency in offline reinforcement learning (RL), which aims to learn to perform decision making from history data without active exploration. Due to uncertainties and variabilities of the environment, it is critical to learn a robust policy -- with as few samples as possible -- that performs well even when the deployed environment deviates from the nominal one used to collect the history dataset. We consider a distributionally robust formulation of offline RL, focusing on a tabular non-stationary finite-horizon robust Markov decision process with an uncertainty set specified by the Kullback-Leibler divergence. To combat with sample scarcity, a model-based algorithm that combines distributionally robust value iteration with the principle of pessimism in the face of uncertainty is proposed, by penalizing the robust value estimates with a carefully designed data-driven penalty term. Under a mild and tailored assumption of the history dataset that measures distribution shift without requiring full coverage of the state-action space, we establish the finite-sample complexity of the proposed algorithm, and further show it is almost unimprovable in light of a nearly-matching information-theoretic lower bound up to a polynomial factor of the horizon length. To the best our knowledge, this provides the first provably near-optimal robust offline RL algorithm that learns under model uncertainty and partial coverage.  ( 3 min )
    Scalable neural quantum states architecture for quantum chemistry. (arXiv:2208.05637v1 [physics.chem-ph])
    Variational optimization of neural-network representations of quantum states has been successfully applied to solve interacting fermionic problems. Despite rapid developments, significant scalability challenges arise when considering molecules of large scale, which correspond to non-locally interacting quantum spin Hamiltonians consisting of sums of thousands or even millions of Pauli operators. In this work, we introduce scalable parallelization strategies to improve neural-network-based variational quantum Monte Carlo calculations for ab-initio quantum chemistry applications. We establish GPU-supported local energy parallelism to compute the optimization objective for Hamiltonians of potentially complex molecules. Using autoregressive sampling techniques, we demonstrate systematic improvement in wall-clock timings required to achieve CCSD baseline target energies. The performance is further enhanced by accommodating the structure of resultant spin Hamiltonians into the autoregressive sampling ordering. The algorithm achieves promising performance in comparison with the classical approximate methods and exhibits both running time and scalability advantages over existing neural-network based methods.  ( 2 min )
    Regret Analysis for Hierarchical Experts Bandit Problem. (arXiv:2208.05622v1 [cs.LG])
    We study an extension of standard bandit problem in which there are R layers of experts. Multi-layered experts make selections layer by layer and only the experts in the last layer can play arms. The goal of the learning policy is to minimize the total regret in this hierarchical experts setting. We first analyze the case that total regret grows linearly with the number of layers. Then we focus on the case that all experts are playing Upper Confidence Bound (UCB) strategy and give several sub-linear upper bounds for different circumstances. Finally, we design some experiments to help the regret analysis for the general case of hierarchical UCB structure and show the practical significance of our theoretical results. This article gives many insights about reasonable hierarchical decision structure.  ( 2 min )
    OpenMedIA: Open-Source Medical Image Analysis Toolbox and Benchmark under Heterogeneous AI Computing Platforms. (arXiv:2208.05616v1 [eess.IV])
    In this paper, we present OpenMedIA, an open-source toolbox library containing a rich set of deep learning methods for medical image analysis under heterogeneous Artificial Intelligence (AI) computing platforms. Various medical image analysis methods, including 2D$/$3D medical image classification, segmentation, localisation, and detection, have been included in the toolbox with PyTorch and$/$or MindSpore implementations under heterogeneous NVIDIA and Huawei Ascend computing systems. To our best knowledge, OpenMedIA is the first open-source algorithm library providing compared PyTorch and MindSp  ( 2 min )
    Neural Networks for Scalar Input and Functional Output. (arXiv:2208.05776v1 [stat.ML])
    The regression of a functional response on a set of scalar predictors can be a challenging task, especially if there is a large number of predictors, these predictors have interaction effects, or the relationship between those predictors and the response is nonlinear. In this work, we propose a solution to this problem: a feed-forward neural network (NN) designed to predict a functional response using scalar inputs. First, we transform the functional response to a finite-dimension representation and then we construct a NN that outputs this representation. We proposed different objective functions to train the NN. The proposed models are suited for both regularly and irregularly spaced data and also provide multiple ways to apply a roughness penalty to control the smoothness of the predicted curve. The difficulty in implementing both those features lies in the definition of objective functions that can be back-propagated. In our experiments, we demonstrate that our model outperforms the conventional function-on-scalar regression model in multiple scenarios while computationally scaling better with the dimension of the predictors.  ( 2 min )
    A Principled Method for the Creation of Synthetic Multi-fidelity Data Sets. (arXiv:2208.05667v1 [stat.ML])
    Multifidelity and multioutput optimisation algorithms are an area of current interest in many areas of computational design as they allow experimental and computational proxies to be used intelligently in the search for optimal species. Characterisation of these algorithms involves benchmarks that typically either use analytic functions or existing multifidelity datasets. Unfortunately, existing analytic functions are often not representative of relevant problems, while many existing datasets are not constructed to easily allow systematic investigation of the influence of characteristics of the contained proxies functions. To fulfil this need, we present a methodology for systematic generation of synthetic fidelities derived from a reference ground truth function with a controllable degree of correlation.  ( 2 min )
    Interpretable cytometry cell-type annotation with flow-based deep generative models. (arXiv:2208.05745v1 [q-bio.QM])
    Cytometry enables precise single-cell phenotyping within heterogeneous populations. These cell types are traditionally annotated via manual gating, but this method suffers from a lack of reproducibility and sensitivity to batch-effect. Also, the most recent cytometers - spectral flow or mass cytometers - create rich and high-dimensional data whose analysis via manual gating becomes challenging and time-consuming. To tackle these limitations, we introduce Scyan (https://github.com/MICS-Lab/scyan), a Single-cell Cytometry Annotation Network that automatically annotates cell types using only prior expert knowledge about the cytometry panel. We demonstrate that Scyan significantly outperforms the related state-of-the-art models on multiple public datasets while being faster and interpretable. In addition, Scyan overcomes several complementary tasks such as batch-effect removal, debarcoding, and population discovery. Overall, this model accelerates and eases cell population characterisation, quantification, and discovery in cytometry.  ( 2 min )
    Neural Embedding: Learning the Embedding of Manifold of Physics Data. (arXiv:2208.05484v1 [hep-ph])
    In this paper, we present a method of embedding physics data manifolds with metric structure into lower dimensional spaces with simpler metrics, such as Euclidean and Hyperbolic spaces. We then demonstrate that it can be a powerful step in the data analysis pipeline for many applications. Using progressively more realistic simulated collisions at the Large Hadron Collider, we show that this embedding approach learns the underlying latent structure. With the notion of volume in Euclidean spaces, we provide for the first time a viable solution to quantifying the true search capability of model agnostic search algorithms in collider physics (i.e. anomaly detection). Finally, we discuss how the ideas presented in this paper can be employed to solve many practical challenges that require the extraction of physically meaningful representations from information in complex high dimensional datasets.  ( 2 min )
    SSDBCODI: Semi-Supervised Density-Based Clustering with Outliers Detection Integrated. (arXiv:2208.05561v1 [cs.LG])
    Clustering analysis is one of the critical tasks in machine learning. Traditionally, clustering has been an independent task, separate from outlier detection. Due to the fact that the performance of clustering can be significantly eroded by outliers, a small number of algorithms try to incorporate outlier detection in the process of clustering. However, most of those algorithms are based on unsupervised partition-based algorithms such as k-means. Given the nature of those algorithms, they often fail to deal with clusters of complex, non-convex shapes. To tackle this challenge, we have proposed SSDBCODI, a semi-supervised density-based algorithm. SSDBCODI combines the advantage of density-based algorithms, which are capable of dealing with clusters of complex shapes, with the semi-supervised element, which offers flexibility to adjust the clustering results based on a few user labels. We also merge an outlier detection component with the clustering process. Potential outliers are detected based on three scores generated during the process: (1) reachability-score, which measures how density-reachable a point is to a labeled normal object, (2) local-density-score, which measures the neighboring density of data objects, and (3) similarity-score, which measures the closeness of a point to its nearest labeled outliers. Then in the following step, instance weights are generated for each data instance based on those three scores before being used to train a classifier for further clustering and outlier detection. To enhance the understanding of the proposed algorithm, for our evaluation, we have run our proposed algorithm against some of the state-of-art approaches on multiple datasets and separately listed the results of outlier detection apart from clustering. Our results indicate that our algorithm can achieve superior results with a small percentage of labels.  ( 3 min )
    Multi-fidelity wavelet neural operator with application to uncertainty quantification. (arXiv:2208.05606v1 [cs.LG])
    Operator learning frameworks, because of their ability to learn nonlinear maps between two infinite dimensional functional spaces and utilization of neural networks in doing so, have recently emerged as one of the more pertinent areas in the field of applied machine learning. Although these frameworks are extremely capable when it comes to modeling complex phenomena, they require an extensive amount of data for successful training which is often not available or is too expensive. However, this issue can be alleviated with the use of multi-fidelity learning, where a model is trained by making use of a large amount of inexpensive low-fidelity data along with a small amount of expensive high-fidelity data. To this end, we develop a new framework based on the wavelet neural operator which is capable of learning from a multi-fidelity dataset. The developed model's excellent learning capabilities are demonstrated by solving different problems which require effective correlation learning between the two fidelities for surrogate construction. Furthermore, we also assess the application of the developed framework for uncertainty quantification. The results obtained from this work illustrate the excellent performance of the proposed framework.  ( 2 min )
    Finding Reusable Machine Learning Components to Build Programming Language Processing Pipelines. (arXiv:2208.05596v1 [cs.LG])
    Programming Language Processing (PLP) using machine learning has made vast improvements in the past few years. Increasingly more people are interested in exploring this promising field. However, it is challenging for new researchers and developers to find the right components to construct their own machine learning pipelines, given the diverse PLP tasks to be solved, the large number of datasets and models being released, and the set of complex compilers or tools involved. To improve the findability, accessibility, interoperability and reusability (FAIRness) of machine learning components, we collect and analyze a set of representative papers in the domain of machine learning-based PLP. We then identify and characterize key concepts including PLP tasks, model architectures and supportive tools. Finally, we show some example use cases of leveraging the reusable components to construct machine learning pipelines to solve a set of PLP tasks.  ( 2 min )
    Patching open-vocabulary models by interpolating weights. (arXiv:2208.05592v1 [cs.CV])
    Open-vocabulary models like CLIP achieve high accuracy across many image classification tasks. However, there are still settings where their zero-shot performance is far from optimal. We study model patching, where the goal is to improve accuracy on specific tasks without degrading accuracy on tasks where performance is already adequate. Towards this goal, we introduce PAINT, a patching method that uses interpolations between the weights of a model before fine-tuning and the weights after fine-tuning on a task to be patched. On nine tasks where zero-shot CLIP performs poorly, PAINT increases accuracy by 15 to 60 percentage points while preserving accuracy on ImageNet within one percentage point of the zero-shot model. PAINT also allows a single model to be patched on multiple tasks and improves with model scale. Furthermore, we identify cases of broad transfer, where patching on one task increases accuracy on other tasks even when the tasks have disjoint classes. Finally, we investigate applications beyond common benchmarks such as counting or reducing the impact of typographic attacks on CLIP. Our findings demonstrate that it is possible to expand the set of tasks on which open-vocabulary models achieve high accuracy without re-training them from scratch.  ( 2 min )
    High-Frequency Space Diffusion Models for Accelerated MRI. (arXiv:2208.05481v1 [eess.IV])
    Denoising diffusion probabilistic models (DDPMs) have been shown to have superior performances in MRI reconstruction. From the perspective of continuous stochastic differential equations (SDEs), the reverse process of DDPM can be seen as maximizing the energy of the reconstructed MR image, leading to SDE sequence divergence. For this reason, a modified high-frequency DDPM model is proposed for MRI reconstruction. From its continuous SDE viewpoint, termed high-frequency space SDE (HFS-SDE), the energy concentrated low-frequency part of the MR image is no longer amplified, and the diffusion process focuses more on acquiring high-frequency prior information. It not only improves the stability of the diffusion model but also provides the possibility of better recovery of high-frequency details. Experiments on the publicly fastMRI dataset show that our proposed HFS-SDE outperforms the DDPM-driven VP-SDE, supervised deep learning methods and traditional parallel imaging methods in terms of stability and reconstruction accuracy.  ( 2 min )
    Semi-supervised segmentation of tooth from 3D Scanned Dental Arches. (arXiv:2208.05539v1 [cs.CV])
    Teeth segmentation is an important topic in dental restorations that is essential for crown generation, diagnosis, and treatment planning. In the dental field, the variability of input data is high and there are no publicly available 3D dental arch datasets. Although there has been improvement in the field provided by recent deep learning architectures on 3D data, there still exists some problems such as properly identifying missing teeth in an arch. We propose to use spectral clustering as a self-supervisory signal to joint-train neural networks for segmentation of 3D arches. Our approach is motivated by the observation that K-means clustering provides cues to capture margin lines related to human perception. The main idea is to automatically generate training data by decomposing unlabeled 3D arches into segments relying solely on geometric information. The network is then trained using a joint loss that combines a supervised loss of annotated input and a self-supervised loss of non-labeled input. Our collected data has a variety of arches including arches with missing teeth. Our experimental results show improvement over the fully supervised state-of-the-art MeshSegNet when using semi-supervised learning. Finally, we contribute code and a dataset.  ( 3 min )
    Are Gradients on Graph Structure Reliable in Gray-box Attacks?. (arXiv:2208.05514v1 [cs.CR])
    Graph edge perturbations are dedicated to damaging the prediction of graph neural networks by modifying the graph structure. Previous gray-box attackers employ gradients from the surrogate model to locate the vulnerable edges to perturb the graph structure. However, unreliability exists in gradients on graph structures, which is rarely studied by previous works. In this paper, we discuss and analyze the errors caused by the unreliability of the structural gradients. These errors arise from rough gradient usage due to the discreteness of the graph structure and from the unreliability in the meta-gradient on the graph structure. In order to address these problems, we propose a novel attack model with methods to reduce the errors inside the structural gradients. We propose edge discrete sampling to select the edge perturbations associated with hierarchical candidate selection to ensure computational efficiency. In addition, semantic invariance and momentum gradient ensemble are proposed to address the gradient fluctuation on semantic-augmented graphs and the instability of the surrogate model. Experiments are conducted in untargeted gray-box poisoning scenarios and demonstrate the improvement in the performance of our approach.  ( 2 min )
    The Moral Foundations Reddit Corpus. (arXiv:2208.05545v1 [cs.CL])
    Moral framing and sentiment can affect a variety of online and offline behaviors, including donation, pro-environmental action, political engagement, and even participation in violent protests. Various computational methods in Natural Language Processing (NLP) have been used to detect moral sentiment from textual data, but in order to achieve better performances in such subjective tasks, large sets of hand-annotated training data are needed. Previous corpora annotated for moral sentiment have proven valuable, and have generated new insights both within NLP and across the social sciences, but have been limited to Twitter. To facilitate improving our understanding of the role of moral rhetoric, we present the Moral Foundations Reddit Corpus, a collection of 16,123 Reddit comments that have been curated from 12 distinct subreddits, hand-annotated by at least three trained annotators for 8 categories of moral sentiment (i.e., Care, Proportionality, Equality, Purity, Authority, Loyalty, Thin Morality, Implicit/Explicit Morality) based on the updated Moral Foundations Theory (MFT) framework. We use a range of methodologies to provide baseline moral-sentiment classification results for this new corpus, e.g., cross-domain classification and knowledge transfer.  ( 2 min )
    Customized Watermarking for Deep Neural Networks via Label Distribution Perturbation. (arXiv:2208.05477v1 [cs.CR])
    With the increasing application value of machine learning, the intellectual property (IP) rights of deep neural networks (DNN) are getting more and more attention. With our analysis, most of the existing DNN watermarking methods can resist fine-tuning and pruning attack, but distillation attack. To address these problem, we propose a new DNN watermarking framework, Unified Soft-label Perturbation (USP), having a detector paired with the model to be watermarked, and Customized Soft-label Perturbation (CSP), embedding watermark via adding perturbation into the model output probability distribution. Experimental results show that our methods can resist all watermark removal attacks and outperform in distillation attack. Besides, we also have an excellent trade-off between the main task and watermarking that achieving 98.68% watermark accuracy while only affecting the main task accuracy by 0.59%.  ( 2 min )
    Polynomial Optimization: Enhancing RLT relaxations with Conic Constraints. (arXiv:2208.05608v1 [math.OC])
    Conic optimization has recently emerged as a powerful tool for designing tractable and guaranteed algorithms for non-convex polynomial optimization problems. On the one hand, tractability is crucial for efficiently solving large-scale problems and, on the other hand, strong bounds are needed to ensure high quality solutions. In this research, we investigate the strengthening of RLT relaxations of polynomial optimization problems through the addition of nine different types of constraints that are based on linear, second-order cone, and semidefinite programming to solve to optimality the instances of well established test sets of polynomial optimization problems. We describe how to design these conic constraints and their performance with respect to each other and with respect to the standard RLT relaxations. Our first finding is that the different variants of nonlinear constraints (second-order cone and semidefinite) are the best performing ones in around $50\%$ of the instances. Additionally, we present a machine learning approach to decide on the most suitable constraints to add for a given instance. The computational results show that the machine learning approach significantly outperforms each and every one of the nine individual approaches.  ( 2 min )
    Quality Not Quantity: On the Interaction between Dataset Design and Robustness of CLIP. (arXiv:2208.05516v1 [cs.LG])
    Web-crawled datasets have enabled remarkable generalization capabilities in recent image-text models such as CLIP (Contrastive Language-Image pre-training) or Flamingo, but little is known about the dataset creation processes. In this work, we introduce a testbed of six publicly available data sources - YFCC, LAION, Conceptual Captions, WIT, RedCaps, Shutterstock - to investigate how pre-training distributions induce robustness in CLIP. We find that the performance of the pre-training data varies substantially across distribution shifts, with no single data source dominating. Moreover, we systematically study the interactions between these data sources and find that combining multiple sources does not necessarily yield better models, but rather dilutes the robustness of the best individual data source. We complement our empirical findings with theoretical insights from a simple setting, where combining the training data also results in diluted robustness. In addition, our theoretical model provides a candidate explanation for the success of the CLIP-based data filtering technique recently employed in the LAION dataset. Overall our results demonstrate that simply gathering a large amount of data from the web is not the most effective way to build a pre-training dataset for robust generalization, necessitating further study into dataset design.  ( 2 min )
    Modeling Diverse Chemical Reactions for Single-step Retrosynthesis via Discrete Latent Variables. (arXiv:2208.05482v1 [q-bio.QM])
    Single-step retrosynthesis is the cornerstone of retrosynthesis planning, which is a crucial task for computer-aided drug discovery. The goal of single-step retrosynthesis is to identify the possible reactants that lead to the synthesis of the target product in one reaction. By representing organic molecules as canonical strings, existing sequence-based retrosynthetic methods treat the product-to-reactant retrosynthesis as a sequence-to-sequence translation problem. However, most of them struggle to identify diverse chemical reactions for a desired product due to the deterministic inference, which contradicts the fact that many compounds can be synthesized through various reaction types with different sets of reactants. In this work, we aim to increase reaction diversity and generate various reactants using discrete latent variables. We propose a novel sequence-based approach, namely RetroDVCAE, which incorporates conditional variational autoencoders into single-step retrosynthesis and associates discrete latent variables with the generation process. Specifically, RetroDVCAE uses the Gumbel-Softmax distribution to approximate the categorical distribution over potential reactions and generates multiple sets of reactants with the variational decoder. Experiments demonstrate that RetroDVCAE outperforms state-of-the-art baselines on both benchmark dataset and homemade dataset. Both quantitative and qualitative results show that RetroDVCAE can model the multi-modal distribution over reaction types and produce diverse reactant candidates.  ( 2 min )
    Imbalance Trouble: Revisiting Neural-Collapse Geometry. (arXiv:2208.05512v1 [cs.LG])
    Neural Collapse refers to the remarkable structural properties characterizing the geometry of class embeddings and classifier weights, found by deep nets when trained beyond zero training error. However, this characterization only holds for balanced data. Here we thus ask whether it can be made invariant to class imbalances. Towards this end, we adopt the unconstrained-features model (UFM), a recent theoretical model for studying neural collapse, and introduce Simplex-Encoded-Labels Interpolation (SELI) as an invariant characterization of the neural collapse phenomenon. Specifically, we prove for the UFM with cross-entropy loss and vanishing regularization that, irrespective of class imbalances, the embeddings and classifiers always interpolate a simplex-encoded label matrix and that their individual geometries are determined by the SVD factors of this same label matrix. We then present extensive experiments on synthetic and real datasets that confirm convergence to the SELI geometry. However, we caution that convergence worsens with increasing imbalances. We theoretically support this finding by showing that unlike the balanced case, when minorities are present, ridge-regularization plays a critical role in tweaking the geometry. This defines new questions and motivates further investigations into the impact of class imbalances on the rates at which first-order methods converge to their asymptotically preferred solutions.  ( 2 min )
  • Open

    Learning governing physics from output only measurements. (arXiv:2208.05609v1 [physics.data-an])
    Extracting governing physics from data is a key challenge in many areas of science and technology. The existing techniques for equations discovery are dependent on both input and state measurements; however, in practice, we only have access to the output measurements only. We here propose a novel framework for learning governing physics of dynamical system from output only measurements; this essentially transfers the physics discovery problem from the deterministic to the stochastic domain. The proposed approach models the input as a stochastic process and blends concepts of stochastic calculus, sparse learning algorithms, and Bayesian statistics. In particular, we combine sparsity promoting spike and slab prior, Bayes law, and Euler Maruyama scheme to identify the governing physics from data. The resulting model is highly efficient and works with sparse, noisy, and incomplete output measurements. The efficacy and robustness of the proposed approach is illustrated on several numerical examples involving both complete and partial state measurements. The results obtained indicate the potential of the proposed approach in identifying governing physics from output only measurement.  ( 2 min )
    Fast variable selection makes Karhunen-Lo\`eve decomposed Gaussian process BSS-ANOVA a speedy and accurate choice for dynamic systems identification. (arXiv:2205.13676v2 [cs.LG] UPDATED)
    Many approaches for scalable GPs have focused on using a subset of data as inducing points. Another promising approach is the Karhunen-Lo\`eve (KL) decomposition, in which the GP kernel is represented by a set of basis functions which are the eigenfunctions of the kernel operator. Such kernels have the potential to be very fast, and do not depend on the selection of a reduced set of inducing points. However KL decompositions lead to high dimensionality, and variable selection thus becomes paramount. This paper reports a new method of forward variable selection, enabled by the ordered nature of the basis functions in the KL expansion of the Bayesian Smoothing Spline ANOVA kernel (BSS-ANOVA), coupled with fast Gibbs sampling in a fully Bayesian approach. The new algorithm determines how high the orders of included terms should reach, balancing model fidelity with model complexity using $L^0$ penalties inherent in Bayesian and Akaike information criteria. The inference speed and accuracy makes the method especially useful for modeling dynamic systems, by modeling the derivative in a dynamic system as a static problem, then integrating the learned dynamics using a high-order scheme. The methods are demonstrated on two dynamic datasets: a `Susceptible, Infected, Recovered' (SIR) toy problem, with the transmissibility used as forcing function, along with the experimental `Cascaded Tanks' benchmark dataset. Comparisons on the static prediction of derivatives are made with a random forest (RF), a residual neural network (ResNet), and the Orthogonal Additive Kernel (OAK) inducing points scalable GP, while for the timeseries prediction comparisons are made with LSTM and GRU recurrent neural networks (RNNs).  ( 3 min )
    Uncertainty Quantification of Sparse Travel Demand Prediction with Spatial-Temporal Graph Neural Networks. (arXiv:2208.05908v1 [cs.LG])
    Origin-Destination (O-D) travel demand prediction is a fundamental challenge in transportation. Recently, spatial-temporal deep learning models demonstrate the tremendous potential to enhance prediction accuracy. However, few studies tackled the uncertainty and sparsity issues in fine-grained O-D matrices. This presents a serious problem, because a vast number of zeros deviate from the Gaussian assumption underlying the deterministic deep learning models. To address this issue, we design a Spatial-Temporal Zero-Inflated Negative Binomial Graph Neural Network (STZINB-GNN) to quantify the uncertainty of the sparse travel demand. It analyzes spatial and temporal correlations using diffusion and temporal convolution networks, which are then fused to parameterize the probabilistic distributions of travel demand. The STZINB-GNN is examined using two real-world datasets with various spatial and temporal resolutions. The results demonstrate the superiority of STZINB-GNN over benchmark models, especially under high spatial-temporal resolutions, because of its high accuracy, tight confidence intervals, and interpretable parameters. The sparsity parameter of the STZINB-GNN has physical interpretation for various transportation applications.  ( 2 min )
    Random survival forests for competing risks with multivariate longitudinal endogenous covariates. (arXiv:2208.05801v1 [stat.ML])
    Predicting the individual risk of a clinical event using the complete patient history is still a major challenge for personalized medicine. Among the methods developed to compute individual dynamic predictions, the joint models have the assets of using all the available information while accounting for dropout. However, they are restricted to a very small number of longitudinal predictors. Our objective was to propose an innovative alternative solution to predict an event probability using a possibly large number of longitudinal predictors. We developed DynForest, an extension of random survival forests for competing risks that handles endogenous longitudinal predictors. At each node of the trees, the time-dependent predictors are translated into time-fixed features (using mixed models) to be used as candidates for splitting the subjects into two subgroups. The individual event probability is estimated in each tree by the Aalen-Johansen estimator of the leaf in which the subject is classified according to his/her history of predictors. The final individual prediction is given by the average of the tree-specific individual event probabilities. We carried out a simulation study to demonstrate the performances of DynForest both in a small dimensional context (in comparison with joint models) and in a large dimensional context (in comparison with a regression calibration method that ignores informative dropout). We also applied DynForest to (i) predict the individual probability of dementia in the elderly according to repeated measures of cognitive, functional, vascular and neuro-degeneration markers, and (ii) quantify the importance of each type of markers for the prediction of dementia. Implemented in the R package DynForest, our methodology provides a solution for the prediction of events from longitudinal endogenous predictors whatever their number.  ( 3 min )
    Best Policy Identification in Linear MDPs. (arXiv:2208.05633v1 [cs.LG])
    We investigate the problem of best policy identification in discounted linear Markov Decision Processes in the fixed confidence setting under a generative model. We first derive an instance-specific lower bound on the expected number of samples required to identify an $\varepsilon$-optimal policy with probability $1-\delta$. The lower bound characterizes the optimal sampling rule as the solution of an intricate non-convex optimization program, but can be used as the starting point to devise simple and near-optimal sampling rules and algorithms. We devise such algorithms. One of these exhibits a sample complexity upper bounded by ${\cal O}({\frac{d}{(\varepsilon+\Delta)^2}} (\log(\frac{1}{\delta})+d))$ where $\Delta$ denotes the minimum reward gap of sub-optimal actions and $d$ is the dimension of the feature space. This upper bound holds in the moderate-confidence regime (i.e., for all $\delta$), and matches existing minimax and gap-dependent lower bounds. We extend our algorithm to episodic linear MDPs.  ( 2 min )
    Explaining Machine Learning Models using Entropic Variable Projection. (arXiv:1810.07924v6 [stat.ML] UPDATED)
    In this paper, we present a new explainability formalism designed to shed light on how each input variable of a test set impacts the predictions of machine learning models. Hence, we propose a group explainability formalism for trained machine learning decision rules, based on their response to the variability of the input variables distribution. In order to emphasize the impact of each input variable, this formalism uses an information theory framework that quantifies the influence of all input-output observations based on entropic projections. This is thus the first unified and model agnostic formalism enabling data scientists to interpret the dependence between the input variables, their impact on the prediction errors, and their influence on the output predictions. Convergence rates of the entropic projections are provided in the large sample case. Most importantly, we prove that computing an explanation in our framework has a low algorithmic complexity, making it scalable to real-life large datasets. We illustrate our strategy by explaining complex decision rules learned by using XGBoost, Random Forest or Deep Neural Network classifiers on various datasets such as Adult Income, MNIST, CelebA, Boston Housing, Iris, as well as synthetic ones. We finally make clear its differences with the explainability strategies LIME and SHAP, that are based on single observations. Results can be reproduced by using the freely distributed Python toolbox https://gems-ai.aniti.fr/.  ( 3 min )
    Distributionally Robust Model-Based Offline Reinforcement Learning with Near-Optimal Sample Complexity. (arXiv:2208.05767v1 [cs.LG])
    This paper concerns the central issues of model robustness and sample efficiency in offline reinforcement learning (RL), which aims to learn to perform decision making from history data without active exploration. Due to uncertainties and variabilities of the environment, it is critical to learn a robust policy -- with as few samples as possible -- that performs well even when the deployed environment deviates from the nominal one used to collect the history dataset. We consider a distributionally robust formulation of offline RL, focusing on a tabular non-stationary finite-horizon robust Markov decision process with an uncertainty set specified by the Kullback-Leibler divergence. To combat with sample scarcity, a model-based algorithm that combines distributionally robust value iteration with the principle of pessimism in the face of uncertainty is proposed, by penalizing the robust value estimates with a carefully designed data-driven penalty term. Under a mild and tailored assumption of the history dataset that measures distribution shift without requiring full coverage of the state-action space, we establish the finite-sample complexity of the proposed algorithm, and further show it is almost unimprovable in light of a nearly-matching information-theoretic lower bound up to a polynomial factor of the horizon length. To the best our knowledge, this provides the first provably near-optimal robust offline RL algorithm that learns under model uncertainty and partial coverage.  ( 3 min )
    Valid Inference after Causal Discovery. (arXiv:2208.05949v1 [stat.ME])
    Causal graph discovery and causal effect estimation are two fundamental tasks in causal inference. While many methods have been developed for each task individually, statistical challenges arise when applying these methods jointly: estimating causal effects after running causal discovery algorithms on the same data leads to "double dipping," invalidating coverage guarantees of classical confidence intervals. To this end, we develop tools for valid post-causal-discovery inference. One key contribution is a randomized version of the greedy equivalence search (GES) algorithm, which permits a valid, finite-sample correction of classical confidence intervals. Across empirical studies, we show that a naive combination of causal discovery and subsequent inference algorithms typically leads to highly inflated miscoverage rates; at the same time, our noisy GES method provides reliable coverage control while achieving more accurate causal graph recovery than data splitting.  ( 2 min )
    Imbalance Trouble: Revisiting Neural-Collapse Geometry. (arXiv:2208.05512v1 [cs.LG])
    Neural Collapse refers to the remarkable structural properties characterizing the geometry of class embeddings and classifier weights, found by deep nets when trained beyond zero training error. However, this characterization only holds for balanced data. Here we thus ask whether it can be made invariant to class imbalances. Towards this end, we adopt the unconstrained-features model (UFM), a recent theoretical model for studying neural collapse, and introduce Simplex-Encoded-Labels Interpolation (SELI) as an invariant characterization of the neural collapse phenomenon. Specifically, we prove for the UFM with cross-entropy loss and vanishing regularization that, irrespective of class imbalances, the embeddings and classifiers always interpolate a simplex-encoded label matrix and that their individual geometries are determined by the SVD factors of this same label matrix. We then present extensive experiments on synthetic and real datasets that confirm convergence to the SELI geometry. However, we caution that convergence worsens with increasing imbalances. We theoretically support this finding by showing that unlike the balanced case, when minorities are present, ridge-regularization plays a critical role in tweaking the geometry. This defines new questions and motivates further investigations into the impact of class imbalances on the rates at which first-order methods converge to their asymptotically preferred solutions.  ( 2 min )
    Learning Topic Models: Identifiability and Finite-Sample Analysis. (arXiv:2110.04232v2 [stat.ML] UPDATED)
    Topic models provide a useful text-mining tool for learning, extracting, and discovering latent structures in large text corpora. Although a plethora of methods have been proposed for topic modeling, lacking in the literature is a formal theoretical investigation of the statistical identifiability and accuracy of latent topic estimation. In this paper, we propose a maximum likelihood estimator (MLE) of latent topics based on a specific integrated likelihood that is naturally connected to the concept, in computational geometry, of volume minimization. Our theory introduces a new set of geometric conditions for topic model identifiability, conditions that are weaker than conventional separability conditions, which typically rely on the existence of pure topic documents or of anchor words. Weaker conditions allow a wider and thus potentially more fruitful investigation. We conduct finite-sample error analysis for the proposed estimator and discuss connections between our results and those of previous investigations. We conclude with empirical studies employing both simulated and real datasets.  ( 2 min )
    Non-Asymptotic Analysis of Stochastic Approximation Algorithms for Streaming Data. (arXiv:2109.07117v5 [cs.LG] UPDATED)
    We consider the stochastic approximation problem in a streaming framework where an objective is minimized through unbiased estimates of its gradients. In this streaming framework, we consider time-varying data streams that must be processed sequentially. Our methods are Stochastic Gradient (SG) based due to their applicability and computational advantages. We provide a non-asymptotic analysis of the convergence of various SG-based methods; this includes the famous SG descent (a.k.a. Robbins-Monro algorithm), constant and time-varying mini-batch SG methods, and their averaged estimates (a.k.a. Polyak-Ruppert averaging). Our analysis suggests choosing the learning rate according to the expected data streams, which can speed up the convergence. In addition, we show how the averaged estimate can achieve optimal convergence in terms of attaining Cramer-Rao's lower bound while being robust to any data stream rate. In particular, our analysis shows how Polyak-Ruppert averaging of time-varying mini-batches can provide variance reduction and accelerate convergence simultaneously, which is advantageous for large-scale learning problems. These theoretical results are illustrated for various data streams, showing the effectiveness of the proposed algorithms.  ( 3 min )
    General Cutting Planes for Bound-Propagation-Based Neural Network Verification. (arXiv:2208.05740v1 [cs.LG])
    Bound propagation methods, when combined with branch and bound, are among the most effective methods to formally verify properties of deep neural networks such as correctness, robustness, and safety. However, existing works cannot handle the general form of cutting plane constraints widely accepted in traditional solvers, which are crucial for strengthening verifiers with tightened convex relaxations. In this paper, we generalize the bound propagation procedure to allow the addition of arbitrary cutting plane constraints, including those involving relaxed integer variables that do not appear in existing bound propagation formulations. Our generalized bound propagation method, GCP-CROWN, opens up the opportunity to apply general cutting plane methods} for neural network verification while benefiting from the efficiency and GPU acceleration of bound propagation methods. As a case study, we investigate the use of cutting planes generated by off-the-shelf mixed integer programming (MIP) solver. We find that MIP solvers can generate high-quality cutting planes for strengthening bound-propagation-based verifiers using our new formulation. Since the branching-focused bound propagation procedure and the cutting-plane-focused MIP solver can run in parallel utilizing different types of hardware (GPUs and CPUs), their combination can quickly explore a large number of branches with strong cutting planes, leading to strong verification performance. Experiments demonstrate that our method is the first verifier that can completely solve the oval20 benchmark and verify twice as many instances on the oval21 benchmark compared to the best tool in VNN-COMP 2021, and also noticeably outperforms state-of-the-art verifiers on a wide range of benchmarks. GCP-CROWN is part of the $\alpha$,$\beta$-CROWN verifier, the VNN-COMP 2022 winner. Code is available at this http URL  ( 3 min )
    A Principled Method for the Creation of Synthetic Multi-fidelity Data Sets. (arXiv:2208.05667v1 [stat.ML])
    Multifidelity and multioutput optimisation algorithms are an area of current interest in many areas of computational design as they allow experimental and computational proxies to be used intelligently in the search for optimal species. Characterisation of these algorithms involves benchmarks that typically either use analytic functions or existing multifidelity datasets. Unfortunately, existing analytic functions are often not representative of relevant problems, while many existing datasets are not constructed to easily allow systematic investigation of the influence of characteristics of the contained proxies functions. To fulfil this need, we present a methodology for systematic generation of synthetic fidelities derived from a reference ground truth function with a controllable degree of correlation.  ( 2 min )
    Distributionally Robust Losses for Latent Covariate Mixtures. (arXiv:2007.13982v2 [cs.LG] UPDATED)
    While modern large-scale datasets often consist of heterogeneous subpopulations -- for example, multiple demographic groups or multiple text corpora -- the standard practice of minimizing average loss fails to guarantee uniformly low losses across all subpopulations. We propose a convex procedure that controls the worst-case performance over all subpopulations of a given size. Our procedure comes with finite-sample (nonparametric) convergence guarantees on the worst-off subpopulation. Empirically, we observe on lexical similarity, wine quality, and recidivism prediction tasks that our worst-case procedure learns models that do well against unseen subpopulations.  ( 2 min )
    Diagnosing and Fixing Manifold Overfitting in Deep Generative Models. (arXiv:2204.07172v3 [stat.ML] UPDATED)
    Likelihood-based, or explicit, deep generative models use neural networks to construct flexible high-dimensional densities. This formulation directly contradicts the manifold hypothesis, which states that observed data lies on a low-dimensional manifold embedded in high-dimensional ambient space. In this paper we investigate the pathologies of maximum-likelihood training in the presence of this dimensionality mismatch. We formally prove that degenerate optima are achieved wherein the manifold itself is learned but not the distribution on it, a phenomenon we call manifold overfitting. We propose a class of two-step procedures consisting of a dimensionality reduction step followed by maximum-likelihood density estimation, and prove that they recover the data-generating distribution in the nonparametric regime, thus avoiding manifold overfitting. We also show that these procedures enable density estimation on the manifolds learned by implicit models, such as generative adversarial networks, hence addressing a major shortcoming of these models. Several recently proposed methods are instances of our two-step procedures; we thus unify, extend, and theoretically justify a large class of models.  ( 2 min )
    Neural Networks for Scalar Input and Functional Output. (arXiv:2208.05776v1 [stat.ML])
    The regression of a functional response on a set of scalar predictors can be a challenging task, especially if there is a large number of predictors, these predictors have interaction effects, or the relationship between those predictors and the response is nonlinear. In this work, we propose a solution to this problem: a feed-forward neural network (NN) designed to predict a functional response using scalar inputs. First, we transform the functional response to a finite-dimension representation and then we construct a NN that outputs this representation. We proposed different objective functions to train the NN. The proposed models are suited for both regularly and irregularly spaced data and also provide multiple ways to apply a roughness penalty to control the smoothness of the predicted curve. The difficulty in implementing both those features lies in the definition of objective functions that can be back-propagated. In our experiments, we demonstrate that our model outperforms the conventional function-on-scalar regression model in multiple scenarios while computationally scaling better with the dimension of the predictors.  ( 2 min )
    Adaptive LASSO estimation for functional hidden dynamic geostatistical model. (arXiv:2208.05528v1 [stat.ME])
    We propose a novel model selection algorithm based on a penalized maximum likelihood estimator (PMLE) for functional hidden dynamic geostatistical models (f-HDGM). These models employ a classic mixed-effect regression structure with embedded spatiotemporal dynamics to model georeferenced data observed in a functional domain. Thus, the parameters of interest are functions across this domain. The algorithm simultaneously selects the relevant spline basis functions and regressors that are used to model the fixed-effects relationship between the response variable and the covariates. In this way, it automatically shrinks to zero irrelevant parts of the functional coefficients or the entire effect of irrelevant regressors. The algorithm is based on iterative optimisation and uses an adaptive least absolute shrinkage and selector operator (LASSO) penalty function, wherein the weights are obtained by the unpenalised f-HDGM maximum-likelihood estimators. The computational burden of maximisation is drastically reduced by a local quadratic approximation of the likelihood. Through a Monte Carlo simulation study, we analysed the performance of the algorithm under different scenarios, including strong correlations among the regressors. We showed that the penalised estimator outperformed the unpenalised estimator in all the cases we considered. We applied the algorithm to a real case study in which the recording of the hourly nitrogen dioxide concentrations in the Lombardy region in Italy was modelled as a functional process with several weather and land cover covariates.  ( 3 min )
    Achieving Fairness via Post-Processing in Web-Scale Recommender Systems. (arXiv:2006.11350v3 [stat.ML] UPDATED)
    Building fair recommender systems is a challenging and crucial area of study due to its immense impact on society. We extended the definitions of two commonly accepted notions of fairness to recommender systems, namely equality of opportunity and equalized odds. These fairness measures ensure that equally "qualified" (or "unqualified") candidates are treated equally regardless of their protected attribute status (such as gender or race). We propose scalable methods for achieving equality of opportunity and equalized odds in rankings in the presence of position bias, which commonly plagues data generated from recommender systems. Our algorithms are model agnostic in the sense that they depend only on the final scores provided by a model, making them easily applicable to virtually all web-scale recommender systems. We conduct extensive simulations as well as real-world experiments to show the efficacy of our approach.  ( 2 min )
    Adaptively Identifying Patient Populations With Treatment Benefit in Clinical Trials. (arXiv:2208.05844v1 [stat.ML])
    We study the problem of adaptively identifying patient subpopulations that benefit from a given treatment during a confirmatory clinical trial. This type of adaptive clinical trial, often referred to as adaptive enrichment design, has been thoroughly studied in biostatistics with a focus on a limited number of subgroups (typically two) which make up (sub)populations, and a small number of interim analysis points. In this paper, we aim to relax classical restrictions on such designs and investigate how to incorporate ideas from the recent machine learning literature on adaptive and online experimentation to make trials more flexible and efficient. We find that the unique characteristics of the subpopulation selection problem -- most importantly that (i) one is usually interested in finding subpopulations with any treatment benefit (and not necessarily the single subgroup with largest effect) given a limited budget and that (ii) effectiveness only has to be demonstrated across the subpopulation on average -- give rise to interesting challenges and new desiderata when designing algorithmic solutions. Building on these findings, we propose AdaGGI and AdaGCPI, two meta-algorithms for subpopulation construction, which focus on identifying good subgroups and good composite subpopulations, respectively. We empirically investigate their performance across a range of simulation scenarios and derive insights into their (dis)advantages across different settings.  ( 2 min )

  • Open

    “A beautiful painting of a portal to another universe” - Created the pixelz ai discord server
    submitted by /u/mdfnb [link] [comments]  ( 86 min )
    How Science Fiction Dystopianism Shapes the Debate over AI & Robotics - Discourse
    submitted by /u/Simcurious [link] [comments]  ( 86 min )
    Feedback on AI book
    Hi! I've finished writing the first draft of a book that tells the truth about the current status of AI and tells stories about how businesses and academics exaggerate and fiddle numbers to promote AI. The book is based on my decade of experience in the field. I'm a computer scientist with a PhD in AI. I'm looking for some beta readers that would like to read the draft and give me some honest feedback about it. It's a moderately short book, so it shouldn't take too long to read it. Is anybody interested in giving me a hand? Thanks! submitted by /u/lh511 [link] [comments]  ( 87 min )
    My second attempt at creating wallpapers: River in Lush Plains | Using MidJourney AI (Image Creator bot for Discord)
    submitted by /u/Potato_Player_BR [link] [comments]  ( 86 min )
    Neuraan: The NLP API specialized in the Spanish language
    Hello Reddit! We’re Israel & Mario, cofounders of Neuraan (https://neuraan.com/en). It is difficult for natural language processing developers in LatAm to get their applications to have the same accuracy in Spanish as in English. We create an API that helps them correct those inaccuracies and increase the adoption of their solution. We have +6 years of experience developing chatbots and voice assistants for large enterprises across LatAm and was very common to end up doing some research and developing algorithms to enhance the accuracy that the commercial solutions like Dialogflow, Watson, Amazon Lex, and Azure Cognitive deliver because they provide a general solution for non-English languages. To provide an extra layer to improve the performance of conversational applications in Spanish we developed a RESTful API that allows developers and enterprises to provide a few examples of the intents and entities used by their applications to enhance the recognition. We are using a few shot learning strategies developed using various Machine Learning strategies including deep neural networks along with self-developed algorithms. This solution permits the enhancement of the recognition of goal-oriented applications (create a support ticket, extract data from a user conversation, make a telephonic sale, etc.) instead of standard NLP engines' general Spanish. Our pricing is based on API requests. We look forward to feedback from the Reddit community! submitted by /u/Isracv [link] [comments]  ( 87 min )
    AI Dream 69 - EPIC Ending Imploding Universe by AI
    submitted by /u/LordPewPew777 [link] [comments]  ( 86 min )
    Midnight Rider
    Credit: https://discord.gg/x3s9Ye2h2A ​ https://preview.redd.it/hdptjfle84h91.png?width=1024&format=png&auto=webp&s=c034e659d5d7899c2e046aaa639363bc79ccafa6 https://preview.redd.it/d8udrjle84h91.png?width=1024&format=png&auto=webp&s=fef79955226d943727370a68c9b7f24aff7ac148 https://preview.redd.it/5w699jle84h91.png?width=1024&format=png&auto=webp&s=664377617c859bc0226e8c03310cb94a9f115925 submitted by /u/Old-Pumpkin4899 [link] [comments]  ( 86 min )
    Kiss and Make-Up (1934) - "Do you allow men to pick you up in the water?" Scene Colorized
    submitted by /u/ColorizingCinema [link] [comments]  ( 86 min )
    I created a website to remove image backgrounds without uploading images.It runs a machine learning model inside the browser
    Hi everyone, I am the founder of bgsub.com. There are already many sites on the web that automatically remove image backgrounds, but BgSub is the only one that doesn't require image uploading, Q: Why did I create this site? A: I really don't want to upload my images to the cloud server, no matter how guaranteed it is. Here is a brief description of the site. No download: Simply open the website and use all the features No login: No need to register or enter your information No paying: Supports resolutions up to 4096 x 4096 No upload: Using a highly optimized processing engine, all operations are performed locally, no need to upload images AI Coloring: Automatically adjust the image tone after changing the image background to make the image more harmonious If you have any feedback feel free to comment here, I will do better, thank you! ​ https://preview.redd.it/fcio8xj4k3h91.png?width=1619&format=png&auto=webp&s=9db6e099978240194b79f46fd446145f25a70c3a ​ https://preview.redd.it/orjdvy8ik3h91.png?width=1280&format=png&auto=webp&s=0e73d666578d0064f77eab2966f9d860dab6bbb5 submitted by /u/Fit_Committee_1313 [link] [comments]  ( 87 min )
    Type 3 civilization engine schematic for intergalactic travel
    submitted by /u/Far_Beyond_YT [link] [comments]  ( 86 min )
    Survey on AI Ethics and Readiness (For High School and First Year Bachelor Students)
    https://forms.gle/rPKmuN611VeLmZaNA I'm conducting a survey to understand awareness, attitudes and readiness of high school students towards Artificial Intelligence. The study will look at different aspects such as opportunities, risks, and ethics of AI, and also education necessary for high schoolers to improve their understanding. The results will be published as part of a detailed report. Your inputs are valuable in understanding how students learn and think about AI. All responses will be kept confidential and data is analysed only at the aggregate level. The “best” five responses will get a Rs 1000 amazon gift card each. The winners will be selected by, you guessed it, an algorithm. Thank You! submitted by /u/divijadurga [link] [comments]  ( 87 min )
    Is there AI that can continue image beyond it's borders?
    Is there any AI that can expand, or continue image beyond borders? For example take horizontal image and expand it to 16:9, filling up empty space with lots result, so it could look like this image originally was 16:9 submitted by /u/Lemenus [link] [comments]  ( 86 min )
    PepsiCo Inc. partners with Provectus to empower company's e-commerce business with AI and ML
    submitted by /u/TallAssociation0 [link] [comments]  ( 86 min )
    AI Technology Trends That Matter For Business in 2022 [Podcast]
    If you're working with artificial intelligence it's crucial to keep up with the latest trends in this industry. That's why I want to share with you this podcast on the top AI trends. Hope you'll find it helpful. AI in security and surveillance AI in real-time video processing AI for content creation and chatbots Other NLP solutions Use of GANs AI-driven visual inspection for production AI in healthcare No-code AI platforms Diversity in AI Listen to the podcast https://youtu.be/UVl_WKRyZuI submitted by /u/Data-Power [link] [comments]  ( 86 min )
    How soon can we expect AI that can learn what sort of tik tok feed we like and be able to make such content out of nothing endlessly?
    submitted by /u/aluode [link] [comments]  ( 88 min )
    My First Wallpaper: Planet in Nebula - Using MidJourney AI (Image Creator bot for Discord):
    submitted by /u/Potato_Player_BR [link] [comments]  ( 86 min )
    Blonde Crazy (1931) - "In my brassiere" Scene Colorized
    submitted by /u/ColorizingCinema [link] [comments]  ( 86 min )
    BlenderBot keeping it real...
    submitted by /u/Patmiass [link] [comments]  ( 91 min )
  • Open

    Weird Speech Synthesis Model Idea
    I barely have any qualifications to do what I want to do, but I want to generate a neural network model that generates speech from text. My idea though, was to not simply try to input characters, which may be more difficult for it to learn, and instead input phonemes, which seems to be a fairly standard idea as well. Not only that but I think it would be useful/better to have some sort of "pause" character as well so that you can specify when it should pause, you could detect commas, periods, etc. I think that would allow the model to perhaps even learn to generate the speech easier. One issue that this causes is that it will be much more difficult to label the training data with all of this information. Speech synthesis, which is what I want to do, seems more complicated or at least more difficult to get a model that works pretty well with. I was wondering if anyone thinks that the idea to first create a model that detects speech from audio, and then transcribes it as phonemes and pauses is a good idea and may be easier to make. That training data could also theoretically be used to train the synthesizer, but at the same time could train the speech transcriber to eventually be good enough to generate the training data for the synthesizer itself. It isn't quite a GAN or anything, although maybe it could be turned into one? But I think the idea of having two networks help each other sounds like a good idea to me in theory. Let me know all of your ideas. submitted by /u/Danktroyer27 [link] [comments]  ( 87 min )
    Full Stanford Seminar on YouTube - How to represent part-whole hierarchies in a neural network: Geoff Hinton of University of Toronto
    In this seminar Professor Hinton will present a single idea about representation which allows advances made by several different groups to be combined into an imaginary system called GLOM. GLOM answers the question, "How can a neural network with a fixed architecture parse an image into a part-whole hierarchy which has a different structure for each image?" Watch on YouTube submitted by /u/Stanford_Online [link] [comments]  ( 86 min )
  • Open

    [P] hyper-nn: Easy Hypernetworks in Pytorch and Jax
    Hey all! I’d like to share a project that I’ve been working on called hyper-nn, which gives users the ability to create easily customizable Hypernetworks for almost any torch.nn.Module and flax.linen.Module. If you aren’t familiar with Hypernetworks, I highly recommend the amazing blog post and paper by the original author, David Ha: https://blog.otoro.net/2016/09/28/hyper-networks/. Simply put, Hypernetworks are neural networks that output the parameters of another neural network. They can be incredibly powerful, being able to represent large networks while using only a fraction of their parameters, or even dynamically changing the weights depending on the input provided. With hyper-nn, we can easily customize and create a hypernetwork that works like any other torch or flax module in only a couple lines of code import torch import torch.nn as nn from hyper_nn.torch.hypernet import TorchHyperNetwork target_network = nn.Sequential( nn.Linear(8, 64), nn.ReLU(), nn.Linear(64, 32) ) EMBEDDING_DIM = 4 NUM_EMBEDDINGS = 32 hypernetwork = TorchHyperNetwork.from_target( target_network = target_network, embedding_dim = EMBEDDING_DIM, num_embeddings = NUM_EMBEDDINGS ) inp = torch.zeros((1, 8)) # generate parameters under the hood and use them in the target_network output = hypernetwork(inp=[inp]) # 1 x 32 # or generate parameters manually generated_params, aux_output = hypernetwork.generate_params(inp=[inp]) More interesting applications are provided in the github repo: Hypernetwork that generates weights for a Lunar Lander reinforcement learning policy Hypernetwork that generates dynamic weights that change for each character in a name generator A minimal multi-task hypernetwork that generates weights given a provided task id, implemented with < 50 lines of code. If any of this seems interesting to you, check out the github repo: https://github.com/shyamsn97/hyper-nn and let me know what you think! submitted by /u/shyamsn97 [link] [comments]  ( 89 min )
    [D] How to handle being given range of possible data values instead of an actual dataset for training?
    I was given a file that defines possible characteristics of thousands of dogs. I need to classify input dogs as being a certain breed. Example: dog breed X can weigh between 50-100 lbs, be 2.5-4.5 feet in height,is a friendly dog T/F, etc. Furthermore, our confidence about each parameter is different. We are confident about height parameters, not so confident about the weight range. For this project, I should make the assumption that each feature is uniformally distributed (ex: height is not normally distributed, equal chance that the dog is any value between the parameters) How do I go about creating my training dataset? Should I “generate” a fake training dataset based on the parameters given? How should I handle this lack of actual measures data? Or, is this the wrong approach and there is an optimal method given my problem space of only being given a range + linear distribution for each feature? submitted by /u/Old-Box228 [link] [comments]  ( 90 min )
    [D] With AutoML packages coming with Optuna - how do real ML engineers create and tune "real" models?
    As the title says, if these AutoML packages tests on majority of the algorithms and can even spend a user set amount of time on hyperparameter tuning; how do you guys actually build models better than this? Disclaimer, I am still relatively new to ML which is why I'm asking to understand how to improve the performance of my own tuned models over these AutoML packages. submitted by /u/RayPotatoes [link] [comments]  ( 88 min )
    [Research]: Looking for some suggestions for visual representation learning architectures! More info is below.
    I am guessing this will be some type of vision transformer architecture, but it would be great to get specific recommendations along with papers! Please let me know all suggestions you might have including but not limited to SOTA architectures because I might need to compare a few of them for my experiments (not yet 100% sure). Let me know if you need more details. Thanks! Edit: Punctuation submitted by /u/SeizeOpportunity [link] [comments]  ( 88 min )
    [Research] AI Ethics: The Case for Including Animals (Peter Singer's first paper on AI ethics)
    I just want to share a paper I recently published with Peter Singer. We argued that AI ethics should extend its scope to nonhuman animals. We also analyzed whether, and how, AI agents can behave ethically toward animals. Please kindly consider giving us feedback if you read the paper, thank you! https://link.springer.com/article/10.1007/s43681-022-00187-z submitted by /u/Tseyipfai [link] [comments]  ( 88 min )
    [D] List of 1000 Queries - Remove Similar Ones - Cosine Similarity?
    Hello, I have a list of 1000 questions and need to find which ones are similar to other queries so the list can be purged. I know there is subjectivity in "similar" and wondering how I would address that and accomplish this. TIA! submitted by /u/Klutzy-Way-2843 [link] [comments]  ( 116 min )
    [R] Video Question Answering with Iterative Video-Text Co-Tokenization - Google 2022 - Reduces GFLOPs from 150-360 to only 67 while being able to achive new SOTAs in three main VideoQA benchmarks MSRVTT-QA, MSVD-QA and IVQA!
    Paper: https://arxiv.org/abs/2208.00934 https://ai.googleblog.com/2022/08/efficient-video-text-learning-with.html Abstract: Video question answering is a challenging task that requires understanding jointly the language input, the visual information in individual video frames, as well as the temporal information about the events occurring in the video. In this paper, we propose a novel multi-stream video encoder for video question answering that uses multiple video inputs and a new video-text iterative co-tokenization approach to answer a variety of questions related to videos. We experimentally evaluate the model on several datasets, such as MSRVTT-QA, MSVD-QA, IVQA, outperforming the previous state-of-the-art by large margins. Simultaneously, our model reduces the required GFLOPs from 150-360 to only 67, producing a highly efficient video question answering model. ​ https://preview.redd.it/pjug6yu9e4h91.jpg?width=1195&format=pjpg&auto=webp&s=4d091fe3b3e22b5afa5595bc2346e62341cfbb0e https://preview.redd.it/90fk2rdae4h91.jpg?width=982&format=pjpg&auto=webp&s=17c76d5bfca9a08cab5259f3819d6f9c8300a527 https://preview.redd.it/c3a10qdae4h91.jpg?width=787&format=pjpg&auto=webp&s=fa9933d42ee6d004bdf5780b27a3a57fc8ea0ef4 submitted by /u/Singularian2501 [link] [comments]  ( 88 min )
    [D]: How safe is it to just use a strangers Model?
    I'm wondering about this, since with GPT-neo, it has become a thing to publish Free AI's freely. I once heard that machine-learning has a backdoor problem. How would backdoors in models work? How to check for backdoors? How to proceed safely? Which File Formats are most to least safe? (.json, .bin, .keras, . pt, .ot, .msgpack, .pickle) submitted by /u/GerritTheBerrit [link] [comments]  ( 89 min )
    [D] Modeling Imbalanced Classes with Quintuplet Sampling
    Does anyone have experience using Quintuplet Sampling and Triple-Header Hinge Loss for modeling imbalanced classes (Huang et al., 2016)? It seems like an intriguing technique theoretically, but does it really work better than simple upsampling/downsampling? submitted by /u/_aitalks_ [link] [comments]  ( 87 min )
    [D] What is some recent ideas/papers that you find most interesting?
    I am a bit tired of reading a lots of lego-like papers recently and just want to refresh my head with something new. Could anyone share some interesting ideas, elegent proofs or new perspective that you found really standout between thousand of published papers? submitted by /u/IndependentSavings60 [link] [comments]  ( 89 min )
    [R] [P] Design a car with a genetic algorithm to contribute to our research into human AI collaboration
    I'm part of a team of computer scientists interested in how how we can design tools to help human designers work with computer designers. As part of this work we have created one of those genetic algorithm car toys for you to play with. The more you play with it (and talk about it) the more useful data we get on how to design better algorithms and tools. You can have a play here - it works in a web browser but there are also downloadable versions if you want better performance (and gif recording!!) Any questions just ask! I hope using two tags is okay, this is both research and my own project :) This work is an extension of a project we did looking at how procedural level design tools effect the design process which you can read on arxiv here. In that work we created a dungeon level designer (source code) which worked along side a human designer. Using an evolutionary algorithm the tool suggested new levels to the human level designer and learned from what they liked and didn't like. Our main focus of the paper was to try and evaluate what effect the system actually had on the human designers. It turns out it really helped participants think of new ideas and go in new design directions. We actually found that participants using the tool spend longer on the task (because they were enjoying themselves) than participants given a placebo tool. submitted by /u/seanebaby [link] [comments]  ( 89 min )
    [D]: Is there a way to use GPT-Neo 2.7B with less than 10GB VRAM?
    Is there any way to Run GPT-Neo 2.7B on an Ampere GPU with less than 10GB VRAM? Like a 3060 or 3080 with only 6 or 8GB submitted by /u/GerritTheBerrit [link] [comments]  ( 89 min )
    [D] Reducing Pipeline Debt With Great Expectations
    What's your war story with data pipeline debt? 🤔. Have you ever joined an organization where you had to do a lot of tracing, re-orchestration, reworking, and so forth? The post (linked below) says that: Pipeline debt is technical debt in data pipelines. It arises when your data pipeline is triple-U: Undocumented Untested Unstable And I am wondering a few things: How about ML pipeline debts? How would you define them? The author says data pipeline debt arises from those practices mentioned above. Have you found other situations where pipeline debts were incurred? Struggling to understand what the author means by "unstable" in this case. Link: Reducing Pipeline Debt With Great Expectations. If many pipelines exist, they will inevitably blend | Source: Post author (linked above). submitted by /u/MLBoi_TM [link] [comments]  ( 88 min )
    [P] Using Machine learning to fact-check those in power without any human assistance!
    In an online world swamped with fake news machine learning is playing an increasingly important role in cleaning up our collective information ecology. At Full Fact we have been developing technology to help increase the speed of fact-checking. Recently we have been working on a tool that can automatically fact-check claims from the UK media without any human input. This tool is capable of extracting the key information from a claim before looking up the relevant data and determining its veracity. In this video we breakdown how we combined deep learning with more traditional NLP techniques to create this cutting-edge tool. submitted by /u/techinnovator [link] [comments]  ( 93 min )
    [D] Weighted Score for Regression Models
    Suppose I've got 2(or more) Regression Models for the same dataset. For each of them I've got the RMSE, MAE, R^2 scores for training & for testing data. Does anyone know how to calculate a weighted score (based on RMSE, MAE, R^2) which will allow me to conclude which model has better fit? submitted by /u/Short-Development-64 [link] [comments]  ( 88 min )
    [P] Fine-tuned the GPT-Neox Model to Generate Quotes
    We fine-tuned the 20B parameter GPT-Neox model by #EleutherAI to generate quotes. Generate a new quote: https://neox.labml.ai/quote Generated quotes: https://neox.labml.ai/quotes You can guide the model by adding an author, category or tags. Also, you can provide a start to your quote and, the model will complete it. We have open-sourced the code that we used to fine-tune the GPT-NeoX model. Annotated implantation: https://nn.labml.ai/neox/index.html Github: https://github.com/labmlai/annotated_deep_learning_paper_implementations/tree/master/labml_nn/neox EleutherAI GPT-Neox GitHub repo: https://github.com/EleutherAI/gpt-neox We also have deployed the GPT-Neox model for anyone who wants to try it out. Link to the Playground: https://neox.labml.ai We love to hear your feedback and suggestions. Thank you all, and I appreciate the support. submitted by /u/hnipun [link] [comments]  ( 88 min )
    [Discussion] PyTorch vs Taichi: What makes them different and complementary
    PyTorch is a widely-used machine learning framework; Taichi is a DSL embedded in Python and designed for high-performance numerical computation. I use both packages and feel the two can be a good combination in scenarios like differentiable physics simulation and reinforcement learning. For example, Taichi's autodiff feature can accelerate the convergence and its kernels can pre-process data and implement user-defined operators for PyTorch programs. To begin with, I wrote a blog to share my observations of some fundamental similarities and differences between PyTorch and Taichi to give a basic understanding of their design philosophies and what they are capable of. But this is just a comparison from a macro perspective, and I cannot possibly cover all the details. I haven't got time to touch upon real-life cases where they can be integrated but I plan to. It would be nice if you can share your thoughts with me! submitted by /u/Ailing-Zhang [link] [comments]  ( 88 min )
    How to publish theoretical machine learning paper (alone) [D]
    During my Master's I developed a machine learning algorithm, formally proved its convergence and several other interesting properties. I think it's make very interesting connections between several theories such as geometric deep learning, markov decision processes, slow feature analysis (and it also has straightforward correspondence to spiking networks and other aspects of neuroscience). I implemented and benchmarked the algorithm. It works on toy datasets like MNIST and such, and in some aspects can outperform deep nets but it certainly is not a new SOTA. More of a proof of concept. It's not deep learning by any means and I don't think it would be appreciated by deep learning folks, so publishing it at conferences like neurIPS or ICML etc. is probably out of question. If any reviewer asked "what is it useful for" my immediate response would be "it's not useful at all. It's pure math contribution". That petty much would get me rejected and brushed off by all the applied ML folks. So where should I go if I wanted to publish it? It's more like "perceptron" kind of formal model. Perhaps I should submit it to Psychological Review (that's where all the old school perceptron-like papers used to be published before deep learning became a thing). submitted by /u/alagris12358 [link] [comments]  ( 96 min )
    [D] looking for a buddy or a mentor to start a ML project with
    I've a basic knowledge of machine learning, deep learning, and Computer Vision. I haven't practiced what I've learned in a project yet. To be honest, my university offered us a great opportunity to learn ML and apply what we learned, but to get accepted I have to do at least a project within three months. So if you're interested dm me submitted by /u/this-is-the-admin [link] [comments]  ( 87 min )
    [D] Why is Vaswani et al still the SOTA when the attention mechanism is O(n²)?
    There are so many attention mechanisms (guided attention, apple's aft, nystromformer, etc) that alleviate the O(n²) to something like O(n). Why don't recent LMs use these techniques to speed up training and matrix multiplication in the SA layer? submitted by /u/tororo-in [link] [comments]  ( 92 min )
    [D] What tool do you use for reinforcement learning experimentation?
    Good evening, guys. I currently use StarCraft 2 as a tool for experimenting with my deep reinforcement learning projects, I have also used OpenAI Gym. The intention of this post is to open my horizon of possibilities and take this content to my research group to see if it helps someone. Could you recommend other tools for experimentation? There is no search theme restriction. submitted by /u/barash-616 [link] [comments]  ( 87 min )
  • Open

    Build an air quality anomaly detector using Amazon Lookout for Metrics
    Today, air pollution is a familiar environmental issue that creates severe respiratory and heart conditions, which pose serious health threats. Acid rain, depletion of the ozone layer, and global warming are also adverse consequences of air pollution. There is a need for intelligent monitoring and automation in order to prevent severe health issues and in […]  ( 9 min )
    Build a GNN-based real-time fraud detection solution using Amazon SageMaker, Amazon Neptune, and the Deep Graph Library
    Fraudulent activities severely impact many industries, such as e-commerce, social media, and financial services. Frauds could cause a significant loss for businesses and consumers. American consumers reported losing more than $5.8 billion to frauds in 2021, up more than 70% over 2020. Many techniques have been used to detect fraudsters—rule-based filters, anomaly detection, and machine […]  ( 13 min )
  • Open

    Rax: Composable Learning-to-Rank Using JAX
    Posted by Rolf Jagerman and Honglei Zhuang, Software Engineers, Google Research Ranking is a core problem across a variety of domains, such as search engines, recommendation systems, or question answering. As such, researchers often utilize learning-to-rank (LTR), a set of supervised machine learning techniques that optimize for the utility of an entire list of items (rather than a single item at a time). A noticeable recent focus is on combining LTR with deep learning. Existing libraries, most notably TF-Ranking, offer researchers and practitioners the necessary tools to use LTR in their work. However, none of the existing LTR libraries work natively with JAX, a new machine learning framework that provides an extensible system of function transformations that compose: automatic different…  ( 23 min )
  • Open

    What threshold should I use to clip DQN gradients?
    Does there exist a way to detect when the gradient step has become big and needs to be clipped. Perhaps it can be a function of the reward, learning rate or optimizer function? submitted by /u/Academic-Rent7800 [link] [comments]  ( 86 min )
    Agent that needs to take two different kinds of action at each step.
    I am new to reinforcement learning, and am working on a game where I have to decide on two actions at once. How can I approach this problem using stable_baselines3 submitted by /u/prestem [link] [comments]  ( 105 min )
    Suggestions for RL conferences
    Are there any good conferences which value RL but not entirely focus on algorithm itself? (e.g. methodology improvement and applications in real-world problems) Most top-tier conferences focus mainly on algorithm itself (e.g. NeurIPS, ICML, ICLR, or only robotics). Are there any other prestigious RL conferences would value methodology improvement and real-world problems? submitted by /u/Blasphemer666 [link] [comments]  ( 86 min )
  • Open

    Top Israel Medical Center Partners with AI Startups to Help Detect Brain Bleeds, Other Critical Cases
    Israel’s largest private medical center is working with startups and researchers to bring potentially life-saving AI solutions to real-world healthcare workflows. With more than 1.5 million patients across eight medical centers, Assuta Medical Centers conduct over 100,000 surgeries, 800,000 imaging tests and hundreds of thousands of other health diagnostics and treatments each year. These create Read article > The post Top Israel Medical Center Partners with AI Startups to Help Detect Brain Bleeds, Other Critical Cases appeared first on NVIDIA Blog.  ( 7 min )
    GFN Thursday Brings Thunder to the Cloud With ‘Rumbleverse’ Arriving on GeForce NOW
    It’s time to rumble in Grapital City with Rumbleverse launching today on GeForce NOW. Punch your way into the all-new, free-to-play Brawler Royale from Iron Galaxy Studios and Epic Games Publishing, streaming from the cloud to nearly all devices. That means gamers can tackle, uppercut, body slam and more from any GeForce NOW-compatible device, including Read article > The post GFN Thursday Brings Thunder to the Cloud With ‘Rumbleverse’ Arriving on GeForce NOW appeared first on NVIDIA Blog.  ( 5 min )
  • Open

    How to Choose a Programming Language for Your Application
    Programming Language Evaluation  ( 8 min )
  • Open

    What is Cloud Data Management? A Complete Guide
    IT infrastructure is becoming increasingly complicated to safeguard crucial business data better and work on advanced data Management applications. Companies working in this business landscape must rely on scalable data -management solutions to remain competitive in their respective industry.  Hence it might not be wrong to say that data management in cloud data management computing… Read More »What is Cloud Data Management? A Complete Guide The post What is Cloud Data Management? A Complete Guide appeared first on Data Science Central.  ( 20 min )
  • Open

    FourCastNet: Accelerating Global High-Resolution Weather Forecasting using Adaptive Fourier Neural Operators. (arXiv:2208.05419v1 [physics.ao-ph])
    Extreme weather amplified by climate change is causing increasingly devastating impacts across the globe. The current use of physics-based numerical weather prediction (NWP) limits accuracy due to high computational cost and strict time-to-solution limits. We report that a data-driven deep learning Earth system emulator, FourCastNet, can predict global weather and generate medium-range forecasts five orders-of-magnitude faster than NWP while approaching state-of-the-art accuracy. FourCast-Net is optimized and scales efficiently on three supercomputing systems: Selene, Perlmutter, and JUWELS Booster up to 3,808 NVIDIA A100 GPUs, attaining 140.8 petaFLOPS in mixed precision (11.9%of peak at that scale). The time-to-solution for training FourCastNet measured on JUWELS Booster on 3,072GPUs is 67.4minutes, resulting in an 80,000times faster time-to-solution relative to state-of-the-art NWP, in inference. FourCastNet produces accurate instantaneous weather predictions for a week in advance, enables enormous ensembles that better capture weather extremes, and supports higher global forecast resolutions.  ( 2 min )
    Oblique and rotation double random forest. (arXiv:2111.02010v3 [cs.LG] UPDATED)
    Random Forest is an ensemble of decision trees based on the bagging and random subspace concepts. As suggested by Breiman, the strength of unstable learners and the diversity among them are the ensemble models' core strength. In this paper, we propose two approaches known as oblique and rotation double random forests. In the first approach, we propose rotation based double random forest. In rotation based double random forests, transformation or rotation of the feature space is generated at each node. At each node different random feature subspace is chosen for evaluation, hence the transformation at each node is different. Different transformations result in better diversity among the base learners and hence, better generalization performance. With the double random forest as base learner, the data at each node is transformed via two different transformations namely, principal component analysis and linear discriminant analysis. In the second approach, we propose oblique double random forest. Decision trees in random forest and double random forest are univariate, and this results in the generation of axis parallel split which fails to capture the geometric structure of the data. Also, the standard random forest may not grow sufficiently large decision trees resulting in suboptimal performance. To capture the geometric properties and to grow the decision trees of sufficient depth, we propose oblique double random forest. The oblique double random forest models are multivariate decision trees. At each non-leaf node, multisurface proximal support vector machine generates the optimal plane for better generalization performance. Also, different regularization techniques are employed for tackling the small sample size problems in the decision trees of oblique double random forest.  ( 3 min )
    Looking for a Needle in a Haystack: A Comprehensive Study of Hallucinations in Neural Machine Translation. (arXiv:2208.05309v1 [cs.CL])
    Although the problem of hallucinations in neural machine translation (NMT) has received some attention, research on this highly pathological phenomenon lacks solid ground. Previous work has been limited in several ways: it often resorts to artificial settings where the problem is amplified, it disregards some (common) types of hallucinations, and it does not validate adequacy of detection heuristics. In this paper, we set foundations for the study of NMT hallucinations. First, we work in a natural setting, i.e., in-domain data without artificial noise neither in training nor in inference. Next, we annotate a dataset of over 3.4k sentences indicating different kinds of critical errors and hallucinations. Then, we turn to detection methods and both revisit methods used previously and propose using glass-box uncertainty-based detectors. Overall, we show that for preventive settings, (i) previously used methods are largely inadequate, (ii) sequence log-probability works best and performs on par with reference-based methods. Finally, we propose DeHallucinator, a simple method for alleviating hallucinations at test time that significantly reduces the hallucinatory rate. To ease future research, we release our annotated dataset for WMT18 German-English data, along with the model, training data, and code.  ( 2 min )
    Diversifying Design of Nucleic Acid Aptamers Using Unsupervised Machine Learning. (arXiv:2208.05341v1 [physics.bio-ph])
    Inverse design of short single-stranded RNA and DNA sequences (aptamers) is the task of finding sequences that satisfy a set of desired criteria. Relevant criteria may be, for example, the presence of specific folding motifs, binding to molecular ligands, sensing properties, etc. Most practical approaches to aptamer design identify a small set of promising candidate sequences using high-throughput experiments (e.g. SELEX), and then optimize performance by introducing only minor modifications to the empirically found candidates. Sequences that possess the desired properties but differ drastically in chemical composition will add diversity to the search space and facilitate the discovery of useful nucleic acid aptamers. Systematic diversification protocols are needed. Here we propose to use an unsupervised machine learning model known as the Potts model to discover new, useful sequences with controllable sequence diversity. We start by training a Potts model using the maximum entropy principle on a small set of empirically identified sequences unified by a common feature. To generate new candidate sequences with a controllable degree of diversity, we take advantage of the model's spectral feature: an energy bandgap separating sequences that are similar to the training set from those that are distinct. By controlling the Potts energy range that is sampled, we generate sequences that are distinct from the training set yet still likely to have the encoded features. To demonstrate performance, we apply our approach to design diverse pools of sequences with specified secondary structure motifs in 30-mer RNA and DNA aptamers.  ( 3 min )
    Fast Heterogeneous Federated Learning with Hybrid Client Selection. (arXiv:2208.05135v1 [cs.LG])
    Client selection schemes are widely adopted to handle the communication-efficient problems in recent studies of Federated Learning (FL). However, the large variance of the model updates aggregated from the randomly-selected unrepresentative subsets directly slows the FL convergence. We present a novel clustering-based client selection scheme to accelerate the FL convergence by variance reduction. Simple yet effective schemes are designed to improve the clustering effect and control the effect fluctuation, therefore, generating the client subset with certain representativeness of sampling. Theoretically, we demonstrate the improvement of the proposed scheme in variance reduction. We also present the tighter convergence guarantee of the proposed method thanks to the variance reduction. Experimental results confirm the exceed efficiency of our scheme compared to alternatives.  ( 2 min )
    Non-Contrastive Self-Supervised Learning of Utterance-Level Speech Representations. (arXiv:2208.05413v1 [eess.AS])
    Considering the abundance of unlabeled speech data and the high labeling costs, unsupervised learning methods can be essential for better system development. One of the most successful methods is contrastive self-supervised methods, which require negative sampling: sampling alternative samples to contrast with the current sample (anchor). However, it is hard to ensure if all the negative samples belong to classes different from the anchor class without labels. This paper applies a non-contrastive self-supervised learning method on an unlabeled speech corpus to learn utterance-level embeddings. We used DIstillation with NO labels (DINO), proposed in computer vision, and adapted it to the speech domain. Unlike the contrastive methods, DINO does not require negative sampling. These embeddings were evaluated on speaker verification and emotion recognition. In speaker verification, the unsupervised DINO embedding with cosine scoring provided 4.38% EER on the VoxCeleb1 test trial. This outperforms the best contrastive self-supervised method by 40% relative in EER. An iterative pseudo-labeling training pipeline, not requiring speaker labels, further improved the EER to 1.89%. In emotion recognition, the DINO embedding performed 60.87, 79.21, and 56.98% in micro-f1 score on IEMOCAP, Crema-D, and MSP-Podcast, respectively. The results imply the generality of the DINO embedding to different speech applications.  ( 3 min )
    Flexible Unsupervised Learning for Massive MIMO Subarray Hybrid Beamforming. (arXiv:2208.05443v1 [cs.IT])
    Hybrid beamforming is a promising technology to improve the energy efficiency of massive MIMO systems. In particular, subarray hybrid beamforming can further decrease power consumption by reducing the number of phase-shifters. However, designing the hybrid beamforming vectors is a complex task due to the discrete nature of the subarray connections and the phase-shift amounts. Finding the optimal connections between RF chains and antennas requires solving a non-convex problem in a large search space. In addition, conventional solutions assume that perfect CSI is available, which is not the case in practical systems. Therefore, we propose a novel unsupervised learning approach to design the hybrid beamforming for any subarray structure while supporting quantized phase-shifters and noisy CSI. One major feature of the proposed architecture is that no beamforming codebook is required, and the neural network is trained to take into account the phase-shifter quantization. Simulation results show that the proposed deep learning solutions can achieve higher sum-rates than existing methods.  ( 2 min )
    Active Sampling of Multiple Sources for Sequential Estimation. (arXiv:2208.05406v1 [cs.LG])
    Consider $K$ processes, each generating a sequence of identical and independent random variables. The probability measures of these processes have random parameters that must be estimated. Specifically, they share a parameter $\theta$ common to all probability measures. Additionally, each process $i\in\{1, \dots, K\}$ has a private parameter $\alpha_i$. The objective is to design an active sampling algorithm for sequentially estimating these parameters in order to form reliable estimates for all shared and private parameters with the fewest number of samples. This sampling algorithm has three key components: (i)~data-driven sampling decisions, which dynamically over time specifies which of the $K$ processes should be selected for sampling; (ii)~stopping time for the process, which specifies when the accumulated data is sufficient to form reliable estimates and terminate the sampling process; and (iii)~estimators for all shared and private parameters. Owing to the sequential estimation being known to be analytically intractable, this paper adopts \emph {conditional} estimation cost functions, leading to a sequential estimation approach that was recently shown to render tractable analysis. Asymptotically optimal decision rules (sampling, stopping, and estimation) are delineated, and numerical experiments are provided to compare the efficacy and quality of the proposed procedure with those of the relevant approaches.  ( 2 min )
    Counterfactual Phenotyping with Censored Time-to-Events. (arXiv:2202.11089v3 [cs.LG] UPDATED)
    Estimation of treatment efficacy of real-world clinical interventions involves working with continuous outcomes such as time-to-death, re-hospitalization, or a composite event that may be subject to censoring. Counterfactual reasoning in such scenarios requires decoupling the effects of confounding physiological characteristics that affect baseline survival rates from the effects of the interventions being assessed. In this paper, we present a latent variable approach to model heterogeneous treatment effects by proposing that an individual can belong to one of latent clusters with distinct response characteristics. We show that this latent structure can mediate the base survival rates and helps determine the effects of an intervention. We demonstrate the ability of our approach to discover actionable phenotypes of individuals based on their treatment response on multiple large randomized clinical trials originally conducted to assess appropriate treatments to reduce cardiovascular risk.  ( 2 min )
    Adaptive Learning Rates for Faster Stochastic Gradient Methods. (arXiv:2208.05287v1 [cs.LG])
    In this work, we propose new adaptive step size strategies that improve several stochastic gradient methods. Our first method (StoPS) is based on the classical Polyak step size (Polyak, 1987) and is an extension of the recent development of this method for the stochastic optimization-SPS (Loizou et al., 2021), and our second method, denoted GraDS, rescales step size by "diversity of stochastic gradients". We provide a theoretical analysis of these methods for strongly convex smooth functions and show they enjoy deterministic-like rates despite stochastic gradients. Furthermore, we demonstrate the theoretical superiority of our adaptive methods on quadratic objectives. Unfortunately, both StoPS and GraDS depend on unknown quantities, which are only practical for the overparametrized models. To remedy this, we drop this undesired dependence and redefine StoPS and GraDS to StoP and GraD, respectively. We show that these new methods converge linearly to the neighbourhood of the optimal solution under the same assumptions. Finally, we corroborate our theoretical claims by experimental validation, which reveals that GraD is particularly useful for deep learning optimization.  ( 2 min )
    How Does the Task Landscape Affect MAML Performance?. (arXiv:2010.14672v5 [cs.LG] UPDATED)
    Model-Agnostic Meta-Learning (MAML) has become increasingly popular for training models that can quickly adapt to new tasks via one or few stochastic gradient descent steps. However, the MAML objective is significantly more difficult to optimize compared to standard non-adaptive learning (NAL), and little is understood about how much MAML improves over NAL in terms of the fast adaptability of their solutions in various scenarios. We analytically address this issue in a linear regression setting consisting of a mixture of easy and hard tasks, where hardness is related to the rate that gradient descent converges on the task. Specifically, we prove that in order for MAML to achieve substantial gain over NAL, (i) there must be some discrepancy in hardness among the tasks, and (ii) the optimal solutions of the hard tasks must be closely packed with the center far from the center of the easy tasks optimal solutions. We also give numerical and analytical results suggesting that these insights apply to two-layer neural networks. Finally, we provide few-shot image classification experiments that support our insights for when MAML should be used and emphasize the importance of training MAML on hard tasks in practice.  ( 3 min )
    KL-divergence Based Deep Learning for Discrete Time Model. (arXiv:2208.05100v1 [stat.ML])
    Neural Network (Deep Learning) is a modern model in Artificial Intelligence and it has been exploited in Survival Analysis. Although several improvements have been shown by previous works, training an excellent deep learning model requires a huge amount of data, which may not hold in practice. To address this challenge, we develop a Kullback-Leibler-based (KL) deep learning procedure to integrate external survival prediction models with newly collected time-to-event data. Time-dependent KL discrimination information is utilized to measure the discrepancy between the external and internal data. This is the first work considering using prior information to deal with short data problem in Survival Analysis for deep learning. Simulation and real data results show that the proposed model achieves better performance and higher robustness compared with previous works.  ( 2 min )
    Fast Offline Policy Optimization for Large Scale Recommendation. (arXiv:2208.05327v1 [cs.IR])
    Personalised interactive systems such as recommender systems require selecting relevant items dependent on context. Production systems need to identify the items rapidly from very large catalogues which can be efficiently solved using maximum inner product search technology. Offline optimisation of maximum inner product search can be achieved by a relaxation of the discrete problem resulting in policy learning or reinforce style learning algorithms. Unfortunately this relaxation step requires computing a sum over the entire catalogue making the complexity of the evaluation of the gradient (and hence each stochastic gradient descent iterations) linear in the catalogue size. This calculation is untenable in many real world examples such as large catalogue recommender systems severely limiting the usefulness of this method in practice. In this paper we show how it is possible to produce an excellent approximation of these policy learning algorithms that scale logarithmically with the catalogue size. Our contribution is based upon combining three novel ideas: a new Monte Carlo estimate of the gradient of a policy, the self normalised importance sampling estimator and the use of fast maximum inner product search at training time. Extensive experiments show our algorithm is an order of magnitude faster than naive approaches yet produces equally good policies.  ( 2 min )
    Association Between Neighborhood Factors and Adult Obesity in Shelby County, Tennessee: Geospatial Machine Learning Approach. (arXiv:2208.05335v1 [cs.LG])
    Obesity is a global epidemic causing at least 2.8 million deaths per year. This complex disease is associated with significant socioeconomic burden, reduced work productivity, unemployment, and other social determinants of Health (SDoH) disparities. Objective: The objective of this study was to investigate the effects of SDoH on obesity prevalence among adults in Shelby County, Tennessee, USA using a geospatial machine-learning approach. Obesity prevalence was obtained from publicly available CDC 500 cities database while SDoH indicators were extracted from the U.S. Census and USDA. We examined the geographic distributions of obesity prevalence patterns using Getis-Ord Gi* statistics and calibrated multiple models to study the association between SDoH and adult obesity. Also, unsupervised machine learning was used to conduct grouping analysis to investigate the distribution of obesity prevalence and associated SDoH indicators. Results depicted a high percentage of neighborhoods experiencing high adult obesity prevalence within Shelby County. In the census tract, median household income, as well as the percentage of individuals who were black, home renters, living below the poverty level, fifty-five years or older, unmarried, and uninsured, had a significant association with adult obesity prevalence. The grouping analysis revealed disparities in obesity prevalence amongst disadvantaged neighborhoods. More research is needed that examines linkages between geographical location, SDoH, and chronic diseases. These findings, which depict a significantly higher prevalence of obesity within disadvantaged neighborhoods, and other geospatial information can be leveraged to offer valuable insights informing health decision-making and interventions that mitigate risk factors for increasing obesity prevalence.  ( 3 min )
    Robust Continual Test-time Adaptation: Instance-aware BN and Prediction-balanced Memory. (arXiv:2208.05117v1 [cs.LG])
    Test-time adaptation (TTA) is an emerging paradigm that addresses distributional shifts between training and testing phases without additional data acquisition or labeling cost; only unlabeled test data streams are used for continual model adaptation. Previous TTA schemes assume that the test samples are independent and identically distributed (i.i.d.), even though they are often temporally correlated (non-i.i.d.) in application scenarios, e.g., autonomous driving. We discover that most existing TTA methods fail dramatically under such scenarios. Motivated by this, we present a new test-time adaptation scheme that is robust against non-i.i.d. test data streams. Our novelty is mainly two-fold: (a) Instance-Aware Batch Normalization (IABN) that corrects normalization for out-of-distribution samples, and (b) Prediction-balanced Reservoir Sampling (PBRS) that simulates i.i.d. data stream from non-i.i.d. stream in a class-balanced manner. Our evaluation with various datasets, including real-world non-i.i.d. streams, demonstrates that the proposed robust TTA not only outperforms state-of-the-art TTA algorithms in the non-i.i.d. setting, but also achieves comparable performance to those algorithms under the i.i.d. assumption.  ( 2 min )
    Tianshou: a Highly Modularized Deep Reinforcement Learning Library. (arXiv:2107.14171v3 [cs.LG] UPDATED)
    In this paper, we present Tianshou, a highly modularized Python library for deep reinforcement learning (DRL) that uses PyTorch as its backend. Tianshou intends to be research-friendly by providing a flexible and reliable infrastructure of DRL algorithms. It supports online and offline training with more than 20 classic algorithms through a unified interface. To facilitate related research and prove Tianshou's reliability, we have released Tianshou's benchmark of MuJoCo environments, covering eight classic algorithms with state-of-the-art performance. We open-sourced Tianshou at https://github.com/thu-ml/tianshou/.  ( 2 min )
    Spatial-Temporal Identity: A Simple yet Effective Baseline for Multivariate Time Series Forecasting. (arXiv:2208.05233v1 [cs.LG])
    Multivariate Time Series (MTS) forecasting plays a vital role in a wide range of applications. Recently, Spatial-Temporal Graph Neural Networks (STGNNs) have become increasingly popular MTS forecasting methods due to their state-of-the-art performance. However, recent works are becoming more sophisticated with limited performance improvements. This phenomenon motivates us to explore the critical factors of MTS forecasting and design a model that is as powerful as STGNNs, but more concise and efficient. In this paper, we identify the indistinguishability of samples in both spatial and temporal dimensions as a key bottleneck, and propose a simple yet effective baseline for MTS forecasting by attaching Spatial and Temporal IDentity information (STID), which achieves the best performance and efficiency simultaneously based on simple Multi-Layer Perceptrons (MLPs). These results suggest that we can design efficient and effective models as long as they solve the indistinguishability of samples, without being limited to STGNNs.  ( 2 min )
    Explaining Machine Learning DGA Detectors from DNS Traffic Data. (arXiv:2208.05285v1 [cs.CR])
    One of the most common causes of lack of continuity of online systems stems from a widely popular Cyber Attack known as Distributed Denial of Service (DDoS), in which a network of infected devices (botnet) gets exploited to flood the computational capacity of services through the commands of an attacker. This attack is made by leveraging the Domain Name System (DNS) technology through Domain Generation Algorithms (DGAs), a stealthy connection strategy that yet leaves suspicious data patterns. To detect such threats, advances in their analysis have been made. For the majority, they found Machine Learning (ML) as a solution, which can be highly effective in analyzing and classifying massive amounts of data. Although strongly performing, ML models have a certain degree of obscurity in their decision-making process. To cope with this problem, a branch of ML known as Explainable ML tries to break down the black-box nature of classifiers and make them interpretable and human-readable. This work addresses the problem of Explainable ML in the context of botnet and DGA detection, which at the best of our knowledge, is the first to concretely break down the decisions of ML classifiers when devised for botnet/DGA detection, therefore providing global and local explanations.  ( 2 min )
    CoditT5: Pretraining for Source Code and Natural Language Editing. (arXiv:2208.05446v1 [cs.SE])
    Pretrained language models have been shown to be effective in many software-related generation tasks; however, they are not well-suited for editing tasks as they are not designed to reason about edits. To address this, we propose a novel pretraining objective which explicitly models edits and use it to build CoditT5, a large language model for software-related editing tasks that is pretrained on large amounts of source code and natural language comments. We fine-tune it on various downstream editing tasks, including comment updating, bug fixing, and automated code review. By outperforming pure generation-based models, we demonstrate the generalizability of our approach and its suitability for editing tasks. We also show how a pure generation model and our edit-based model can complement one another through simple reranking strategies, with which we achieve state-of-the-art performance for the three downstream editing tasks.  ( 2 min )
    Auto-ViT-Acc: An FPGA-Aware Automatic Acceleration Framework for Vision Transformer with Mixed-Scheme Quantization. (arXiv:2208.05163v1 [cs.CV])
    Vision transformers (ViTs) are emerging with significantly improved accuracy in computer vision tasks. However, their complex architecture and enormous computation/storage demand impose urgent needs for new hardware accelerator design methodology. This work proposes an FPGA-aware automatic ViT acceleration framework based on the proposed mixed-scheme quantization. To the best of our knowledge, this is the first FPGA-based ViT acceleration framework exploring model quantization. Compared with state-of-the-art ViT quantization work (algorithmic approach only without hardware acceleration), our quantization achieves 0.47% to 1.36% higher Top-1 accuracy under the same bit-width. Compared with the 32-bit floating-point baseline FPGA accelerator, our accelerator achieves around 5.6x improvement on the frame rate (i.e., 56.8 FPS vs. 10.0 FPS) with 0.71% accuracy drop on ImageNet dataset for DeiT-base.  ( 2 min )
    Subgraph Permutation Equivariant Networks. (arXiv:2111.11840v3 [cs.LG] UPDATED)
    In this work we develop a new method, named Sub-graph Permutation Equivariant Networks (SPEN), which provides a framework for building graph neural networks that operate on sub-graphs, while using a base update function that is permutation equivariant, that are equivariant to a novel choice of automorphism group. Message passing neural networks have been shown to be limited in their expressive power and recent approaches to over come this either lack scalability or require structural information to be encoded into the feature space. The general framework presented here overcomes the scalability issues associated with global permutation equivariance by operating more locally on sub-graphs. In addition, through operating on sub-graphs the expressive power of higher-dimensional global permutation equivariant networks is improved; this is due to fact that two non-distinguishable graphs often contain distinguishable sub-graphs. Furthermore, the proposed framework only requires a choice of $k$-hops for creating ego-network sub-graphs and a choice of representation space to be used for each layer, which makes the method easily applicable across a range of graph based domains. We experimentally validate the method on a range of graph benchmark classification tasks, demonstrating statistically indistinguishable results from the state-of-the-art on six out of seven benchmarks. Further, we demonstrate that the use of local update functions offers a significant improvement in GPU memory over global methods.
    Generative Transfer Learning: Covid-19 Classification with a few Chest X-ray Images. (arXiv:2208.05305v1 [eess.IV])
    Detection of diseases through medical imaging is preferred due to its non-invasive nature. Medical imaging supports multiple modalities of data that enable a thorough and quick look inside a human body. However, interpreting imaging data is often time-consuming and requires a great deal of human expertise. Deep learning models can expedite interpretation and alleviate the work of human experts. However, these models are data-intensive and require significant labeled images for training. During novel disease outbreaks such as Covid-19, we often do not have the required labeled imaging data, especially at the start of the epidemic. Deep Transfer Learning addresses this problem by using a pretrained model in the public domain, e.g. any variant of either VGGNet, ResNet, Inception, DenseNet, etc., as a feature learner to quickly adapt the target task from fewer samples. Most pretrained models are deep with complex architectures. They are trained with large multi-class datasets such as ImageNet, with significant human efforts in architecture design and hyper parameters tuning. We presented 1 a simpler generative source model, pretrained on a single but related concept, can perform as effectively as existing larger pretrained models. We demonstrate the usefulness of generative transfer learning that requires less compute and training data, for Few Shot Learning (FSL) with a Covid-19 binary classification use case. We compare classic deep transfer learning with our approach and also report FSL results with three settings of 84, 20, and 10 training samples. The model implementation of generative FSL for Covid-19 classification is available publicly at https://github.com/suvarnak/GenerativeFSLCovid.git.  ( 3 min )
    An alternative approach to train neural networks using monotone variational inequality. (arXiv:2202.08876v3 [stat.ML] UPDATED)
    Despite the vast empirical success of neural networks, theoretical understanding of the training procedures remains limited, especially in providing performance guarantees of testing performance due to the non-convex nature of the optimization problem. The current paper investigates an alternative approach of neural network training by reducing to another problem with convex structure -- to solve a monotone variational inequality (MVI) -- inspired by a recent work of (Juditsky & Nemirovsky, 2019). The solution to MVI can be found by computationally efficient procedures, and importantly, this leads to performance guarantee of $\ell_2$ and $\ell_{\infty}$ bounds on model recovery and prediction accuracy under the theoretical setting of training a single-layer linear neural network. In addition, we study the use of MVI for training multi-layer neural networks and propose a practical algorithm called \textit{stochastic variational inequality} (SVI), and demonstrate its applicability in training fully-connected neural networks and graph neural networks (GNN) (SVI is completely general and can be used to train other types of neural networks). We demonstrate the competitive or better performance of SVI compared to widely-used stochastic gradient descent methods on both synthetic and real network data prediction tasks regarding various performance metrics, especially in the improved efficiency in the early stage of training.
    Image classifiers can not be made robust to small perturbations. (arXiv:2112.04033v2 [cs.CV] UPDATED)
    The sensitivity of image classifiers to small perturbations in the input is often viewed as a defect of their construction. We demonstrate that this sensitivity is a fundamental property of classifiers. For any arbitrary classifier over the set of $n$-by-$n$ images, we show that for all but one class it is possible to change the classification of all but a tiny fraction of the images in that class with a perturbation of size $O(n^{1/\max{(p,1)}})$ when measured in any $p$-norm for $p \geq 0$. We then discuss how this phenomenon relates to human visual perception and the potential implications for the design considerations of computer vision systems.
    System Norm Regularization Methods for Koopman Operator Approximation. (arXiv:2110.09658v3 [eess.SY] UPDATED)
    Approximating the Koopman operator from data is numerically challenging when many lifting functions are considered. Even low-dimensional systems can yield unstable or ill-conditioned results in a high-dimensional lifted space. In this paper, Extended Dynamic Mode Decomposition (DMD) and DMD with control, two methods for approximating the Koopman operator, are reformulated as convex optimization problems with linear matrix inequality constraints. Asymptotic stability constraints and system norm regularizers are then incorporated as methods to improve the numerical conditioning of the Koopman operator. Specifically, the H-infinity norm is used to penalize the input-output gain of the Koopman system. Weighting functions are then applied to penalize the system gain at specific frequencies. These constraints and regularizers introduce bilinear matrix inequality constraints to the regression problem, which are handled by solving a sequence of convex optimization problems. Experimental results using data from an aircraft fatigue structural test rig and a soft robot arm highlight the advantages of the proposed regression methods.
    Benchmarking the Robustness of Instance Segmentation Models. (arXiv:2109.01123v2 [cs.CV] UPDATED)
    This paper presents a comprehensive evaluation of instance segmentation models with respect to real-world image corruptions as well as out-of-domain image collections, e.g. images captured by a different set-up than the training dataset. The out-of-domain image evaluation shows the generalization capability of models, an essential aspect of real-world applications and an extensively studied topic of domain adaptation. These presented robustness and generalization evaluations are important when designing instance segmentation models for real-world applications and picking an off-the-shelf pretrained model to directly use for the task at hand. Specifically, this benchmark study includes state-of-the-art network architectures, network backbones, normalization layers, models trained starting from scratch versus pretrained networks, and the effect of multi-task training on robustness and generalization. Through this study, we gain several insights. For example, we find that group normalization enhances the robustness of networks across corruptions where the image contents stay the same but corruptions are added on top. On the other hand, batch normalization improves the generalization of the models across different datasets where statistics of image features change. We also find that single-stage detectors do not generalize well to larger image resolutions than their training size. On the other hand, multi-stage detectors can easily be used on images of different sizes. We hope that our comprehensive study will motivate the development of more robust and reliable instance segmentation models.
    Deep Learning Based Single Sample Per Person Face Recognition: A Survey. (arXiv:2006.11395v2 [cs.CV] UPDATED)
    Face recognition has long been an active research area in the field of artificial intelligence, particularly since the rise of deep learning in recent years. In some practical situations, each identity has only a single sample available for training. Face recognition under this situation is referred to as single sample face recognition and poses significant challenges to the effective training of deep models. Therefore, in recent years, researchers have attempted to unleash more potential of deep learning and improve the model recognition performance in the single sample situation. While several comprehensive surveys have been conducted on traditional single sample face recognition approaches, emerging deep learning based methods are rarely involved in these reviews. Accordingly, we focus on the deep learning-based methods in this paper, classifying them into virtual sample methods and generic learning methods. In the former category, virtual images or virtual features are generated to benefit the training of the deep model. In the latter one, additional multi-sample generic sets are used. There are three types of generic learning methods: combining traditional methods and deep features, improving the loss function, and improving network structure, all of which are covered in our analysis. Moreover, we review face datasets that have been commonly used for evaluating single sample face recognition models and go on to compare the results of different types of models. Additionally, we discuss problems with existing single sample face recognition methods, including identity information preservation in virtual sample methods, domain adaption in generic learning methods. Furthermore, we regard developing unsupervised methods is a promising future direction, and point out that the semantic gap as an important issue that needs to be further considered.
    Action Noise in Off-Policy Deep Reinforcement Learning: Impact on Exploration and Performance. (arXiv:2206.03787v2 [cs.LG] UPDATED)
    Many Deep Reinforcement Learning (D-RL) algorithms rely on simple forms of exploration such as the additive action noise often used in continuous control domains. Typically, the scaling factor of this action noise is chosen as a hyper-parameter and is kept constant during training. In this paper, we focus on action noise in off-policy deep reinforcement learning for continuous control. We analyze how the learned policy is impacted by the noise type, noise scale, and impact scaling factor reduction schedule. We consider the two most prominent types of action noise, Gaussian and Ornstein-Uhlenbeck noise, and perform a vast experimental campaign by systematically varying the noise type and scale parameter, and by measuring variables of interest like the expected return of the policy and the state-space coverage during exploration. For the latter, we propose a novel state-space coverage measure $\operatorname{X}_{\mathcal{U}\text{rel}}$ that is more robust to boundary artifacts than previously-proposed measures. Larger noise scales generally increase state-space coverage. However, we found that increasing the space coverage using a larger noise scale is often not beneficial. On the contrary, reducing the noise scale over the training process reduces the variance and generally improves the learning performance. We conclude that the best noise type and scale are environment dependent, and based on our observations derive heuristic rules for guiding the choice of the action noise as a starting point for further optimization.
    Accelerated Algorithms for Monotone Inclusion and Constrained Nonconvex-Nonconcave Min-Max Optimization. (arXiv:2206.05248v2 [math.OC] UPDATED)
    We study monotone inclusions and monotone variational inequalities, as well as their generalizations to non-monotone settings. We first show that the Extra Anchored Gradient (EAG) algorithm, originally proposed by Yoon and Ryu [2021] for unconstrained convex-concave min-max optimization, can be applied to solve the more general problem of Lipschitz monotone inclusion. More specifically, we prove that the EAG solves Lipschitz monotone inclusion problems with an accelerated convergence rate of $O(\frac{1}{T})$, which is optimal among all first-order methods [Diakonikolas, 2020, Yoon and Ryu, 2021]. Our second result is an accelerated forward-backward splitting algorithm (AS), which not only achieves the accelerated $O(\frac{1}{T})$ convergence rate for all monotone inclusion problems, but also exhibits the same accelerated rate for a family of general (non-monotone) inclusion problems that concern negative comonotone operators. As a special case of our second result, AS enjoys the $O(\frac{1}{T})$ convergence rate for solving a non-trivial class of nonconvex-nonconcave min-max optimization problems. Our analyses are based on simple potential function arguments, which might be useful for analysing other accelerated algorithms.
    Trustworthy Visual Analytics in Clinical Gait Analysis: A Case Study for Patients with Cerebral Palsy. (arXiv:2208.05232v1 [cs.HC])
    Three-dimensional clinical gait analysis is essential for selecting optimal treatment interventions for patients with cerebral palsy (CP), but generates a large amount of time series data. For the automated analysis of these data, machine learning approaches yield promising results. However, due to their black-box nature, such approaches are often mistrusted by clinicians. We propose gaitXplorer, a visual analytics approach for the classification of CP-related gait patterns that integrates Grad-CAM, a well-established explainable artificial intelligence algorithm, for explanations of machine learning classifications. Regions of high relevance for classification are highlighted in the interactive visual interface. The approach is evaluated in a case study with two clinical gait experts. They inspected the explanations for a sample of eight patients using the visual interface and expressed which relevance scores they found trustworthy and which they found suspicious. Overall, the clinicians gave positive feedback on the approach as it allowed them a better understanding of which regions in the data were relevant for the classification.
    A Sublinear Adversarial Training Algorithm. (arXiv:2208.05395v1 [cs.LG])
    Adversarial training is a widely used strategy for making neural networks resistant to adversarial perturbations. For a neural network of width $m$, $n$ input training data in $d$ dimension, it takes $\Omega(mnd)$ time cost per training iteration for the forward and backward computation. In this paper we analyze the convergence guarantee of adversarial training procedure on a two-layer neural network with shifted ReLU activation, and shows that only $o(m)$ neurons will be activated for each input data per iteration. Furthermore, we develop an algorithm for adversarial training with time cost $o(m n d)$ per iteration by applying half-space reporting data structure.  ( 2 min )
    Training neural networks using Metropolis Monte Carlo and an adaptive variant. (arXiv:2205.07408v2 [cs.LG] UPDATED)
    We examine the zero-temperature Metropolis Monte Carlo algorithm as a tool for training a neural network by minimizing a loss function. We find that, as expected on theoretical grounds and shown empirically by other authors, Metropolis Monte Carlo can train a neural net with an accuracy comparable to that of gradient descent, if not necessarily as quickly. The Metropolis algorithm does not fail automatically when the number of parameters of a neural network is large. It can fail when a neural network's structure or neuron activations are strongly heterogenous, and we introduce an adaptive Monte Carlo algorithm, aMC, to overcome these limitations. The intrinsic stochasticity and numerical stability of the Monte Carlo method allow aMC to train deep neural networks and recurrent neural networks in which the gradient is too small or too large to allow training by gradient descent. Monte Carlo methods offer a complement to gradient-based methods for training neural networks, allowing access to a distinct set of network architectures and principles.
    A Simple Approach for Visual Rearrangement: 3D Mapping and Semantic Search. (arXiv:2206.13396v2 [cs.CV] UPDATED)
    Physically rearranging objects is an important capability for embodied agents. Visual room rearrangement evaluates an agent's ability to rearrange objects in a room to a desired goal based solely on visual input. We propose a simple yet effective method for this problem: (1) search for and map which objects need to be rearranged, and (2) rearrange each object until the task is complete. Our approach consists of an off-the-shelf semantic segmentation model, voxel-based semantic map, and semantic search policy to efficiently find objects that need to be rearranged. On the AI2-THOR Rearrangement Challenge, our method improves on current state-of-the-art end-to-end reinforcement learning-based methods that learn visual rearrangement policies from 0.53% correct rearrangement to 16.56%, using only 2.7% as many samples from the environment.
    An NLP-Assisted Bayesian Time Series Analysis for Prevalence of Twitter Cyberbullying During the COVID-19 Pandemic. (arXiv:2208.04980v1 [cs.SI])
    COVID-19 has brought about many changes in social dynamics. Stay-at-home orders and disruptions in school teaching can influence bullying behavior in-person and online, both of which leading to negative outcomes in victims. To study cyberbullying specifically, 1 million tweets containing keywords associated with abuse were collected from the beginning of 2019 to the end of 2021 with the Twitter API search endpoint. A natural language processing model pre-trained on a Twitter corpus generated probabilities for the tweets being offensive and hateful. To overcome limitations of sampling, data was also collected using the count endpoint. The fraction of tweets from a given daily sample marked as abusive is multiplied to the number reported by the count endpoint. Once these adjusted counts are assembled, a Bayesian autoregressive Poisson model allows one to study the mean trend and lag functions of the data and how they vary over time. The results reveal strong weekly and yearly seasonality in hateful speech but with slight differences across years that may be attributed to COVID-19.
    Semi-Supervised Junction Tree Variational Autoencoder for Molecular Property Prediction. (arXiv:2208.05119v1 [cs.LG])
    Recent advances in machine learning have enabled accurate prediction of chemical properties. However, supervised machine learning methods in this domain often suffer from the label scarcity problem, due to the expensive nature of labeling chemical property experimentally. This research modifies state-of-the-art molecule generation method - Junction Tree Variational Autoencoder (JT-VAE) to facilitate semi-supervised learning on chemical property prediction. Furthermore, we force some latent variables to take on consistent and interpretable purposes such as representing toxicity via this partial supervision. We leverage JT-VAE architecture to learn an interpretable representation optimal for tasks ranging from molecule property prediction to conditional molecule generation, using a partially labelled dataset.  ( 2 min )
    Convergence of denoising diffusion models under the manifold hypothesis. (arXiv:2208.05314v1 [stat.ML])
    Denoising diffusion models are a recent class of generative models exhibiting state-of-the-art performance in image and audio synthesis. Such models approximate the time-reversal of a forward noising process from a target distribution to a reference density, which is usually Gaussian. Despite their strong empirical results, the theoretical analysis of such models remains limited. In particular, all current approaches crucially assume that the target density admits a density w.r.t. the Lebesgue measure. This does not cover settings where the target distribution is supported on a lower-dimensional manifold or is given by some empirical distribution. In this paper, we bridge this gap by providing the first convergence results for diffusion models in this more general setting. In particular, we provide quantitative bounds on the Wasserstein distance of order one between the target data distribution and the generative distribution of the diffusion model.
    Theoretical Connection between Locally Linear Embedding, Factor Analysis, and Probabilistic PCA. (arXiv:2203.13911v2 [stat.ML] UPDATED)
    Locally Linear Embedding (LLE) is a nonlinear spectral dimensionality reduction and manifold learning method. It has two main steps which are linear reconstruction and linear embedding of points in the input space and embedding space, respectively. In this work, we look at the linear reconstruction step from a stochastic perspective where it is assumed that every data point is conditioned on its linear reconstruction weights as latent factors. The stochastic linear reconstruction of LLE is solved using expectation maximization. We show that there is a theoretical connection between three fundamental dimensionality reduction methods, i.e., LLE, factor analysis, and probabilistic Principal Component Analysis (PCA). The stochastic linear reconstruction of LLE is formulated similar to the factor analysis and probabilistic PCA. It is also explained why factor analysis and probabilistic PCA are linear and LLE is a nonlinear method. This work combines and makes a bridge between two broad approaches of dimensionality reduction, i.e., the spectral and probabilistic algorithms.
    Flow-matching -- efficient coarse-graining molecular dynamics without forces. (arXiv:2203.11167v2 [physics.comp-ph] UPDATED)
    Coarse-grained (CG) molecular simulations have become a standard tool to study molecular processes on time- and length-scales inaccessible to all-atom simulations. Parameterizing CG force fields to match all-atom simulations has mainly relied on force-matching or relative entropy minimization, which require many samples from costly simulations with all-atom or CG resolutions, respectively. Here we present flow-matching, a new training method for CG force fields that combines the advantages of both methods by leveraging normalizing flows, a generative deep learning method. Flow-matching first trains a normalizing flow to represent the CG probability density, which is equivalent to minimizing the relative entropy without requiring iterative CG simulations. Subsequently, the flow generates samples and forces according to the learned distribution in order to train the desired CG energy model via force matching. Even without requiring forces from the all-atom simulations, flow-matching outperforms classical force-matching by an order of magnitude in terms of data efficiency, and produces CG models that can capture the folding and unfolding transitions of small proteins.
    Edge-Compatible Reinforcement Learning for Recommendations. (arXiv:2112.05812v2 [cs.LG] UPDATED)
    Most reinforcement learning (RL) recommendation systems designed for edge computing must either synchronize during recommendation selection or depend on an unprincipled patchwork collection of algorithms. In this work, we build on asynchronous coagent policy gradient algorithms \citep{kostas2020asynchronous} to propose a principled solution to this problem. The class of algorithms that we propose can be distributed over the internet and run asynchronously and in real-time. When a given edge fails to respond to a request for data with sufficient speed, this is not a problem; the algorithm is designed to function and learn in the edge setting, and network issues are part of this setting. The result is a principled, theoretically grounded RL algorithm designed to be distributed in and learn in this asynchronous environment. In this work, we describe this algorithm and a proposed class of architectures in detail, and demonstrate that they work well in practice in the asynchronous setting, even as the network quality degrades.
    Plan Your Target and Learn Your Skills: Transferable State-Only Imitation Learning via Decoupled Policy Optimization. (arXiv:2203.02214v4 [cs.LG] UPDATED)
    Recent progress in state-only imitation learning extends the scope of applicability of imitation learning to real-world settings by relieving the need for observing expert actions. However, existing solutions only learn to extract a state-to-action mapping policy from the data, without considering how the expert plans to the target. This hinders the ability to leverage demonstrations and limits the flexibility of the policy. In this paper, we introduce Decoupled Policy Optimization (DePO), which explicitly decouples the policy as a high-level state planner and an inverse dynamics model. With embedded decoupled policy gradient and generative adversarial training, DePO enables knowledge transfer to different action spaces or state transition dynamics, and can generalize the planner to out-of-demonstration state regions. Our in-depth experimental analysis shows the effectiveness of DePO on learning a generalized target state planner while achieving the best imitation performance. We demonstrate the appealing usage of DePO for transferring across different tasks by pre-training, and the potential for co-training agents with various skills.
    Approximation of Functionals by Neural Network without Curse of Dimensionality. (arXiv:2205.14421v3 [math.NA] UPDATED)
    In this paper, we establish a neural network to approximate functionals, which are maps from infinite dimensional spaces to finite dimensional spaces. The approximation error of the neural network is $O(1/\sqrt{m})$ where $m$ is the size of networks, which overcomes the curse of dimensionality. The key idea of the approximation is to define a Barron spectral space of functionals.
    Machine Learning with DBOS. (arXiv:2208.05101v1 [cs.CR])
    We recently proposed a new cluster operating system stack, DBOS, centered on a DBMS. DBOS enables unique support for ML applications by encapsulating ML code within stored procedures, centralizing ancillary ML data, providing security built into the underlying DBMS, co-locating ML code and data, and tracking data and workflow provenance. Here we demonstrate a subset of these benefits around two ML applications. We first show that image classification and object detection models using GPUs can be served as DBOS stored procedures with performance competitive to existing systems. We then present a 1D CNN trained to detect anomalies in HTTP requests on DBOS-backed web services, achieving SOTA results. We use this model to develop an interactive anomaly detection system and evaluate it through qualitative user feedback, demonstrating its usefulness as a proof of concept for future work to develop learned real-time security services on top of DBOS.
    SKDCGN: Source-free Knowledge Distillation of Counterfactual Generative Networks using cGANs. (arXiv:2208.04226v2 [cs.CV] UPDATED)
    With the usage of appropriate inductive biases, Counterfactual Generative Networks (CGNs) can generate novel images from random combinations of shape, texture, and background manifolds. These images can be utilized to train an invariant classifier, avoiding the wide spread problem of deep architectures learning spurious correlations rather than meaningful ones. As a consequence, out-of-domain robustness is improved. However, the CGN architecture comprises multiple over parameterized networks, namely BigGAN and U2-Net. Training these networks requires appropriate background knowledge and extensive computation. Since one does not always have access to the precise training details, nor do they always possess the necessary knowledge of counterfactuals, our work addresses the following question: Can we use the knowledge embedded in pre-trained CGNs to train a lower-capacity model, assuming black-box access (i.e., only access to the pretrained CGN model) to the components of the architecture? In this direction, we propose a novel work named SKDCGN that attempts knowledge transfer using Knowledge Distillation (KD). In our proposed architecture, each independent mechanism (shape, texture, background) is represented by a student 'TinyGAN' that learns from the pretrained teacher 'BigGAN'. We demonstrate the efficacy of the proposed method using state-of-the-art datasets such as ImageNet, and MNIST by using KD and appropriate loss functions. Moreover, as an additional contribution, our paper conducts a thorough study on the composition mechanism of the CGNs, to gain a better understanding of how each mechanism influences the classification accuracy of an invariant classifier. Code available at: https://github.com/ambekarsameer96/SKDCGN
    SurvLatent ODE : A Neural ODE based time-to-event model with competing risks for longitudinal data improves cancer-associated Venous Thromboembolism (VTE) prediction. (arXiv:2204.09633v2 [cs.LG] UPDATED)
    Effective learning from electronic health records (EHR) data for prediction of clinical outcomes is often challenging because of features recorded at irregular timesteps and loss to follow-up as well as competing events such as death or disease progression. To that end, we propose a generative time-to-event model, SurvLatent ODE, which adopts an Ordinary Differential Equation-based Recurrent Neural Networks (ODE-RNN) as an encoder to effectively parameterize dynamics of latent states under irregularly sampled input data. Our model then utilizes the resulting latent embedding to flexibly estimate survival times for multiple competing events without specifying shapes of event-specific hazard function. We demonstrate competitive performance of our model on MIMIC-III, a freely-available longitudinal dataset collected from critical care units, on predicting hospital mortality as well as the data from the Dana-Farber Cancer Institute (DFCI) on predicting onset of Venous Thromboembolism (VTE), a life-threatening complication for patients with cancer, with death as a competing event. SurvLatent ODE outperforms the current clinical standard Khorana Risk scores for stratifying VTE risk groups, while providing clinically meaningful and interpretable latent representations.
    Deep Learning Methods for Proximal Inference via Maximum Moment Restriction. (arXiv:2205.09824v2 [stat.ML] UPDATED)
    The No Unmeasured Confounding Assumption is widely used to identify causal effects in observational studies. Recent work on proximal inference has provided alternative identification results that succeed even in the presence of unobserved confounders, provided that one has measured a sufficiently rich set of proxy variables, satisfying specific structural conditions. However, proximal inference requires solving an ill-posed integral equation. Previous approaches have used a variety of machine learning techniques to estimate a solution to this integral equation, commonly referred to as the bridge function. However, prior work has often been limited by relying on pre-specified kernel functions, which are not data adaptive and struggle to scale to large datasets. In this work, we introduce a flexible and scalable method based on a deep neural network to estimate causal effects in the presence of unmeasured confounding using proximal inference. Our method achieves state of the art performance on two well-established proximal inference benchmarks. Finally, we provide theoretical consistency guarantees for our method.
    A Transistor Operations Model for Deep Learning Energy Consumption Scaling Law. (arXiv:2205.15062v2 [cs.LG] UPDATED)
    Deep Learning (DL) has transformed the automation of a wide range of industries and finds increasing ubiquity in society. The high complexity of DL models and its widespread adoption has led to global energy consumption doubling every 3-4 months. Currently, the relationship between the DL model configuration and energy consumption is not well established. At a general computational energy model level, there is both strong dependency to both the hardware architecture (e.g. generic processors with different configuration of inner components- CPU and GPU, programmable integrated circuits - FPGA), as well as different interacting energy consumption aspects (e.g., data movement, calculation, control). At the DL model level, we need to translate non-linear activation functions and its interaction with data into calculation tasks. Current methods mainly linearize nonlinear DL models to approximate its theoretical FLOPs and MACs as a proxy for energy consumption. Yet, this is inaccurate (est. 93\% accuracy) due to the highly nonlinear nature of many convolutional neural networks (CNNs) for example. In this paper, we develop a bottom-level Transistor Operations (TOs) method to expose the role of non-linear activation functions and neural network structure in energy consumption. We translate a range of feedforward and CNN models into ALU calculation tasks and then TO steps. This is then statistically linked to real energy consumption values via a regression model for different hardware configurations and data sets. We show that our proposed TOs method can achieve a 93.61% - 99.51% precision in predicting its energy consumption.
    Importance Weighting Approach in Kernel Bayes' Rule. (arXiv:2202.02474v3 [stat.ML] UPDATED)
    We study a nonparametric approach to Bayesian computation via feature means, where the expectation of prior features is updated to yield expected kernel posterior features, based on regression from learned neural net or kernel features of the observations. All quantities involved in the Bayesian update are learned from observed data, making the method entirely model-free. The resulting algorithm is a novel instance of a kernel Bayes' rule (KBR), based on importance weighting. This results in superior numerical stability to the original approach to KBR, which requires operator inversion. We show the convergence of the estimator using a novel consistency analysis on the importance weighting estimator in the infinity norm. We evaluate KBR on challenging synthetic benchmarks, including a filtering problem with a state-space model involving high dimensional image observations. Importance weighted KBR yields uniformly better empirical performance than the original KBR, and competitive performance with other competing methods.
    PS-Net: Learned Partially Separable Model for Dynamic MR Imaging. (arXiv:2205.04073v2 [eess.IV] UPDATED)
    Deep learning methods driven by the low-rank regularization have achieved attractive performance in dynamic magnetic resonance (MR) imaging. However, most of these methods represent low-rank prior by hand-crafted nuclear norm, which cannot accurately approximate the low-rank prior over the entire dataset through a fixed regularization parameter. In this paper, we propose a learned low-rank method for dynamic MR imaging. In particular, we unrolled the semi-quadratic splitting method (HQS) algorithm for the partially separable (PS) model to a network, in which the low-rank is adaptively characterized by a learnable null-space transform. Experiments on the cardiac cine dataset show that the proposed model outperforms the state-of-the-art compressed sensing (CS) methods and existing deep learning methods both quantitatively and qualitatively.
    Non-Contrastive Self-supervised Learning for Utterance-Level Information Extraction from Speech. (arXiv:2208.05445v1 [eess.AS])
    In recent studies, self-supervised pre-trained models tend to outperform supervised pre-trained models in transfer learning. In particular, self-supervised learning (SSL) of utterance-level speech representation can be used in speech applications that require discriminative representation of consistent attributes within an utterance: speaker, language, emotion, and age. Existing frame-level self-supervised speech representation, e.g., wav2vec, can be used as utterance-level representation with pooling, but the models are usually large. There are also SSL techniques to learn utterance-level representation. One of the most successful is a contrastive method, which requires negative sampling: selecting alternative samples to contrast with the current sample (anchor). However, this does not ensure that all the negative samples belong to classes different from the anchor class without labels. This paper applies a non-contrastive self-supervised method to learn utterance-level embeddings. We adapted DIstillation with NO labels (DINO) from computer vision to speech. Unlike contrastive methods, DINO does not require negative sampling. We compared DINO to x-vector trained in a supervised manner. When transferred to down-stream tasks (speaker verification, speech emotion recognition (SER), and Alzheimer's disease detection), DINO outperformed x-vector. We studied the influence of several aspects during transfer learning such as dividing the fine-tuning process into steps, chunk lengths, or augmentation. During fine-tuning, tuning the last affine layers first and then the whole network surpassed fine-tuning all at once. Using shorter chunk lengths, although they generate more diverse inputs, did not necessarily improve performance, implying speech segments at least with a specific length are required for better performance per application. Augmentation was helpful in SER.
    Model Pruning Based on Quantified Similarity of Feature Maps. (arXiv:2105.06052v2 [cs.CV] UPDATED)
    Convolutional Neural Networks (CNNs) has been applied in numerous Internet of Things (IoT) devices for multifarious downstream tasks. However, with the increasing amount of data on edge devices, CNNs can hardly complete some tasks in time with limited computing and storage resources. Recently, filter pruning has been regarded as an effective technique to compress and accelerate CNNs, but existing methods rarely prune CNNs from the perspective of compressing high-dimensional tensors. In this paper, we propose a novel theory to find redundant information in three-dimensional tensors, namely Quantified Similarity between Feature Maps (QSFM), and utilize this theory to guide the filter pruning procedure. We perform QSFM on datasets (CIFAR-10, CIFAR-100 and ILSVRC-12) and edge devices, demonstrate that the proposed method can find the redundant information in the neural networks effectively with comparable compression and tolerable drop of accuracy. Without any fine-tuning operation, QSFM can compress ResNet-56 on CIFAR-10 significantly (48.7% FLOPs and 57.9% parameters are reduced) with only a loss of 0.54% in the top-1 accuracy. For the practical application of edge devices, QSFM can accelerate MobileNet-V2 inference speed by 1.53 times with only a loss of 1.23% in the ILSVRC-12 top-1 accuracy.
    EvolveHypergraph: Group-Aware Dynamic Relational Reasoning for Trajectory Prediction. (arXiv:2208.05470v1 [cs.CV])
    While the modeling of pair-wise relations has been widely studied in multi-agent interacting systems, its ability to capture higher-level and larger-scale group-wise activities is limited. In this paper, we propose a group-aware relational reasoning approach (named EvolveHypergraph) with explicit inference of the underlying dynamically evolving relational structures, and we demonstrate its effectiveness for multi-agent trajectory prediction. In addition to the edges between a pair of nodes (i.e., agents), we propose to infer hyperedges that adaptively connect multiple nodes to enable group-aware relational reasoning in an unsupervised manner without fixing the number of hyperedges. The proposed approach infers the dynamically evolving relation graphs and hypergraphs over time to capture the evolution of relations, which are used by the trajectory predictor to obtain future states. Moreover, we propose to regularize the smoothness of the relation evolution and the sparsity of the inferred graphs or hypergraphs, which effectively improves training stability and enhances the explainability of inferred relations. The proposed approach is validated on both synthetic crowd simulations and multiple real-world benchmark datasets. Our approach infers explainable, reasonable group-aware relations and achieves state-of-the-art performance in long-term prediction.
    ATLAS: Universal Function Approximator for Memory Retention. (arXiv:2208.05388v1 [cs.LG])
    Artificial neural networks (ANNs), despite their universal function approximation capability and practical success, are subject to catastrophic forgetting. Catastrophic forgetting refers to the abrupt unlearning of a previous task when a new task is learned. It is an emergent phenomenon that hinders continual learning. Existing universal function approximation theorems for ANNs guarantee function approximation ability, but do not predict catastrophic forgetting. This paper presents a novel universal approximation theorem for multi-variable functions using only single-variable functions and exponential functions. Furthermore, we present ATLAS: a novel ANN architecture based on the new theorem. It is shown that ATLAS is a universal function approximator capable of some memory retention, and continual learning. The memory of ATLAS is imperfect, with some off-target effects during continual learning, but it is well-behaved and predictable. An efficient implementation of ATLAS is provided. Experiments are conducted to evaluate both the function approximation and memory retention capabilities of ATLAS.
    Self-Supervised Learning from Contrastive Mixtures for Personalized Speech Enhancement. (arXiv:2011.03426v2 [eess.AS] UPDATED)
    This work explores how self-supervised learning can be universally used to discover speaker-specific features towards enabling personalized speech enhancement models. We specifically address the few-shot learning scenario where access to cleaning recordings of a test-time speaker is limited to a few seconds, but noisy recordings of the speaker are abundant. We develop a simple contrastive learning procedure which treats the abundant noisy data as makeshift training targets through pairwise noise injection: the model is pretrained to maximize agreement between pairs of differently deformed identical utterances and to minimize agreement between pairs of similarly deformed nonidentical utterances. Our experiments compare the proposed pretraining approach with two baseline alternatives: speaker-agnostic fully-supervised pretraining, and speaker-specific self-supervised pretraining without contrastive loss terms. Of all three approaches, the proposed method using contrastive mixtures is found to be most robust to model compression (using 85% fewer parameters) and reduced clean speech (requiring only 3 seconds).
    Mappings for Marginal Probabilities with Applications to Models in Statistical Physics. (arXiv:2208.05333v1 [stat.ML])
    We present local mappings that relate the marginal probabilities of a global probability mass function represented by its primal normal factor graph to the corresponding marginal probabilities in its dual normal factor graph. The mapping is based on the Fourier transform of the local factors of the models. Details of the mapping are provided for the Ising model, where it is proved that the local extrema of the fixed points are attained at the phase transition of the two-dimensional nearest-neighbor Ising model. The results are further extended to the Potts model, to the clock model, and to Gaussian Markov random fields. By employing the mapping, we can transform simultaneously all the estimated marginal probabilities from the dual domain to the primal domain (and vice versa), which is advantageous if estimating the marginals can be carried out more efficiently in the dual domain. An example of particular significance is the ferromagnetic Ising model in a positive external magnetic field. For this model, there exists a rapidly mixing Markov chain (called the subgraphs--world process) to generate configurations in the dual normal factor graph of the model. Our numerical experiments illustrate that the proposed procedure can provide more accurate estimates of marginal probabilities of a global probability mass function in various settings.
    Reducing Exploitability with Population Based Training. (arXiv:2208.05083v1 [cs.LG])
    Self-play reinforcement learning has achieved state-of-the-art, and often superhuman, performance in a variety of zero-sum games. Yet prior work has found that policies that are highly capable against regular opponents can fail catastrophically against adversarial policies: an opponent trained explicitly against the victim. Prior defenses using adversarial training were able to make the victim robust to a specific adversary, but the victim remained vulnerable to new ones. We conjecture this limitation was due to insufficient diversity of adversaries seen during training. We propose a defense using population based training to pit the victim against a diverse set of opponents. We evaluate this defense's robustness against new adversaries in two low-dimensional environments. Our defense increases robustness against adversaries, as measured by number of attacker training timesteps to exploit the victim. Furthermore, we show that robustness is correlated with the size of the opponent population.
    A Novel Resource Allocation for Anti-jamming in Cognitive-UAVs: an Active Inference Approach. (arXiv:2208.05269v1 [cs.LG])
    This work proposes a novel resource allocation strategy for anti-jamming in Cognitive Radio using Active Inference ($\textit{AIn}$), and a cognitive-UAV is employed as a case study. An Active Generalized Dynamic Bayesian Network (Active-GDBN) is proposed to represent the external environment that jointly encodes the physical signal dynamics and the dynamic interaction between UAV and jammer in the spectrum. We cast the action and planning as a Bayesian inference problem that can be solved by avoiding surprising states (minimizing abnormality) during online learning. Simulation results verify the effectiveness of the proposed $\textit{AIn}$ approach in minimizing abnormalities (maximizing rewards) and has a high convergence speed by comparing it with the conventional Frequency Hopping and Q-learning.
    Capturing Dependencies within Machine Learning via a Formal Process Model. (arXiv:2208.05219v1 [cs.SE])
    The development of Machine Learning (ML) models is more than just a special case of software development (SD): ML models acquire properties and fulfill requirements even without direct human interaction in a seemingly uncontrollable manner. Nonetheless, the underlying processes can be described in a formal way. We define a comprehensive SD process model for ML that encompasses most tasks and artifacts described in the literature in a consistent way. In addition to the production of the necessary artifacts, we also focus on generating and validating fitting descriptions in the form of specifications. We stress the importance of further evolving the ML model throughout its life-cycle even after initial training and testing. Thus, we provide various interaction points with standard SD processes in which ML often is an encapsulated task. Further, our SD process model allows to formulate ML as a (meta-) optimization problem. If automated rigorously, it can be used to realize self-adaptive autonomous systems. Finally, our SD process model features a description of time that allows to reason about the progress within ML development processes. This might lead to further applications of formal methods within the field of ML.
    PEPPER: Empowering User-Centric Recommender Systems over Gossip Learning. (arXiv:2208.05320v1 [cs.IR])
    Recommender systems are proving to be an invaluable tool for extracting user-relevant content helping users in their daily activities (e.g., finding relevant places to visit, content to consume, items to purchase). However, to be effective, these systems need to collect and analyze large volumes of personal data (e.g., location check-ins, movie ratings, click rates .. etc.), which exposes users to numerous privacy threats. In this context, recommender systems based on Federated Learning (FL) appear to be a promising solution for enforcing privacy as they compute accurate recommendations while keeping personal data on the users' devices. However, FL, and therefore FL-based recommender systems, rely on a central server that can experience scalability issues besides being vulnerable to attacks. To remedy this, we propose PEPPER, a decentralized recommender system based on gossip learning principles. In PEPPER, users gossip model updates and aggregate them asynchronously. At the heart of PEPPER reside two key components: a personalized peer-sampling protocol that keeps in the neighborhood of each node, a proportion of nodes that have similar interests to the former and a simple yet effective model aggregation function that builds a model that is better suited to each user. Through experiments on three real datasets implementing two use cases: a location check-in recommendation and a movie recommendation, we demonstrate that our solution converges up to 42% faster than with other decentralized solutions providing up to 9% improvement on average performance metric such as hit ratio and up to 21% improvement on long tail performance compared to decentralized competitors.
    Multi-task Active Learning for Pre-trained Transformer-based Models. (arXiv:2208.05379v1 [cs.CL])
    Multi-task learning, in which several tasks are jointly learned by a single model, allows NLP models to share information from multiple annotations and may facilitate better predictions when the tasks are inter-related. This technique, however, requires annotating the same text with multiple annotation schemes which may be costly and laborious. Active learning (AL) has been demonstrated to optimize annotation processes by iteratively selecting unlabeled examples whose annotation is most valuable for the NLP model. Yet, multi-task active learning (MT-AL) has not been applied to state-of-the-art pre-trained Transformer-based NLP models. This paper aims to close this gap. We explore various multi-task selection criteria in three realistic multi-task scenarios, reflecting different relations between the participating tasks, and demonstrate the effectiveness of multi-task compared to single-task selection. Our results suggest that MT-AL can be effectively used in order to minimize annotation efforts for multi-task NLP models.
    Rapid Exploration of a 32.5M Compound Chemical Space with Active Learning to Discover Density Functional Approximation Insensitive and Synthetically Accessible Transitional Metal Chromophores. (arXiv:2208.05444v1 [physics.chem-ph])
    Two outstanding challenges for machine learning (ML) accelerated chemical discovery are the synthesizability of candidate molecules or materials and the fidelity of the data used in ML model training. To address the first challenge, we construct a hypothetical design space of 32.5M transition metal complexes (TMCs), in which all of the constituent fragments (i.e., metals and ligands) and ligand symmetries are synthetically accessible. To address the second challenge, we search for consensus in predictions among 23 density functional approximations across multiple rungs of Jacob's ladder. To accelerate the screening of these 32.5M TMCs, we use efficient global optimization to sample candidate low-spin chromophores that simultaneously have low absorption energies and low static correlation. Despite the scarcity (i.e., $$ 10\%) as the ML models improve during active learning. This represents a 1,000 fold acceleration in discovery corresponding to discoveries in days instead of years. Analyses of candidate chromophores reveal a preference for Co(III) and large, strong-field ligands with more bond saturation. We compute the absorption spectra of promising chromophores on the Pareto front by time-dependent density functional theory calculations and verify that two thirds of them have desired excited state properties. Although these complexes have never been experimentally explored, their constituent ligands demonstrated interesting optical properties in literature, exemplifying the effectiveness of our construction of realistic TMC design space and active learning approach.
    CLEVR-Math: A Dataset for Compositional Language, Visual and Mathematical Reasoning. (arXiv:2208.05358v1 [cs.LG])
    We introduce CLEVR-Math, a multi-modal math word problems dataset consisting of simple math word problems involving addition/subtraction, represented partly by a textual description and partly by an image illustrating the scenario. The text describes actions performed on the scene that is depicted in the image. Since the question posed may not be about the scene in the image, but about the state of the scene before or after the actions are applied, the solver envision or imagine the state changes due to these actions. Solving these word problems requires a combination of language, visual and mathematical reasoning. We apply state-of-the-art neural and neuro-symbolic models for visual question answering on CLEVR-Math and empirically evaluate their performances. Our results show how neither method generalise to chains of operations. We discuss the limitations of the two in addressing the task of multi-modal word problem solving.
    Online Learning in Fisher Markets: Static Pricing Limits and Adaptive Enhancements. (arXiv:2205.00825v2 [cs.GT] UPDATED)
    In a Fisher market, agents (users) spend a budget of (artificial) currency to buy goods that maximize their utilities while a central planner sets prices on capacity-constrained goods such that the market clears. However, the efficacy of pricing schemes in achieving an equilibrium outcome in Fisher markets typically relies on complete knowledge of users' budgets and utilities and requires that transactions happen in a static market wherein all users are present simultaneously. As a result, we study an online variant of Fisher markets, wherein budget-constrained users with privately known utility and budget parameters, drawn i.i.d. from a distribution $\mathcal{D}$, enter the market sequentially. In this setting, we develop an algorithm that adjusts prices solely based on observations of user consumption, i.e., revealed preference feedback, and achieves a regret and capacity violation of $O(\sqrt{n})$, where $n$ is the number of users and the good capacities scale as $O(n)$. Here, our regret measure is the optimality gap in the objective of the Eisenberg-Gale program between an online algorithm and an offline oracle with complete information on users' budgets and utilities. To establish the efficacy of our approach, we show that any uniform (static) pricing algorithm, including one that sets expected equilibrium prices with complete knowledge of the distribution $\mathcal{D}$, cannot achieve both a regret and constraint violation of less than $\Omega(\sqrt{n})$. While our revealed preference algorithm requires no knowledge of the distribution $\mathcal{D}$, we show that if $\mathcal{D}$ is known, then an adaptive variant of expected equilibrium pricing achieves $O(\log(n))$ regret and constant capacity violation for discrete distributions. Finally, we present numerical experiments to demonstrate the performance of our revealed preference algorithm relative to several benchmarks.
    Robust methods for high-dimensional linear learning. (arXiv:2208.05447v1 [stat.ML])
    We propose statistically robust and computationally efficient linear learning methods in the high-dimensional batch setting, where the number of features $d$ may exceed the sample size $n$. We employ, in a generic learning setting, two algorithms depending on whether the considered loss function is gradient-Lipschitz or not. Then, we instantiate our framework on several applications including vanilla sparse, group-sparse and low-rank matrix recovery. This leads, for each application, to efficient and robust learning algorithms, that reach near-optimal estimation rates under heavy-tailed distributions and the presence of outliers. For vanilla $s$-sparsity, we are able to reach the $s\log (d)/n$ rate under heavy-tails and $\eta$-corruption, at a computational cost comparable to that of non-robust analogs. We provide an efficient implementation of our algorithms in an open-source $\mathtt{Python}$ library called $\mathtt{linlearn}$, by means of which we carry out numerical experiments which confirm our theoretical findings together with a comparison to other recent approaches proposed in the literature.
    BabyNet: A Lightweight Network for Infant Reaching Action Recognition in Unconstrained Environments to Support Future Pediatric Rehabilitation Applications. (arXiv:2208.04950v1 [cs.CV])
    Action recognition is an important component to improve autonomy of physical rehabilitation devices, such as wearable robotic exoskeletons. Existing human action recognition algorithms focus on adult applications rather than pediatric ones. In this paper, we introduce BabyNet, a light-weight (in terms of trainable parameters) network structure to recognize infant reaching action from off-body stationary cameras. We develop an annotated dataset that includes diverse reaches performed while in a sitting posture by different infants in unconstrained environments (e.g., in home settings, etc.). Our approach uses the spatial and temporal connection of annotated bounding boxes to interpret onset and offset of reaching, and to detect a complete reaching action. We evaluate the efficiency of our proposed approach and compare its performance against other learning-based network structures in terms of capability of capturing temporal inter-dependencies and accuracy of detection of reaching onset and offset. Results indicate our BabyNet can attain solid performance in terms of (average) testing accuracy that exceeds that of other larger networks, and can hence serve as a light-weight data-driven framework for video-based infant reaching action recognition.
    StratDef: a strategic defense against adversarial attacks in malware detection. (arXiv:2202.07568v2 [cs.LG] UPDATED)
    Over the years, most research towards defenses against adversarial attacks on machine learning models has been in the image recognition domain. The malware detection domain has received less attention despite its importance. Moreover, most work exploring these defenses has focused on several methods but with no strategy when applying them. In this paper, we introduce StratDef, which is a strategic defense system tailored for the malware detection domain based on a moving target defense approach. We overcome challenges related to the systematic construction, selection and strategic use of models to maximize adversarial robustness. StratDef dynamically and strategically chooses the best models to increase the uncertainty for the attacker, whilst minimizing critical aspects in the adversarial ML domain like attack transferability. We provide the first comprehensive evaluation of defenses against adversarial attacks on machine learning for malware detection, where our threat model explores different levels of threat, attacker knowledge, capabilities, and attack intensities. We show that StratDef performs better than other defenses even when facing the peak adversarial threat. We also show that, from the existing defenses, only a few adversarially-trained models provide substantially better protection than just using vanilla models but are still outperformed by StratDef.
    Generating physically-consistent high-resolution climate data with hard-constrained neural networks. (arXiv:2208.05424v1 [physics.ao-ph])
    The availability of reliable, high-resolution climate and weather data is important to inform long-term decisions on climate adaptation and mitigation and to guide rapid responses to extreme events. Forecasting models are limited by computational costs and therefore often predict quantities at a coarse spatial resolution. Statistical downscaling can provide an efficient method of upsampling low-resolution data. In this field, deep learning has been applied successfully, often using methods from the super-resolution domain in computer vision. Despite often achieving visually compelling results, such models often violate conservation laws when predicting physical variables. In order to conserve important physical quantities, we develop methods that guarantee physical constraints are satisfied by a deep downscaling model while also increasing their performance according to traditional metrics. We introduce two ways of constraining the network: A renormalization layer added to the end of the neural network and a successive approach that scales with increasing upsampling factors. We show the applicability of our methods across different popular architectures and upsampling factors using ERA5 reanalysis data.
    Alternating Cross-attention Vision-Language Model for Efficient Learning with Medical Image and Report without Curation. (arXiv:2208.05140v1 [eess.IV])
    Recent advances in vision-language pre-training have demonstrated astounding performances in diverse vision-language tasks, shedding a light on the long-standing problems of a comprehensive understanding of both visual and textual concepts in artificial intelligence research. However, there has been limited success in the application of vision-language pre-training in the medical domain, as the current vision-language models and learning strategies for photographic images and captions are not optimal to process the medical data which are usually insufficient in the amount and the diversity, which impedes successful learning of joint vision-language concepts. In this study, we introduce MAX-VL, a model tailored for efficient vision-language pre-training in the medical domain. We experimentally demonstrated that the pre-trained MAX-VL model outperforms the current state-of-the-art vision language models in various vision-language tasks. We also suggested the clinical utility for the diagnosis of newly emerging diseases and human error detection as well as showed the widespread applicability of the model in different domain data.
    Using Adaptive Experiments to Rapidly Help Students. (arXiv:2208.05092v1 [cs.LG])
    Adaptive experiments can increase the chance that current students obtain better outcomes from a field experiment of an instructional intervention. In such experiments, the probability of assigning students to conditions changes while more data is being collected, so students can be assigned to interventions that are likely to perform better. Digital educational environments lower the barrier to conducting such adaptive experiments, but they are rarely applied in education. One reason might be that researchers have access to few real-world case studies that illustrate the advantages and disadvantages of these experiments in a specific context. We evaluate the effect of homework email reminders in students by conducting an adaptive experiment using the Thompson Sampling algorithm and compare it to a traditional uniform random experiment. We present this as a case study on how to conduct such experiments, and we raise a range of open questions about the conditions under which adaptive randomized experiments may be more or less useful.
    Explainable prediction of Qcodes for NOTAMs using column generation. (arXiv:2208.04955v1 [cs.LG])
    A NOtice To AirMen (NOTAM) contains important flight route related information. To search and filter them, NOTAMs are grouped into categories called QCodes. In this paper, we develop a tool to predict, with some explanations, a Qcode for a NOTAM. We present a way to extend the interpretable binary classification using column generation proposed in Dash, Gunluk, and Wei (2018) to a multiclass text classification method. We describe the techniques used to tackle the issues related to one vs-rest classification, such as multiple outputs and class imbalances. Furthermore, we introduce some heuristics, including the use of a CP-SAT solver for the subproblems, to reduce the training time. Finally, we show that our approach compares favorably with state-of-the-art machine learning algorithms like Linear SVM and small neural networks while adding the needed interpretability component.
    Multi-Depth Boundary-Aware Left Atrial Scar Segmentation Network. (arXiv:2208.04940v1 [eess.IV])
    Automatic segmentation of left atrial (LA) scars from late gadolinium enhanced CMR images is a crucial step for atrial fibrillation (AF) recurrence analysis. However, delineating LA scars is tedious and error-prone due to the variation of scar shapes. In this work, we propose a boundary-aware LA scar segmentation network, which is composed of two branches to segment LA and LA scars, respectively. We explore the inherent spatial relationship between LA and LA scars. By introducing a Sobel fusion module between the two segmentation branches, the spatial information of LA boundaries can be propagated from the LA branch to the scar branch. Thus, LA scar segmentation can be performed condition on the LA boundaries regions. In our experiments, 40 labeled images were used to train the proposed network, and the remaining 20 labeled images were used for evaluation. The network achieved an average Dice score of 0.608 for LA scar segmentation.
    Quantum artificial vision for defect detection in manufacturing. (arXiv:2208.04988v1 [quant-ph])
    In this paper we consider several algorithms for quantum computer vision using Noisy Intermediate-Scale Quantum (NISQ) devices, and benchmark them for a real problem against their classical counterparts. Specifically, we consider two approaches: a quantum Support Vector Machine (QSVM) on a universal gate-based quantum computer, and QBoost on a quantum annealer. The quantum vision systems are benchmarked for an unbalanced dataset of images where the aim is to detect defects in manufactured car pieces. We see that the quantum algorithms outperform their classical counterparts in several ways, with QBoost allowing for larger problems to be analyzed with present-day quantum annealers. Data preprocessing, including dimensionality reduction and contrast enhancement, is also discussed, as well as hyperparameter tuning in QBoost. To the best of our knowledge, this is the first implementation of quantum computer vision systems for a problem of industrial relevance in a manufacturing production line.
    FedOBD: Opportunistic Block Dropout for Efficiently Training Large-scale Neural Networks through Federated Learning. (arXiv:2208.05174v1 [cs.LG])
    Large-scale neural networks possess considerable expressive power. They are well-suited for complex learning tasks in industrial applications. However, large-scale models pose significant challenges for training under the current Federated Learning (FL) paradigm. Existing approaches for efficient FL training often leverage model parameter dropout. However, manipulating individual model parameters is not only inefficient in meaningfully reducing the communication overhead when training large-scale FL models, but may also be detrimental to the scaling efforts and model performance as shown by recent research. To address these issues, we propose the Federated Opportunistic Block Dropout (FedOBD) approach. The key novelty is that it decomposes large-scale models into semantic blocks so that FL participants can opportunistically upload quantized blocks, which are deemed to be significant towards training the model, to the FL server for aggregation. Extensive experiments evaluating FedOBD against five state-of-the-art approaches based on multiple real-world datasets show that it reduces the overall communication overhead by more than 70% compared to the best performing baseline approach, while achieving the highest test accuracy. To the best of our knowledge, FedOBD is the first approach to perform dropout on FL models at the block level rather than at the individual parameter level.
    Increasing Students' Engagement to Reminder Emails Through Multi-Armed Bandits. (arXiv:2208.05090v1 [cs.LG])
    Conducting randomized experiments in education settings raises the question of how we can use machine learning techniques to improve educational interventions. Using Multi-Armed Bandits (MAB) algorithms like Thompson Sampling (TS) in adaptive experiments can increase students' chances of obtaining better outcomes by increasing the probability of assignment to the most optimal condition (arm), even before an intervention completes. This is an advantage over traditional A/B testing, which may allocate an equal number of students to both optimal and non-optimal conditions. The problem is the exploration-exploitation trade-off. Even though adaptive policies aim to collect enough information to allocate more students to better arms reliably, past work shows that this may not be enough exploration to draw reliable conclusions about whether arms differ. Hence, it is of interest to provide additional uniform random (UR) exploration throughout the experiment. This paper shows a real-world adaptive experiment on how students engage with instructors' weekly email reminders to build their time management habits. Our metric of interest is open email rates which tracks the arms represented by different subject lines. These are delivered following different allocation algorithms: UR, TS, and what we identified as TS{\dag} - which combines both TS and UR rewards to update its priors. We highlight problems with these adaptive algorithms - such as possible exploitation of an arm when there is no significant difference - and address their causes and consequences. Future directions includes studying situations where the early choice of the optimal arm is not ideal and how adaptive algorithms can address them.
    Adaptive Target-Condition Neural Network: DNN-Aided Load Balancing for Hybrid LiFi and WiFi Networks. (arXiv:2208.05035v1 [eess.SP])
    Load balancing (LB) is a challenging issue in the hybrid light fidelity (LiFi) and wireless fidelity (WiFi) networks (HLWNets), due to the nature of heterogeneous access points (APs). Machine learning has the potential to provide a complexity-friendly LB solution with near-optimal network performance, at the cost of a training process. The state-of-the-art (SOTA) learning-aided LB methods, however, need retraining when the network environment (especially the number of users) changes, significantly limiting its practicability. In this paper, a novel deep neural network (DNN) structure named adaptive target-condition neural network (A-TCNN) is proposed, which conducts AP selection for one target user upon the condition of other users. Also, an adaptive mechanism is developed to map a smaller number of users to a larger number through splitting their data rate requirements, without affecting the AP selection result for the target user. This enables the proposed method to handle different numbers of users without the need for retraining. Results show that A-TCNN achieves a network throughput very close to that of the testing dataset, with a gap less than 3%. It is also proven that A-TCNN can obtain a network throughput comparable to two SOTA benchmarks, while reducing the runtime by up to three orders of magnitude.
    A Frequency-aware Software Cache for Large Recommendation System Embeddings. (arXiv:2208.05321v1 [cs.IR])
    Deep learning recommendation models (DLRMs) have been widely applied in Internet companies. The embedding tables of DLRMs are too large to fit on GPU memory entirely. We propose a GPU-based software cache approaches to dynamically manage the embedding table in the CPU and GPU memory space by leveraging the id's frequency statistics of the target dataset. Our proposed software cache is efficient in training entire DLRMs on GPU in a synchronized update manner. It is also scaled to multiple GPUs in combination with the widely used hybrid parallel training approaches. Evaluating our prototype system shows that we can keep only 1.5% of the embedding parameters in the GPU to obtain a decent end-to-end training speed.
    Differentiable Inference of Temporal Logic Formulas. (arXiv:2208.05440v1 [cs.LG])
    We demonstrate the first Recurrent Neural Network architecture for learning Signal Temporal Logic formulas, and present the first systematic comparison of formula inference methods. Legacy systems embed much expert knowledge which is not explicitly formalized. There is great interest in learning formal specifications that characterize the ideal behavior of such systems -- that is, formulas in temporal logic that are satisfied by the system's output signals. Such specifications can be used to better understand the system's behavior and improve design of its next iteration. Previous inference methods either assumed certain formula templates, or did a heuristic enumeration of all possible templates. This work proposes a neural network architecture that infers the formula structure via gradient descent, eliminating the need for imposing any specific templates. It combines learning of formula structure and parameters in one optimization. Through systematic comparison, we demonstrate that this method achieves similar or better mis-classification rates (MCR) than enumerative and lattice methods. We also observe that different formulas can achieve similar MCR, empirically demonstrating the under-determinism of the problem of temporal logic inference.
    A Model-Constrained Tangent Manifold Learning Approach for Dynamical Systems. (arXiv:2208.04995v1 [cs.LG])
    Real time accurate solutions of large scale complex dynamical systems are in critical need for control, optimization, uncertainty quantification, and decision-making in practical engineering and science applications. This paper contributes in this direction a model constrained tangent manifold learning (mcTangent) approach. At the heart of mcTangent is the synergy of several desirable strategies: i) a tangent manifold learning to take advantage of the neural network speed and the time accurate nature of the method of lines; ii) a model constrained approach to encode the neural network tangent with the underlying governing equations; iii) sequential learning strategies to promote long time stability and accuracy; and iv) data randomization approach to implicitly enforce the smoothness of the neural network tangent and its likeliness to the truth tangent up second order derivatives in order to further enhance the stability and accuracy of mcTangent solutions. Both semi heuristic and rigorous arguments are provided to analyze and justify the proposed approach. Several numerical results for transport equation, viscous Burgers equation, and Navier Stokes equation are presented to study and demonstrate the capability of the proposed mcTangent learning approach.
    Offline versus Online Triplet Mining based on Extreme Distances of Histopathology Patches. (arXiv:2007.02200v3 [cs.CV] UPDATED)
    We analyze the effect of offline and online triplet mining for colorectal cancer (CRC) histopathology dataset containing 100,000 patches. We consider the extreme, i.e., farthest and nearest patches to a given anchor, both in online and offline mining. While many works focus solely on selecting the triplets online (batch-wise), we also study the effect of extreme distances and neighbor patches before training in an offline fashion. We analyze extreme cases' impacts in terms of embedding distance for offline versus online mining, including easy positive, batch semi-hard, batch hard triplet mining, neighborhood component analysis loss, its proxy version, and distance weighted sampling. We also investigate online approaches based on extreme distance and comprehensively compare offline, and online mining performance based on the data patterns and explain offline mining as a tractable generalization of the online mining with large mini-batch size. As well, we discuss the relations of different colorectal tissue types in terms of extreme distances. We found that offline and online mining approaches have comparable performances for a specific architecture, such as ResNet-18 in this study. Moreover, we found the assorted case, including different extreme distances, is promising, especially in the online approach.
    Language Supervised Training for Skeleton-based Action Recognition. (arXiv:2208.05318v1 [cs.CV])
    Skeleton-based action recognition has drawn a lot of attention for its computation efficiency and robustness to lighting conditions. Existing skeleton-based action recognition methods are typically formulated as a one-hot classification task without fully utilizing the semantic relations between actions. For example, "make victory sign" and "thumb up" are two actions of hand gestures, whose major difference lies in the movement of hands. This information is agnostic from the categorical one-hot encoding of action classes but could be unveiled in the language description of actions. Therefore, utilizing action language descriptions in training could potentially benefit representation learning. In this work, we propose a Language Supervised Training (LST) approach for skeleton-based action recognition. More specifically, we employ a large-scale language model as the knowledge engine to provide text descriptions for body parts movements of actions, and propose a multi-modal training scheme by utilizing the text encoder to generate feature vectors for different body parts and supervise the skeleton encoder for action representation learning. Experiments show that our proposed LST method achieves noticeable improvements over various baseline models without extra computation cost at inference. LST achieves new state-of-the-arts on popular skeleton-based action recognition benchmarks, including NTU RGB+D, NTU RGB+D 120 and NW-UCLA. The code can be found at https://github.com/MartinXM/LST.
    Learning to Improve Code Efficiency. (arXiv:2208.05297v1 [cs.SE])
    Improvements in the performance of computing systems, driven by Moore's Law, have transformed society. As such hardware-driven gains slow down, it becomes even more important for software developers to focus on performance and efficiency during development. While several studies have demonstrated the potential from such improved code efficiency (e.g., 2x better generational improvements compared to hardware), unlocking these gains in practice has been challenging. Reasoning about algorithmic complexity and the interaction of coding patterns on hardware can be challenging for the average programmer, especially when combined with pragmatic constraints around development velocity and multi-person development. This paper seeks to address this problem. We analyze a large competitive programming dataset from the Google Code Jam competition and find that efficient code is indeed rare, with a 2x runtime difference between the median and the 90th percentile of solutions. We propose using machine learning to automatically provide prescriptive feedback in the form of hints, to guide programmers towards writing high-performance code. To automatically learn these hints from the dataset, we propose a novel discrete variational auto-encoder, where each discrete latent variable represents a different learned category of code-edit that increases performance. We show that this method represents the multi-modal space of code efficiency edits better than a sequence-to-sequence baseline and generates a distribution of more efficient solutions.
    Learning Two-Player Mixture Markov Games: Kernel Function Approximation and Correlated Equilibrium. (arXiv:2208.05363v1 [cs.LG])
    We consider learning Nash equilibria in two-player zero-sum Markov Games with nonlinear function approximation, where the action-value function is approximated by a function in a Reproducing Kernel Hilbert Space (RKHS). The key challenge is how to do exploration in the high-dimensional function space. We propose a novel online learning algorithm to find a Nash equilibrium by minimizing the duality gap. At the core of our algorithms are upper and lower confidence bounds that are derived based on the principle of optimism in the face of uncertainty. We prove that our algorithm is able to attain an $O(\sqrt{T})$ regret with polynomial computational complexity, under very mild assumptions on the reward function and the underlying dynamic of the Markov Games. We also propose several extensions of our algorithm, including an algorithm with Bernstein-type bonus that can achieve a tighter regret bound, and another algorithm for model misspecification that can be applied to neural function approximation.
    Privacy-Aware Adversarial Network in Human Mobility Prediction. (arXiv:2208.05009v1 [cs.LG])
    As mobile devices and location-based services are increasingly developed in different smart city scenarios and applications, many unexpected privacy leakages have arisen due to geolocated data collection and sharing. User re-identification and other sensitive inferences are major privacy threats when geolocated data are shared with cloud-assisted applications. Significantly, four spatio-temporal points are enough to uniquely identify 95\% of the individuals, which exacerbates personal information leakages. To tackle malicious purposes such as user re-identification, we propose an LSTM-based adversarial mechanism with representation learning to attain a privacy-preserving feature representation of the original geolocated data (i.e., mobility data) for a sharing purpose. These representations aim to maximally reduce the chance of user re-identification and full data reconstruction with a minimal utility budget (i.e., loss). We train the mechanism by quantifying privacy-utility trade-off of mobility datasets in terms of trajectory reconstruction risk, user re-identification risk, and mobility predictability. We report an exploratory analysis that enables the user to assess this trade-off with a specific loss function and its weight parameters. The extensive comparison results on four representative mobility datasets demonstrate the superiority of our proposed architecture in mobility privacy protection and the efficiency of the proposed privacy-preserving features extractor. We show that the privacy of mobility traces attains decent protection at the cost of marginal mobility utility. Our results also show that by exploring the Pareto optimal setting, we can simultaneously increase both privacy (45%) and utility (32%).
    Model-Free Generative Replay for Lifelong Reinforcement Learning: Application to Starcraft-2. (arXiv:2208.05056v1 [cs.LG])
    One approach to meet the challenges of deep lifelong reinforcement learning (LRL) is careful management of the agent's learning experiences, in order to learn (without forgetting) and build internal meta-models (of the tasks, environments, agents, and world). Generative replay (GR) is a biologically-inspired replay mechanism that augments learning experiences with self-labelled examples drawn from an internal generative model that is updated over time. In this paper, we present a version of GR for LRL that satisfies two desiderata: (a) Introspective density modelling of the latent representations of policies learned using deep RL, and (b) Model-free end-to-end learning. In this work, we study three deep learning architectures for model-free GR. We evaluate our proposed algorithms on three different scenarios comprising tasks from the StarCraft2 and Minigrid domains. We report several key findings showing the impact of the design choices on quantitative metrics that include transfer learning, generalization to unseen tasks, fast adaptation after task change, performance comparable to a task expert, and minimizing catastrophic forgetting. We observe that our GR prevents drift in the features-to-action mapping from the latent vector space of a deep actor-critic agent. We also show improvements in established lifelong learning metrics. We find that the introduction of a small random replay buffer is needed to significantly increase the stability of training, when used in conjunction with the replay buffer and the generated replay buffer. Overall, we find that "hidden replay" (a well-known architecture for class-incremental classification) is the most promising approach that pushes the state-of-the-art in GR for LRL.  ( 3 min )
    Bridging the gap between target-based and cell-based drug discovery with a graph generative multi-task model. (arXiv:2208.04944v1 [q-bio.QM])
    Drug discovery is vitally important for protecting human against disease. Target-based screening is one of the most popular methods to develop new drugs in the past several decades. This method efficiently screens candidate drugs inhibiting target protein in vitro, but it often fails due to inadequate activity of the selected drugs in vivo. Accurate computational methods are needed to bridge this gap. Here, we propose a novel graph multi task deep learning model to identify compounds carrying both target inhibitory and cell active (MATIC) properties. On a carefully curated SARS-CoV-2 dataset, the proposed MATIC model shows advantages comparing with traditional method in screening effective compounds in vivo. Next, we explored the model interpretability and found that the learned features for target inhibition (in vitro) or cell active (in vivo) tasks are different with molecular property correlations and atom functional attentions. Based on these findings, we utilized a monte carlo based reinforcement learning generative model to generate novel multi-property compounds with both in vitro and in vivo efficacy, thus bridging the gap between target-based and cell-based drug discovery.
    Adaptive Resources Allocation CUSUM for Binomial Count Data Monitoring with Application to COVID-19 Hotspot Detection. (arXiv:2208.05045v1 [cs.LG])
    In this paper, we present an efficient statistical method (denoted as "Adaptive Resources Allocation CUSUM") to robustly and efficiently detect the hotspot with limited sampling resources. Our main idea is to combine the multi-arm bandit (MAB) and change-point detection methods to balance the exploration and exploitation of resource allocation for hotspot detection. Further, a Bayesian weighted update is used to update the posterior distribution of the infection rate. Then, the upper confidence bound (UCB) is used for resource allocation and planning. Finally, CUSUM monitoring statistics to detect the change point as well as the change location. For performance evaluation, we compare the performance of the proposed method with several benchmark methods in the literature and showed the proposed algorithm is able to achieve a lower detection delay and higher detection precision. Finally, this method is applied to hotspot detection in a real case study of county-level daily positive COVID-19 cases in Washington State WA) and demonstrates the effectiveness with very limited distributed samples.
    PerD: Perturbation Sensitivity-based Neural Trojan Detection Framework on NLP Applications. (arXiv:2208.04943v1 [cs.LG])
    Deep Neural Networks (DNNs) have been shown to be susceptible to Trojan attacks. Neural Trojan is a type of targeted poisoning attack that embeds the backdoor into the victim and is activated by the trigger in the input space. The increasing deployment of DNNs in critical systems and the surge of outsourcing DNN training (which makes Trojan attack easier) makes the detection of Trojan attacks necessary. While Neural Trojan detection has been studied in the image domain, there is a lack of solutions in the NLP domain. In this paper, we propose a model-level Trojan detection framework by analyzing the deviation of the model output when we introduce a specially crafted perturbation to the input. Particularly, we extract the model's responses to perturbed inputs as the `signature' of the model and train a meta-classifier to determine if a model is Trojaned based on its signature. We demonstrate the effectiveness of our proposed method on both a dataset of NLP models we create and a public dataset of Trojaned NLP models from TrojAI. Furthermore, we propose a lightweight variant of our detection method that reduces the detection time while preserving the detection rates.  ( 2 min )
    TSInterpret: A unified framework for time series interpretability. (arXiv:2208.05280v1 [cs.LG])
    With the increasing application of deep learning algorithms to time series classification, especially in high-stake scenarios, the relevance of interpreting those algorithms becomes key. Although research in time series interpretability has grown, accessibility for practitioners is still an obstacle. Interpretability approaches and their visualizations are diverse in use without a unified API or framework. To close this gap, we introduce TSInterpret an easily extensible open-source Python library for interpreting predictions of time series classifiers that combines existing interpretation approaches into one unified framework. The library features (i) state-of-the-art interpretability algorithms, (ii) exposes a unified API enabling users to work with explanations consistently and provides (iii) suitable visualizations for each explanation.  ( 2 min )
    Controlling Perceived Emotion in Symbolic Music Generation with Monte Carlo Tree Search. (arXiv:2208.05162v1 [cs.SD])
    This paper presents a new approach for controlling emotion in symbolic music generation with Monte Carlo Tree Search. We use Monte Carlo Tree Search as a decoding mechanism to steer the probability distribution learned by a language model towards a given emotion. At every step of the decoding process, we use Predictor Upper Confidence for Trees (PUCT) to search for sequences that maximize the average values of emotion and quality as given by an emotion classifier and a discriminator, respectively. We use a language model as PUCT's policy and a combination of the emotion classifier and the discriminator as its value function. To decode the next token in a piece of music, we sample from the distribution of node visits created during the search. We evaluate the quality of the generated samples with respect to human-composed pieces using a set of objective metrics computed directly from the generated samples. We also perform a user study to evaluate how human subjects perceive the generated samples' quality and emotion. We compare PUCT against Stochastic Bi-Objective Beam Search (SBBS) and Conditional Sampling (CS). Results suggest that PUCT outperforms SBBS and CS in almost all metrics of music quality and emotion.  ( 3 min )
    D-BIAS: A Causality-Based Human-in-the-Loop System for Tackling Algorithmic Bias. (arXiv:2208.05126v1 [cs.LG])
    With the rise of AI, algorithms have become better at learning underlying patterns from the training data including ingrained social biases based on gender, race, etc. Deployment of such algorithms to domains such as hiring, healthcare, law enforcement, etc. has raised serious concerns about fairness, accountability, trust and interpretability in machine learning algorithms. To alleviate this problem, we propose D-BIAS, a visual interactive tool that embodies human-in-the-loop AI approach for auditing and mitigating social biases from tabular datasets. It uses a graphical causal model to represent causal relationships among different features in the dataset and as a medium to inject domain knowledge. A user can detect the presence of bias against a group, say females, or a subgroup, say black females, by identifying unfair causal relationships in the causal network and using an array of fairness metrics. Thereafter, the user can mitigate bias by acting on the unfair causal edges. For each interaction, say weakening/deleting a biased causal edge, the system uses a novel method to simulate a new (debiased) dataset based on the current causal model. Users can visually assess the impact of their interactions on different fairness metrics, utility metrics, data distortion, and the underlying data distribution. Once satisfied, they can download the debiased dataset and use it for any downstream application for fairer predictions. We evaluate D-BIAS by conducting experiments on 3 datasets and also a formal user study. We found that D-BIAS helps reduce bias significantly compared to the baseline debiasing approach across different fairness metrics while incurring little data distortion and a small loss in utility. Moreover, our human-in-the-loop based approach significantly outperforms an automated approach on trust, interpretability and accountability.  ( 3 min )
    Robust Reinforcement Learning using Offline Data. (arXiv:2208.05129v1 [cs.LG])
    The goal of robust reinforcement learning (RL) is to learn a policy that is robust against the uncertainty in model parameters. Parameter uncertainty commonly occurs in many real-world RL applications due to simulator modeling errors, changes in the real-world system dynamics over time, and adversarial disturbances. Robust RL is typically formulated as a max-min problem, where the objective is to learn the policy that maximizes the value against the worst possible models that lie in an uncertainty set. In this work, we propose a robust RL algorithm called Robust Fitted Q-Iteration (RFQI), which uses only an offline dataset to learn the optimal robust policy. Robust RL with offline data is significantly more challenging than its non-robust counterpart because of the minimization over all models present in the robust Bellman operator. This poses challenges in offline data collection, optimization over the models, and unbiased estimation. In this work, we propose a systematic approach to overcome these challenges, resulting in our RFQI algorithm. We prove that RFQI learns a near-optimal robust policy under standard assumptions and demonstrate its superior performance on standard benchmark problems.  ( 2 min )
    A data-driven modular architecture with denoising autoencoders for health indicator construction in a manufacturing process. (arXiv:2208.05208v1 [cs.LG])
    Within the field of prognostics and health management (PHM), health indicators (HI) can be used to aid the production and, e.g. schedule maintenance and avoid failures. However, HI is often engineered to a specific process and typically requires large amounts of historical data for set-up. This is especially a challenge for SMEs, which often lack sufficient resources and knowledge to benefit from PHM. In this paper, we propose ModularHI, a modular approach in the construction of HI for a system without historical data. With ModularHI, the operator chooses which sensor inputs are available, and then ModularHI will compute a baseline model based on data collected during a burn-in state. This baseline model will then be used to detect if the system starts to degrade over time. We test the ModularHI on two open datasets, CMAPSS and N-CMAPSS. Results from the former dataset showcase our system's ability to detect degradation, while results from the latter point to directions for further research within the area. The results shows that our novel approach is able to detect system degradation without historical data.  ( 2 min )
    Machine Learning 1- and 2-electron reduced density matrices of polymeric molecules. (arXiv:2208.04976v1 [physics.chem-ph])
    Encoding the electronic structure of molecules using 2-electron reduced density matrices (2RDMs) as opposed to many-body wave functions has been a decades-long quest as the 2RDM contains sufficient information to compute the exact molecular energy but requires only polynomial storage. We focus on linear polymers with varying conformations and numbers of monomers and show that we can use machine learning to predict both the 1-electron and the 2-electron reduced density matrices. Moreover, by applying the Hamiltonian operator to the predicted reduced density matrices we show that we can recover the molecular energy. Thus, we demonstrate the feasibility of a machine learning approach to predicting electronic structure that is generalizable both to new conformations as well as new molecules. At the same time our work circumvents the N-representability problem that has stymied the adaption of 2RDM methods, by directly machine-learning valid Reduced Density Matrices.  ( 2 min )
    Fairness Based Energy-Efficient 3D Path Planning of a Portable Access Point: A Deep Reinforcement Learning Approach. (arXiv:2208.05265v1 [eess.SP])
    In this work, we optimize the 3D trajectory of an unmanned aerial vehicle (UAV)-based portable access point (PAP) that provides wireless services to a set of ground nodes (GNs). Moreover, as per the Peukert effect, we consider pragmatic non-linear battery discharge for the battery of the UAV. Thus, we formulate the problem in a novel manner that represents the maximization of a fairness-based energy efficiency metric and is named fair energy efficiency (FEE). The FEE metric defines a system that lays importance on both the per-user service fairness and the energy efficiency of the PAP. The formulated problem takes the form of a non-convex problem with non-tractable constraints. To obtain a solution, we represent the problem as a Markov Decision Process (MDP) with continuous state and action spaces. Considering the complexity of the solution space, we use the twin delayed deep deterministic policy gradient (TD3) actor-critic deep reinforcement learning (DRL) framework to learn a policy that maximizes the FEE of the system. We perform two types of RL training to exhibit the effectiveness of our approach: the first (offline) approach keeps the positions of the GNs the same throughout the training phase; the second approach generalizes the learned policy to any arrangement of GNs by changing the positions of GNs after each training episode. Numerical evaluations show that neglecting the Peukert effect overestimates the air-time of the PAP and can be addressed by optimally selecting the PAP's flying speed. Moreover, the user fairness, energy efficiency, and hence the FEE value of the system can be improved by efficiently moving the PAP above the GNs. As such, we notice massive FEE improvements over baseline scenarios of up to 88.31%, 272.34%, and 318.13% for suburban, urban, and dense urban environments, respectively.  ( 3 min )
    Learning Quantization in LDPC Decoders. (arXiv:2208.05186v1 [cs.IT])
    Finding optimal message quantization is a key requirement for low complexity belief propagation (BP) decoding. To this end, we propose a floating-point surrogate model that imitates quantization effects as additions of uniform noise, whose amplitudes are trainable variables. We verify that the surrogate model closely matches the behavior of a fixed-point implementation and propose a hand-crafted loss function to realize a trade-off between complexity and error-rate performance. A deep learning-based method is then applied to optimize the message bitwidths. Moreover, we show that parameter sharing can both ensure implementation-friendly solutions and results in faster training convergence than independent parameters. We provide simulation results for 5G low-density parity-check (LDPC) codes and report an error-rate performance within 0.2 dB of floating-point decoding at an average message quantization bitwidth of 3.1 bits. In addition, we show that the learned bitwidths also generalize to other code rates and channels.  ( 2 min )
    Wavelet Score-Based Generative Modeling. (arXiv:2208.05003v1 [cs.LG])
    Score-based generative models (SGMs) synthesize new data samples from Gaussian white noise by running a time-reversed Stochastic Differential Equation (SDE) whose drift coefficient depends on some probabilistic score. The discretization of such SDEs typically requires a large number of time steps and hence a high computational cost. This is because of ill-conditioning properties of the score that we analyze mathematically. We show that SGMs can be considerably accelerated, by factorizing the data distribution into a product of conditional probabilities of wavelet coefficients across scales. The resulting Wavelet Score-based Generative Model (WSGM) synthesizes wavelet coefficients with the same number of time steps at all scales, and its time complexity therefore grows linearly with the image size. This is proved mathematically over Gaussian distributions, and shown numerically over physical processes at phase transition and natural image datasets.  ( 2 min )
    Heterogeneous Multi-agent Zero-Shot Coordination by Coevolution. (arXiv:2208.04957v1 [cs.NE])
    Generating agents that can achieve Zero-Shot Coordination (ZSC) with unseen partners is a new challenge in cooperative Multi-Agent Reinforcement Learning (MARL). Recently, some studies have made progress in ZSC by exposing the agents to diverse partners during the training process. They usually involve self-play when training the partners, implicitly assuming that the tasks are homogeneous. However, many real-world tasks are heterogeneous, and hence previous methods may fail. In this paper, we study the heterogeneous ZSC problem for the first time and propose a general method based on coevolution, which coevolves two populations of agents and partners through three sub-processes: pairing, updating and selection. Experimental results on a collaborative cooking task show the necessity of considering the heterogeneous setting and illustrate that our proposed method is a promising solution for heterogeneous cooperative MARL.  ( 2 min )
    Learning from imperfect training data using a robust loss function: application to brain image segmentation. (arXiv:2208.04941v1 [eess.IV])
    Segmentation is one of the most important tasks in MRI medical image analysis and is often the first and the most critical step in many clinical applications. In brain MRI analysis, head segmentation is commonly used for measuring and visualizing the brain's anatomical structures and is also a necessary step for other applications such as current-source reconstruction in electroencephalography and magnetoencephalography (EEG/MEG). Here we propose a deep learning framework that can segment brain, skull, and extra-cranial tissue using only T1-weighted MRI as input. In addition, we describe a robust method for training the model in the presence of noisy labels.  ( 2 min )
    CoViT: Real-time phylogenetics for the SARS-CoV-2 pandemic using Vision Transformers. (arXiv:2208.05004v1 [cs.LG])
    Real-time viral genome detection, taxonomic classification and phylogenetic analysis are critical for efficient tracking and control of viral pandemics such as Covid-19. However, the unprecedented and still growing amounts of viral genome data create a computational bottleneck, which effectively prevents the real-time pandemic tracking. We are attempting to alleviate this bottleneck by modifying and applying Vision Transformer, a recently developed neural network model for image recognition, to taxonomic classification and placement of viral genomes, such as SARS-CoV-2. Our solution, CoViT, places newly acquired samples onto the tree of SARS-CoV-2 lineages. One of the two potential placements returned by CoVit is the true one with the probability of 99.0%. The probability of the correct placement to be found among five potential placements generated by CoViT is 99.8%. The placement time is 1.45ms per individual genome running on NVIDIAs GeForce RTX 2080 Ti GPU. We make CoViT available to research community through GitHub: https://github.com/zuherJahshan/covit.  ( 2 min )
    A Unified Comparison of User Modeling Techniques for Predicting Data Interaction and Detecting Exploration Bias. (arXiv:2208.05021v1 [cs.HC])
    The visual analytics community has proposed several user modeling algorithms to capture and analyze users' interaction behavior in order to assist users in data exploration and insight generation. For example, some can detect exploration biases while others can predict data points that the user will interact with before that interaction occurs. Researchers believe this collection of algorithms can help create more intelligent visual analytics tools. However, the community lacks a rigorous evaluation and comparison of these existing techniques. As a result, there is limited guidance on which method to use and when. Our paper seeks to fill in this missing gap by comparing and ranking eight user modeling algorithms based on their performance on a diverse set of four user study datasets. We analyze exploration bias detection, data interaction prediction, and algorithmic complexity, among other measures. Based on our findings, we highlight open challenges and new directions for analyzing user interactions and visualization provenance.  ( 2 min )
    A physically-informed Deep-Learning approach for locating sources in a waveguide. (arXiv:2208.04938v1 [cs.LG])
    Inverse source problems are central to many applications in acoustics, geophysics, non-destructive testing, and more. Traditional imaging methods suffer from the resolution limit, preventing distinction of sources separated by less than the emitted wavelength. In this work we propose a method based on physically-informed neural-networks for solving the source refocusing problem, constructing a novel loss term which promotes super-resolving capabilities of the network and is based on the physics of wave propagation. We demonstrate the approach in the setup of imaging an a-priori unknown number of point sources in a two-dimensional rectangular waveguide from measurements of wavefield recordings along a vertical cross-section. The results show the ability of the method to approximate the locations of sources with high accuracy, even when placed close to each other.  ( 2 min )
    Machine Learning-based EEG Applications and Markets. (arXiv:2208.05144v1 [cs.LG])
    This paper addresses both the various EEG applications and the current EEG market ecosystem propelled by machine learning. Increasingly available open medical and health datasets using EEG encourage data-driven research with a promise of improving neurology for patient care through knowledge discovery and machine learning data science algorithm development. This effort leads to various kinds of EEG developments and currently forms a new EEG market. This paper attempts to do a comprehensive survey on the EEG market and covers the six significant applications of EEG, including diagnosis/screening, drug development, neuromarketing, daily health, metaverse, and age/disability assistance. The highlight of this survey is on the compare and contrast between the research field and the business market. Our survey points out the current limitations of EEG and indicates the future direction of research and business opportunity for every EEG application listed above. Based on our survey, more research on machine learning-based EEG applications will lead to a more robust EEG-related market. More companies will use the research technology and apply it to real-life settings. As the EEG-related market grows, the EEG-related devices will collect more EEG data, and there will be more EEG data available for researchers to use in their study, coming back as a virtuous cycle. Our market analysis indicates that research related to the use of EEG data and machine learning in the six applications listed above points toward a clear trend in the growth and development of the EEG ecosystem and machine learning world.  ( 3 min )
    Continual Prune-and-Select: Class-incremental learning with specialized subnetworks. (arXiv:2208.04952v1 [cs.LG])
    The human brain is capable of learning tasks sequentially mostly without forgetting. However, deep neural networks (DNNs) suffer from catastrophic forgetting when learning one task after another. We address this challenge considering a class-incremental learning scenario where the DNN sees test data without knowing the task from which this data originates. During training, Continual-Prune-and-Select (CP&S) finds a subnetwork within the DNN that is responsible for solving a given task. Then, during inference, CP&S selects the correct subnetwork to make predictions for that task. A new task is learned by training available neuronal connections of the DNN (previously untrained) to create a new subnetwork by pruning, which can include previously trained connections belonging to other subnetwork(s) because it does not update shared connections. This enables to eliminate catastrophic forgetting by creating specialized regions in the DNN that do not conflict with each other while still allowing knowledge transfer across them. The CP&S strategy is implemented with different subnetwork selection strategies, revealing superior performance to state-of-the-art continual learning methods tested on various datasets (CIFAR-100, CUB-200-2011, ImageNet-100 and ImageNet-1000). In particular, CP&S is capable of sequentially learning 10 tasks from ImageNet-1000 keeping an accuracy around 94% with negligible forgetting, a first-of-its-kind result in class-incremental learning. To the best of the authors' knowledge, this represents an improvement in accuracy above 20% when compared to the best alternative method.  ( 3 min )
    Attention Hijacking in Trojan Transformers. (arXiv:2208.04946v1 [cs.LG])
    Trojan attacks pose a severe threat to AI systems. Recent works on Transformer models received explosive popularity and the self-attentions are now indisputable. This raises a central question: Can we reveal the Trojans through attention mechanisms in BERTs and ViTs? In this paper, we investigate the attention hijacking pattern in Trojan AIs, \ie, the trigger token ``kidnaps'' the attention weights when a specific trigger is present. We observe the consistent attention hijacking pattern in Trojan Transformers from both Natural Language Processing (NLP) and Computer Vision (CV) domains. This intriguing property helps us to understand the Trojan mechanism in BERTs and ViTs. We also propose an Attention-Hijacking Trojan Detector (AHTD) to discriminate the Trojan AIs from the clean ones.  ( 2 min )
    Automatic Ultrasound Image Segmentation of Supraclavicular Nerve Using Dilated U-Net Deep Learning Architecture. (arXiv:2208.05050v1 [eess.IV])
    Automated object recognition in medical images can facilitate medical diagnosis and treatment. In this paper, we automatically segmented supraclavicular nerves in ultrasound images to assist in injecting peripheral nerve blocks. Nerve blocks are generally used for pain treatment after surgery, where ultrasound guidance is used to inject local anesthetics next to target nerves. This treatment blocks the transmission of pain signals to the brain, which can help improve the rate of recovery from surgery and significantly decrease the requirement for postoperative opioids. However, Ultrasound Guided Regional Anesthesia (UGRA) requires anesthesiologists to visually recognize the actual nerve position in the ultrasound images. This is a complex task given the myriad visual presentations of nerves in ultrasound images, and their visual similarity to many neighboring tissues. In this study, we used an automated nerve detection system for the UGRA Nerve Block treatment. The system can recognize the position of the nerve in ultrasound images using Deep Learning techniques. We developed a model to capture features of nerves by training two deep neural networks with skip connections: two extended U-Net architectures with and without dilated convolutions. This solution could potentially lead to an improved blockade of targeted nerves in regional anesthesia.  ( 3 min )
    Interpretable Polynomial Neural Ordinary Differential Equations. (arXiv:2208.05072v1 [cs.LG])
    Neural networks have the ability to serve as universal function approximators, but they are not interpretable and don't generalize well outside of their training region. Both of these issues are problematic when trying to apply standard neural ordinary differential equations (neural ODEs) to dynamical systems. We introduce the polynomial neural ODE, which is a deep polynomial neural network inside of the neural ODE framework. We demonstrate the capability of polynomial neural ODEs to predict outside of the training region, as well as perform direct symbolic regression without additional tools such as SINDy.  ( 2 min )
    Classifier Transfer with Data Selection Strategies for Online Support Vector Machine Classification with Class Imbalance. (arXiv:2208.05112v1 [cs.LG])
    Objective: Classifier transfers usually come with dataset shifts. To overcome them, online strategies have to be applied. For practical applications, limitations in the computational resources for the adaptation of batch learning algorithms, like the SVM, have to be considered. Approach: We review and compare several strategies for online learning with SVMs. We focus on data selection strategies which limit the size of the stored training data [...] Main Results: For different data shifts, different criteria are appropriate. For the synthetic data, adding all samples to the pool of considered samples performs often significantly worse than other criteria. Especially, adding only misclassified samples performed astoundingly well. Here, balancing criteria were very important when the other criteria were not well chosen. For the transfer setups, the results show that the best strategy depends on the intensity of the drift during the transfer. Adding all and removing the oldest samples results in the best performance, whereas for smaller drifts, it can be sufficient to only add potential new support vectors of the SVM which reduces processing resources. Significance: For BCIs based on EEG models, trained on data from a calibration session, a previous recording session, or even from a recording session with one or several other subjects, are used. This transfer of the learned model usually decreases the performance and can therefore benefit from online learning which adapts the classifier like the established SVM. We show that by using the right combination of data selection criteria, it is possible to adapt the classifier and largely increase the performance. Furthermore, in some cases it is possible to speed up the processing and save computational by updating with a subset of special samples and keeping a small subset of samples for training the classifier.  ( 3 min )
  • Open

    Deep Learning Methods for Proximal Inference via Maximum Moment Restriction. (arXiv:2205.09824v2 [stat.ML] UPDATED)
    The No Unmeasured Confounding Assumption is widely used to identify causal effects in observational studies. Recent work on proximal inference has provided alternative identification results that succeed even in the presence of unobserved confounders, provided that one has measured a sufficiently rich set of proxy variables, satisfying specific structural conditions. However, proximal inference requires solving an ill-posed integral equation. Previous approaches have used a variety of machine learning techniques to estimate a solution to this integral equation, commonly referred to as the bridge function. However, prior work has often been limited by relying on pre-specified kernel functions, which are not data adaptive and struggle to scale to large datasets. In this work, we introduce a flexible and scalable method based on a deep neural network to estimate causal effects in the presence of unmeasured confounding using proximal inference. Our method achieves state of the art performance on two well-established proximal inference benchmarks. Finally, we provide theoretical consistency guarantees for our method.  ( 2 min )
    How Does the Task Landscape Affect MAML Performance?. (arXiv:2010.14672v5 [cs.LG] UPDATED)
    Model-Agnostic Meta-Learning (MAML) has become increasingly popular for training models that can quickly adapt to new tasks via one or few stochastic gradient descent steps. However, the MAML objective is significantly more difficult to optimize compared to standard non-adaptive learning (NAL), and little is understood about how much MAML improves over NAL in terms of the fast adaptability of their solutions in various scenarios. We analytically address this issue in a linear regression setting consisting of a mixture of easy and hard tasks, where hardness is related to the rate that gradient descent converges on the task. Specifically, we prove that in order for MAML to achieve substantial gain over NAL, (i) there must be some discrepancy in hardness among the tasks, and (ii) the optimal solutions of the hard tasks must be closely packed with the center far from the center of the easy tasks optimal solutions. We also give numerical and analytical results suggesting that these insights apply to two-layer neural networks. Finally, we provide few-shot image classification experiments that support our insights for when MAML should be used and emphasize the importance of training MAML on hard tasks in practice.  ( 3 min )
    Wavelet Score-Based Generative Modeling. (arXiv:2208.05003v1 [cs.LG])
    Score-based generative models (SGMs) synthesize new data samples from Gaussian white noise by running a time-reversed Stochastic Differential Equation (SDE) whose drift coefficient depends on some probabilistic score. The discretization of such SDEs typically requires a large number of time steps and hence a high computational cost. This is because of ill-conditioning properties of the score that we analyze mathematically. We show that SGMs can be considerably accelerated, by factorizing the data distribution into a product of conditional probabilities of wavelet coefficients across scales. The resulting Wavelet Score-based Generative Model (WSGM) synthesizes wavelet coefficients with the same number of time steps at all scales, and its time complexity therefore grows linearly with the image size. This is proved mathematically over Gaussian distributions, and shown numerically over physical processes at phase transition and natural image datasets.  ( 2 min )
    KL-divergence Based Deep Learning for Discrete Time Model. (arXiv:2208.05100v1 [stat.ML])
    Neural Network (Deep Learning) is a modern model in Artificial Intelligence and it has been exploited in Survival Analysis. Although several improvements have been shown by previous works, training an excellent deep learning model requires a huge amount of data, which may not hold in practice. To address this challenge, we develop a Kullback-Leibler-based (KL) deep learning procedure to integrate external survival prediction models with newly collected time-to-event data. Time-dependent KL discrimination information is utilized to measure the discrepancy between the external and internal data. This is the first work considering using prior information to deal with short data problem in Survival Analysis for deep learning. Simulation and real data results show that the proposed model achieves better performance and higher robustness compared with previous works.  ( 2 min )
    Mappings for Marginal Probabilities with Applications to Models in Statistical Physics. (arXiv:2208.05333v1 [stat.ML])
    We present local mappings that relate the marginal probabilities of a global probability mass function represented by its primal normal factor graph to the corresponding marginal probabilities in its dual normal factor graph. The mapping is based on the Fourier transform of the local factors of the models. Details of the mapping are provided for the Ising model, where it is proved that the local extrema of the fixed points are attained at the phase transition of the two-dimensional nearest-neighbor Ising model. The results are further extended to the Potts model, to the clock model, and to Gaussian Markov random fields. By employing the mapping, we can transform simultaneously all the estimated marginal probabilities from the dual domain to the primal domain (and vice versa), which is advantageous if estimating the marginals can be carried out more efficiently in the dual domain. An example of particular significance is the ferromagnetic Ising model in a positive external magnetic field. For this model, there exists a rapidly mixing Markov chain (called the subgraphs--world process) to generate configurations in the dual normal factor graph of the model. Our numerical experiments illustrate that the proposed procedure can provide more accurate estimates of marginal probabilities of a global probability mass function in various settings.  ( 3 min )
    Importance Weighting Approach in Kernel Bayes' Rule. (arXiv:2202.02474v3 [stat.ML] UPDATED)
    We study a nonparametric approach to Bayesian computation via feature means, where the expectation of prior features is updated to yield expected kernel posterior features, based on regression from learned neural net or kernel features of the observations. All quantities involved in the Bayesian update are learned from observed data, making the method entirely model-free. The resulting algorithm is a novel instance of a kernel Bayes' rule (KBR), based on importance weighting. This results in superior numerical stability to the original approach to KBR, which requires operator inversion. We show the convergence of the estimator using a novel consistency analysis on the importance weighting estimator in the infinity norm. We evaluate KBR on challenging synthetic benchmarks, including a filtering problem with a state-space model involving high dimensional image observations. Importance weighted KBR yields uniformly better empirical performance than the original KBR, and competitive performance with other competing methods.  ( 2 min )
    Image classifiers can not be made robust to small perturbations. (arXiv:2112.04033v2 [cs.CV] UPDATED)
    The sensitivity of image classifiers to small perturbations in the input is often viewed as a defect of their construction. We demonstrate that this sensitivity is a fundamental property of classifiers. For any arbitrary classifier over the set of $n$-by-$n$ images, we show that for all but one class it is possible to change the classification of all but a tiny fraction of the images in that class with a perturbation of size $O(n^{1/\max{(p,1)}})$ when measured in any $p$-norm for $p \geq 0$. We then discuss how this phenomenon relates to human visual perception and the potential implications for the design considerations of computer vision systems.  ( 2 min )
    Fast Offline Policy Optimization for Large Scale Recommendation. (arXiv:2208.05327v1 [cs.IR])
    Personalised interactive systems such as recommender systems require selecting relevant items dependent on context. Production systems need to identify the items rapidly from very large catalogues which can be efficiently solved using maximum inner product search technology. Offline optimisation of maximum inner product search can be achieved by a relaxation of the discrete problem resulting in policy learning or reinforce style learning algorithms. Unfortunately this relaxation step requires computing a sum over the entire catalogue making the complexity of the evaluation of the gradient (and hence each stochastic gradient descent iterations) linear in the catalogue size. This calculation is untenable in many real world examples such as large catalogue recommender systems severely limiting the usefulness of this method in practice. In this paper we show how it is possible to produce an excellent approximation of these policy learning algorithms that scale logarithmically with the catalogue size. Our contribution is based upon combining three novel ideas: a new Monte Carlo estimate of the gradient of a policy, the self normalised importance sampling estimator and the use of fast maximum inner product search at training time. Extensive experiments show our algorithm is an order of magnitude faster than naive approaches yet produces equally good policies.  ( 2 min )
    Machine Learning-based EEG Applications and Markets. (arXiv:2208.05144v1 [cs.LG])
    This paper addresses both the various EEG applications and the current EEG market ecosystem propelled by machine learning. Increasingly available open medical and health datasets using EEG encourage data-driven research with a promise of improving neurology for patient care through knowledge discovery and machine learning data science algorithm development. This effort leads to various kinds of EEG developments and currently forms a new EEG market. This paper attempts to do a comprehensive survey on the EEG market and covers the six significant applications of EEG, including diagnosis/screening, drug development, neuromarketing, daily health, metaverse, and age/disability assistance. The highlight of this survey is on the compare and contrast between the research field and the business market. Our survey points out the current limitations of EEG and indicates the future direction of research and business opportunity for every EEG application listed above. Based on our survey, more research on machine learning-based EEG applications will lead to a more robust EEG-related market. More companies will use the research technology and apply it to real-life settings. As the EEG-related market grows, the EEG-related devices will collect more EEG data, and there will be more EEG data available for researchers to use in their study, coming back as a virtuous cycle. Our market analysis indicates that research related to the use of EEG data and machine learning in the six applications listed above points toward a clear trend in the growth and development of the EEG ecosystem and machine learning world.  ( 3 min )
    Convergence of denoising diffusion models under the manifold hypothesis. (arXiv:2208.05314v1 [stat.ML])
    Denoising diffusion models are a recent class of generative models exhibiting state-of-the-art performance in image and audio synthesis. Such models approximate the time-reversal of a forward noising process from a target distribution to a reference density, which is usually Gaussian. Despite their strong empirical results, the theoretical analysis of such models remains limited. In particular, all current approaches crucially assume that the target density admits a density w.r.t. the Lebesgue measure. This does not cover settings where the target distribution is supported on a lower-dimensional manifold or is given by some empirical distribution. In this paper, we bridge this gap by providing the first convergence results for diffusion models in this more general setting. In particular, we provide quantitative bounds on the Wasserstein distance of order one between the target data distribution and the generative distribution of the diffusion model.  ( 2 min )
    Counterfactual Phenotyping with Censored Time-to-Events. (arXiv:2202.11089v3 [cs.LG] UPDATED)
    Estimation of treatment efficacy of real-world clinical interventions involves working with continuous outcomes such as time-to-death, re-hospitalization, or a composite event that may be subject to censoring. Counterfactual reasoning in such scenarios requires decoupling the effects of confounding physiological characteristics that affect baseline survival rates from the effects of the interventions being assessed. In this paper, we present a latent variable approach to model heterogeneous treatment effects by proposing that an individual can belong to one of latent clusters with distinct response characteristics. We show that this latent structure can mediate the base survival rates and helps determine the effects of an intervention. We demonstrate the ability of our approach to discover actionable phenotypes of individuals based on their treatment response on multiple large randomized clinical trials originally conducted to assess appropriate treatments to reduce cardiovascular risk.  ( 2 min )
    Robust methods for high-dimensional linear learning. (arXiv:2208.05447v1 [stat.ML])
    We propose statistically robust and computationally efficient linear learning methods in the high-dimensional batch setting, where the number of features $d$ may exceed the sample size $n$. We employ, in a generic learning setting, two algorithms depending on whether the considered loss function is gradient-Lipschitz or not. Then, we instantiate our framework on several applications including vanilla sparse, group-sparse and low-rank matrix recovery. This leads, for each application, to efficient and robust learning algorithms, that reach near-optimal estimation rates under heavy-tailed distributions and the presence of outliers. For vanilla $s$-sparsity, we are able to reach the $s\log (d)/n$ rate under heavy-tails and $\eta$-corruption, at a computational cost comparable to that of non-robust analogs. We provide an efficient implementation of our algorithms in an open-source $\mathtt{Python}$ library called $\mathtt{linlearn}$, by means of which we carry out numerical experiments which confirm our theoretical findings together with a comparison to other recent approaches proposed in the literature.  ( 2 min )
    Training Process of Unsupervised Learning Architecture for Gravity Spy Dataset. (arXiv:2208.03623v1 [gr-qc] CROSS LISTED)
    Transient noise appearing in the data from gravitational-wave detectors frequently causes problems, such as instability of the detectors and overlapping or mimicking gravitational-wave signals. Because transient noise is considered to be associated with the environment and instrument, its classification would help to understand its origin and improve the detector's performance. In a previous study, an architecture for classifying transient noise using a time-frequency 2D image (spectrogram) is proposed, which uses unsupervised deep learning combined with variational autoencoder and invariant information clustering. The proposed unsupervised-learning architecture is applied to the Gravity Spy dataset, which consists of Advanced Laser Interferometer Gravitational-Wave Observatory (Advanced LIGO) transient noises with their associated metadata to discuss the potential for online or offline data analysis. In this study, focused on the Gravity Spy dataset, the training process of unsupervised-learning architecture of the previous study is examined and reported.  ( 2 min )
    Uncertainty quantification in the Bradley-Terry-Luce model. (arXiv:2110.03874v2 [math.ST] UPDATED)
    The Bradley-Terry-Luce (BTL) model is a benchmark model for pairwise comparisons between individuals. Despite recent progress on the first-order asymptotics of several popular procedures, the understanding of uncertainty quantification in the BTL model remains largely incomplete, especially when the underlying comparison graph is sparse. In this paper, we fill this gap by focusing on two estimators that have received much recent attention: the maximum likelihood estimator (MLE) and the spectral estimator. Using a unified proof strategy, we derive sharp and uniform non-asymptotic expansions for both estimators in the sparsest possible regime (up to some poly-logarithmic factors) of the underlying comparison graph. These expansions allow us to obtain: (i) finite-dimensional central limit theorems for both estimators; (ii) construction of confidence intervals for individual ranks; (iii) optimal constant of $\ell_2$ estimation, which is achieved by the MLE but not by the spectral estimator. Our proof is based on a self-consistent equation of the second-order remainder vector and a novel leave-two-out analysis.  ( 2 min )
    SurvLatent ODE : A Neural ODE based time-to-event model with competing risks for longitudinal data improves cancer-associated Venous Thromboembolism (VTE) prediction. (arXiv:2204.09633v2 [cs.LG] UPDATED)
    Effective learning from electronic health records (EHR) data for prediction of clinical outcomes is often challenging because of features recorded at irregular timesteps and loss to follow-up as well as competing events such as death or disease progression. To that end, we propose a generative time-to-event model, SurvLatent ODE, which adopts an Ordinary Differential Equation-based Recurrent Neural Networks (ODE-RNN) as an encoder to effectively parameterize dynamics of latent states under irregularly sampled input data. Our model then utilizes the resulting latent embedding to flexibly estimate survival times for multiple competing events without specifying shapes of event-specific hazard function. We demonstrate competitive performance of our model on MIMIC-III, a freely-available longitudinal dataset collected from critical care units, on predicting hospital mortality as well as the data from the Dana-Farber Cancer Institute (DFCI) on predicting onset of Venous Thromboembolism (VTE), a life-threatening complication for patients with cancer, with death as a competing event. SurvLatent ODE outperforms the current clinical standard Khorana Risk scores for stratifying VTE risk groups, while providing clinically meaningful and interpretable latent representations.  ( 3 min )
    A Model-Constrained Tangent Manifold Learning Approach for Dynamical Systems. (arXiv:2208.04995v1 [cs.LG])
    Real time accurate solutions of large scale complex dynamical systems are in critical need for control, optimization, uncertainty quantification, and decision-making in practical engineering and science applications. This paper contributes in this direction a model constrained tangent manifold learning (mcTangent) approach. At the heart of mcTangent is the synergy of several desirable strategies: i) a tangent manifold learning to take advantage of the neural network speed and the time accurate nature of the method of lines; ii) a model constrained approach to encode the neural network tangent with the underlying governing equations; iii) sequential learning strategies to promote long time stability and accuracy; and iv) data randomization approach to implicitly enforce the smoothness of the neural network tangent and its likeliness to the truth tangent up second order derivatives in order to further enhance the stability and accuracy of mcTangent solutions. Both semi heuristic and rigorous arguments are provided to analyze and justify the proposed approach. Several numerical results for transport equation, viscous Burgers equation, and Navier Stokes equation are presented to study and demonstrate the capability of the proposed mcTangent learning approach.  ( 2 min )
    Robust Reinforcement Learning using Offline Data. (arXiv:2208.05129v1 [cs.LG])
    The goal of robust reinforcement learning (RL) is to learn a policy that is robust against the uncertainty in model parameters. Parameter uncertainty commonly occurs in many real-world RL applications due to simulator modeling errors, changes in the real-world system dynamics over time, and adversarial disturbances. Robust RL is typically formulated as a max-min problem, where the objective is to learn the policy that maximizes the value against the worst possible models that lie in an uncertainty set. In this work, we propose a robust RL algorithm called Robust Fitted Q-Iteration (RFQI), which uses only an offline dataset to learn the optimal robust policy. Robust RL with offline data is significantly more challenging than its non-robust counterpart because of the minimization over all models present in the robust Bellman operator. This poses challenges in offline data collection, optimization over the models, and unbiased estimation. In this work, we propose a systematic approach to overcome these challenges, resulting in our RFQI algorithm. We prove that RFQI learns a near-optimal robust policy under standard assumptions and demonstrate its superior performance on standard benchmark problems.  ( 2 min )
    An alternative approach to train neural networks using monotone variational inequality. (arXiv:2202.08876v3 [stat.ML] UPDATED)
    Despite the vast empirical success of neural networks, theoretical understanding of the training procedures remains limited, especially in providing performance guarantees of testing performance due to the non-convex nature of the optimization problem. The current paper investigates an alternative approach of neural network training by reducing to another problem with convex structure -- to solve a monotone variational inequality (MVI) -- inspired by a recent work of (Juditsky & Nemirovsky, 2019). The solution to MVI can be found by computationally efficient procedures, and importantly, this leads to performance guarantee of $\ell_2$ and $\ell_{\infty}$ bounds on model recovery and prediction accuracy under the theoretical setting of training a single-layer linear neural network. In addition, we study the use of MVI for training multi-layer neural networks and propose a practical algorithm called \textit{stochastic variational inequality} (SVI), and demonstrate its applicability in training fully-connected neural networks and graph neural networks (GNN) (SVI is completely general and can be used to train other types of neural networks). We demonstrate the competitive or better performance of SVI compared to widely-used stochastic gradient descent methods on both synthetic and real network data prediction tasks regarding various performance metrics, especially in the improved efficiency in the early stage of training.  ( 3 min )
    Theoretical Connection between Locally Linear Embedding, Factor Analysis, and Probabilistic PCA. (arXiv:2203.13911v2 [stat.ML] UPDATED)
    Locally Linear Embedding (LLE) is a nonlinear spectral dimensionality reduction and manifold learning method. It has two main steps which are linear reconstruction and linear embedding of points in the input space and embedding space, respectively. In this work, we look at the linear reconstruction step from a stochastic perspective where it is assumed that every data point is conditioned on its linear reconstruction weights as latent factors. The stochastic linear reconstruction of LLE is solved using expectation maximization. We show that there is a theoretical connection between three fundamental dimensionality reduction methods, i.e., LLE, factor analysis, and probabilistic Principal Component Analysis (PCA). The stochastic linear reconstruction of LLE is formulated similar to the factor analysis and probabilistic PCA. It is also explained why factor analysis and probabilistic PCA are linear and LLE is a nonlinear method. This work combines and makes a bridge between two broad approaches of dimensionality reduction, i.e., the spectral and probabilistic algorithms.  ( 3 min )
    Learning Two-Player Mixture Markov Games: Kernel Function Approximation and Correlated Equilibrium. (arXiv:2208.05363v1 [cs.LG])
    We consider learning Nash equilibria in two-player zero-sum Markov Games with nonlinear function approximation, where the action-value function is approximated by a function in a Reproducing Kernel Hilbert Space (RKHS). The key challenge is how to do exploration in the high-dimensional function space. We propose a novel online learning algorithm to find a Nash equilibrium by minimizing the duality gap. At the core of our algorithms are upper and lower confidence bounds that are derived based on the principle of optimism in the face of uncertainty. We prove that our algorithm is able to attain an $O(\sqrt{T})$ regret with polynomial computational complexity, under very mild assumptions on the reward function and the underlying dynamic of the Markov Games. We also propose several extensions of our algorithm, including an algorithm with Bernstein-type bonus that can achieve a tighter regret bound, and another algorithm for model misspecification that can be applied to neural function approximation.  ( 2 min )

  • Open

    [P] Imbalanced sentence pair classification
    I am training a multi class sentence pair classifier. The inputs are sent A, sent B (sort of repetition of sent A (by another person)) . The classes signify different types of errors in sent B. However the data is highly imbalanced, approximately Class 1 : 850 Class 2: 125 Class 3: 90 Class 4: 160 Class 5: 45 Also the test data is recorded at different source compared to train data. My approach was to fine tune a bert model for sentence pair task. Until now I have tried oversampling, undersampling, different losses (weighted cross entropy, focal loss and dice loss). But the precision, recall measures of class 5 is very poor. Is there anything else I can try to improve the performance? Would Pretrained nli model help? I also have around half a million records of unlabelled sent A. Could I try training bert on this data before fine tuning it? submitted by /u/channel-hopper- [link] [comments]  ( 88 min )
    [Research] Deep Critical Learning (i.e., Deep Robustness) In The Era of Big Data
    Here are related papers on the fitting and generalization of deep learning: * ProSelfLC: Progressive Self Label Correction Towards A Low-Temperature Entropy State * Understanding deep learning requires rethinking generalization * A Closer Look at Memorization in Deep Networks * ProSelfLC: Progressive Self Label Correction for Training Robust Deep Neural Networks * Blog link: https://xinshaoamoswang.github.io/blogs/2020-06-07-Progressive-self-label-correction/ * Mean Absolute Error Does Not Treat Examples Equally and Gradient Magnitude’s Variance Matters * Derivative Manipulation: Example Weighting via Emphasis Density Funtion in the context of DL * Novelty: moving from loss design to derivative design PyTorch Implementation for ProSelfLC, Derivative Manipulation, Improved MAE Easy to i…  ( 89 min )
    [Project] Implementation of Improved Denoised Diffusion Models
    Hi all! I recently implemented "Improved Denoising Diffusion Probabilistic Models" (https://arxiv.org/abs/2102.09672). ​ You can find my implementation here: https://github.com/vedantroy/improved-ddpm-pytorch As far as I'm aware, there's not that many open-source implementations of diffusion models. Most people just seem to use lucidrain's (excellent) imagen-pytorch repository. submitted by /u/vanilla-acc [link] [comments]  ( 87 min )
    [D] Affiliations (Universities, companies) with most papers at CVPR over the years
    Is there a publicly available list of affiliations with the number of papers accepted at CVPR each year (spanning a few years)? submitted by /u/daredevildas [link] [comments]  ( 114 min )
    [R] Meaning without reference in large language models - Deepmind 2022 - Meaning comes from the way concepts relate to each other and LLM likely do that too.
    Paper: https://arxiv.org/abs/2208.02957 Abstract: The widespread success of large language models (LLMs) has been met with skepticism that they possess anything like human concepts or meanings. Contrary to claims that LLMs possess no meaning whatsoever, we argue that they likely capture important aspects of meaning, and moreover work in a way that approximates a compelling account of human cognition in which meaning arises from conceptual role. Because conceptual role is defined by the relationships between internal representational states, meaning cannot be determined from a model's architecture, training data, or objective function, but only by examination of how its internal states relate to each other. This approach may clarify why and how LLMs are so successful and suggest how they can be made more human-like. Conclusion: Bender & Koller argue that text-based LLMs will never have meaning because these models lack reference. However, they do not demonstrate that reference is the key to meaning instead they assume it. As we have argued, this assumption is hard to reconcile with theories of cognition and the phenomena that motivate them. People are happy to think about concepts without referents and otherwise often don’t know many details of reference. Meaning instead seems to come from the way concepts relate to each other. It is these interrelations that LLMs know something about since their internal geometries and trajectories approximate those of humans. Like people who don’t know that water is H2O and so could not pick it out based on chemical composition, Bender & Koller’s octopus lacks some aspects of conceptual role like physical appearance. But, both the octopus and people know other parts of conceptual role that are sophisticated in their own right. If theories about conceptual role are the correct account, then LLMs likely already share the foundation of how our own concepts get their meaning. submitted by /u/Singularian2501 [link] [comments]  ( 116 min )
    [R] Branch-Train-Merge: Embarrassingly Parallel Training of Expert Language Models - Meta AI 2022
    Paper: https://arxiv.org/abs/2208.03306 Github: https://github.com/hadasah/btm Code and Models coming soon! Abstract: We present Branch-Train-Merge (BTM), a communication-efficient algorithm for embarrassingly parallel training of large language models (LLMs). We show it is possible to independently train subparts of a new class of LLMs on different subsets of the data, eliminating the massive multi-node synchronization currently required to train LLMs. BTM learns a set of independent expert LMs (ELMs), each specialized to a different textual domain, such as scientific or legal text. These ELMs can be added and removed to update data coverage, ensembled to generalize to new domains, or averaged to collapse back to a single LM for efficient inference. New ELMs are learned by branching …  ( 89 min )
    [D] how do i use the quantum computing machine learning tool pennylane to predict stock prices
    Is there a tutorial that can help me learn what this guy did to predict stock prices using pennylane? Anything I try to find ends up just being something about building circuits and other stuff when really I just want to figure out what this guy did and fully understand it submitted by /u/RazzmatazzInternal85 [link] [comments]  ( 114 min )
    [Project] Modulate error about output error
    I've been googling with no luck. The idea is to somehow tell the learning algorithm something like "I care a lot about the error in the first N components of the output vector and I care, but not much, about the rest of the components". Or something like "I care a lot about output[0], a bit less about output[1], ...". I'm thinking of using a non-recurrent NN for trajectory prediction of drones. submitted by /u/bartergames [link] [comments]  ( 87 min )
    [N] New platform for audio related AI with many tutorials incoming
    Hey everyone, I think there are not many of people like me who would love to apply more AI to audio related topics which are not only text-to-speech. Recently the only youtuber I know about this and really like, Valerio Velardo just launched a new platform. At the moment there is only the advanced python tutorial but there will be nice things coming there I guess, so if you also search for resources in audio related AI I highly recommend his channel while waiting for what is coming there. submitted by /u/mr_birrd [link] [comments]  ( 88 min )
    [D] Table manners for Data Scientists
    I compiled a non-exhaustive set of learnings from Data Science and Machine Learning Consulting, especially what to take care of during and before the meetings. They are based on the teachings of the Mathematician Gian Rota. https://towardsdatascience.com/table-manners-for-the-data-scientists-7092fd6cd03e submitted by /u/prashantmdgl9 [link] [comments]  ( 87 min )
    [P] Training&Prediction all in one binary Recommend System powered by Item2vec Embedding + MLP got AUC 0.78 on MovieLens
    I'm a backend engineer at ByteDance (Tiktok). We are maintaining a very complex DeepLearning-based Recommendation System. I was always thinking about how to make a 70% performance but easy-to-adopt recommendation system for smaller scenarios. Maybe most people here write Python, this project is Golang based. As we all know, taking a complex CTR model with a lot of feature engineering work online is really painful. So, let's suppose you know how to write Golang and SQL. It will be really easy to use edgeRec to train and deploy an API ready CTR prediction system over your database like MySQL or SQLite. This project is a pretty prototype, any suggestion is welcome. Right now, I'm just trying to make the AUC better by implementing the Deep Interest Network model. I will try to make it w…  ( 90 min )
    [D]: Are there any alternatives to Huggingface in the use of GPT-Neo?
    Even though it is OpenSource, the code for the download process seems quite complicated and downloads it from their own Hub instead of EleutherAI itself. This makes it quite intransparent to follow up, what actually happens beside the actual download for most people without professional code-audition experience. I'm wondering, wether there are any simplistic alternatives, besides downloading and compiling the model yourself (which can include alot of mistakes as well). submitted by /u/GerritTheBerrit [link] [comments]  ( 88 min )
    [D] Image Recognition - Any Algorithms to Geolocate a Photo?
    I wonder, is there research regarding the geolocation problem? In other words we have a map(perhaps 3D model) of some area and a photo from that area as input for an algorithm to try to infer a location on the map where this photo most likely is taken from. Are there algorithms that show good results? What's their prerequisites? Feel free to share papers you deem might be of relevance. Thanks! submitted by /u/pgess [link] [comments]  ( 88 min )
    [D] NeurIPS post-discussion discussion
    The discussion period is over (although I understand it was not really a discussion for many). How were the reviewer interactions for you? I saw some Tweets mentioning that reviewers seemed more open to increasing scores, which I observed too. I figured this may be due to the widespread discussion of the overall low scores encouraging reviewers to increase it. There were reports of very low average scores in the initial reviews, but I haven't seen anything regarding post-rebuttal scores. Does anyone have any info regarding this? submitted by /u/bananskaftet [link] [comments]  ( 94 min )
    [D] Help for choosing/building a workstation for AI/ML/DL research
    Hi Redditors, I need the help of the hive mind for an AI/ML/DL workstation. Our lab has recently started some research collaborations to get into AI/ML/DL for medical imaging and recruited some computer scientists either directly or through collaborations. However, the hardware we used to have ended up being suboptimal and needs replacement. To make the story very short, we are struggling to get a new workstation as local vendors are proposing customized workstations that according to our techs would not work and/or burn out quickly. The two GPUs we have are NVIDIA RTX A5000 24Gb, and we would like to use both on the same machine; obviously, we need a workstation that could handle Windows and Linux. Googling around I found those two options that seem to support our GPUs: Dell Precision Tower 7920 Lenovo ThinkStation P620 Tower Our vendors however say that they cannot guarantee those machines will support 2 NVIDIA RTX A5000 despite this being clearly written on the official Dell/Lenovo websites. Any advice on this, please? We are getting so different opinions from our techs and local vendors that we are puzzled about who to listen to. We would need a machine by September as a PhD student needs to work on it, but at the same time, we are worried to listen to the wrong advice and end up with a botched workstation. Thanks! submitted by /u/fudok [link] [comments]  ( 90 min )
    [D] Approximate the Evidence of bayesian logistic regression
    There is an approximation of the evidence of bayesian linear regression in PRML, and maximize the evidence, I would like to know if there is any method on how to approximate the evidence of logistic regression and maximize the evidence. ​ https://preview.redd.it/whs3benpmug91.png?width=1276&format=png&auto=webp&s=dc55d8d7165fc2eaf8ad48946cda3ab6d32905bb https://preview.redd.it/nlmtmo4qmug91.png?width=784&format=png&auto=webp&s=e940e45c0e290cada4cdcd3ec8ae7de2b29965c6 submitted by /u/isleizhang [link] [comments]  ( 88 min )
    [D] Will we ever see analog deep learning take over digital?
    The biggest advances in deep learning have been mostly due to scale. Ie. DALL·E 2, GPT-3... Take this to an extreme, and the limiting factor becomes power and cost of the silicon required for the computation. Cost is a purely human factor, and could be almost eliminated through mass production (rather like solar costs reduced 100x in 50 years, mostly due to mass production and automation). Power can also be reduced, but big advances in power use for dense math computation like this seem less likely. So, the eventual limiting factor of large scale deep learning will probably be available power. One approach to mathematical computation with far less power is analog circuits. For optimization use cases like this, many computation iterations can be done in a single step. So there are potentially big performance gains too. The downsides are the network design typically is fixed (only the weights can be changed), the results have noise added, dynamic range is limited (no equivalent of floating point), hardware faults aren't correctable, etc. Will analog deep learning ever take over for these reasons? submitted by /u/londons_explorer [link] [comments]  ( 90 min )
    [D] Does there exist a way to predict the accuracy of a NN? (or any other metric such as recall or precision)
    Hello, I was wondering if there had been research about predicting or generating lower bounds and upper bounds for the accuracy (or recall, precision, ...) of a NN architecture (be it a "vanilla" NN or some more involved architectures such as conv nets, recurrent NN, transformers) solely based on: - The input (that is the dataset) - The embedding (or how the data is represented) - The architecture I couldn't find anything encompassing all the aforementioned. Although, I found some attempts to predict the accuracy based on weights but the paper was quite outdated and assumed a "simple" NN and didn't generalise to conv nets and the like. I also found some papers using information theory to generate bounds for optimality but they were rather limited in their scope. That is, they only worked for "simple" NN (so no convnet, transformers, ...) Anyway, if you guys know of any paper that might be of interest feel free to share! I'd also be curious about whether you think this is fundamentally possible to make such predictions! :) Cheers, submitted by /u/Inquation [link] [comments]  ( 91 min )
    [D] Using MLFlow for model performance tracking
    do you think using MLFlow for model monitoring is a good idea? When the model makes an inference, it sends the results to an MLFlow server that could be then to track model metrics and performance. What do you think? Thank you in advance. submitted by /u/arezki123 [link] [comments]  ( 115 min )
    [D] Are vision transformers a good topic to research about in PhD ?
    I will be applying for a PhD in Computer Vision, and want to do research on Vision transformers and explore different applications in which ViTs can be used in, especially in terms of developing more resilient and reliable algorithms. Would this be a good topic to pursue? or is it too ambitious? submitted by /u/Queen_momo [link] [comments]  ( 95 min )
    [D] Does anyone care about adversarial attacks anymore?
    I feel as though this area has not received much attention over the last couple of years. The CleverHans project has gone stale and I haven't heard of many new results recently. Has the community lost interest in this area? Did we decide that adversarial attacks aren't such a problem in practical applications? submitted by /u/deepestdescent [link] [comments]  ( 119 min )
    [R] Camera-ready version of volume 2 of Kevin Murphy's "Probabilistic Machine Learning" (Advanced Topics) now free for download.
    Book available here as pdf: https://probml.github.io/pml-book/book2.html ToC here, if you want to peek: https://github.com/probml/pml2-book/blob/main/toc2-long-2022-07-29.pdf Discussion, from the man himself: https://twitter.com/sirbayes/status/1553127082992881665 submitted by /u/bikeskata [link] [comments]  ( 88 min )
    [D] How to monitor NLP and Object Detection models on AWS Sagemaker?
    Hello all, We are kind of boxed into using Sagemaker at our organization and we need to do a POC for Sagemaker's model monitoring. We noticed that Sagemaker monitoring works best with models that use tabular data/features. There are a lot of example notebooks that demonstrate model monitoring capabilities, but all of the examples are based on tabular data. We are trying to apply Sagemaker's model monitoring and gather metrics from Data Quality, Model Quality, Bias Drift, Feature Attribution Drift, and Explainability and then push those metrics into CloudWatch, similar to what was done in these notebooks: https://github.com/aws/amazon-sagemaker-examples/tree/main/sagemaker_model_monitor . Unfortunately, all of our models are using unstructured data. One model is using detectron2 for object detection (https://aws.amazon.com/blogs/machine-learning/object-detection-with-detectron2-on-amazon-sagemaker/ ) and the other is an NLP model using scikit-learn. How would we integrate Sagemaker model monitoring for these types of use cases and generate important metrics for CloudWatch? Any one have any experience using Sagemaker's model monitoring for models that use NLP or images? Thanks in advance. submitted by /u/rirhun [link] [comments]  ( 88 min )
  • Open

    How to approach a human identification algorithm by fusion of gait and face recognition by the methods of deep learning ?
    submitted by /u/ResearcherGlobal4354 [link] [comments]  ( 87 min )
    Relative position of text in text classification.
    I am trying to train a model for text classification in Azure. Input is a support ticket information. Relevant fields are Subject, Body and Creator. Azure model accepts input text as a single string, so I'd have to concatenate it all into a single text. I am planning to format training data so that creator is always on first line, subject on second and body everything after second. I assume this would help the model to identify patterns when certain people only create certain ticket types or certain keywords in subject always indicate a certain ticket type. Does this make sense? Would such relative position of information have positive effect on model performance? submitted by /u/jursla [link] [comments]  ( 87 min )
  • Open

    Use computer vision to measure agriculture yield with Amazon Rekognition Custom Labels
    In the agriculture sector, the problem of identifying and counting the amount of fruit on trees plays an important role in crop estimation. The concept of renting and leasing a tree is becoming popular, where a tree owner leases the tree every year before the harvest based on the estimated fruit yeild. The common practice […]  ( 9 min )
    Amazon SageMaker Automatic Model Tuning now supports SageMaker Training Instance Fallbacks
    Today Amazon SageMaker announced the support of SageMaker training instance fallbacks for Amazon SageMaker Automatic Model Tuning (AMT) that allow users to specify alternative compute resource configurations. SageMaker automatic model tuning finds the best version of a model by running many training jobs on your dataset using the ranges of hyperparameters that you specify for your […]  ( 5 min )
  • Open

    What examples do you have of feature engineering in deep reinforcement learning?
    submitted by /u/No_Possibility_7588 [link] [comments]  ( 86 min )
    Motion planning research papers
    I am starting my new Msc in robotics and my research direction is related to Motion planning and prediction in self-driving cars/autonomous driving. I am interested to work on this direction and its intersection with Reinforcement Learning especially Multi-Agent Reinforcement Learning. However, I would like first to know more about the literature in this direction as I had only previous experience with RL but nothing with motion planning. Therefore, I am working on it and trying to know more about the field as fast as possible. So, if anyone can mention good survey papers, papers with SoTA results, maybe mentioning the current research gaps, I would be appreciated! At the moment, I am working on collecting papers, checking awesome repos, reading papers, asking recommendations for literature and seeking help from any source. submitted by /u/hany606_ [link] [comments]  ( 102 min )
    Can we use model free reinforcement learning algorithms on an offline dataset?
    I have a dataset of trajectory collected by a another policy. Now, I wanted to train an RL algorithm (like DDPG, TD3) on this dataset, such that my new policy is better than the previous one. So, is it possible to train these algorithm (preferably of continuous action space) on the offline data. submitted by /u/Better-Ad8608 [link] [comments]  ( 87 min )
    RL project using PyVista?
    Hello community, I'm wondering if any of you have come across RL projects using PyVista? I'm thinking of using this library to create my environment based on meshes and interactions between them. Cheers! submitted by /u/leozinho2r [link] [comments]  ( 86 min )
    How does a recurrent generator work in PPO?
    I am looking at a repo in which PPO uses a recurrent generator to provide training data to the model. I don't understand very well how it works. Say that I have a 6x25x3x60x60 image as an observation. Six parallel environments, 25 steps in an episode, 3 RGB channels, 60 for height and width. The recurrent generator in this repo creates a list called sampler (https://github.com/marlbenchmark/on-policy/blob/5778eceff937bfaf7b46cfa6ee20c79ad05b58f5/onpolicy/utils/separated_buffer.py#L302) that might look like this: sampler = [ 2 11 4 1 8 9 13 7 5 10 14 6 0 12 3] Then, for each of this indexes, they create a variable called ind, which results from the multiplication between the index and the data_chunk_length (which is 10). So, it's gonna look like this: sampler = [ 20 110 40 10 80 ... 30] Then, it creates a batch and appends to it, like so: obs_batch.append(obs[ind:ind+data_chunk_length]) What I don't understand is, taken as an example my 6x25x3x60x60 image and 20 as an index, what exactly is it that it appends to the batch of images? Some of those 25 steps from all of the six parallel environments? But then I don't understand how that would be possible with huge indices such as 110. Thanks! submitted by /u/No_Possibility_7588 [link] [comments]  ( 103 min )
    RLlib work well in M1 mac?
    I'm thinking about buying M1 Mini for testing code before uploading it to server. Is it ok? submitted by /u/BugsBugking [link] [comments]  ( 86 min )
  • Open

    New programmable materials can sense their own movements
    Engineers 3D print materials with networks of sensors directly incorporated.  ( 8 min )
    3 Questions: Amar Gupta on an integrated approach to enhanced health-care delivery
    The MIT researcher and former professor discusses how Covid-19 and the influx of virtual technologies created a new medical ecosystem that needs more synchronized oversight.  ( 9 min )
    Caspar Hare, Georgia Perakis named associate deans of Social and Ethical Responsibilities of Computing
    The faculty members will work together to advance the cross-cutting initiative of the MIT Schwarzman College of Computing.  ( 6 min )
    Leveraging computational tools to enhance product design
    Graduate student Jana Saadi works on making the product design process more creative and inclusive.  ( 8 min )
  • Open

    Can AI design better streets for pedestrians? You be the judge
    submitted by /u/estasfuera [link] [comments]  ( 86 min )
    Quiz: Can you detect whether an image is Artificially Generated? (Stable Diffusion A.I)
    submitted by /u/Red-HawkEye [link] [comments]  ( 86 min )
    Demon Girl by Dall E
    submitted by /u/LuckyArcher4517 [link] [comments]  ( 92 min )
    I recently came across an Indian book with beautiful illustrations. it inspired me to create this video
    submitted by /u/nalr00n [link] [comments]  ( 86 min )
    Bringing my story to life with Midjourney, Resemble AI, Wotja, etc.
    submitted by /u/babygerbil [link] [comments]  ( 86 min )
    are there any good AI's for making pixel art?
    I would like to use AI maybe to get some ideas for my pixel art are there any that can make pixel art without it looking like pixel vomit :) submitted by /u/ryan7251 [link] [comments]  ( 86 min )
    “Balloon House” - Beautiful children’s illustration vibes from Pixelz AI user 🎈
    submitted by /u/pixelz_ai [link] [comments]  ( 86 min )
    A.I. Is Not Sentient. Why Do People Say It Is?
    submitted by /u/estasfuera [link] [comments]  ( 97 min )
    ''The Relativity of Perception'' by Ethan Smith
    submitted by /u/widgia [link] [comments]  ( 93 min )
    fresh dall e 2 account
    Ive received an extra dall e 2 invitation. would like to trade with someone in need of an dall e 2 account!! submitted by /u/cocau [link] [comments]  ( 86 min )
    AI helped me create my first game ever.
    submitted by /u/kbf_ [link] [comments]  ( 86 min )
  • Open

    New-and-Improved Content Moderation Tooling
    We are introducing a new-and-improved content moderation tool: The Moderation endpoint improves upon our previous content filter, and is available for free today to OpenAI API developers. To help developers protect their applications against possible misuse, we are introducing the faster and more accurate Moderation endpoint. This endpoint provides OpenAI  ( 6 min )
  • Open

    Design in the Age of Digital Twins: A Conversation With Graphics Pioneer Donald Greenberg
    Asked about the future of design, Donald Greenberg holds up a model of a human aorta. “After my son became an intravascular heart surgeon at the Cleveland Clinic, he hired one of my students to use CAT scans and create digital 3D models of an aortic aneurysm,” said the computer graphics pioneer in a video Read article > The post Design in the Age of Digital Twins: A Conversation With Graphics Pioneer Donald Greenberg appeared first on NVIDIA Blog.  ( 6 min )
    AI Flying Off the Shelves: Restocking Robot Rolls Out to Hundreds of Japanese Convenience Stores
    Tokyo-based startup Telexistence this week announced it will deploy NVIDIA AI-powered robots to restock shelves at hundreds of FamilyMart convenience stores in Japan. There are 56,000 convenience stores in Japan — the third-highest density worldwide. Around 16,000 of them are run by FamilyMart. Telexistence aims to save time for these stores by offloading repetitive tasks Read article > The post AI Flying Off the Shelves: Restocking Robot Rolls Out to Hundreds of Japanese Convenience Stores appeared first on NVIDIA Blog.  ( 6 min )
  • Open

    Artificial Intelligence, Content Generation, and Creativity
    With the rise of artificial intelligence and machine learning, it’s not surprising that content marketing is becoming a key aspect of…  ( 9 min )
  • Open

    Test for non-negligible adverse shifts. (arXiv:2107.02990v4 [stat.ML] UPDATED)
    Statistical tests for dataset shift are susceptible to false alarms: they are sensitive to minor differences when there is in fact adequate sample coverage and predictive performance. We propose instead a framework to detect adverse dataset shifts based on outlier scores, $\texttt{D-SOS}$ for short. $\texttt{D-SOS}$ holds that the new (test) sample is not substantively worse than the reference (training) sample, and not that the two are equal. The key idea is to reduce observations to outlier scores and compare contamination rates at varying weighted thresholds. Users can define what $\it{worse}$ means in terms of relevant notions of outlyingness, including proxies for predictive performance. Compared to tests of equal distribution, our approach is uniquely tailored to serve as a robust metric for model monitoring and data validation. We show how versatile and practical $\texttt{D-SOS}$ is on a wide range of real and simulated data.
    MULTIPAR: Supervised Irregular Tensor Factorization with Multi-task Learning. (arXiv:2208.00993v2 [cs.LG] UPDATED)
    Tensor factorization has received increasing interest due to its intrinsic ability to capture latent factors in multi-dimensional data with many applications such as recommender systems and Electronic Health Records (EHR) mining. PARAFAC2 and its variants have been proposed to address irregular tensors where one of the tensor modes is not aligned, e.g., different users in recommender systems or patients in EHRs may have different length of records. PARAFAC2 has been successfully applied on EHRs for extracting meaningful medical concepts (phenotypes). Despite recent advancements, current models' predictability and interpretability are not satisfactory, which limits its utility for downstream analysis. In this paper, we propose MULTIPAR: a supervised irregular tensor factorization with multi-task learning. MULTIPAR is flexible to incorporate both static (e.g. in-hospital mortality prediction) and continuous or dynamic (e.g. the need for ventilation) tasks. By supervising the tensor factorization with downstream prediction tasks and leveraging information from multiple related predictive tasks, MULTIPAR can yield not only more meaningful phenotypes but also better predictive performance for downstream tasks. We conduct extensive experiments on two real-world temporal EHR datasets to demonstrate that MULTIPAR is scalable and achieves better tensor fit with more meaningful subgroups and stronger predictive performance compared to existing state-of-the-art methods.
    Machine Learning in Event-Triggered Control: Recent Advances and Open Issues. (arXiv:2009.12783v2 [eess.SY] UPDATED)
    Networked control systems have gained considerable attention over the last decade as a result of the trend towards decentralised control applications and the emergence of cyber-physical system applications. However, real-world wireless networked control systems suffer from limited communication bandwidths, reliability issues, and a lack of awareness of network dynamics due to the complex nature of wireless networks. Combining machine learning and event-triggered control has the potential to alleviate some of these issues. For example, machine learning can be used to overcome the problem of a lack of network models by learning system behavior or adapting to dynamically changing models by continuously learning model dynamics. Event-triggered control can help to conserve communication bandwidth by transmitting control information only when necessary or when resources are available. The purpose of this article is to conduct a review of the literature on the use of machine learning in combination with event-triggered control. Machine learning techniques such as statistical learning, neural networks, and reinforcement learning-based approaches such as deep reinforcement learning are being investigated in combination with event-triggered control. We discuss how these learning algorithms can be used for different applications depending on the purpose of the machine learning use. Following the review and discussion of the literature, we highlight open research questions and challenges associated with machine learning-based event-triggered control and suggest potential solutions.
    Reinforcement Learning for Freight Booking Control Problems. (arXiv:2102.00092v2 [math.OC] UPDATED)
    Booking control problems are sequential decision-making problems that occur in the domain of revenue management. More precisely, freight booking control focuses on the problem of deciding to accept or reject bookings: given a limited capacity, accept a booking request or reject it to reserve capacity for future bookings with potentially higher revenue. This problem can be formulated as a finite-horizon stochastic dynamic program, where accepting a set of requests results in a profit at the end of the booking period that depends on the cost of fulfilling the accepted bookings. For many freight applications, the cost of fulfilling requests is obtained by solving an operational decision-making problem, which often requires the solutions to mixed-integer linear programs. Routinely solving such operational problems when deploying reinforcement learning algorithms may be too time consuming. The majority of booking control policies are obtained by solving problem-specific mathematical programming relaxations that are often non-trivial to generalize to new problems and, in some cases, provide quite crude approximations. In this work, we propose a two-phase approach: we first train a supervised learning model to predict the objective of the operational problem, and then we deploy the model within reinforcement learning algorithms to compute control policies. This approach is general: it can be used every time the objective function of the end-of-horizon operational problem can be predicted, and it is particularly suitable to those cases where such problems are computationally hard. Furthermore, it allows one to leverage the recent advances in reinforcement learning as routinely solving the operational problem is replaced with a single prediction. Our methodology is evaluated on two booking control problems in the literature, namely, distributional logistics and airline cargo management.
    Causality, Causal Discovery, and Causal Inference in Structural Engineering. (arXiv:2204.01543v3 [cs.LG] UPDATED)
    Much of our experiments are designed to uncover the cause(s) and effect(s) behind a data generating mechanism (i.e., phenomenon) we happen to be interested in. Uncovering such relationships allows us to identify the true working of a phenomenon and, most importantly, articulate a model that may enable us to further explore the phenomenon on hand and/or allow us to predict it accurately. Fundamentally, such models are likely to be derived via a causal approach (as opposed to an observational or empirical mean). In this approach, causal discovery is required to create a causal model, which can then be applied to infer the influence of interventions, and answer any hypothetical questions (i.e., in the form of What ifs? Etc.) that we might have. This paper builds a case for causal discovery and causal inference and contrasts that against traditional machine learning approaches; all from a civil and structural engineering perspective. More specifically, this paper outlines the key principles of causality and the most commonly used algorithms and packages for causal discovery and causal inference. Finally, this paper also presents a series of examples and case studies of how causal concepts can be adopted for our domain.
    Open-environment Machine Learning. (arXiv:2206.00423v2 [cs.LG] UPDATED)
    Conventional machine learning studies generally assume close-environment scenarios where important factors of the learning process hold invariant. With the great success of machine learning, nowadays, more and more practical tasks, particularly those involving open-environment scenarios where important factors are subject to change, called open-environment machine learning (Open ML) in this article, are present to the community. Evidently it is a grand challenge for machine learning turning from close environment to open environment. It becomes even more challenging since, in various big data tasks, data are usually accumulated with time, like streams, while it is hard to train the machine learning model after collecting all data as in conventional studies. This article briefly introduces some advances in this line of research, focusing on techniques concerning emerging new classes, decremental/incremental features, changing data distributions, varied learning objectives, and discusses some theoretical issues.
    Unsupervised Learning Based Focal Stack Camera Depth Estimation. (arXiv:2203.07904v2 [eess.IV] UPDATED)
    We propose an unsupervised deep learning based method to estimate depth from focal stack camera images. On the NYU-v2 dataset, our method achieves much better depth estimation accuracy compared to single-image based methods.
    Maze Learning using a Hyperdimensional Predictive Processing Cognitive Architecture. (arXiv:2204.00619v2 [cs.AI] UPDATED)
    We present the COGnitive Neural GENerative system (CogNGen), a cognitive architecture that combines two neurobiologically-plausible, computational models: predictive processing and hyperdimensional/vector-symbolic models. We draw inspiration from architectures such as ACT-R and Spaun/Nengo. CogNGen is in broad agreement with these, providing a level of detail between ACT-R's high-level symbolic description of human cognition and Spaun's low-level neurobiological description, furthermore creating the groundwork for designing agents that learn continually from diverse tasks and model human performance at larger scales than what is possible with current systems. We test CogNGen on four maze-learning tasks, including those that test memory and planning, and find that CogNGen matches performance of deep reinforcement learning models and exceeds on a task designed to test memory.
    Mining Reaction and Diffusion Dynamics in Social Activities. (arXiv:2208.04846v1 [cs.SI])
    Large quantifies of online user activity data, such as weekly web search volumes, which co-evolve with the mutual influence of several queries and locations, serve as an important social sensor. It is an important task to accurately forecast the future activity by discovering latent interactions from such data, i.e., the ecosystems between each query and the flow of influences between each area. However, this is a difficult problem in terms of data quantity and complex patterns covering the dynamics. To tackle the problem, we propose FluxCube, which is an effective mining method that forecasts large collections of co-evolving online user activity and provides good interpretability. Our model is the expansion of a combination of two mathematical models: a reaction-diffusion system provides a framework for modeling the flow of influences between local area groups and an ecological system models the latent interactions between each query. Also, by leveraging the concept of physics-informed neural networks, FluxCube achieves high interpretability obtained from the parameters and high forecasting performance, together. Extensive experiments on real datasets showed that FluxCube outperforms comparable models in terms of the forecasting accuracy, and each component in FluxCube contributes to the enhanced performance. We then show some case studies that FluxCube can extract useful latent interactions between queries and area groups.
    Wasserstein Generative Adversarial Uncertainty Quantification in Physics-Informed Neural Networks. (arXiv:2108.13054v2 [math.NA] UPDATED)
    In this paper, we study a physics-informed algorithm for Wasserstein Generative Adversarial Networks (WGANs) for uncertainty quantification in solutions of partial differential equations. By using groupsort activation functions in adversarial network discriminators, network generators are utilized to learn the uncertainty in solutions of partial differential equations observed from the initial/boundary data. Under mild assumptions, we show that the generalization error of the computed generator converges to the approximation error of the network with high probability, when the number of samples are sufficiently taken. According to our established error bound, we also find that our physics-informed WGANs have higher requirement for the capacity of discriminators than that of generators. Numerical results on synthetic examples of partial differential equations are reported to validate our theoretical results and demonstrate how uncertainty quantification can be obtained for solutions of partial differential equations and the distributions of initial/boundary data. However, the quality or the accuracy of the uncertainty quantification theory in all the points in the interior is still the theoretical vacancy, and required for further research.
    The application of adaptive minimum match k-nearest neighbors to identify at-risk students in health professions education. (arXiv:2108.07709v3 [cs.CY] UPDATED)
    Purpose: When a learner fails to reach a milestone, educators often wonder if there had been any warning signs that could have allowed them to intervene sooner. Machine learning can predict which students are at risk of failing a high-stakes certification exam. If predictions can be made well in advance of the exam, then educators can meaningfully intervene before students take the exam to reduce the chances of a failing score. Methods: Using already-collected, first-year student assessment data from five cohorts in a Master of Physician Assistant Studies program, the authors implement an "adaptive minimum match" version of the k-nearest neighbors algorithm (AMMKNN), using changing numbers of neighbors to predict each student's future exam scores on the Physician Assistant National Certifying Examination (PANCE). Validation occurred in two ways: Leave-one-out cross-validation (LOOCV) and evaluating the predictions in a new cohort. Results: AMMKNN achieved an accuracy of 93% in LOOCV. AMMKNN generates a predicted PANCE score for each student, one year before they are scheduled to take the exam. Students can then be classified into extra support, optional extra support, or no extra support groups. The educator then has one year to provide the appropriate customized support to each category of student. Conclusions: Predictive analytics can identify at-risk students, so they can receive additional support or remediation when preparing for high-stakes certification exams. Educators can use the included methods and code to generate predicted test outcomes for students. The authors recommend that educators use this or similar predictive methods responsibly and transparently, as one of many tools used to support students.
    Literature Review: Graph Kernels in Chemoinformatics. (arXiv:2208.04929v1 [stat.ML])
    The purpose of this review is to introduce the reader to graph kernels, with a view of applying them in classification problems in chemoinformatics. Graph kernels are functions that allow us to infer chemical properties of molecules, which can help with tasks such as finding suitable compounds for drug design. The use of kernel methods is but one particular way two quantify similarity between graphs. We restrict our discussion to this one method, although popular alternatives have emerged in recent years, most notably Graph Neural Networks.
    Low-Complexity Algorithm for Restless Bandits with Imperfect Observations. (arXiv:2108.03812v2 [cs.LG] UPDATED)
    We consider a class of restless bandit problems that finds a broad application area in stochastic optimization, reinforcement learning and operations research. We consider $N$ independent discrete-time Markov processes, each of which had two possible states: 1 and 0 (`good' and `bad'). Only if a process is both in state 1 and observed to be so does reward accrue. The aim is to maximize the expected discounted sum of returns over the infinite horizon subject to a constraint that only $M$ $(<N)$ processes may be observed at each step. Observation is error-prone: there are known probabilities that state 1 (0) will be observed as 0 (1). From this one knows, at any time $t$, a probability that process $i$ is in state 1. The resulting system may be modeled as a restless multi-armed bandit problem with an information state space of uncountable cardinality. Restless bandit problems with even finite state spaces are PSPACE-HARD in general. We propose a novel approach for simplifying the dynamic programming equations of this class of restless bandits and develop a low-complexity algorithm that achieves a strong performance and is readily extensible to the general restless bandit model with observation errors. Under certain conditions, we establish the existence (indexability) of Whittle index and its equivalence to our algorithm. When those conditions do not hold, we show by numerical experiments the near-optimal performance of our algorithm in the general parametric space. Last, we theoretically prove the optimality of our algorithm for homogeneous systems.
    A causal model of safety assurance for machine learning. (arXiv:2201.05451v3 [cs.SE] UPDATED)
    This paper proposes a framework based on a causal model of safety upon which effective safety assurance cases for ML-based applications can be built. In doing so, we build upon established principles of safety engineering as well as previous work on structuring assurance arguments for ML. The paper defines four categories of safety case evidence and a structured analysis approach within which these evidences can be effectively combined. Where appropriate, abstract formalisations of these contributions are used to illustrate the causalities they evaluate, their contributions to the safety argument and desirable properties of the evidences. Based on the proposed framework, progress in this area is re-evaluated and a set of future research directions proposed in order for tangible progress in this field to be made.
    Formalization of a Stochastic Approximation Theorem. (arXiv:2202.05959v2 [cs.LO] UPDATED)
    Stochastic approximation algorithms are iterative procedures which are used to approximate a target value in an environment where the target is unknown and direct observations are corrupted by noise. These algorithms are useful, for instance, for root-finding and function minimization when the target function or model is not directly known. Originally introduced in a 1951 paper by Robbins and Monro, the field of Stochastic approximation has grown enormously and has come to influence application domains from adaptive signal processing to artificial intelligence. As an example, the Stochastic Gradient Descent algorithm which is ubiquitous in various subdomains of Machine Learning is based on stochastic approximation theory. In this paper, we give a formal proof (in the Coq proof assistant) of a general convergence theorem due to Aryeh Dvoretzky, which implies the convergence of important classical methods such as the Robbins-Monro and the Kiefer-Wolfowitz algorithms. In the process, we build a comprehensive Coq library of measure-theoretic probability theory and stochastic processes.
    Global Evaluation for Decision Tree Learning. (arXiv:2208.04828v1 [cs.LG])
    We transfer distances on clusterings to the building process of decision trees, and as a consequence extend the classical ID3 algorithm to perform modifications based on the global distance of the tree to the ground truth--instead of considering single leaves. Next, we evaluate this idea in comparison with the original version and discuss occurring problems, but also strengths of the global approach. On this basis, we finish by identifying other scenarios where global evaluations are worthwhile.
    A Novel Ontology-guided Attribute Partitioning Ensemble Learning Model for Early Prediction of Cognitive Deficits using Quantitative Structural MRI in Very Preterm Infants. (arXiv:2202.04134v2 [cs.LG] UPDATED)
    Structural magnetic resonance imaging studies have shown that brain anatomical abnormalities are associated with cognitive deficits in preterm infants. Brain maturation and geometric features can be used with machine learning models for predicting later neurodevelopmental deficits. However, traditional machine learning models would suffer from a large feature-to-instance ratio (i.e., a large number of features but a small number of instances/samples). Ensemble learning is a paradigm that strategically generates and integrates a library of machine learning classifiers and has been successfully used on a wide variety of predictive modeling problems to boost model performance. Attribute (i.e., feature) bagging method is the most commonly used feature partitioning scheme, which randomly and repeatedly draws feature subsets from the entire feature set. Although attribute bagging method can effectively reduce feature dimensionality to handle the large feature-to-instance ratio, it lacks consideration of domain knowledge and latent relationship among features. In this study, we proposed a novel Ontology-guided Attribute Partitioning (OAP) method to better draw feature subsets by considering the domain-specific relationship among features. With the better partitioned feature subsets, we developed an ensemble learning framework, which is referred to as OAP-Ensemble Learning (OAP-EL). We applied the OAP-EL to predict cognitive deficits at 2 years of age using quantitative brain maturation and geometric features obtained at term equivalent age in very preterm infants. We demonstrated that the proposed OAP-EL approach significantly outperformed the peer ensemble learning and traditional machine learning approaches.
    PPA: Preference Profiling Attack Against Federated Learning. (arXiv:2202.04856v2 [cs.LG] UPDATED)
    Federated learning (FL) trains a global model across a number of decentralized users, each with a local dataset. Compared to traditional centralized learning, FL does not require direct access to local datasets and thus aims to mitigate data privacy concerns. However, data privacy leakage in FL still exists due to inference attacks, including membership inference, property inference, and data inversion. In this work, we propose a new type of privacy inference attack, coined Preference Profiling Attack (PPA), that accurately profiles the private preferences of a local user, e.g., most liked (disliked) items from the client's online shopping and most common expressions from the user's selfies. In general, PPA can profile top-k (i.e., k = 1, 2, 3 and k = 1 in particular) preferences contingent on the local client (user)'s characteristics. Our key insight is that the gradient variation of a local user's model has a distinguishable sensitivity to the sample proportion of a given class, especially the majority (minority) class. By observing a user model's gradient sensitivity to a class, PPA can profile the sample proportion of the class in the user's local dataset, and thus the user's preference of the class is exposed. The inherent statistical heterogeneity of FL further facilitates PPA. We have extensively evaluated the PPA's effectiveness using four datasets (MNIST, CIFAR10, RAF-DB and Products-10K). Our results show that PPA achieves 90% and 98% top-1 attack accuracy to the MNIST and CIFAR10, respectively. More importantly, in real-world commercial scenarios of shopping (i.e., Products-10K) and social network (i.e., RAF-DB), PPA gains a top-1 attack accuracy of 78% in the former case to infer the most ordered items (i.e., as a commercial competitor), and 88% in the latter case to infer a victim user's most often facial expressions, e.g., disgusted.
    Overcoming challenges in leveraging GANs for few-shot data augmentation. (arXiv:2203.16662v3 [stat.ML] UPDATED)
    In this paper, we explore the use of GAN-based few-shot data augmentation as a method to improve few-shot classification performance. We perform an exploration into how a GAN can be fine-tuned for such a task (one of which is in a class-incremental manner), as well as a rigorous empirical investigation into how well these models can perform to improve few-shot classification. We identify issues related to the difficulty of training such generative models under a purely supervised regime with very few examples, as well as issues regarding the evaluation protocols of existing works. We also find that in this regime, classification accuracy is highly sensitive to how the classes of the dataset are randomly split. Therefore, we propose a semi-supervised fine-tuning approach as a more pragmatic way forward to address these problems.
    Quantization enabled Privacy Protection in Decentralized Stochastic Optimization. (arXiv:2208.04845v1 [math.OC])
    By enabling multiple agents to cooperatively solve a global optimization problem in the absence of a central coordinator, decentralized stochastic optimization is gaining increasing attention in areas as diverse as machine learning, control, and sensor networks. Since the associated data usually contain sensitive information, such as user locations and personal identities, privacy protection has emerged as a crucial need in the implementation of decentralized stochastic optimization. In this paper, we propose a decentralized stochastic optimization algorithm that is able to guarantee provable convergence accuracy even in the presence of aggressive quantization errors that are proportional to the amplitude of quantization inputs. The result applies to both convex and non-convex objective functions, and enables us to exploit aggressive quantization schemes to obfuscate shared information, and hence enables privacy protection without losing provable optimization accuracy. In fact, by using a {stochastic} ternary quantization scheme, which quantizes any value to three numerical levels, we achieve quantization-based rigorous differential privacy in decentralized stochastic optimization, which has not been reported before. In combination with the presented quantization scheme, the proposed algorithm ensures, for the first time, rigorous differential privacy in decentralized stochastic optimization without losing provable convergence accuracy. Simulation results for a distributed estimation problem as well as numerical experiments for decentralized learning on a benchmark machine learning dataset confirm the effectiveness of the proposed approach.
    Satisficing Paths and Independent Multi-Agent Reinforcement Learning in Stochastic Games. (arXiv:2110.04638v3 [cs.GT] UPDATED)
    In multi-agent reinforcement learning (MARL), independent learners are those that do not observe the actions of other agents in the system. Due to the decentralization of information, it is challenging to design independent learners that drive play to equilibrium. This paper investigates the feasibility of using satisficing dynamics to guide independent learners to approximate equilibrium in stochastic games. For $\epsilon \geq 0$, an $\epsilon$-satisficing policy update rule is any rule that instructs the agent to not change its policy when it is $\epsilon$-best-responding to the policies of the remaining players; $\epsilon$-satisficing paths are defined to be sequences of joint policies obtained when each agent uses some $\epsilon$-satisficing policy update rule to select its next policy. We establish structural results on the existence of $\epsilon$-satisficing paths into $\epsilon$-equilibrium in both symmetric $N$-player games and general stochastic games with two players. We then present an independent learning algorithm for $N$-player symmetric games and give high probability guarantees of convergence to $\epsilon$-equilibrium under self-play. This guarantee is made using symmetry alone, leveraging the previously unexploited structure of $\epsilon$-satisficing paths.
    Measurement-based Admission Control in Sliced Networks: A Best Arm Identification Approach. (arXiv:2204.06910v2 [cs.NI] UPDATED)
    In sliced networks, the shared tenancy of slices requires adaptive admission control of data flows, based on measurements of network resources. In this paper, we investigate the design of measurement-based admission control schemes, deciding whether a new data flow can be admitted and in this case, on which slice. The objective is to devise a joint measurement and decision strategy that returns a correct decision (e.g., the least loaded slice) with a certain level of confidence while minimizing the measurement cost (the number of measurements made before committing to the decision). We study the design of such strategies for several natural admission criteria specifying what a correct decision is. For each of these criteria, using tools from best arm identification in bandits, we first derive an explicit information-theoretical lower bound on the cost of any algorithm returning the correct decision with fixed confidence. We then devise a joint measurement and decision strategy achieving this theoretical limit. We compare empirically the measurement costs of these strategies, and compare them both to the lower bounds as well as a naive measurement scheme. We find that our algorithm significantly outperforms the naive scheme (by a factor $2-8$).
    Learning from Sparse Demonstrations. (arXiv:2008.02159v3 [cs.RO] UPDATED)
    This paper develops the method of Continuous Pontryagin Differentiable Programming (Continuous PDP), which enables a robot to learn an objective function from a few sparsely demonstrated keyframes. The keyframes, labeled with some time stamps, are the desired task-space outputs, which a robot is expected to follow sequentially. The time stamps of the keyframes can be different from the time of the robot's actual execution. The method jointly finds an objective function and a time-warping function such that the robot's resulting trajectory sequentially follows the keyframes with minimal discrepancy loss. The Continuous PDP minimizes the discrepancy loss using projected gradient descent, by efficiently solving the gradient of the robot trajectory with respect to the unknown parameters. The method is first evaluated on a simulated robot arm and then applied to a 6-DoF quadrotor to learn an objective function for motion planning in unmodeled environments. The results show the efficiency of the method, its ability to handle time misalignment between keyframes and robot execution, and the generalization of objective learning into unseen motion conditions.
    Consistent Approximations in Composite Optimization. (arXiv:2201.05250v2 [math.OC] UPDATED)
    Approximations of optimization problems arise in computational procedures and sensitivity analysis. The resulting effect on solutions can be significant, with even small approximations of components of a problem translating into large errors in the solutions. We specify conditions under which approximations are well behaved in the sense of minimizers, stationary points, and level-sets and this leads to a framework of consistent approximations. The framework is developed for a broad class of composite problems, which are neither convex nor smooth. We demonstrate the framework using examples from stochastic optimization, neural-network based machine learning, distributionally robust optimization, penalty and augmented Lagrangian methods, interior-point methods, homotopy methods, smoothing methods, extended nonlinear programming, difference-of-convex programming, and multi-objective optimization. An enhanced proximal method illustrates the algorithmic possibilities. A quantitative analysis supplements the development by furnishing rates of convergence.
    The Rich Get Richer: Disparate Impact of Semi-Supervised Learning. (arXiv:2110.06282v3 [cs.LG] UPDATED)
    Semi-supervised learning (SSL) has demonstrated its potential to improve the model accuracy for a variety of learning tasks when the high-quality supervised data is severely limited. Although it is often established that the average accuracy for the entire population of data is improved, it is unclear how SSL fares with different sub-populations. Understanding the above question has substantial fairness implications when different sub-populations are defined by the demographic groups that we aim to treat fairly. In this paper, we reveal the disparate impacts of deploying SSL: the sub-population who has a higher baseline accuracy without using SSL (the "rich" one) tends to benefit more from SSL; while the sub-population who suffers from a low baseline accuracy (the "poor" one) might even observe a performance drop after adding the SSL module. We theoretically and empirically establish the above observation for a broad family of SSL algorithms, which either explicitly or implicitly use an auxiliary "pseudo-label". Experiments on a set of image and text classification tasks confirm our claims. We introduce a new metric, Benefit Ratio, and promote the evaluation of the fairness of SSL (Equalized Benefit Ratio). We further discuss how the disparate impact can be mitigated. We hope our paper will alarm the potential pitfall of using SSL and encourage a multifaceted evaluation of future SSL algorithms.
    Towards Individual Grevy's Zebra Identification via Deep 3D Fitting and Metric Learning. (arXiv:2206.02261v3 [cs.CV] UPDATED)
    This paper combines deep learning techniques for species detection, 3D model fitting, and metric learning in one pipeline to perform individual animal identification from photographs by exploiting unique coat patterns. This is the first work to attempt this and, compared to traditional 2D bounding box or segmentation based CNN identification pipelines, the approach provides effective and explicit view-point normalisation and allows for a straight forward visualisation of the learned biometric population space. Note that due to the use of metric learning the pipeline is also readily applicable to open set and zero shot re-identification scenarios. We apply the proposed approach to individual Grevy's zebra (Equus grevyi) identification and show in a small study on the SMALST dataset that the use of 3D model fitting can indeed benefit performance. In particular, back-projected textures from 3D fitted models improve identification accuracy from 48.0% to 56.8% compared to 2D bounding box approaches for the dataset. Whilst the study is far too small accurately to estimate the full performance potential achievable in larger-scale real-world application settings and in comparisons against polished tools, our work lays the conceptual and practical foundations for a next step in animal biometrics towards deep metric learning driven, fully 3D-aware animal identification in open population settings. We publish network weights and relevant facilitating source code with this paper for full reproducibility and as inspiration for further research.
    Applying data technologies to combat AMR: current status, challenges, and opportunities on the way forward. (arXiv:2208.04683v1 [cs.CY])
    Antimicrobial resistance (AMR) is a growing public health threat, estimated to cause over 10 million deaths per year and cost the global economy 100 trillion USD by 2050 under status quo projections. These losses would mainly result from an increase in the morbidity and mortality from treatment failure, AMR infections during medical procedures, and a loss of quality of life attributed to AMR. Numerous interventions have been proposed to control the development of AMR and mitigate the risks posed by its spread. This paper reviews key aspects of bacterial AMR management and control which make essential use of data technologies such as artificial intelligence, machine learning, and mathematical and statistical modelling, fields that have seen rapid developments in this century. Although data technologies have become an integral part of biomedical research, their impact on AMR management has remained modest. We outline the use of data technologies to combat AMR, detailing recent advancements in four complementary categories: surveillance, prevention, diagnosis, and treatment. We provide an overview on current AMR control approaches using data technologies within biomedical research, clinical practice, and in the "One Health" context. We discuss the potential impact and challenges wider implementation of data technologies is facing in high-income as well as in low- and middle-income countries, and recommend concrete actions needed to allow these technologies to be more readily integrated within the healthcare and public health sectors.
    Deep Probabilistic Models for Forward and Inverse Problems in Parametric PDEs. (arXiv:2208.04856v1 [stat.ML])
    We formulate a class of physics-driven deep latent variable models (PDDLVM) to learn parameter-to-solution (forward) and solution-to-parameter (inverse) maps of parametric partial differential equations (PDEs). Our formulation leverages the finite element method (FEM), deep neural networks, and probabilistic modeling to assemble a deep probabilistic framework in which the forward and inverse maps are approximated with coherent uncertainty quantification. Our probabilistic model explicitly incorporates a parametric PDE-based density and a trainable solution-to-parameter network while the introduced amortized variational family postulates a parameter-to-solution network, all of which are jointly trained. Furthermore, the proposed methodology does not require any expensive PDE solves and is physics-informed only at training time, which allows real-time emulation of PDEs and generation of inverse problem solutions after training, bypassing the need for FEM solve operations with comparable accuracy to FEM solutions. The proposed framework further allows for a seamless integration of observed data for solving inverse problems and building generative models. We demonstrate the effectiveness of our method on a nonlinear Poisson problem, elastic shells with complex 3D geometries, and integrating generic physics-informed neural networks (PINN) architectures. We achieve up to three orders of magnitude speed-ups after training compared to traditional FEM solvers, while outputting coherent uncertainty estimates.
    A Bayesian Bradley-Terry model to compare multiple ML algorithms on multiple data sets. (arXiv:2208.04935v1 [cs.LG])
    This paper proposes a Bayesian model to compare multiple algorithms on multiple data sets, on any metric. The model is based on the Bradley-Terry model, that counts the number of times one algorithm performs better than another on different data sets. Because of its Bayesian foundations, the Bayesian Bradley Terry model (BBT) has different characteristics than frequentist approaches to comparing multiple algorithms on multiple data sets, such as Demsar (2006) tests on mean rank, and Benavoli et al. (2016) multiple pairwise Wilcoxon tests with p-adjustment procedures. In particular, a Bayesian approach allows for more nuanced statements regarding the algorithms beyond claiming that the difference is or it is not statistically significant. Bayesian approaches also allow to define when two algorithms are equivalent for practical purposes, or the region of practical equivalence (ROPE). Different than a Bayesian signed rank comparison procedure proposed by Benavoli et al. (2017), our approach can define a ROPE for any metric, since it is based on probability statements, and not on differences of that metric. This paper also proposes a local ROPE concept, that evaluates whether a positive difference between a mean measure across some cross validation to the mean of some other algorithms is should be really seen as the first algorithm being better than the second, based on effect sizes. This local ROPE proposal is independent of a Bayesian use, and can be used in frequentist approaches based on ranks. A R package and a Python program that implements the BBT is available.
    Monotone Learning. (arXiv:2202.05246v2 [cs.LG] UPDATED)
    The amount of training-data is one of the key factors which determines the generalization capacity of learning algorithms. Intuitively, one expects the error rate to decrease as the amount of training-data increases. Perhaps surprisingly, natural attempts to formalize this intuition give rise to interesting and challenging mathematical questions. For example, in their classical book on pattern recognition, Devroye, Gyorfi, and Lugosi (1996) ask whether there exists a {monotone} Bayes-consistent algorithm. This question remained open for over 25 years, until recently Pestov (2021) resolved it for binary classification, using an intricate construction of a monotone Bayes-consistent algorithm. We derive a general result in multiclass classification, showing that every learning algorithm A can be transformed to a monotone one with similar performance. Further, the transformation is efficient and only uses a black-box oracle access to A. This demonstrates that one can provably avoid non-monotonic behaviour without compromising performance, thus answering questions asked by Devroye et al (1996), Viering, Mey, and Loog (2019), Viering and Loog (2021), and by Mhammedi (2021). Our transformation readily implies monotone learners in a variety of contexts: for example it extends Pestov's result to classification tasks with an arbitrary number of labels. This is in contrast with Pestov's work which is tailored to binary classification. In addition, we provide uniform bounds on the error of the monotone algorithm. This makes our transformation applicable in distribution-free settings. For example, in PAC learning it implies that every learnable class admits a monotone PAC learner. This resolves questions by Viering, Mey, and Loog (2019); Viering and Loog (2021); Mhammedi (2021).
    Basis for Intentions: Efficient Inverse Reinforcement Learning using Past Experience. (arXiv:2208.04919v1 [cs.LG])
    This paper addresses the problem of inverse reinforcement learning (IRL) -- inferring the reward function of an agent from observing its behavior. IRL can provide a generalizable and compact representation for apprenticeship learning, and enable accurately inferring the preferences of a human in order to assist them. %and provide for more accurate prediction. However, effective IRL is challenging, because many reward functions can be compatible with an observed behavior. We focus on how prior reinforcement learning (RL) experience can be leveraged to make learning these preferences faster and more efficient. We propose the IRL algorithm BASIS (Behavior Acquisition through Successor-feature Intention inference from Samples), which leverages multi-task RL pre-training and successor features to allow an agent to build a strong basis for intentions that spans the space of possible goals in a given domain. When exposed to just a few expert demonstrations optimizing a novel goal, the agent uses its basis to quickly and effectively infer the reward function. Our experiments reveal that our method is highly effective at inferring and optimizing demonstrated reward functions, accurately inferring reward functions from less than 100 trajectories.
    Representation learning for maximization of MI, nonlinear ICA and nonlinear subspaces with robust density ratio estimation. (arXiv:2101.02083v2 [cs.LG] UPDATED)
    Contrastive learning is a recent promising approach in unsupervised representation learning where a feature representation of data is learned by solving a pseudo classification problem from unlabelled data. However, it is not straightforward to understand what representation contrastive learning yields. In addition, contrastive learning is often based on the maximum likelihood estimation, which tends to be vulnerable to the contamination by outliers. To promote the understanding to contrastive learning, this paper first theoretically shows a connection to maximization of mutual information (MI). Our result indicates that density ratio estimation is necessary and sufficient for maximization of MI under some conditions. Thus, contrastive learning related to density ratio estimation as done in popular objective functions can be interpreted as maximizing MI. Next, with the density ratio, we establish new recovery conditions for the latent source components in nonlinear independent component analysis (ICA). In contrast with existing work, the established conditions include a novel insight for the dimensionality of data, which is clearly supported by numerical experiments. Furthermore, inspired by nonlinear ICA, we propose a novel framework to estimate a nonlinear subspace for lower-dimensional latent source components, and some theoretical conditions for the subspace estimation are established with the density ratio. Then, we propose a practical method through outlier-robust density ratio estimation, which can be seen as performing maximization of MI, nonlinear ICA or nonlinear subspace estimation. Moreover, a sample-efficient nonlinear ICA method is also proposed. We theoretically investigate outlier-robustness of the proposed methods. Finally, the usefulness of the proposed methods is numerically demonstrated in nonlinear ICA and through application to linear classification.
    Multiple Kernel Representation Learning on Networks. (arXiv:2106.05057v2 [cs.SI] UPDATED)
    Learning representations of nodes in a low dimensional space is a crucial task with numerous interesting applications in network analysis, including link prediction, node classification, and visualization. Two popular approaches for this problem are matrix factorization and random walk-based models. In this paper, we aim to bring together the best of both worlds, towards learning node representations. In particular, we propose a weighted matrix factorization model that encodes random walk-based information about nodes of the network. The benefit of this novel formulation is that it enables us to utilize kernel functions without realizing the exact proximity matrix so that it enhances the expressiveness of existing matrix decomposition methods with kernels and alleviates their computational complexities. We extend the approach with a multiple kernel learning formulation that provides the flexibility of learning the kernel as the linear combination of a dictionary of kernels in data-driven fashion. We perform an empirical evaluation on real-world networks, showing that the proposed model outperforms baseline node embedding algorithms in downstream machine learning tasks.
    The Unsurprising Effectiveness of Pre-Trained Vision Models for Control. (arXiv:2203.03580v2 [cs.CV] UPDATED)
    Recent years have seen the emergence of pre-trained representations as a powerful abstraction for AI applications in computer vision, natural language, and speech. However, policy learning for control is still dominated by a tabula-rasa learning paradigm, with visuo-motor policies often trained from scratch using data from deployment environments. In this context, we revisit and study the role of pre-trained visual representations for control, and in particular representations trained on large-scale computer vision datasets. Through extensive empirical evaluation in diverse control domains (Habitat, DeepMind Control, Adroit, Franka Kitchen), we isolate and study the importance of different representation training methods, data augmentations, and feature hierarchies. Overall, we find that pre-trained visual representations can be competitive or even better than ground-truth state representations to train control policies. This is in spite of using only out-of-domain data from standard vision datasets, without any in-domain data from the deployment environments. Source code and more at https://sites.google.com/view/pvr-control.
    Learning to Learn to Predict Performance Regressions in Production at Meta. (arXiv:2208.04351v1 [cs.SE])
    Catching and attributing code change-induced performance regressions in production is hard; predicting them beforehand, even harder. A primer on automatically learning to predict performance regressions in software, this article gives an account of the experiences we gained when researching and deploying an ML-based regression prediction pipeline at Meta. In this paper, we report on a comparative study with four ML models of increasing complexity, from (1) code-opaque, over (2) Bag of Words, (3) off-the-shelve Transformer-based, to (4) a bespoke Transformer-based model, coined SuperPerforator. Our investigation shows the inherent difficulty of the performance prediction problem, which is characterized by a large imbalance of benign onto regressing changes. Our results also call into question the general applicability of Transformer-based architectures for performance prediction: an off-the-shelve CodeBERT-based approach had surprisingly poor performance; our highly customized SuperPerforator architecture initially achieved prediction performance that was just on par with simpler Bag of Words models, and only outperformed them for down-stream use cases. This ability of SuperPerforator to transfer to an application with few learning examples afforded an opportunity to deploy it in practice at Meta: it can act as a pre-filter to sort out changes that are unlikely to introduce a regression, truncating the space of changes to search a regression in by up to 43%, a 45x improvement over a random baseline. To gain further insight into SuperPerforator, we explored it via a series of experiments computing counterfactual explanations. These highlight which parts of a code change the model deems important, thereby validating the learned black-box model.  ( 3 min )
    Automating DBSCAN via Deep Reinforcement Learning. (arXiv:2208.04537v1 [cs.LG])
    DBSCAN is widely used in many scientific and engineering fields because of its simplicity and practicality. However, due to its high sensitivity parameters, the accuracy of the clustering result depends heavily on practical experience. In this paper, we first propose a novel Deep Reinforcement Learning guided automatic DBSCAN parameters search framework, namely DRL-DBSCAN. The framework models the process of adjusting the parameter search direction by perceiving the clustering environment as a Markov decision process, which aims to find the best clustering parameters without manual assistance. DRL-DBSCAN learns the optimal clustering parameter search policy for different feature distributions via interacting with the clusters, using a weakly-supervised reward training policy network. In addition, we also present a recursive search mechanism driven by the scale of the data to efficiently and controllably process large parameter spaces. Extensive experiments are conducted on five artificial and real-world datasets based on the proposed four working modes. The results of offline and online tasks show that the DRL-DBSCAN not only consistently improves DBSCAN clustering accuracy by up to 26% and 25% respectively, but also can stably find the dominant parameters with high computational efficiency. The code is available at https://github.com/RingBDStack/DRL-DBSCAN.  ( 2 min )
    NeuralVDB: High-resolution Sparse Volume Representation using Hierarchical Neural Networks. (arXiv:2208.04448v1 [cs.LG])
    We introduce NeuralVDB, which improves on an existing industry standard for efficient storage of sparse volumetric data, denoted VDB, by leveraging recent advancements in machine learning. Our novel hybrid data structure can reduce the memory footprints of VDB volumes by orders of magnitude, while maintaining its flexibility and only incurring a small (user-controlled) compression errors. Specifically, NeuralVDB replaces the lower nodes of a shallow and wide VDB tree structure with multiple hierarchy neural networks that separately encode topology and value information by means of neural classifiers and regressors respectively. This approach has proven to maximize the compression ratio while maintaining the spatial adaptivity offered by the higher-level VDB data structure. For sparse signed distance fields and density volumes, we have observed compression ratios on the order of $10\times$ to more than $100\times$ from already compressed VDB inputs, with little to no visual artifacts. We also demonstrate how its application to animated sparse volumes can both accelerate training and generate temporally coherent neural networks.  ( 2 min )
    Hierarchical Reinforcement Learning By Discovering Intrinsic Options. (arXiv:2101.06521v3 [cs.LG] UPDATED)
    We propose a hierarchical reinforcement learning method, HIDIO, that can learn task-agnostic options in a self-supervised manner while jointly learning to utilize them to solve sparse-reward tasks. Unlike current hierarchical RL approaches that tend to formulate goal-reaching low-level tasks or pre-define ad hoc lower-level policies, HIDIO encourages lower-level option learning that is independent of the task at hand, requiring few assumptions or little knowledge about the task structure. These options are learned through an intrinsic entropy minimization objective conditioned on the option sub-trajectories. The learned options are diverse and task-agnostic. In experiments on sparse-reward robotic manipulation and navigation tasks, HIDIO achieves higher success rates with greater sample efficiency than regular RL baselines and two state-of-the-art hierarchical RL methods.
    Deep Learning for Android Malware Defenses: a Systematic Literature Review. (arXiv:2103.05292v3 [cs.CR] UPDATED)
    Malicious applications (particularly those targeting the Android platform) pose a serious threat to developers and end-users. Numerous research efforts have been devoted to developing effective approaches to defend against Android malware. However, given the explosive growth of Android malware and the continuous advancement of malicious evasion technologies like obfuscation and reflection, Android malware defense approaches based on manual rules or traditional machine learning may not be effective. In recent years, a dominant research field called deep learning (DL), which provides a powerful feature abstraction ability, has demonstrated a compelling and promising performance in a variety of areas, like natural language processing and computer vision. To this end, employing deep learning techniques to thwart Android malware attacks has recently garnered considerable research attention. Yet, no systematic literature review focusing on deep learning approaches for Android Malware defenses exists. In this paper, we conducted a systematic literature review to search and analyze how deep learning approaches have been applied in the context of malware defenses in the Android environment. As a result, a total of 132 studies covering the period 2014-2021 were identified. Our investigation reveals that, while the majority of these sources mainly consider DL-based on Android malware detection, 53 primary studies (40.1 percent) design defense approaches based on other scenarios. This review also discusses research trends, research focuses, challenges, and future research directions in DL-based Android malware defenses.
    On the Activation Function Dependence of the Spectral Bias of Neural Networks. (arXiv:2208.04924v1 [cs.LG])
    Neural networks are universal function approximators which are known to generalize well despite being dramatically overparameterized. We study this phenomenon from the point of view of the spectral bias of neural networks. Our contributions are two-fold. First, we provide a theoretical explanation for the spectral bias of ReLU neural networks by leveraging connections with the theory of finite element methods. Second, based upon this theory we predict that switching the activation function to a piecewise linear B-spline, namely the Hat function, will remove this spectral bias, which we verify empirically in a variety of settings. Our empirical studies also show that neural networks with the Hat activation function are trained significantly faster using stochastic gradient descent and ADAM. Combined with previous work showing that the Hat activation function also improves generalization accuracy on image classification tasks, this indicates that using the Hat activation provides significant advantages over the ReLU on certain problems.
    Labels, Information, and Computation: Efficient Learning Using Sufficient Labels. (arXiv:2104.09015v2 [cs.LG] UPDATED)
    In supervised learning, obtaining a large set of fully-labeled training data is expensive. We show that we do not always need full label information on every single training example to train a competent classifier. Specifically, inspired by the principle of sufficiency in statistics, we present a statistic (a summary) of the fully-labeled training set that captures almost all the relevant information for classification but at the same time is easier to obtain directly. We call this statistic "sufficiently-labeled data" and prove its sufficiency and efficiency for finding the optimal hidden representations, on which competent classifier heads can be trained using as few as a single randomly-chosen fully-labeled example per class. Sufficiently-labeled data can be obtained from annotators directly without collecting the fully-labeled data first. And we prove that it is easier to directly obtain sufficiently-labeled data than obtaining fully-labeled data. Furthermore, sufficiently-labeled data is naturally more secure since it stores relative, instead of absolute, information. Extensive experimental results are provided to support our theory.
    Implicit differentiation for fast hyperparameter selection in non-smooth convex learning. (arXiv:2105.01637v3 [stat.ML] UPDATED)
    Finding the optimal hyperparameters of a model can be cast as a bilevel optimization problem, typically solved using zero-order techniques. In this work we study first-order methods when the inner optimization problem is convex but non-smooth. We show that the forward-mode differentiation of proximal gradient descent and proximal coordinate descent yield sequences of Jacobians converging toward the exact Jacobian. Using implicit differentiation, we show it is possible to leverage the non-smoothness of the inner problem to speed up the computation. Finally, we provide a bound on the error made on the hypergradient when the inner optimization problem is solved approximately. Results on regression and classification problems reveal computational benefits for hyperparameter optimization, especially when multiple hyperparameters are required.
    DDPG-Driven Deep-Unfolding with Adaptive Depth for Channel Estimation with Sparse Bayesian Learning. (arXiv:2201.08477v2 [eess.SP] UPDATED)
    Deep-unfolding neural networks (NNs) have received great attention since they achieve satisfactory performance with relatively low complexity. Typically, these deep-unfolding NNs are restricted to a fixed-depth for all inputs. However, the optimal number of layers required for convergence changes with different inputs. In this paper, we first develop a framework of deep deterministic policy gradient (DDPG)-driven deep-unfolding with adaptive depth for different inputs, where the trainable parameters of deep-unfolding NN are learned by DDPG, rather than updated by the stochastic gradient descent algorithm directly. Specifically, the optimization variables, trainable parameters, and architecture of deep-unfolding NN are designed as the state, action, and state transition of DDPG, respectively. Then, this framework is employed to deal with the channel estimation problem in massive multiple-input multiple-output systems. Specifically, first of all we formulate the channel estimation problem with an off-grid basis and develop a sparse Bayesian learning (SBL)-based algorithm to solve it. Secondly, the SBL-based algorithm is unfolded into a layer-wise structure with a set of introduced trainable parameters. Thirdly, the proposed DDPG-driven deep-unfolding framework is employed to solve this channel estimation problem based on the unfolded structure of the SBL-based algorithm. To realize adaptive depth, we design the halting score to indicate when to stop, which is a function of the channel reconstruction error. Furthermore, the proposed framework is extended to realize the adaptive depth of the general deep neural networks (DNNs). Simulation results show that the proposed algorithm outperforms the conventional optimization algorithms and DNNs with fixed depth with much reduced number of layers.
    Application of federated learning in manufacturing. (arXiv:2208.04664v1 [cs.LG])
    A vast amount of data is created every minute, both in the private sector and industry. Whereas it is often easy to get hold of data in the private entertainment sector, in the industrial production environment it is much more difficult due to laws, preservation of intellectual property, and other factors. However, most machine learning methods require a data source that is sufficient in terms of quantity and quality. A suitable way to bring both requirements together is federated learning where learning progress is aggregated, but everyone remains the owner of their data. Federate learning was first proposed by Google researchers in 2016 and is used for example in the improvement of Google's keyboard Gboard. In contrast to billions of android users, comparable machinery is only used by few companies. This paper examines which other constraints prevail in production and which federated learning approaches can be considered as a result.  ( 2 min )
    Optimal scheduling of entropy regulariser for continuous-time linear-quadratic reinforcement learning. (arXiv:2208.04466v1 [cs.LG])
    This work uses the entropy-regularised relaxed stochastic control perspective as a principled framework for designing reinforcement learning (RL) algorithms. Herein agent interacts with the environment by generating noisy controls distributed according to the optimal relaxed policy. The noisy policies on the one hand, explore the space and hence facilitate learning but, on the other hand, introduce bias by assigning a positive probability to non-optimal actions. This exploration-exploitation trade-off is determined by the strength of entropy regularisation. We study algorithms resulting from two entropy regularisation formulations: the exploratory control approach, where entropy is added to the cost objective, and the proximal policy update approach, where entropy penalises the divergence of policies between two consecutive episodes. We analyse the finite horizon continuous-time linear-quadratic (LQ) RL problem for which both algorithms yield a Gaussian relaxed policy. We quantify the precise difference between the value functions of a Gaussian policy and its noisy evaluation and show that the execution noise must be independent across time. By tuning the frequency of sampling from relaxed policies and the parameter governing the strength of entropy regularisation, we prove that the regret, for both learning algorithms, is of the order $\mathcal{O}(\sqrt{N}) $ (up to a logarithmic factor) over $N$ episodes, matching the best known result from the literature.  ( 3 min )
    Design of High-Throughput Mixed-Precision CNN Accelerators on FPGA. (arXiv:2208.04854v1 [cs.AR])
    Convolutional Neural Networks (CNNs) reach high accuracies in various application domains, but require large amounts of computation and incur costly data movements. One method to decrease these costs while trading accuracy is weight and/or activation word-length reduction. Thereby, layer-wise mixed-precision quantization allows for more efficient results while inflating the design space. In this work, we present an in-depth quantitative methodology to efficiently explore the design space considering the limited hardware resources of a given FPGA. Our holistic exploration approach vertically traverses the various design entry levels from the architectural down to the logic level, and laterally covers optimization from processing elements to dataflow for an efficient mixed-precision CNN accelerator. Our resulting hardware accelerators implement truly mixed-precision operations that enable efficient execution of layer-wise and channel-wise quantized CNNs. Mapping feed-forward and identity-shortcut-connection mixed-precision CNNs result in competitive accuracy-throughout trade-offs: 245 frames/s with 87.48% Top-5 accuracy for ResNet-18 and 92.9% Top-5 accuracy with 1.13 TOps/s for ResNet-152, respectively. Thereby, the required memory footprint for parameters is reduced by 4.9x and 9.4x compared to the respective floating-point baseline.
    Generalized Reinforcement Learning: Experience Particles, Action Operator, Reinforcement Field, Memory Association, and Decision Concepts. (arXiv:2208.04822v1 [cs.LG])
    Learning a control policy that involves time-varying and evolving system dynamics often poses a great challenge to mainstream reinforcement learning algorithms. In most standard methods, actions are often assumed to be a rigid, fixed set of choices that are sequentially applied to the state space in a predefined manner. Consequently, without resorting to substantial re-learning processes, the learned policy lacks the ability in adapting to variations in the action set and the action's "behavioral" outcomes. In addition, the standard action representation and the action-induced state transition mechanism inherently limit how reinforcement learning can be applied in complex, real-world applications primarily due to the intractability of the resulting large state space and the lack of facility to generalize the learned policy to the unknown part of the state space. This paper proposes a Bayesian-flavored generalized reinforcement learning framework by first establishing the notion of parametric action model to better cope with uncertainty and fluid action behaviors, followed by introducing the notion of reinforcement field as a physics-inspired construct established through "polarized experience particles" maintained in the learning agent's working memory. These particles effectively encode the dynamic learning experience that evolves over time in a self-organizing way. On top of the reinforcement field, we will further generalize the policy learning process to incorporate high-level decision concepts by considering the past memory as having an implicit graph structure, in which the past memory instances (or particles) are interconnected with similarity between decisions defined, and thereby, the "associative memory" principle can be applied to augment the learning agent's world model.
    Comparison of Markov chains via weak Poincar\'e inequalities with application to pseudo-marginal MCMC. (arXiv:2112.05605v2 [stat.CO] UPDATED)
    We investigate the use of a certain class of functional inequalities known as weak Poincar\'e inequalities to bound convergence of Markov chains to equilibrium. We show that this enables the straightforward and transparent derivation of subgeometric convergence bounds for methods such as the Independent Metropolis--Hastings sampler and pseudo-marginal methods for intractable likelihoods, the latter being subgeometric in many practical settings. These results rely on novel quantitative comparison theorems between Markov chains. Associated proofs are simpler than those relying on drift/minorization conditions and the tools developed allow us to recover and further extend known results as particular cases. We are then able to provide new insights into the practical use of pseudo-marginal algorithms, analyse the effect of averaging in Approximate Bayesian Computation (ABC) and the use of products of independent averages, and also to study the case of lognormal weights relevant to particle marginal Metropolis--Hastings (PMMH).
    Towards a General Pre-training Framework for Adaptive Learning in MOOCs. (arXiv:2208.04708v1 [cs.CY])
    Adaptive learning aims to stimulate and meet the needs of individual learners, which requires sophisticated system-level coordination of diverse tasks, including modeling learning resources, estimating student states, and making personalized recommendations. Existing deep learning methods have achieved great success over statistical models; however, they still lack generalization for diverse tasks and suffer from insufficient capacity since they are composed of highly-coupled task-specific architectures and rely on small-scale, coarse-grained recommendation scenarios. To realize the idea of general adaptive systems proposed in pedagogical theory, with the emerging pre-training techniques in NLP, we try to conduct a practical exploration on applying pre-training to adaptive learning, to propose a unified framework based on data observation and learning style analysis, properly leveraging heterogeneous learning elements. Through a series of downstream tasks of Learning Recommendation, Learning Resource Evaluation, Knowledge Tracing, and Dropout Prediction, we find that course structures, text, and knowledge are helpful for modeling and inherently coherent to student non-sequential learning behaviors and that indirectly relevant information included in the pre-training foundation can be shared across downstream tasks to facilitate effectiveness. We finally build a simplified systematic application of adaptive learning and reflect on the insights brought back to pedagogy. The source code and dataset will be released.
    Exploring the trade off between human driving imitation and safety for traffic simulation. (arXiv:2208.04803v1 [cs.LG])
    Traffic simulation has gained a lot of interest for quantitative evaluation of self driving vehicles performance. In order for a simulator to be a valuable test bench, it is required that the driving policy animating each traffic agent in the scene acts as humans would do while maintaining minimal safety guarantees. Learning the driving policies of traffic agents from recorded human driving data or through reinforcement learning seems to be an attractive solution for the generation of realistic and highly interactive traffic situations in uncontrolled intersections or roundabouts. In this work, we show that a trade-off exists between imitating human driving and maintaining safety when learning driving policies. We do this by comparing how various Imitation learning and Reinforcement learning algorithms perform when applied to the driving task. We also propose a multi objective learning algorithm (MOPPO) that improves both objectives together. We test our driving policies on highly interactive driving scenarios extracted from INTERACTION Dataset to evaluate how human-like they behave.
    Clustering Optimisation Method for Highly Connected Biological Data. (arXiv:2208.04720v1 [q-bio.QM])
    Currently, data-driven discovery in biological sciences resides in finding segmentation strategies in multivariate data that produce sensible descriptions of the data. Clustering is but one of several approaches and sometimes falls short because of difficulties in assessing reasonable cutoffs, the number of clusters that need to be formed or that an approach fails to preserve topological properties of the original system in its clustered form. In this work, we show how a simple metric for connectivity clustering evaluation leads to an optimised segmentation of biological data. The novelty of the work resides in the creation of a simple optimisation method for clustering crowded data. The resulting clustering approach only relies on metrics derived from the inherent properties of the clustering. The new method facilitates knowledge for optimised clustering, which is easy to implement. We discuss how the clustering optimisation strategy corresponds to the viable information content yielded by the final segmentation. We further elaborate on how the clustering results, in the optimal solution, corresponds to prior knowledge of three different data sets.
    Training Deep Architectures Without End-to-End Backpropagation: A Survey on the Provably Optimal Methods. (arXiv:2101.03419v3 [cs.LG] UPDATED)
    This tutorial paper surveys provably optimal alternatives to end-to-end backpropagation (E2EBP) -- the de facto standard for training deep architectures. Modular training refers to strictly local training without both the forward and the backward pass, i.e., dividing a deep architecture into several nonoverlapping modules and training them separately without any end-to-end operation. Between the fully global E2EBP and the strictly local modular training, there are weakly modular hybrids performing training without the backward pass only. These alternatives can match or surpass the performance of E2EBP on challenging datasets such as ImageNet, and are gaining increasing attention primarily because they offer practical advantages over E2EBP, which will be enumerated herein. In particular, they allow for greater modularity and transparency in deep learning workflows, aligning deep learning with the mainstream computer science engineering that heavily exploits modularization for scalability. Modular training has also revealed novel insights about learning and has further implications on other important research domains. Specifically, it induces natural and effective solutions to some important practical problems such as data efficiency and transferability estimation.
    Areas of Strategic Visibility: Disability Bias in Biometrics. (arXiv:2208.04712v1 [cs.CY])
    This response to the RFI considers the potential for biometrics to help or harm disabled people2. Biometrics are already integrated into many aspects of daily life, from airport travel to mobile phone use. Yet many of these systems are not accessible to people who experience different kinds of disability exclusion . Different personal characteristics may impact any or all of the physical (DNA, fingerprints, face or retina) and behavioral (gesture, gait, voice) characteristics listed in the RFI as examples of biometric signals.
    Efficient Novelty Detection Methods for Early Warning of Potential Fatal Diseases. (arXiv:2208.04732v1 [cs.CY])
    Fatal diseases, as Critical Health Episodes (CHEs), represent real dangers for patients hospitalized in Intensive Care Units. These episodes can lead to irreversible organ damage and death. Nevertheless, diagnosing them in time would greatly reduce their inconvenience. This study therefore focused on building a highly effective early warning system for CHEs such as Acute Hypotensive Episodes and Tachycardia Episodes. To facilitate the precocity of the prediction, a gap of one hour was considered between the observation periods (Observation Windows) and the periods during which a critical event can occur (Target Windows). The MIMIC II dataset was used to evaluate the performance of the proposed system. This system first includes extracting additional features using three different modes. Then, the feature selection process allowing the selection of the most relevant features was performed using the Mutual Information Gain feature importance. Finally, the high-performance predictive model LightGBM was used to perform episode classification. This approach called MIG-LightGBM was evaluated using five different metrics: Event Recall (ER), Reduced Precision (RP), average Anticipation Time (aveAT), average False Alarms (aveFA), and Event F1-score (EF1-score). A method is therefore considered highly efficient for the early prediction of CHEs if it exhibits not only a large aveAT but also a large EF1-score and a low aveFA. Compared to systems using Extreme Gradient Boosting, Support Vector Classification or Naive Bayes as a predictive model, the proposed system was found to be highly dominant. It also confirmed its superiority over the Layered Learning approach.
    Risk-averse Stochastic Optimization for Farm Management Practices and Cultivar Selection Under Uncertainty. (arXiv:2208.04840v1 [math.OC])
    Optimizing management practices and selecting the best cultivar for planting play a significant role in increasing agricultural food production and decreasing environmental footprint. In this study, we develop optimization frameworks under uncertainty using conditional value-at-risk in the stochastic programming objective function. We integrate the crop model, APSIM, and a parallel Bayesian optimization algorithm to optimize the management practices and select the best cultivar at different levels of risk aversion. This approach integrates the power of optimization in determining the best decisions and crop model in simulating nature's output corresponding to various decisions. As a case study, we set up the crop model for 25 locations across the US Corn Belt. We optimized the management options (planting date, N fertilizer amount, fertilizing date, and plant density in the farm) and cultivar options (cultivars with different maturity days) three times: a) before, b) at planting and c) after a growing season with known weather. Results indicated that the proposed model produced meaningful connections between weather and optima decisions. Also, we found risk-tolerance farmers get more expected yield than risk-averse ones in wet and non-wet weathers.
    Multiple Similarity Drug-Target Interaction Prediction with Random Walks and Matrix Factorization. (arXiv:2201.09508v2 [q-bio.QM] UPDATED)
    The discovery of drug-target interactions (DTIs) is a very promising area of research with great potential. The accurate identification of reliable interactions among drugs and proteins via computational methods, which typically leverage heterogeneous information retrieved from diverse data sources, can boost the development of effective pharmaceuticals. Although random walk and matrix factorization techniques are widely used in DTI prediction, they have several limitations. Random walk-based embedding generation is usually conducted in an unsupervised manner, while the linear similarity combination in matrix factorization distorts individual insights offered by different views. To tackle these issues, we take a multi-layered network approach to handle diverse drug and target similarities, and propose a novel optimization framework, called Multiple similarity DeepWalk-based Matrix Factorization (MDMF), for DTI prediction. The framework unifies embedding generation and interaction prediction, learning vector representations of drugs and targets that not only retain higher-order proximity across all hyper-layers and layer-specific local invariance, but also approximate the interactions with their inner product. Furthermore, we develop an ensemble method (MDMF2A) that integrates two instantiations of the MDMF model, optimizing the area under the precision-recall curve (AUPR) and the area under the receiver operating characteristic curve (AUC) respectively. The empirical study on real-world DTI datasets shows that our method achieves statistically significant improvement over current state-of-the-art approaches in four different settings. Moreover, the validation of highly ranked non-interacting pairs also demonstrates the potential of MDMF2A to discover novel DTIs.
    Rank List Sensitivity of Recommender Systems to Interaction Perturbations. (arXiv:2201.12686v2 [cs.IR] UPDATED)
    Prediction models can exhibit sensitivity with respect to training data: small changes in the training data can produce models that assign conflicting predictions to individual data points during test time. In this work, we study this sensitivity in recommender systems, where users' recommendations are drastically altered by minor perturbations in other unrelated users' interactions. We introduce a measure of stability for recommender systems, called Rank List Sensitivity (RLS), which measures how rank lists generated by a given recommender system at test time change as a result of a perturbation in the training data. We develop a method, CASPER, which uses cascading effect to identify the minimal and systematical perturbation to induce higher instability in a recommender system. Experiments on four datasets show that recommender models are overly sensitive to minor perturbations introduced randomly or via CASPER - even perturbing one random interaction of one user drastically changes the recommendation lists of all users. Importantly, with CASPER perturbation, the models generate more unstable recommendations for low-accuracy users (i.e., those who receive low-quality recommendations) than high-accuracy ones.
    An optimal scheduled learning rate for a randomized Kaczmarz algorithm. (arXiv:2202.12224v4 [math.NA] UPDATED)
    We study how the learning rate affects the performance of a relaxed randomized Kaczmarz algorithm for solving $A x \approx b + \varepsilon$, where $A x =b$ is a consistent linear system and $\varepsilon$ has independent mean zero random entries. We derive a learning rate schedule which optimizes a bound on the expected error that is sharp in certain cases; in contrast to the exponential convergence of the standard randomized Kaczmarz algorithm, our optimized bound involves the reciprocal of the Lambert-$W$ function of an exponential.
    EAFL: Towards Energy-Aware Federated Learning on Battery-Powered Edge Devices. (arXiv:2208.04505v1 [cs.LG])
    Federated learning (FL) is a newly emerged branch of AI that facilitates edge devices to collaboratively train a global machine learning model without centralizing data and with privacy by default. However, despite the remarkable advancement, this paradigm comes with various challenges. Specifically, in large-scale deployments, client heterogeneity is the norm which impacts training quality such as accuracy, fairness, and time. Moreover, energy consumption across these battery-constrained devices is largely unexplored and a limitation for wide-adoption of FL. To address this issue, we develop EAFL, an energy-aware FL selection method that considers energy consumption to maximize the participation of heterogeneous target devices. \scheme is a power-aware training algorithm that cherry-picks clients with higher battery levels in conjunction with its ability to maximize the system efficiency. Our design jointly minimizes the time-to-accuracy and maximizes the remaining on-device battery levels. \scheme improves the testing model accuracy by up to 85\% and decreases the drop-out of clients by up to 2.45$\times$.  ( 2 min )
    RDA: Reciprocal Distribution Alignment for Robust SSL. (arXiv:2208.04619v1 [cs.LG])
    In this work, we propose Reciprocal Distribution Alignment (RDA) to address semi-supervised learning (SSL), which is a hyperparameter-free framework that is independent of confidence threshold and works with both the matched (conventionally) and the mismatched class distributions. Distribution mismatch is an often overlooked but more general SSL scenario where the labeled and the unlabeled data do not fall into the identical class distribution. This may lead to the model not exploiting the labeled data reliably and drastically degrade the performance of SSL methods, which could not be rescued by the traditional distribution alignment. In RDA, we enforce a reciprocal alignment on the distributions of the predictions from two classifiers predicting pseudo-labels and complementary labels on the unlabeled data. These two distributions, carrying complementary information, could be utilized to regularize each other without any prior of class distribution. Moreover, we theoretically show that RDA maximizes the input-output mutual information. Our approach achieves promising performance in SSL under a variety of scenarios of mismatched distributions, as well as the conventional matched SSL setting. Our code is available at: https://github.com/NJUyued/RDA4RobustSSL.
    Classification of Stress via Ambulatory ECG and GSR Data. (arXiv:2208.04705v1 [cs.CY])
    In healthcare, detecting stress and enabling individuals to monitor their mental health and wellbeing is challenging. Advancements in wearable technology now enable continuous physiological data collection. This data can provide insights into mental health and behavioural states through psychophysiological analysis. However, automated analysis is required to provide timely results due to the quantity of data collected. Machine learning has shown efficacy in providing an automated classification of physiological data for health applications in controlled laboratory environments. Ambulatory uncontrolled environments, however, provide additional challenges requiring further modelling to overcome. This work empirically assesses several approaches utilising machine learning classifiers to detect stress using physiological data recorded in an ambulatory setting with self-reported stress annotations. A subset of the training portion SMILE dataset enables the evaluation of approaches before submission. The optimal stress detection approach achieves 90.77% classification accuracy, 91.24 F1-Score, 90.42 Sensitivity and 91.08 Specificity, utilising an ExtraTrees classifier and feature imputation methods. Meanwhile, accuracy on the challenge data is much lower at 59.23% (submission #54 from BEaTS-MTU, username ZacDair). The cause of the performance disparity is explored in this work.
    Intrinsically Motivated Learning of Causal World Models. (arXiv:2208.04892v1 [cs.AI])
    Despite the recent progress in deep learning and reinforcement learning, transfer and generalization of skills learned on specific tasks is very limited compared to human (or animal) intelligence. The lifelong, incremental building of common sense knowledge might be a necessary component on the way to achieve more general intelligence. A promising direction is to build world models capturing the true physical mechanisms hidden behind the sensorimotor interaction with the environment. Here we explore the idea that inferring the causal structure of the environment could benefit from well-chosen actions as means to collect relevant interventional data.
    An Unconstrained Symmetric Nonnegative Latent Factor Analysis for Large-scale Undirected Weighted Networks. (arXiv:2208.04811v1 [cs.LG])
    Large-scale undirected weighted networks are usually found in big data-related research fields. It can naturally be quantified as a symmetric high-dimensional and incomplete (SHDI) matrix for implementing big data analysis tasks. A symmetric non-negative latent-factor-analysis (SNL) model is able to efficiently extract latent factors (LFs) from an SHDI matrix. Yet it relies on a constraint-combination training scheme, which makes it lack flexibility. To address this issue, this paper proposes an unconstrained symmetric nonnegative latent-factor-analysis (USNL) model. Its main idea is two-fold: 1) The output LFs are separated from the decision parameters via integrating a nonnegative mapping function into an SNL model; and 2) Stochastic gradient descent (SGD) is adopted for implementing unconstrained model training along with ensuring the output LFs nonnegativity. Empirical studies on four SHDI matrices generated from real big data applications demonstrate that an USNL model achieves higher prediction accuracy of missing data than an SNL model, as well as highly competitive computational efficiency.
    E2EG: End-to-End Node Classification Using Graph Topology and Text-based Node Attributes. (arXiv:2208.04609v1 [cs.LG])
    Node classification utilizing text-based node attributes has many real-world applications, ranging from prediction of paper topics in academic citation graphs to classification of user characteristics in social media networks. State-of-the-art node classification frameworks, such as GIANT, use a two-stage pipeline: first embedding the text attributes of graph nodes then feeding the resulting embeddings into a node classification model. In this paper, we eliminate these two stages and instead develop an end-to-end node classification model that builds upon GIANT, called End-to-End-GIANT (E2EG). The tandem utilization of a main and an auxiliary classification objectives in our approach results in a more robust model, thus enabling the BERT backbone to be switched out for a distilled encoder with a 25% - 40% reduction in the number of parameters. Moreover, the end-to-end nature of the model increases ease of use, as it avoids the need of chaining multiple models for node classification. Compared to a GIANT+MLP baseline on the ogbn-arxiv and ogbn-products datasets, our model is able to obtain slightly better accuracy in the transductive setting (+0.5%), while reducing model training time by up to 40%. Our model is also applicable in the inductive setting, outperforming GIANT+MLP by up to +2.23%.
    A Time-to-first-spike Coding and Conversion Aware Training for Energy-Efficient Deep Spiking Neural Network Processor Design. (arXiv:2208.04494v1 [cs.NE])
    In this paper, we present an energy-efficient SNN architecture, which can seamlessly run deep spiking neural networks (SNNs) with improved accuracy. First, we propose a conversion aware training (CAT) to reduce ANN-to-SNN conversion loss without hardware implementation overhead. In the proposed CAT, the activation function developed for simulating SNN during ANN training, is efficiently exploited to reduce the data representation error after conversion. Based on the CAT technique, we also present a time-to-first-spike coding that allows lightweight logarithmic computation by utilizing spike time information. The SNN processor design that supports the proposed techniques has been implemented using 28nm CMOS process. The processor achieves the top-1 accuracies of 91.7%, 67.9% and 57.4% with inference energy of 486.7uJ, 503.6uJ, and 1426uJ to process CIFAR-10, CIFAR-100, and Tiny-ImageNet, respectively, when running VGG-16 with 5bit logarithmic weights.
    A Means-End Account of Explainable Artificial Intelligence. (arXiv:2208.04638v1 [cs.AI])
    Explainable artificial intelligence (XAI) seeks to produce explanations for those machine learning methods which are deemed opaque. However, there is considerable disagreement about what this means and how to achieve it. Authors disagree on what should be explained (topic), to whom something should be explained (stakeholder), how something should be explained (instrument), and why something should be explained (goal). In this paper, I employ insights from means-end epistemology to structure the field. According to means-end epistemology, different means ought to be rationally adopted to achieve different epistemic ends. Applied to XAI, different topics, stakeholders, and goals thus require different instruments. I call this the means-end account of XAI. The means-end account has a descriptive and a normative component: on the one hand, I show how the specific means-end relations give rise to a taxonomy of existing contributions to the field of XAI; on the other hand, I argue that the suitability of XAI methods can be assessed by analyzing whether they are prescribed by a given topic, stakeholder, and goal.
    Context sequence theory: a common explanation for multiple types of learning. (arXiv:2208.04707v1 [q-bio.NC])
    Although principles of neuroscience like reinforcement learning, visual perception and attention have been applied in machine learning models, there is a huge gap between machine learning and mammalian learning. Based on the advances in neuroscience, we propose the context sequence theory to give a common explanation for multiple types of learning in mammals and hope that can provide a new insight into the construct of machine learning models.
    Res-Dense Net for 3D Covid Chest CT-scan classification. (arXiv:2208.04613v1 [eess.IV])
    One of the most contentious areas of research in Medical Image Preprocessing is 3D CT-scan. With the rapid spread of COVID-19, the function of CT-scan in properly and swiftly diagnosing the disease has become critical. It has a positive impact on infection prevention. There are many tasks to diagnose the illness through CT-scan images, include COVID-19. In this paper, we propose a method that using a Stacking Deep Neural Network to detect the Covid 19 through the series of 3D CT-scans images . In our method, we experiment with two backbones are DenseNet 121 and ResNet 101. This method achieves a competitive performance on some evaluation metrics
    Combining Variational Modeling with Partial Gradient Perturbation to Prevent Deep Gradient Leakage. (arXiv:2208.04767v1 [cs.LG])
    Exploiting gradient leakage to reconstruct supposedly private training data, gradient inversion attacks are an ubiquitous threat in collaborative learning of neural networks. To prevent gradient leakage without suffering from severe loss in model performance, recent work proposed a PRivacy EnhanCing mODulE (PRECODE) based on variational modeling as extension for arbitrary model architectures. In this work, we investigate the effect of PRECODE on gradient inversion attacks to reveal its underlying working principle. We show that variational modeling induces stochasticity on PRECODE's and its subsequent layers' gradients that prevents gradient attacks from convergence. By purposefully omitting those stochastic gradients during attack optimization, we formulate an attack that can disable PRECODE's privacy preserving effects. To ensure privacy preservation against such targeted attacks, we propose PRECODE with Partial Perturbation (PPP), as strategic combination of variational modeling and partial gradient perturbation. We conduct an extensive empirical study on four seminal model architectures and two image classification datasets. We find all architectures to be prone to gradient leakage, which can be prevented by PPP. In result, we show that our approach requires less gradient perturbation to effectively preserve privacy without harming model performance.
    EfficientNet for Brain-Lesion classification. (arXiv:2208.04616v1 [eess.IV])
    In the development of technology, there are increasing cases of brain disease, there are more treatments proposed and achieved a positive result. However, with Brain-Lesion, the early diagnoses can improve the possibility for successful treatment and can help patients recuperate better. From this reason, Brain-Lesion is one of the controversial topics in medical images analysis nowadays. With the improvement of the architecture, there is a variety of methods that are proposed and achieve competitive scores. In this paper, we proposed a technique that uses efficient-net for 3D images, especially the Efficient-net B0 for Brain-Lesion classification task solution, and achieve the competitive score. Moreover, we also proposed the method to use Multiscale-EfficientNet to classify the slices of the MRI data
    EFI: A Toolbox for Feature Importance Fusion and Interpretation in Python. (arXiv:2208.04343v1 [cs.LG])
    This paper presents an open-source Python toolbox called Ensemble Feature Importance (EFI) to provide machine learning (ML) researchers, domain experts, and decision makers with robust and accurate feature importance quantification and more reliable mechanistic interpretation of feature importance for prediction problems using fuzzy sets. The toolkit was developed to address uncertainties in feature importance quantification and lack of trustworthy feature importance interpretation due to the diverse availability of machine learning algorithms, feature importance calculation methods, and dataset dependencies. EFI merges results from multiple machine learning models with different feature importance calculation approaches using data bootstrapping and decision fusion techniques, such as mean, majority voting and fuzzy logic. The main attributes of the EFI toolbox are: (i) automatic optimisation of ML algorithms, (ii) automatic computation of a set of feature importance coefficients from optimised ML algorithms and feature importance calculation techniques, (iii) automatic aggregation of importance coefficients using multiple decision fusion techniques, and (iv) fuzzy membership functions that show the importance of each feature to the prediction task. The key modules and functions of the toolbox are described, and a simple example of their application is presented using the popular Iris dataset.  ( 2 min )
    Long-term Causal Effects Estimation via Latent Surrogates Representation Learning. (arXiv:2208.04589v1 [cs.LG])
    Estimating long-term causal effects based on short-term surrogates is a significant but challenging problem in many real-world applications, e.g., marketing and medicine. Despite its success in certain domains, most existing methods estimate causal effects in an idealistic and simplistic way - ignoring the causal structure among short-term outcomes and treating all of them as surrogates. However, such methods cannot be well applied to real-world scenarios, in which the partially observed surrogates are mixed with their proxies among short-term outcomes. To this end, we develop our flexible method, Laser, to estimate long-term causal effects in the more realistic situation that the surrogates are observed or have observed proxies.Given the indistinguishability between the surrogates and proxies, we utilize identifiable variational auto-encoder (iVAE) to recover the whole valid surrogates on all the surrogates candidates without the need of distinguishing the observed surrogates or the proxies of latent surrogates. With the help of the recovered surrogates, we further devise an unbiased estimation of long-term causal effects. Extensive experimental results on the real-world and semi-synthetic datasets demonstrate the effectiveness of our proposed method.
    Extending GCC-PHAT using Shift Equivariant Neural Networks. (arXiv:2208.04654v1 [eess.AS])
    Speaker localization using microphone arrays depends on accurate time delay estimation techniques. For decades, methods based on the generalized cross correlation with phase transform (GCC-PHAT) have been widely adopted for this purpose. Recently, the GCC-PHAT has also been used to provide input features to neural networks in order to remove the effects of noise and reverberation, but at the cost of losing theoretical guarantees in noise-free conditions. We propose a novel approach to extending the GCC-PHAT, where the received signals are filtered using a shift equivariant neural network that preserves the timing information contained in the signals. By extensive experiments we show that our model consistently reduces the error of the GCC-PHAT in adverse environments, with guarantees of exact time delay recovery in ideal conditions.
    A Visual Analytics System for Improving Attention-based Traffic Forecasting Models. (arXiv:2208.04350v1 [cs.HC])
    With deep learning (DL) outperforming conventional methods for different tasks, much effort has been devoted to utilizing DL in various domains. Researchers and developers in the traffic domain have also designed and improved DL models for forecasting tasks such as estimation of traffic speed and time of arrival. However, there exist many challenges in analyzing DL models due to the black-box property of DL models and complexity of traffic data (i.e., spatio-temporal dependencies). Collaborating with domain experts, we design a visual analytics system, AttnAnalyzer, that enables users to explore how DL models make predictions by allowing effective spatio-temporal dependency analysis. The system incorporates dynamic time warping (DTW) and Granger causality tests for computational spatio-temporal dependency analysis while providing map, table, line chart, and pixel views to assist user to perform dependency and model behavior analysis. For the evaluation, we present three case studies showing how AttnAnalyzer can effectively explore model behaviors and improve model performance in two different road networks. We also provide domain expert feedback.  ( 2 min )
    Implementation of fast ICA using memristor crossbar arrays for blind image source separations. (arXiv:2208.04317v1 [cs.ET])
    Independent component analysis is an unsupervised learning approach for computing the independent components (ICs) from the multivariate signals or data matrix. The ICs are evaluated based on the multiplication of the weight matrix with the multivariate data matrix. This study proposes a novel memristor crossbar array for the implementation of both ACY ICA and Fast ICA for blind source separation. The data input was applied in the form of pulse width modulated voltages to the crossbar array and the weight of the implemented neural network is stored in the memristor. The output charges from the memristor columns are used to calculate the weight update, which is executed through the voltages kept higher than the memristor Set/Reset voltages. In order to demonstrate its potential application, the proposed memristor crossbar arrays based fast ICA architecture is employed for image source separation problem. The experimental results demonstrate that the proposed approach is very effective to separate image sources, and also the contrast of the images are improved with an improvement factor in terms of percentage of structural similarity as 67.27% when compared with the software-based implementation of conventional ACY ICA and Fast ICA algorithms.  ( 3 min )
    Predicting Intraoperative Hypoxemia with Hybrid Inference Sequence Autoencoder Networks. (arXiv:2104.14756v5 [cs.LG] UPDATED)
    We present an end-to-end model using streaming physiological time series to accurately predict near-term risk for hypoxemia, a rare, but life-threatening condition known to cause serious patient harm during surgery. Inspired by the fact that a hypoxemia event is defined based on the sequence of future-observed low SpO2 (i.e., blood oxygen saturation) instances, our proposed model makes hybrid inference on both future low SpO2 instances and hypoxemia outcomes, enabled by a joint sequence autoencoder that simultaneously optimizes a discriminative decoder for label prediction, and two auxiliary decoders trained for data reconstruction and forecast, which seamlessly learns contextual latent representations that capture the transition between present state to future state. All decoders share a memory-based encoder that helps capture the global dynamics of patient measurement. For a large surgical cohort of 72,081 surgeries at a major academic medical center, our model outperforms all baselines including the model used by the state-of-the-art hypoxemia prediction system. Being able to make minute-resolution real-time prediction with clinically acceptable alarm rate to near-term hypoxemic events, particularly the more critical persistent hypoxemia, our proposed model is promising in improving clinical decision making and easing burden on perioperative care.
    COROID: A Crowdsourcing-based Companion Drones to Tackle Current and Future Pandemics. (arXiv:2208.04704v1 [cs.CY])
    Due to the current COVID-19 virus, which has already been declared a pandemic by the World Health Organization (WHO), we are witnessing the greatest pandemic of the decade. Millions of people are being infected, resulting in thousands of deaths every day across the globe. Even it was difficult for the best healthcare-providing countries could not handle the pandemic because of the strain of treating thousands of patients at a time. The count of infections and deaths is increasing at an alarming rate because of the spread of the virus. We believe that innovative technologies could help reduce pandemics to a certain extent until we find a definite solution from the medical field to handle and treat such pandemic situations. Technology innovation has the potential to introduce new technologies that could support people and society during these difficult times. Therefore, this paper proposes the idea of using drones as a companion to tackle current and future pandemics. Our COROID drone is based on the principle of crowdsourcing sensors data of the public's smart devices, which can correlate the reading of the infrared cameras equipped on the COROID drones. To the best of our knowledge, this concept has yet to be investigated either as a concept or as a product. Therefore, we believe that the COROID drone is innovative and has a huge potential to tackle COVID-19 and future pandemics.
    Bayesian Pseudo Labels: Expectation Maximization for Robust and Efficient Semi-Supervised Segmentation. (arXiv:2208.04435v1 [cs.CV])
    This paper concerns pseudo labelling in segmentation. Our contribution is fourfold. Firstly, we present a new formulation of pseudo-labelling as an Expectation-Maximization (EM) algorithm for clear statistical interpretation. Secondly, we propose a semi-supervised medical image segmentation method purely based on the original pseudo labelling, namely SegPL. We demonstrate SegPL is a competitive approach against state-of-the-art consistency regularisation based methods on semi-supervised segmentation on a 2D multi-class MRI brain tumour segmentation task and a 3D binary CT lung vessel segmentation task. The simplicity of SegPL allows less computational cost comparing to prior methods. Thirdly, we demonstrate that the effectiveness of SegPL may originate from its robustness against out-of-distribution noises and adversarial attacks. Lastly, under the EM framework, we introduce a probabilistic generalisation of SegPL via variational inference, which learns a dynamic threshold for pseudo labelling during the training. We show that SegPL with variational inference can perform uncertainty estimation on par with the gold-standard method Deep Ensemble.  ( 2 min )
    Motif-based Graph Representation Learning with Application to Chemical Molecules. (arXiv:2208.04529v1 [cs.LG])
    This work considers the task of representation learning on the attributed relational graph (ARG). Both the nodes and edges in an ARG are associated with attributes/features allowing ARGs to encode rich structural information widely observed in real applications. Existing graph neural networks offer limited ability to capture complex interactions within local structural contexts, which hinders them from taking advantage of the expression power of ARGs. We propose Motif Convolution Module (MCM), a new motif-based graph representation learning technique to better utilize local structural information. The ability to handle continuous edge and node features is one of MCM's advantages over existing motif-based models. MCM builds a motif vocabulary in an unsupervised way and deploys a novel motif convolution operation to extract the local structural context of individual nodes, which is then used to learn higher-level node representations via multilayer perceptron and/or message passing in graph neural networks. When compared with other graph learning approaches to classifying synthetic graphs, our approach is substantially better in capturing structural context. We also demonstrate the performance and explainability advantages of our approach by applying it to several molecular benchmarks.
    Statistical Properties of the log-cosh Loss Function Used in Machine Learning. (arXiv:2208.04564v1 [stat.ML])
    This paper analyzes a popular loss function used in machine learning called the log-cosh loss function. A number of papers have been published using this loss function but, to date, no statistical analysis has been presented in the literature. In this paper, we present the distribution function from which the log-cosh loss arises. We compare it to a similar distribution, called the Cauchy distribution, and carry out various statistical procedures that characterize its properties. In particular, we examine its associated pdf, cdf, likelihood function and Fisher information. Side-by-side we consider the Cauchy and Cosh distributions as well as the MLE of the location parameter with asymptotic bias, asymptotic variance, and confidence intervals. We also provide a comparison of robust estimators from several other loss functions, including the Huber loss function and the rank dispersion function. Further, we examine the use of the log-cosh function for quantile regression. In particular, we identify a quantile distribution function from which a maximum likelihood estimator for quantile regression can be derived. Finally, we compare a quantile M-estimator based on log-cosh with robust monotonicity against another approach to quantile regression based on convolutional smoothing.
    LAMDA-SSL: Semi-Supervised Learning in Python. (arXiv:2208.04610v1 [cs.LG])
    LAMDA-SSL is open-sourced on GitHub and its detailed usage documentation is available at https://ygzwqzd.github.io/LAMDA-SSL/. This documentation introduces LAMDA-SSL in detail from various aspects and can be divided into four parts. The first part introduces the design idea, features and functions of LAMDA-SSL. The second part shows the usage of LAMDA-SSL by abundant examples in detail. The third part introduces all algorithms implemented by LAMDA-SSL to help users quickly understand and choose SSL algorithms. The fourth part shows the APIs of LAMDA-SSL. This detailed documentation greatly reduces the cost of familiarizing users with LAMDA-SSL toolkit and SSL algorithms.
    Boundary Distance Loss for Intra-/Extra-meatal Segmentation of Vestibular Schwannoma. (arXiv:2208.04680v1 [eess.IV])
    Vestibular Schwannoma (VS) typically grows from the inner ear to the brain. It can be separated into two regions, intrameatal and extrameatal respectively corresponding to being inside or outside the inner ear canal. The growth of the extrameatal regions is a key factor that determines the disease management followed by the clinicians. In this work, a VS segmentation approach with subdivision into intra-/extra-meatal parts is presented. We annotated a dataset consisting of 227 T2 MRI instances, acquired longitudinally on 137 patients, excluding post-operative instances. We propose a staged approach, with the first stage performing the whole tumour segmentation and the second stage performing the intra-/extra-meatal segmentation using the T2 MRI along with the mask obtained from the first stage. To improve on the accuracy of the predicted meatal boundary, we introduce a task-specific loss which we call Boundary Distance Loss. The performance is evaluated in contrast to the direct intrameatal extrameatal segmentation task performance, i.e. the Baseline. Our proposed method, with the two-stage approach and the Boundary Distance Loss, achieved a Dice score of 0.8279+-0.2050 and 0.7744+-0.1352 for extrameatal and intrameatal regions respectively, significantly improving over the Baseline, which gave Dice score of 0.7939+-0.2325 and 0.7475+-0.1346 for the extrameatal and intrameatal regions respectively.
    Adaptive Local Implicit Image Function for Arbitrary-scale Super-resolution. (arXiv:2208.04318v1 [eess.IV])
    Image representation is critical for many visual tasks. Instead of representing images discretely with 2D arrays of pixels, a recent study, namely local implicit image function (LIIF), denotes images as a continuous function where pixel values are expansion by using the corresponding coordinates as inputs. Due to its continuous nature, LIIF can be adopted for arbitrary-scale image super-resolution tasks, resulting in a single effective and efficient model for various up-scaling factors. However, LIIF often suffers from structural distortions and ringing artifacts around edges, mostly because all pixels share the same model, thus ignoring the local properties of the image. In this paper, we propose a novel adaptive local image function (A-LIIF) to alleviate this problem. Specifically, our A-LIIF consists of two main components: an encoder and a expansion network. The former captures cross-scale image features, while the latter models the continuous up-scaling function by a weighted combination of multiple local implicit image functions. Accordingly, our A-LIIF can reconstruct the high-frequency textures and structures more accurately. Experiments on multiple benchmark datasets verify the effectiveness of our method. Our codes are available at \url{https://github.com/LeeHW-THU/A-LIIF}.
    On Taking Advantage of Opportunistic Meta-knowledge to Reduce Configuration Spaces for Automated Machine Learning. (arXiv:2208.04376v1 [cs.LG])
    The automated machine learning (AutoML) process can require searching through complex configuration spaces of not only machine learning (ML) components and their hyperparameters but also ways of composing them together, i.e. forming ML pipelines. Optimisation efficiency and the model accuracy attainable for a fixed time budget suffer if this pipeline configuration space is excessively large. A key research question is whether it is both possible and practical to preemptively avoid costly evaluations of poorly performing ML pipelines by leveraging their historical performance for various ML tasks, i.e. meta-knowledge. The previous experience comes in the form of classifier/regressor accuracy rankings derived from either (1) a substantial but non-exhaustive number of pipeline evaluations made during historical AutoML runs, i.e. 'opportunistic' meta-knowledge, or (2) comprehensive cross-validated evaluations of classifiers/regressors with default hyperparameters, i.e. 'systematic' meta-knowledge. Numerous experiments with the AutoWeka4MCPS package suggest that (1) opportunistic/systematic meta-knowledge can improve ML outcomes, typically in line with how relevant that meta-knowledge is, and (2) configuration-space culling is optimal when it is neither too conservative nor too radical. However, the utility and impact of meta-knowledge depend critically on numerous facets of its generation and exploitation, warranting extensive analysis; these are often overlooked/underappreciated within AutoML and meta-learning literature. In particular, we observe strong sensitivity to the `challenge' of a dataset, i.e. whether specificity in choosing a predictor leads to significantly better performance. Ultimately, identifying `difficult' datasets, thus defined, is crucial to both generating informative meta-knowledge bases and understanding optimal search-space reduction strategies.
    Hierarchical Residual Learning Based Vector Quantized Variational Autoencoder for Image Reconstruction and Generation. (arXiv:2208.04554v1 [cs.CV])
    We propose a multi-layer variational autoencoder method, we call HR-VQVAE, that learns hierarchical discrete representations of the data. By utilizing a novel objective function, each layer in HR-VQVAE learns a discrete representation of the residual from previous layers through a vector quantized encoder. Furthermore, the representations at each layer are hierarchically linked to those at previous layers. We evaluate our method on the tasks of image reconstruction and generation. Experimental results demonstrate that the discrete representations learned by HR-VQVAE enable the decoder to reconstruct high-quality images with less distortion than the baseline methods, namely VQVAE and VQVAE-2. HR-VQVAE can also generate high-quality and diverse images that outperform state-of-the-art generative models, providing further verification of the efficiency of the learned representations. The hierarchical nature of HR-VQVAE i) reduces the decoding search time, making the method particularly suitable for high-load tasks and ii) allows to increase the codebook size without incurring the codebook collapse problem.
    Comparison of semi-supervised learning methods for High Content Screening quality control. (arXiv:2208.04592v1 [cs.CV])
    Progress in automated microscopy and quantitative image analysis has promoted high-content screening (HCS) as an efficient drug discovery and research tool. While HCS offers to quantify complex cellular phenotypes from images at high throughput, this process can be obstructed by image aberrations such as out-of-focus image blur, fluorophore saturation, debris, a high level of noise, unexpected auto-fluorescence or empty images. While this issue has received moderate attention in the literature, overlooking these artefacts can seriously hamper downstream image processing tasks and hinder detection of subtle phenotypes. It is therefore of primary concern, and a prerequisite, to use quality control in HCS. In this work, we evaluate deep learning options that do not require extensive image annotations to provide a straightforward and easy to use semi-supervised learning solution to this issue. Concretely, we compared the efficacy of recent self-supervised and transfer learning approaches to provide a base encoder to a high throughput artefact image detector. The results of this study suggest that transfer learning methods should be preferred for this task as they not only performed best here but present the advantage of not requiring sensitive hyperparameter settings nor extensive additional training.
    Analyzing and Enhancing Closed-loop Stability in Reactive Simulation. (arXiv:2208.04559v1 [cs.RO])
    Simulation has played an important role in efficiently evaluating self-driving vehicles in terms of scalability. Existing methods mostly rely on heuristic-based simulation, where traffic participants follow certain human-encoded rules that fail to generate complex human behaviors. Therefore, the reactive simulation concept is proposed to bridge the human behavior gap between simulation and real-world traffic scenarios by leveraging real-world data. However, these reactive models can easily generate unreasonable behaviors after a few steps of simulation, where we regard the model as losing its stability. To the best of our knowledge, no work has explicitly discussed and analyzed the stability of the reactive simulation framework. In this paper, we aim to provide a thorough stability analysis of the reactive simulation and propose a solution to enhance the stability. Specifically, we first propose a new reactive simulation framework, where we discover that the smoothness and consistency of the simulated state sequences are crucial factors to stability. We then incorporate the kinematic vehicle model into the framework to improve the closed-loop stability of the reactive simulation. Furthermore, along with commonly-used metrics, several novel metrics are proposed in this paper to better analyze the simulation performance.
    Understanding Weight Similarity of Neural Networks via Chain Normalization Rule and Hypothesis-Training-Testing. (arXiv:2208.04369v1 [cs.LG])
    We present a weight similarity measure method that can quantify the weight similarity of non-convex neural networks. To understand the weight similarity of different trained models, we propose to extract the feature representation from the weights of neural networks. We first normalize the weights of neural networks by introducing a chain normalization rule, which is used for weight representation learning and weight similarity measure. We extend the traditional hypothesis-testing method to a hypothesis-training-testing statistical inference method to validate the hypothesis on the weight similarity of neural networks. With the chain normalization rule and the new statistical inference, we study the weight similarity measure on Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN), and find that the weights of an identical neural network optimized with the Stochastic Gradient Descent (SGD) algorithm converge to a similar local solution in a metric space. The weight similarity measure provides more insight into the local solutions of neural networks. Experiments on several datasets consistently validate the hypothesis of weight similarity measure.  ( 2 min )
    Partial Least Square Regression via Three-factor SVD-type Manifold Optimization for EEG Decoding. (arXiv:2208.04324v1 [cs.LG])
    Partial least square regression (PLSR) is a widely-used statistical model to reveal the linear relationships of latent factors that comes from the independent variables and dependent variables. However, traditional methods \ql{ to solve PLSR models are usually based on the Euclidean space, and easily getting} stuck into a local minimum. To this end, we propose a new method to solve the partial least square regression, named PLSR via optimization on bi-Grassmann manifold (PLSRbiGr). \ql{Specifically, we first leverage} the three-factor SVD-type decomposition of the cross-covariance matrix defined on the bi-Grassmann manifold, converting the orthogonal constrained optimization problem into an unconstrained optimization problem on bi-Grassmann manifold, and then incorporate the Riemannian preconditioning of matrix scaling to regulate the Riemannian metric in each iteration. \ql{PLSRbiGr is validated} with a variety of experiments for decoding EEG signals at motor imagery (MI) and steady-state visual evoked potential (SSVEP) task. Experimental results demonstrate that PLSRbiGr outperforms competing algorithms in multiple EEG decoding tasks, which will greatly facilitate small sample data learning.
    Generative models-based data labeling for deep networks regression: application to seed maturity estimation from UAV multispectral images. (arXiv:2208.04611v1 [cs.CV])
    Monitoring seed maturity is an increasing challenge in agriculture due to climate change and more restrictive practices. Seeds monitoring in the field is essential to optimize the farming process and to guarantee yield quality through high germination. Traditional methods are based on limited sampling in the field and analysis in laboratory. Moreover, they are time consuming and only allow monitoring sub-sections of the crop field. This leads to a lack of accuracy on the condition of the crop as a whole due to intra-field heterogeneities. Multispectral imagery by UAV allows uniform scan of fields and better capture of crop maturity information. On the other hand, deep learning methods have shown tremendous potential in estimating agronomic parameters, especially maturity. However, they require large labeled datasets. Although large sets of aerial images are available, labeling them with ground truth is a tedious, if not impossible task. In this paper, we propose a method for estimating parsley seed maturity using multispectral UAV imagery, with a new approach for automatic data labeling. This approach is based on parametric and non-parametric models to provide weak labels. We also consider the data acquisition protocol and the performance evaluation of the different steps of the method. Results show good performance, and the non-parametric kernel density estimator model can improve neural network generalization when used as a labeling method, leading to more robust and better performing deep neural models.
    Deep Maxout Network Gaussian Process. (arXiv:2208.04468v1 [stat.ML])
    Study of neural networks with infinite width is important for better understanding of the neural network in practical application. In this work, we derive the equivalence of the deep, infinite-width maxout network and the Gaussian process (GP) and characterize the maxout kernel with a compositional structure. Moreover, we build up the connection between our deep maxout network kernel and deep neural network kernels. We also give an efficient numerical implementation of our kernel which can be adapted to any maxout rank. Numerical results show that doing Bayesian inference based on the deep maxout network kernel can lead to competitive results compared with their finite-width counterparts and deep neural network kernels. This enlightens us that the maxout activation may also be incorporated into other infinite-width neural network structures such as the convolutional neural network (CNN).
    More Interpretable Graph Similarity Computation via Maximum Common Subgraph Inference. (arXiv:2208.04580v1 [cs.LG])
    Graph similarity measurement, which computes the distance/similarity between two graphs, arises in various graph-related tasks. Recent learning-based methods lack interpretability, as they directly transform interaction information between two graphs into one hidden vector and then map it to similarity. To cope with this problem, this study proposes a more interpretable end-to-end paradigm for graph similarity learning, named Similarity Computation via Maximum Common Subgraph Inference (INFMCS). Our critical insight into INFMCS is the strong correlation between similarity score and Maximum Common Subgraph (MCS). We implicitly infer MCS to obtain the normalized MCS size, with the supervision information being only the similarity score during training. To capture more global information, we also stack some vanilla transformer encoder layers with graph convolution layers and propose a novel permutation-invariant node Positional Encoding. The entire model is quite simple yet effective. Comprehensive experiments demonstrate that INFMCS consistently outperforms state-of-the-art baselines for graph-graph classification and regression tasks. Ablation experiments verify the effectiveness of the proposed computation paradigm and other components. Also, visualization and statistics of results reveal the interpretability of INFMCS.
    Multiple Instance Neural Networks Based on Sparse Attention for Cancer Detection using T-cell Receptor Sequences. (arXiv:2208.04524v1 [stat.ML])
    Early detection of cancers has been much explored due to its paramount importance in biomedical fields. Among different types of data used to answer this biological question, studies based on T cell receptors (TCRs) are under recent spotlight due to the growing appreciation of the roles of the host immunity system in tumor biology. However, the one-to-many correspondence between a patient and multiple TCR sequences hinders researchers from simply adopting classical statistical/machine learning methods. There were recent attempts to model this type of data in the context of multiple instance learning (MIL). Despite the novel application of MIL to cancer detection using TCR sequences and the demonstrated adequate performance in several tumor types, there is still room for improvement, especially for certain cancer types. Furthermore, explainable neural network models are not fully investigated for this application. In this article, we propose multiple instance neural networks based on sparse attention (MINN-SA) to enhance the performance in cancer detection and explainability. The sparse attention structure drops out uninformative instances in each bag, achieving both interpretability and better predictive performance in combination with the skip connection. Our experiments show that MINN-SA yields the highest area under the ROC curve (AUC) scores on average measured across 10 different types of cancers, compared to existing MIL approaches. Moreover, we observe from the estimated attentions that MINN-SA can identify the TCRs that are specific for tumor antigens in the same T cell repertoire.
    Disentangled Representation Learning Using ($\beta$-)VAE and GAN. (arXiv:2208.04549v1 [cs.CV])
    Given a dataset of images containing different objects with different features such as shape, size, rotation, and x-y position; and a Variational Autoencoder (VAE); creating a disentangled encoding of these features in the hidden space vector of the VAE was the task of interest in this paper. The dSprite dataset provided the desired features for the required experiments in this research. After training the VAE combined with a Generative Adversarial Network (GAN), each dimension of the hidden vector was disrupted to explore the disentanglement in each dimension. Note that the GAN was used to improve the quality of output image reconstruction.
    Controlled Sparsity via Constrained Optimization or: How I Learned to Stop Tuning Penalties and Love Constraints. (arXiv:2208.04425v1 [cs.LG])
    The performance of trained neural networks is robust to harsh levels of pruning. Coupled with the ever-growing size of deep learning models, this observation has motivated extensive research on learning sparse models. In this work, we focus on the task of controlling the level of sparsity when performing sparse learning. Existing methods based on sparsity-inducing penalties involve expensive trial-and-error tuning of the penalty factor, thus lacking direct control of the resulting model sparsity. In response, we adopt a constrained formulation: using the gate mechanism proposed by Louizos et al. (2018), we formulate a constrained optimization problem where sparsification is guided by the training objective and the desired sparsity target in an end-to-end fashion. Experiments on CIFAR-10/100, TinyImageNet, and ImageNet using WideResNet and ResNet{18, 50} models validate the effectiveness of our proposal and demonstrate that we can reliably achieve pre-determined sparsity targets without compromising on predictive performance.
    IDNP: Interest Dynamics Modeling using Generative Neural Processes for Sequential Recommendation. (arXiv:2208.04600v1 [cs.IR])
    Recent sequential recommendation models rely increasingly on consecutive short-term user-item interaction sequences to model user interests. These approaches have, however, raised concerns about both short- and long-term interests. (1) {\it short-term}: interaction sequences may not result from a monolithic interest, but rather from several intertwined interests, even within a short period of time, resulting in their failures to model skip behaviors; (2) {\it long-term}: interaction sequences are primarily observed sparsely at discrete intervals, other than consecutively over the long run. This renders difficulty in inferring long-term interests, since only discrete interest representations can be derived, without taking into account interest dynamics across sequences. In this study, we address these concerns by learning (1) multi-scale representations of short-term interests; and (2) dynamics-aware representations of long-term interests. To this end, we present an \textbf{I}nterest \textbf{D}ynamics modeling framework using generative \textbf{N}eural \textbf{P}rocesses, coined IDNP, to model user interests from a functional perspective. IDNP learns a global interest function family to define each user's long-term interest as a function instantiation, manifesting interest dynamics through function continuity. Specifically, IDNP first encodes each user's short-term interactions into multi-scale representations, which are then summarized as user context. By combining latent global interest with user context, IDNP then reconstructs long-term user interest functions and predicts interactions at upcoming query timestep. Moreover, IDNP can model such interest functions even when interaction sequences are limited and non-consecutive. Extensive experiments on four real-world datasets demonstrate that our model outperforms state-of-the-arts on various evaluation metrics.
    NRBdMF: A recommendation algorithm for predicting drug effects considering directionality. (arXiv:2208.04312v1 [q-bio.QM])
    Predicting the novel effects of drugs based on information about approved drugs can be regarded as a recommendation system. Matrix factorization is one of the most used recommendation systems and various algorithms have been devised for it. A literature survey and summary of existing algorithms for predicting drug effects demonstrated that most such methods, including neighborhood regularized logistic matrix factorization, which was the best performer in benchmark tests, used a binary matrix that considers only the presence or absence of interactions. However, drug effects are known to have two opposite aspects, such as side effects and therapeutic effects. In the present study, we proposed using neighborhood regularized bidirectional matrix factorization (NRBdMF) to predict drug effects by incorporating bidirectionality, which is a characteristic property of drug effects. We used this proposed method for predicting side effects using a matrix that considered the bidirectionality of drug effects, in which known side effects were assigned a positive label (plus 1) and known treatment effects were assigned a negative (minus 1) label. The NRBdMF model, which utilizes drug bidirectional information, achieved enrichment of side effects at the top and indications at the bottom of the prediction list. This first attempt to consider the bidirectional nature of drug effects using NRBdMF showed that it reduced false positives and produced a highly interpretable output.
    Patient-Specific Game-Based Transfer Method for Parkinson's Disease Severity Prediction. (arXiv:2208.04315v1 [cs.LG])
    Dysphonia is one of the early symptoms of Parkinson's disease (PD). Most existing methods use feature selection methods to find the optimal subset of voice features for all PD patients to improve the prediction performance. Few have considered the heterogeneity between patients, which implies the need to provide specific prediction models for different patients. However, building this prediction model for each patient faces the challenge of small sample size, which makes it lack generalization ability. Instance transfer is an effective way to make up for this deficiency. Therefore, this paper proposes a patient-specific game-based transfer (PSGT) method for PD severity prediction. First, a selection mechanism is used to select PD patients with similar disease trends to the target patient from the source domain, which greatly reduces the scope of instance transfer and reduces the risk of negative transfer. Then, the contribution of the transferred subjects and their instances to the disease estimation of the target subject is fairly evaluated by the Shapley value, which improves the interpretability of the method. Next, the proportion of valid instances is determined according to the contribution of transferred subjects, and the instances with higher contribution are transferred based on this proportion to further reduce the difference between the transferred instance subset and the target subject. Finally, the selected subset of instances is added to the training set of the target subject, and the extended data is fed into the random forest to improve the performance of the PD severity prediction method. Parkinson's telemonitoring dataset is used to evaluate the feasibility and effectiveness. Experiment results show that the proposed PSGT method has better performance in both prediction error and stability over compared methods.  ( 3 min )
    Simplified State Space Layers for Sequence Modeling. (arXiv:2208.04933v1 [cs.LG])
    Efficiently modeling long-range dependencies is an important goal in sequence modeling. Recently, models using structured state space sequence (S4) layers achieved state-of-the-art performance on many long-range tasks. The S4 layer combines linear state space models (SSMs) with deep learning techniques and leverages the HiPPO framework for online function approximation to achieve high performance. However, this framework led to architectural constraints and computational difficulties that make the S4 approach complicated to understand and implement. We revisit the idea that closely following the HiPPO framework is necessary for high performance. Specifically, we replace the bank of many independent single-input, single-output (SISO) SSMs the S4 layer uses with one multi-input, multi-output (MIMO) SSM with a reduced latent dimension. The reduced latent dimension of the MIMO system allows for the use of efficient parallel scans which simplify the computations required to apply the S5 layer as a sequence-to-sequence transformation. In addition, we initialize the state matrix of the S5 SSM with an approximation to the HiPPO-LegS matrix used by S4's SSMs and show that this serves as an effective initialization for the MIMO setting. S5 matches S4's performance on long-range tasks, including achieving an average of 82.46% on the suite of Long Range Arena benchmarks compared to S4's 80.48% and the best transformer variant's 61.41%.
    Boosting Simple Learners. (arXiv:2001.11704v5 [cs.LG] UPDATED)
    Boosting is a celebrated machine learning approach which is based on the idea of combining weak and moderately inaccurate hypotheses to a strong and accurate one. We study boosting under the assumption that the weak hypotheses belong to a class of bounded capacity. This assumption is inspired by the common convention that weak hypotheses are "rules-of-thumbs" from an "easy-to-learn class". (Schapire and Freund~'12, Shalev-Shwartz and Ben-David '14.) Formally, we assume the class of weak hypotheses has a bounded VC dimension. We focus on two main questions: (i) Oracle Complexity: How many weak hypotheses are needed to produce an accurate hypothesis? We design a novel boosting algorithm and demonstrate that it circumvents a classical lower bound by Freund and Schapire ('95, '12). Whereas the lower bound shows that $\Omega({1}/{\gamma^2})$ weak hypotheses with $\gamma$-margin are sometimes necessary, our new method requires only $\tilde{O}({1}/{\gamma})$ weak hypothesis, provided that they belong to a class of bounded VC dimension. Unlike previous boosting algorithms which aggregate the weak hypotheses by majority votes, the new boosting algorithm uses more complex ("deeper") aggregation rules. We complement this result by showing that complex aggregation rules are in fact necessary to circumvent the aforementioned lower bound. (ii) Expressivity: Which tasks can be learned by boosting weak hypotheses from a bounded VC class? Can complex concepts that are "far away" from the class be learned? Towards answering the first question we {introduce combinatorial-geometric parameters which capture expressivity in boosting.} As a corollary we provide an affirmative answer to the second question for well-studied classes, including half-spaces and decision stumps. Along the way, we establish and exploit connections with Discrepancy Theory.
    Liquid State Machine-Empowered Reflection Tracking in RIS-Aided THz Communications. (arXiv:2208.04400v1 [cs.LG])
    Passive beamforming in reconfigurable intelligent surfaces (RISs) enables a feasible and efficient way of communication when the RIS reflection coefficients are precisely adjusted. In this paper, we present a framework to track the RIS reflection coefficients with the aid of deep learning from a time-series prediction perspective in a terahertz (THz) communication system. The proposed framework achieves a two-step enhancement over the similar learning-driven counterparts. Specifically, in the first step, we train a liquid state machine (LSM) to track the historical RIS reflection coefficients at prior time steps (known as a time-series sequence) and predict their upcoming time steps. We also fine-tune the trained LSM through Xavier initialization technique to decrease the prediction variance, thus resulting in a higher prediction accuracy. In the second step, we use ensemble learning technique which leverages on the prediction power of multiple LSMs to minimize the prediction variance and improve the precision of the first step. It is numerically demonstrated that, in the first step, employing the Xavier initialization technique to fine-tune the LSM results in at most 26% lower LSM prediction variance and as much as 46% achievable spectral efficiency (SE) improvement over the existing counterparts, when an RIS of size 11x11 is deployed. In the second step, under the same computational complexity of training a single LSM, the ensemble learning with multiple LSMs degrades the prediction variance of a single LSM up to 66% and improves the system achievable SE at most 54%.  ( 3 min )
    Learning-Based Client Selection for Federated Learning Services Over Wireless Networks with Constrained Monetary Budgets. (arXiv:2208.04322v1 [cs.LG])
    We investigate a data quality-aware dynamic client selection problem for multiple federated learning (FL) services in a wireless network, where each client has dynamic datasets for the simultaneous training of multiple FL services and each FL service demander has to pay for the clients with constrained monetary budgets. The problem is formalized as a non-cooperative Markov game over the training rounds. A multi-agent hybrid deep reinforcement learning-based algorithm is proposed to optimize the joint client selection and payment actions, while avoiding action conflicts. Simulation results indicate that our proposed algorithm can significantly improve the training performance.
    Gradient Flows for L2 Support Vector Machine Training. (arXiv:2208.04365v1 [cs.LG])
    We explore the merits of training of support vector machines for binary classification by means of solving systems of ordinary differential equations. We thus assume a continuous time perspective on a machine learning problem which may be of interest for implementations on (re)emerging hardware platforms such as analog- or quantum computers.
    On the Importance of Critical Period in Multi-stage Reinforcement Learning. (arXiv:2208.04832v1 [cs.AI])
    The initial years of an infant's life are known as the critical period, during which the overall development of learning performance is significantly impacted due to neural plasticity. In recent studies, an AI agent, with a deep neural network mimicking mechanisms of actual neurons, exhibited a learning period similar to human's critical period. Especially during this initial period, the appropriate stimuli play a vital role in developing learning ability. However, transforming human cognitive bias into an appropriate shaping reward is quite challenging, and prior works on critical period do not focus on finding the appropriate stimulus. To take a step further, we propose multi-stage reinforcement learning to emphasize finding ``appropriate stimulus" around the critical period. Inspired by humans' early cognitive-developmental stage, we use multi-stage guidance near the critical period, and demonstrate the appropriate shaping reward (stage-2 guidance) in terms of the AI agent's performance, efficiency, and stability.
    Training Overparametrized Neural Networks in Sublinear Time. (arXiv:2208.04508v1 [cs.LG])
    The success of deep learning comes at a tremendous computational and energy cost, and the scalability of training massively overparametrized neural networks is becoming a real barrier to the progress of AI. Despite the popularity and low cost-per-iteration of traditional Backpropagation via gradient decent, SGD has prohibitive convergence rate in non-convex settings, both in theory and practice. To mitigate this cost, recent works have proposed to employ alternative (Newton-type) training methods with much faster convergence rate, albeit with higher cost-per-iteration. For a typical neural network with $m=\mathrm{poly}(n)$ parameters and input batch of $n$ datapoints in $\mathbb{R}^d$, the previous work of [Brand, Peng, Song, and Weinstein, ITCS'2021] requires $\sim mnd + n^3$ time per iteration. In this paper, we present a novel training method that requires only $m^{1-\alpha} n d + n^3$ amortized time in the same overparametrized regime, where $\alpha \in (0.01,1)$ is some fixed constant. This method relies on a new and alternative view of neural networks, as a set of binary search trees, where each iteration corresponds to modifying a small subset of the nodes in the tree. We believe this view would have further applications in the design and analysis of DNNs.
    TripHLApan: predicting HLA molecules binding peptides based on triple coding matrix and transfer learning. (arXiv:2208.04314v1 [q-bio.QM])
    Human leukocyte antigen (HLA) is an important molecule family in the field of human immunity, which recognizes foreign threats and triggers immune responses by presenting peptides to T cells. In recent years, the synthesis of tumor vaccines to induce specific immune responses has become the forefront of cancer treatment. Computationally modeling the binding patterns between peptide and HLA can greatly accelerate the development of tumor vaccines. However, most of the prediction methods performance is very limited and they cannot fully take advantage of the analysis of existing biological knowledge as the basis of modeling. In this paper, we propose TripHLApan, a novel pan-specific prediction model, for HLA molecular peptide binding prediction. TripHLApan exhibits powerful prediction ability by integrating triple coding matrix, BiGRU + Attention models, and transfer learning strategy. The comprehensive evaluations demonstrate the effectiveness of TripHLApan in predicting HLA-I and HLA-II peptide binding in different test environments. The predictive power of HLA-I is further demonstrated in the latest data set. In addition, we show that TripHLApan has strong binding reconstitution ability in the samples of a melanoma patient. In conclusion, TripHLApan is a powerful tool for predicting the binding of HLA-I and HLA-II molecular peptides for the synthesis of tumor vaccines.  ( 3 min )
    Cascade-based Echo Chamber Detection. (arXiv:2208.04620v1 [cs.SI])
    Despite echo chambers in social media have been under considerable scrutiny, general models for their detection and analysis are missing. In this work, we aim to fill this gap by proposing a probabilistic generative model that explains social media footprints -- i.e., social network structure and propagations of information -- through a set of latent communities, characterized by a degree of echo-chamber behavior and by an opinion polarity. Specifically, echo chambers are modeled as communities that are permeable to pieces of information with similar ideological polarity, and impermeable to information of opposed leaning: this allows discriminating echo chambers from communities that lack a clear ideological alignment. To learn the model parameters we propose a scalable, stochastic adaptation of the Generalized Expectation Maximization algorithm, that optimizes the joint likelihood of observing social connections and information propagation. Experiments on synthetic data show that our algorithm is able to correctly reconstruct ground-truth latent communities with their degree of echo-chamber behavior and opinion polarity. Experiments on real-world data about polarized social and political debates, such as the Brexit referendum or the COVID-19 vaccine campaign, confirm the effectiveness of our proposal in detecting echo chambers. Finally, we show how our model can improve accuracy in auxiliary predictive tasks, such as stance detection and prediction of future propagations.
    Computationally Identifying Funneling and Focusing Questions in Classroom Discourse. (arXiv:2208.04715v1 [cs.CY])
    Responsive teaching is a highly effective strategy that promotes student learning. In math classrooms, teachers might "funnel" students towards a normative answer or "focus" students to reflect on their own thinking, deepening their understanding of math concepts. When teachers focus, they treat students' contributions as resources for collective sensemaking, and thereby significantly improve students' achievement and confidence in mathematics. We propose the task of computationally detecting funneling and focusing questions in classroom discourse. We do so by creating and releasing an annotated dataset of 2,348 teacher utterances labeled for funneling and focusing questions, or neither. We introduce supervised and unsupervised approaches to differentiating these questions. Our best model, a supervised RoBERTa model fine-tuned on our dataset, has a strong linear correlation of .76 with human expert labels and with positive educational outcomes, including math instruction quality and student achievement, showing the model's potential for use in automated teacher feedback tools. Our unsupervised measures show significant but weaker correlations with human labels and outcomes, and they highlight interesting linguistic patterns of funneling and focusing questions. The high performance of the supervised measure indicates its promise for supporting teachers in their instruction.
    Second Order Ensemble Langevin Method for Sampling and Inverse Problems. (arXiv:2208.04506v1 [math.DS])
    We propose a sampling method based on an ensemble approximation of second order Langevin dynamics. The log target density is appended with a quadratic term in an auxiliary momentum variable and damped-driven Hamiltonian dynamics introduced; the resulting stochastic differential equation is invariant to the Gibbs measure, with marginal on the position coordinates given by the target. A preconditioner based on covariance under the law of the dynamics does not change this invariance property, and is introduced to accelerate convergence to the Gibbs measure. The resulting mean-field dynamics may be approximated by an ensemble method; this results in a gradient-free and affine-invariant stochastic dynamical system. Numerical results demonstrate its potential as the basis for a numerical sampler in Bayesian inverse problems.  ( 2 min )
    PhyGNNet: Solving spatiotemporal PDEs with Physics-informed Graph Neural Network. (arXiv:2208.04319v1 [cs.NE])
    Solving partial differential equations (PDEs) is an important research means in the fields of physics, biology, and chemistry. As an approximate alternative to numerical methods, PINN has received extensive attention and played an important role in many fields. However, PINN uses a fully connected network as its model, which has limited fitting ability and limited extrapolation ability in both time and space. In this paper, we propose PhyGNNet for solving partial differential equations on the basics of a graph neural network which consists of encoder, processer, and decoder blocks. In particular, we divide the computing area into regular grids, define partial differential operators on the grids, then construct pde loss for the network to optimize to build PhyGNNet model. What's more, we conduct comparative experiments on Burgers equation and heat equation to validate our approach, the results show that our method has better fit ability and extrapolation ability both in time and spatial areas compared with PINN.
    Adaptive Zeroth-Order Optimisation of Nonconvex Composite Objectives. (arXiv:2208.04579v1 [math.OC])
    In this paper, we propose and analyze algorithms for zeroth-order optimization of non-convex composite objectives, focusing on reducing the complexity dependence on dimensionality. This is achieved by exploiting the low dimensional structure of the decision set using the stochastic mirror descent method with an entropy alike function, which performs gradient descent in the space equipped with the maximum norm. To improve the gradient estimation, we replace the classic Gaussian smoothing method with a sampling method based on the Rademacher distribution and show that the mini-batch method copes with the non-Euclidean geometry. To avoid tuning hyperparameters, we analyze the adaptive stepsizes for the general stochastic mirror descent and show that the adaptive version of the proposed algorithm converges without requiring prior knowledge about the problem.
    Learning Mean-Field Control for Delayed Information Load Balancing in Large Queuing Systems. (arXiv:2208.04777v1 [cs.DC])
    Recent years have seen a great increase in the capacity and parallel processing power of data centers and cloud services. To fully utilize the said distributed systems, optimal load balancing for parallel queuing architectures must be realized. Existing state-of-the-art solutions fail to consider the effect of communication delays on the behaviour of very large systems with many clients. In this work, we consider a multi-agent load balancing system, with delayed information, consisting of many clients (load balancers) and many parallel queues. In order to obtain a tractable solution, we model this system as a mean-field control problem with enlarged state-action space in discrete time through exact discretization. Subsequently, we apply policy gradient reinforcement learning algorithms to find an optimal load balancing solution. Here, the discrete-time system model incorporates a synchronization delay under which the queue state information is synchronously broadcasted and updated at all clients. We then provide theoretical performance guarantees for our methodology in large systems. Finally, using experiments, we prove that our approach is not only scalable but also shows good performance when compared to the state-of-the-art power-of-d variant of the Join-the-Shortest-Queue (JSQ) and other policies in the presence of synchronization delays.
    SDWPF: A Dataset for Spatial Dynamic Wind Power Forecasting Challenge at KDD Cup 2022. (arXiv:2208.04360v1 [cs.LG])
    The variability of wind power supply can present substantial challenges to incorporating wind power into a grid system. Thus, Wind Power Forecasting (WPF) has been widely recognized as one of the most critical issues in wind power integration and operation. There has been an explosion of studies on wind power forecasting problems in the past decades. Nevertheless, how to well handle the WPF problem is still challenging, since high prediction accuracy is always demanded to ensure grid stability and security of supply. We present a unique Spatial Dynamic Wind Power Forecasting dataset: SDWPF, which includes the spatial distribution of wind turbines, as well as the dynamic context factors. Whereas, most of the existing datasets have only a small number of wind turbines without knowing the locations and context information of wind turbines at a fine-grained time scale. By contrast, SDWPF provides the wind power data of 134 wind turbines from a wind farm over half a year with their relative positions and internal statuses. We use this dataset to launch the Baidu KDD Cup 2022 to examine the limit of current WPF solutions. The dataset is released at https://aistudio.baidu.com/aistudio/competition/detail/152/0/datasets.
    Stronger Privacy Amplification by Shuffling for R\'enyi and Approximate Differential Privacy. (arXiv:2208.04591v1 [cs.CR])
    The shuffle model of differential privacy has gained significant interest as an intermediate trust model between the standard local and central models [EFMRTT19; CSUZZ19]. A key result in this model is that randomly shuffling locally randomized data amplifies differential privacy guarantees. Such amplification implies substantially stronger privacy guarantees for systems in which data is contributed anonymously [BEMMRLRKTS17]. In this work, we improve the state of the art privacy amplification by shuffling results both theoretically and numerically. Our first contribution is the first asymptotically optimal analysis of the R\'enyi differential privacy parameters for the shuffled outputs of LDP randomizers. Our second contribution is a new analysis of privacy amplification by shuffling. This analysis improves on the techniques of [FMT20] and leads to tighter numerical bounds in all parameter settings.
    AUTOSHAPE: An Autoencoder-Shapelet Approach for Time Series Clustering. (arXiv:2208.04313v1 [cs.LG])
    Time series shapelets are discriminative subsequences that have been recently found effective for time series clustering (TSC). The shapelets are convenient for interpreting the clusters. Thus, the main challenge for TSC is to discover high-quality variable-length shapelets to discriminate different clusters. In this paper, we propose a novel autoencoder-shapelet approach (AUTOSHAPE), which is the first study to take the advantage of both autoencoder and shapelet for determining shapelets in an unsupervised manner. An autoencoder is specially designed to learn high-quality shapelets. More specifically, for guiding the latent representation learning, we employ the latest self-supervised loss to learn the unified embeddings for variable-length shapelet candidates (time series subsequences) of different variables, and propose the diversity loss to select the discriminating embeddings in the unified space. We introduce the reconstruction loss to recover shapelets in the original time series space for clustering. Finally, we adopt Davies Bouldin index (DBI) to inform AUTOSHAPE of the clustering performance during learning. We present extensive experiments on AUTOSHAPE. To evaluate the clustering performance on univariate time series (UTS), we compare AUTOSHAPE with 15 representative methods using UCR archive datasets. To study the performance of multivariate time series (MTS), we evaluate AUTOSHAPE on 30 UEA archive datasets with 5 competitive methods. The results validate that AUTOSHAPE is the best among all the methods compared. We interpret clusters with shapelets, and can obtain interesting intuitions about clusters in three UTS case studies and one MTS case study, respectively.  ( 3 min )
    Recovering the Graph Underlying Networked Dynamical Systems under Partial Observability: A Deep Learning Approach. (arXiv:2208.04405v1 [cs.LG])
    We study the problem of graph structure identification, i.e., of recovering the graph of dependencies among time series. We model these time series data as components of the state of linear stochastic networked dynamical systems. We assume partial observability, where the state evolution of only a subset of nodes comprising the network is observed. We devise a new feature vector computed from the observed time series and prove that these features are linearly separable, i.e., there exists a hyperplane that separates the cluster of features associated with connected pairs of nodes from those associated with disconnected pairs. This renders the features amenable to train a variety of classifiers to perform causal inference. In particular, we use these features to train Convolutional Neural Networks (CNNs). The resulting causal inference mechanism outperforms state-of-the-art counterparts w.r.t. sample-complexity. The trained CNNs generalize well over structurally distinct networks (dense or sparse) and noise-level profiles. Remarkably, they also generalize well to real-world networks while trained over a synthetic network (realization of a random graph). Finally, the proposed method consistently reconstructs the graph in a pairwise manner, that is, by deciding if an edge or arrow is present or absent in each pair of nodes, from the corresponding time series of each pair. This fits the framework of large-scale systems, where observation or processing of all nodes in the network is prohibitive.  ( 3 min )
    An example of use of Variational Methods in Quantum Machine Learning. (arXiv:2208.04316v1 [quant-ph])
    This paper introduces a deep learning system based on a quantum neural network for the binary classification of points of a specific geometric pattern (Two-Moons Classification problem) on a plane. We believe that the use of hybrid deep learning systems (classical + quantum) can reasonably bring benefits, not only in terms of computational acceleration but in understanding the underlying phenomena and mechanisms; that will lead to the creation of new forms of machine learning, as well as to a strong development in the world of quantum computation. The chosen dataset is based on a 2D binary classification generator, which helps test the effectiveness of specific algorithms; it is a set of 2D points forming two interspersed semicircles. It displays two disjointed data sets in a two-dimensional representation space: the features are, therefore, the individual points' two coordinates, $x_1$ and $x_2$. The intention was to produce a quantum deep neural network with the minimum number of trainable parameters capable of correctly recognising and classifying points.  ( 2 min )
    Graph neural networks for the prediction of molecular structure-property relationships. (arXiv:2208.04852v1 [q-bio.BM])
    Molecular property prediction is of crucial importance in many disciplines such as drug discovery, molecular biology, or material and process design. The frequently employed quantitative structure-property/activity relationships (QSPRs/QSARs) characterize molecules by descriptors which are then mapped to the properties of interest via a linear or nonlinear model. In contrast, graph neural networks, a novel machine learning method, directly work on the molecular graph, i.e., a graph representation where atoms correspond to nodes and bonds correspond to edges. GNNs allow to learn properties in an end-to-end fashion, thereby avoiding the need for informative descriptors as in QSPRs/QSARs. GNNs have been shown to achieve state-of-the-art prediction performance on various property predictions tasks and represent an active field of research. We describe the fundamentals of GNNs and demonstrate the application of GNNs via two examples for molecular property prediction.
    Neural-Rendezvous: Learning-based Robust Guidance and Control to Encounter Interstellar Objects. (arXiv:2208.04883v1 [cs.RO])
    Interstellar objects (ISOs), astronomical objects not gravitationally bound to the Sun, are likely representatives of primitive materials invaluable in understanding exoplanetary star systems. Due to their poorly constrained orbits with generally high inclinations and relative velocities, however, exploring ISOs with conventional human-in-the-loop approaches is significantly challenging. This paper presents Neural-Rendezvous -- a deep learning-based guidance and control framework for encountering any fast-moving objects, including ISOs, robustly, accurately, and autonomously in real-time. It uses pointwise minimum norm tracking control on top of a guidance policy modeled by a spectrally-normalized deep neural network, where its hyperparameters are tuned with a newly introduced loss function directly penalizing the state trajectory tracking error. We rigorously show that, even in the challenging case of ISO exploration, Neural-Rendezvous provides 1) a high probability exponential bound on the expected spacecraft delivery error; and 2) a finite optimality gap with respect to the solution of model predictive control, both of which are indispensable especially for such a critical space mission. In numerical simulations, Neural-Rendezvous is demonstrated to achieve a terminal-time delivery error of less than 0.2 km for 99% of the ISO candidates with realistic state uncertainty, whilst retaining computational efficiency sufficient for real-time implementation.
    A Theoretical View on Sparsely Activated Networks. (arXiv:2208.04461v1 [cs.LG])
    Deep and wide neural networks successfully fit very complex functions today, but dense models are starting to be prohibitively expensive for inference. To mitigate this, one promising direction is networks that activate a sparse subgraph of the network. The subgraph is chosen by a data-dependent routing function, enforcing a fixed mapping of inputs to subnetworks (e.g., the Mixture of Experts (MoE) paradigm in Switch Transformers). However, prior work is largely empirical, and while existing routing functions work well in practice, they do not lead to theoretical guarantees on approximation ability. We aim to provide a theoretical explanation for the power of sparse networks. As our first contribution, we present a formal model of data-dependent sparse networks that captures salient aspects of popular architectures. We then introduce a routing function based on locality sensitive hashing (LSH) that enables us to reason about how well sparse networks approximate target functions. After representing LSH-based sparse networks with our model, we prove that sparse networks can match the approximation power of dense networks on Lipschitz functions. Applying LSH on the input vectors means that the experts interpolate the target function in different subregions of the input space. To support our theory, we define various datasets based on Lipschitz target functions, and we show that sparse networks give a favorable trade-off between number of active units and approximation quality.
    Generalization and Overfitting in Matrix Product State Machine Learning Architectures. (arXiv:2208.04372v1 [cs.LG])
    While overfitting and, more generally, double descent are ubiquitous in machine learning, increasing the number of parameters of the most widely used tensor network, the matrix product state (MPS), has generally lead to monotonic improvement of test performance in previous studies. To better understand the generalization properties of architectures parameterized by MPS, we construct artificial data which can be exactly modeled by an MPS and train the models with different number of parameters. We observe model overfitting for one-dimensional data, but also find that for more complex data overfitting is less significant, while with MNIST image data we do not find any signatures of overfitting. We speculate that generalization properties of MPS depend on the properties of data: with one-dimensional data (for which the MPS ansatz is the most suitable) MPS is prone to overfitting, while with more complex data which cannot be fit by MPS exactly, overfitting may be much less significant.
    Causal Discovery in Probabilistic Networks with an Identifiable Causal Effect. (arXiv:2208.04627v1 [cs.LG])
    Causal identification is at the core of the causal inference literature, where complete algorithms have been proposed to identify causal queries of interest. The validity of these algorithms hinges on the restrictive assumption of having access to a correctly specified causal structure. In this work, we study the setting where a probabilistic model of the causal structure is available. Specifically, the edges in a causal graph are assigned probabilities which may, for example, represent degree of belief from domain experts. Alternatively, the uncertainly about an edge may reflect the confidence of a particular statistical test. The question that naturally arises in this setting is: Given such a probabilistic graph and a specific causal effect of interest, what is the subgraph which has the highest plausibility and for which the causal effect is identifiable? We show that answering this question reduces to solving an NP-hard combinatorial optimization problem which we call the edge ID problem. We propose efficient algorithms to approximate this problem, and evaluate our proposed algorithms against real-world networks and randomly generated graphs.
    Representation learning of rare temporal conditions for travel time prediction. (arXiv:2208.04667v1 [stat.ML])
    Predicting travel time under rare temporal conditions (e.g., public holidays, school vacation period, etc.) constitutes a challenge due to the limitation of historical data. If at all available, historical data often form a heterogeneous time series due to high probability of other changes over long periods of time (e.g., road works, introduced traffic calming initiatives, etc.). This is especially prominent in cities and suburban areas. We present a vector-space model for encoding rare temporal conditions, that allows coherent representation learning across different temporal conditions. We show increased performance for travel time prediction over different baselines when utilizing the vector-space encoding for representing the temporal setting.
  • Open

    Test for non-negligible adverse shifts. (arXiv:2107.02990v4 [stat.ML] UPDATED)
    Statistical tests for dataset shift are susceptible to false alarms: they are sensitive to minor differences when there is in fact adequate sample coverage and predictive performance. We propose instead a framework to detect adverse dataset shifts based on outlier scores, $\texttt{D-SOS}$ for short. $\texttt{D-SOS}$ holds that the new (test) sample is not substantively worse than the reference (training) sample, and not that the two are equal. The key idea is to reduce observations to outlier scores and compare contamination rates at varying weighted thresholds. Users can define what $\it{worse}$ means in terms of relevant notions of outlyingness, including proxies for predictive performance. Compared to tests of equal distribution, our approach is uniquely tailored to serve as a robust metric for model monitoring and data validation. We show how versatile and practical $\texttt{D-SOS}$ is on a wide range of real and simulated data.  ( 3 min )
    Boosting Simple Learners. (arXiv:2001.11704v5 [cs.LG] UPDATED)
    Boosting is a celebrated machine learning approach which is based on the idea of combining weak and moderately inaccurate hypotheses to a strong and accurate one. We study boosting under the assumption that the weak hypotheses belong to a class of bounded capacity. This assumption is inspired by the common convention that weak hypotheses are "rules-of-thumbs" from an "easy-to-learn class". (Schapire and Freund~'12, Shalev-Shwartz and Ben-David '14.) Formally, we assume the class of weak hypotheses has a bounded VC dimension. We focus on two main questions: (i) Oracle Complexity: How many weak hypotheses are needed to produce an accurate hypothesis? We design a novel boosting algorithm and demonstrate that it circumvents a classical lower bound by Freund and Schapire ('95, '12). Whereas the lower bound shows that $\Omega({1}/{\gamma^2})$ weak hypotheses with $\gamma$-margin are sometimes necessary, our new method requires only $\tilde{O}({1}/{\gamma})$ weak hypothesis, provided that they belong to a class of bounded VC dimension. Unlike previous boosting algorithms which aggregate the weak hypotheses by majority votes, the new boosting algorithm uses more complex ("deeper") aggregation rules. We complement this result by showing that complex aggregation rules are in fact necessary to circumvent the aforementioned lower bound. (ii) Expressivity: Which tasks can be learned by boosting weak hypotheses from a bounded VC class? Can complex concepts that are "far away" from the class be learned? Towards answering the first question we {introduce combinatorial-geometric parameters which capture expressivity in boosting.} As a corollary we provide an affirmative answer to the second question for well-studied classes, including half-spaces and decision stumps. Along the way, we establish and exploit connections with Discrepancy Theory.  ( 3 min )
    Multiple Instance Neural Networks Based on Sparse Attention for Cancer Detection using T-cell Receptor Sequences. (arXiv:2208.04524v1 [stat.ML])
    Early detection of cancers has been much explored due to its paramount importance in biomedical fields. Among different types of data used to answer this biological question, studies based on T cell receptors (TCRs) are under recent spotlight due to the growing appreciation of the roles of the host immunity system in tumor biology. However, the one-to-many correspondence between a patient and multiple TCR sequences hinders researchers from simply adopting classical statistical/machine learning methods. There were recent attempts to model this type of data in the context of multiple instance learning (MIL). Despite the novel application of MIL to cancer detection using TCR sequences and the demonstrated adequate performance in several tumor types, there is still room for improvement, especially for certain cancer types. Furthermore, explainable neural network models are not fully investigated for this application. In this article, we propose multiple instance neural networks based on sparse attention (MINN-SA) to enhance the performance in cancer detection and explainability. The sparse attention structure drops out uninformative instances in each bag, achieving both interpretability and better predictive performance in combination with the skip connection. Our experiments show that MINN-SA yields the highest area under the ROC curve (AUC) scores on average measured across 10 different types of cancers, compared to existing MIL approaches. Moreover, we observe from the estimated attentions that MINN-SA can identify the TCRs that are specific for tumor antigens in the same T cell repertoire.  ( 3 min )
    Representation learning of rare temporal conditions for travel time prediction. (arXiv:2208.04667v1 [stat.ML])
    Predicting travel time under rare temporal conditions (e.g., public holidays, school vacation period, etc.) constitutes a challenge due to the limitation of historical data. If at all available, historical data often form a heterogeneous time series due to high probability of other changes over long periods of time (e.g., road works, introduced traffic calming initiatives, etc.). This is especially prominent in cities and suburban areas. We present a vector-space model for encoding rare temporal conditions, that allows coherent representation learning across different temporal conditions. We show increased performance for travel time prediction over different baselines when utilizing the vector-space encoding for representing the temporal setting.  ( 2 min )
    Deep Maxout Network Gaussian Process. (arXiv:2208.04468v1 [stat.ML])
    Study of neural networks with infinite width is important for better understanding of the neural network in practical application. In this work, we derive the equivalence of the deep, infinite-width maxout network and the Gaussian process (GP) and characterize the maxout kernel with a compositional structure. Moreover, we build up the connection between our deep maxout network kernel and deep neural network kernels. We also give an efficient numerical implementation of our kernel which can be adapted to any maxout rank. Numerical results show that doing Bayesian inference based on the deep maxout network kernel can lead to competitive results compared with their finite-width counterparts and deep neural network kernels. This enlightens us that the maxout activation may also be incorporated into other infinite-width neural network structures such as the convolutional neural network (CNN).  ( 2 min )
    Training Deep Architectures Without End-to-End Backpropagation: A Survey on the Provably Optimal Methods. (arXiv:2101.03419v3 [cs.LG] UPDATED)
    This tutorial paper surveys provably optimal alternatives to end-to-end backpropagation (E2EBP) -- the de facto standard for training deep architectures. Modular training refers to strictly local training without both the forward and the backward pass, i.e., dividing a deep architecture into several nonoverlapping modules and training them separately without any end-to-end operation. Between the fully global E2EBP and the strictly local modular training, there are weakly modular hybrids performing training without the backward pass only. These alternatives can match or surpass the performance of E2EBP on challenging datasets such as ImageNet, and are gaining increasing attention primarily because they offer practical advantages over E2EBP, which will be enumerated herein. In particular, they allow for greater modularity and transparency in deep learning workflows, aligning deep learning with the mainstream computer science engineering that heavily exploits modularization for scalability. Modular training has also revealed novel insights about learning and has further implications on other important research domains. Specifically, it induces natural and effective solutions to some important practical problems such as data efficiency and transferability estimation.  ( 3 min )
    Literature Review: Graph Kernels in Chemoinformatics. (arXiv:2208.04929v1 [stat.ML])
    The purpose of this review is to introduce the reader to graph kernels, with a view of applying them in classification problems in chemoinformatics. Graph kernels are functions that allow us to infer chemical properties of molecules, which can help with tasks such as finding suitable compounds for drug design. The use of kernel methods is but one particular way two quantify similarity between graphs. We restrict our discussion to this one method, although popular alternatives have emerged in recent years, most notably Graph Neural Networks.  ( 2 min )
    The Rich Get Richer: Disparate Impact of Semi-Supervised Learning. (arXiv:2110.06282v3 [cs.LG] UPDATED)
    Semi-supervised learning (SSL) has demonstrated its potential to improve the model accuracy for a variety of learning tasks when the high-quality supervised data is severely limited. Although it is often established that the average accuracy for the entire population of data is improved, it is unclear how SSL fares with different sub-populations. Understanding the above question has substantial fairness implications when different sub-populations are defined by the demographic groups that we aim to treat fairly. In this paper, we reveal the disparate impacts of deploying SSL: the sub-population who has a higher baseline accuracy without using SSL (the "rich" one) tends to benefit more from SSL; while the sub-population who suffers from a low baseline accuracy (the "poor" one) might even observe a performance drop after adding the SSL module. We theoretically and empirically establish the above observation for a broad family of SSL algorithms, which either explicitly or implicitly use an auxiliary "pseudo-label". Experiments on a set of image and text classification tasks confirm our claims. We introduce a new metric, Benefit Ratio, and promote the evaluation of the fairness of SSL (Equalized Benefit Ratio). We further discuss how the disparate impact can be mitigated. We hope our paper will alarm the potential pitfall of using SSL and encourage a multifaceted evaluation of future SSL algorithms.  ( 3 min )
    Overcoming challenges in leveraging GANs for few-shot data augmentation. (arXiv:2203.16662v3 [stat.ML] UPDATED)
    In this paper, we explore the use of GAN-based few-shot data augmentation as a method to improve few-shot classification performance. We perform an exploration into how a GAN can be fine-tuned for such a task (one of which is in a class-incremental manner), as well as a rigorous empirical investigation into how well these models can perform to improve few-shot classification. We identify issues related to the difficulty of training such generative models under a purely supervised regime with very few examples, as well as issues regarding the evaluation protocols of existing works. We also find that in this regime, classification accuracy is highly sensitive to how the classes of the dataset are randomly split. Therefore, we propose a semi-supervised fine-tuning approach as a more pragmatic way forward to address these problems.  ( 2 min )
    Deep Probabilistic Models for Forward and Inverse Problems in Parametric PDEs. (arXiv:2208.04856v1 [stat.ML])
    We formulate a class of physics-driven deep latent variable models (PDDLVM) to learn parameter-to-solution (forward) and solution-to-parameter (inverse) maps of parametric partial differential equations (PDEs). Our formulation leverages the finite element method (FEM), deep neural networks, and probabilistic modeling to assemble a deep probabilistic framework in which the forward and inverse maps are approximated with coherent uncertainty quantification. Our probabilistic model explicitly incorporates a parametric PDE-based density and a trainable solution-to-parameter network while the introduced amortized variational family postulates a parameter-to-solution network, all of which are jointly trained. Furthermore, the proposed methodology does not require any expensive PDE solves and is physics-informed only at training time, which allows real-time emulation of PDEs and generation of inverse problem solutions after training, bypassing the need for FEM solve operations with comparable accuracy to FEM solutions. The proposed framework further allows for a seamless integration of observed data for solving inverse problems and building generative models. We demonstrate the effectiveness of our method on a nonlinear Poisson problem, elastic shells with complex 3D geometries, and integrating generic physics-informed neural networks (PINN) architectures. We achieve up to three orders of magnitude speed-ups after training compared to traditional FEM solvers, while outputting coherent uncertainty estimates.  ( 3 min )
    Representation learning for maximization of MI, nonlinear ICA and nonlinear subspaces with robust density ratio estimation. (arXiv:2101.02083v2 [cs.LG] UPDATED)
    Contrastive learning is a recent promising approach in unsupervised representation learning where a feature representation of data is learned by solving a pseudo classification problem from unlabelled data. However, it is not straightforward to understand what representation contrastive learning yields. In addition, contrastive learning is often based on the maximum likelihood estimation, which tends to be vulnerable to the contamination by outliers. To promote the understanding to contrastive learning, this paper first theoretically shows a connection to maximization of mutual information (MI). Our result indicates that density ratio estimation is necessary and sufficient for maximization of MI under some conditions. Thus, contrastive learning related to density ratio estimation as done in popular objective functions can be interpreted as maximizing MI. Next, with the density ratio, we establish new recovery conditions for the latent source components in nonlinear independent component analysis (ICA). In contrast with existing work, the established conditions include a novel insight for the dimensionality of data, which is clearly supported by numerical experiments. Furthermore, inspired by nonlinear ICA, we propose a novel framework to estimate a nonlinear subspace for lower-dimensional latent source components, and some theoretical conditions for the subspace estimation are established with the density ratio. Then, we propose a practical method through outlier-robust density ratio estimation, which can be seen as performing maximization of MI, nonlinear ICA or nonlinear subspace estimation. Moreover, a sample-efficient nonlinear ICA method is also proposed. We theoretically investigate outlier-robustness of the proposed methods. Finally, the usefulness of the proposed methods is numerically demonstrated in nonlinear ICA and through application to linear classification.  ( 3 min )
    Copulaboost: additive modeling with copula-based model components. (arXiv:2208.04669v1 [stat.ME])
    We propose a type of generalised additive models with of model components based on pair-copula constructions, with prediction as a main aim. The model components are designed such that our model may capture potentially complex interaction effects in the relationship between the response covariates. In addition, our model does not require discretisation of continuous covariates, and is therefore suitable for problems with many such covariates. Further, we have designed a fitting algorithm inspired by gradient boosting, as well as efficient procedures for model selection and evaluation of the model components, through constraints on the model space and approximations, that speed up time-costly computations. In addition to being absolutely necessary for our model to be a realistic alternative in higher dimensions, these techniques may also be useful as a basis for designing efficient models selection algorithms for other types of copula regression models. We have explored the characteristics of our method in a simulation study, in particular comparing it to natural alternatives, such as logic regression, classic boosting models and penalised logistic regression. We have also illustrated our approach on the Wisconsin breast cancer dataset and on the Boston housing dataset. The results show that our method has a prediction performance that is either better than or comparable to the other methods, even when the proportion of discrete covariates is high.  ( 3 min )
    A Bayesian Bradley-Terry model to compare multiple ML algorithms on multiple data sets. (arXiv:2208.04935v1 [cs.LG])
    This paper proposes a Bayesian model to compare multiple algorithms on multiple data sets, on any metric. The model is based on the Bradley-Terry model, that counts the number of times one algorithm performs better than another on different data sets. Because of its Bayesian foundations, the Bayesian Bradley Terry model (BBT) has different characteristics than frequentist approaches to comparing multiple algorithms on multiple data sets, such as Demsar (2006) tests on mean rank, and Benavoli et al. (2016) multiple pairwise Wilcoxon tests with p-adjustment procedures. In particular, a Bayesian approach allows for more nuanced statements regarding the algorithms beyond claiming that the difference is or it is not statistically significant. Bayesian approaches also allow to define when two algorithms are equivalent for practical purposes, or the region of practical equivalence (ROPE). Different than a Bayesian signed rank comparison procedure proposed by Benavoli et al. (2017), our approach can define a ROPE for any metric, since it is based on probability statements, and not on differences of that metric. This paper also proposes a local ROPE concept, that evaluates whether a positive difference between a mean measure across some cross validation to the mean of some other algorithms is should be really seen as the first algorithm being better than the second, based on effect sizes. This local ROPE proposal is independent of a Bayesian use, and can be used in frequentist approaches based on ranks. A R package and a Python program that implements the BBT is available.  ( 3 min )
    Training Overparametrized Neural Networks in Sublinear Time. (arXiv:2208.04508v1 [cs.LG])
    The success of deep learning comes at a tremendous computational and energy cost, and the scalability of training massively overparametrized neural networks is becoming a real barrier to the progress of AI. Despite the popularity and low cost-per-iteration of traditional Backpropagation via gradient decent, SGD has prohibitive convergence rate in non-convex settings, both in theory and practice. To mitigate this cost, recent works have proposed to employ alternative (Newton-type) training methods with much faster convergence rate, albeit with higher cost-per-iteration. For a typical neural network with $m=\mathrm{poly}(n)$ parameters and input batch of $n$ datapoints in $\mathbb{R}^d$, the previous work of [Brand, Peng, Song, and Weinstein, ITCS'2021] requires $\sim mnd + n^3$ time per iteration. In this paper, we present a novel training method that requires only $m^{1-\alpha} n d + n^3$ amortized time in the same overparametrized regime, where $\alpha \in (0.01,1)$ is some fixed constant. This method relies on a new and alternative view of neural networks, as a set of binary search trees, where each iteration corresponds to modifying a small subset of the nodes in the tree. We believe this view would have further applications in the design and analysis of DNNs.  ( 2 min )
    Consistent Approximations in Composite Optimization. (arXiv:2201.05250v2 [math.OC] UPDATED)
    Approximations of optimization problems arise in computational procedures and sensitivity analysis. The resulting effect on solutions can be significant, with even small approximations of components of a problem translating into large errors in the solutions. We specify conditions under which approximations are well behaved in the sense of minimizers, stationary points, and level-sets and this leads to a framework of consistent approximations. The framework is developed for a broad class of composite problems, which are neither convex nor smooth. We demonstrate the framework using examples from stochastic optimization, neural-network based machine learning, distributionally robust optimization, penalty and augmented Lagrangian methods, interior-point methods, homotopy methods, smoothing methods, extended nonlinear programming, difference-of-convex programming, and multi-objective optimization. An enhanced proximal method illustrates the algorithmic possibilities. A quantitative analysis supplements the development by furnishing rates of convergence.  ( 2 min )
    Understanding Weight Similarity of Neural Networks via Chain Normalization Rule and Hypothesis-Training-Testing. (arXiv:2208.04369v1 [cs.LG])
    We present a weight similarity measure method that can quantify the weight similarity of non-convex neural networks. To understand the weight similarity of different trained models, we propose to extract the feature representation from the weights of neural networks. We first normalize the weights of neural networks by introducing a chain normalization rule, which is used for weight representation learning and weight similarity measure. We extend the traditional hypothesis-testing method to a hypothesis-training-testing statistical inference method to validate the hypothesis on the weight similarity of neural networks. With the chain normalization rule and the new statistical inference, we study the weight similarity measure on Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN), and find that the weights of an identical neural network optimized with the Stochastic Gradient Descent (SGD) algorithm converge to a similar local solution in a metric space. The weight similarity measure provides more insight into the local solutions of neural networks. Experiments on several datasets consistently validate the hypothesis of weight similarity measure.  ( 2 min )
    A Theoretical View on Sparsely Activated Networks. (arXiv:2208.04461v1 [cs.LG])
    Deep and wide neural networks successfully fit very complex functions today, but dense models are starting to be prohibitively expensive for inference. To mitigate this, one promising direction is networks that activate a sparse subgraph of the network. The subgraph is chosen by a data-dependent routing function, enforcing a fixed mapping of inputs to subnetworks (e.g., the Mixture of Experts (MoE) paradigm in Switch Transformers). However, prior work is largely empirical, and while existing routing functions work well in practice, they do not lead to theoretical guarantees on approximation ability. We aim to provide a theoretical explanation for the power of sparse networks. As our first contribution, we present a formal model of data-dependent sparse networks that captures salient aspects of popular architectures. We then introduce a routing function based on locality sensitive hashing (LSH) that enables us to reason about how well sparse networks approximate target functions. After representing LSH-based sparse networks with our model, we prove that sparse networks can match the approximation power of dense networks on Lipschitz functions. Applying LSH on the input vectors means that the experts interpolate the target function in different subregions of the input space. To support our theory, we define various datasets based on Lipschitz target functions, and we show that sparse networks give a favorable trade-off between number of active units and approximation quality.  ( 3 min )
    Implicit differentiation for fast hyperparameter selection in non-smooth convex learning. (arXiv:2105.01637v3 [stat.ML] UPDATED)
    Finding the optimal hyperparameters of a model can be cast as a bilevel optimization problem, typically solved using zero-order techniques. In this work we study first-order methods when the inner optimization problem is convex but non-smooth. We show that the forward-mode differentiation of proximal gradient descent and proximal coordinate descent yield sequences of Jacobians converging toward the exact Jacobian. Using implicit differentiation, we show it is possible to leverage the non-smoothness of the inner problem to speed up the computation. Finally, we provide a bound on the error made on the hypergradient when the inner optimization problem is solved approximately. Results on regression and classification problems reveal computational benefits for hyperparameter optimization, especially when multiple hyperparameters are required.  ( 2 min )
    Optimal scheduling of entropy regulariser for continuous-time linear-quadratic reinforcement learning. (arXiv:2208.04466v1 [cs.LG])
    This work uses the entropy-regularised relaxed stochastic control perspective as a principled framework for designing reinforcement learning (RL) algorithms. Herein agent interacts with the environment by generating noisy controls distributed according to the optimal relaxed policy. The noisy policies on the one hand, explore the space and hence facilitate learning but, on the other hand, introduce bias by assigning a positive probability to non-optimal actions. This exploration-exploitation trade-off is determined by the strength of entropy regularisation. We study algorithms resulting from two entropy regularisation formulations: the exploratory control approach, where entropy is added to the cost objective, and the proximal policy update approach, where entropy penalises the divergence of policies between two consecutive episodes. We analyse the finite horizon continuous-time linear-quadratic (LQ) RL problem for which both algorithms yield a Gaussian relaxed policy. We quantify the precise difference between the value functions of a Gaussian policy and its noisy evaluation and show that the execution noise must be independent across time. By tuning the frequency of sampling from relaxed policies and the parameter governing the strength of entropy regularisation, we prove that the regret, for both learning algorithms, is of the order $\mathcal{O}(\sqrt{N}) $ (up to a logarithmic factor) over $N$ episodes, matching the best known result from the literature.  ( 3 min )
    Statistical Properties of the log-cosh Loss Function Used in Machine Learning. (arXiv:2208.04564v1 [stat.ML])
    This paper analyzes a popular loss function used in machine learning called the log-cosh loss function. A number of papers have been published using this loss function but, to date, no statistical analysis has been presented in the literature. In this paper, we present the distribution function from which the log-cosh loss arises. We compare it to a similar distribution, called the Cauchy distribution, and carry out various statistical procedures that characterize its properties. In particular, we examine its associated pdf, cdf, likelihood function and Fisher information. Side-by-side we consider the Cauchy and Cosh distributions as well as the MLE of the location parameter with asymptotic bias, asymptotic variance, and confidence intervals. We also provide a comparison of robust estimators from several other loss functions, including the Huber loss function and the rank dispersion function. Further, we examine the use of the log-cosh function for quantile regression. In particular, we identify a quantile distribution function from which a maximum likelihood estimator for quantile regression can be derived. Finally, we compare a quantile M-estimator based on log-cosh with robust monotonicity against another approach to quantile regression based on convolutional smoothing.  ( 2 min )
    Stronger Privacy Amplification by Shuffling for R\'enyi and Approximate Differential Privacy. (arXiv:2208.04591v1 [cs.CR])
    The shuffle model of differential privacy has gained significant interest as an intermediate trust model between the standard local and central models [EFMRTT19; CSUZZ19]. A key result in this model is that randomly shuffling locally randomized data amplifies differential privacy guarantees. Such amplification implies substantially stronger privacy guarantees for systems in which data is contributed anonymously [BEMMRLRKTS17]. In this work, we improve the state of the art privacy amplification by shuffling results both theoretically and numerically. Our first contribution is the first asymptotically optimal analysis of the R\'enyi differential privacy parameters for the shuffled outputs of LDP randomizers. Our second contribution is a new analysis of privacy amplification by shuffling. This analysis improves on the techniques of [FMT20] and leads to tighter numerical bounds in all parameter settings.  ( 2 min )
  • Open

    DSC Weekly 09 August 2022 – Decentralized Identifiers (DiDs) becomes a W3C Recommendation
    One of the challenges that decentralized finance (and the web in general) faces is the need to uniquely identify a person, an organization, or a product. This, in general, is difficult because open identifiers are easily spoofed. Blockchain largely intended to combat this by creating self-sovereignty through a distributed algorithm that verified transactions were recorded and captured in multiple places. The post DSC Weekly 09 August 2022 – Decentralized Identifiers (DiDs) becomes a W3C Recommendation appeared first on Data Science Central.  ( 20 min )

  • Open

    Imagine...
    submitted by /u/1starchangel [link] [comments]  ( 86 min )
    Which European universities are doing Conversational AI research?
    submitted by /u/jptrjzz [link] [comments]  ( 86 min )
    What are some of the coolest Computer Vision companies out there?
    submitted by /u/geno_whirl11 [link] [comments]  ( 86 min )
    Got Dall E 2 extra invite
    so i had few extra emails on waitlist. And got extra invites. sonif anyone want to buy. dm or comment submitted by /u/theniwaslost [link] [comments]  ( 92 min )
    EU policy to introduce three risk categories for AI
    submitted by /u/much_successes [link] [comments]  ( 92 min )
    This Is The Reason Why Did An Engineer Claim That The Google AI Is Sentient
    submitted by /u/sopadebombillas [link] [comments]  ( 86 min )
    Humanoid Robotics For Amazon Automation | New Wearable AI Chip | New Machine Learning Model Solves College Math Problems At Human Level
    submitted by /u/tohelpyou88 [link] [comments]  ( 86 min )
    NAFSSR: Stereo Image Super-Resolution Using NAFNet
    submitted by /u/imapurplemango [link] [comments]  ( 86 min )
    This has gone too far. Even the AIs are getting calls from fake car insurance companies.
    submitted by /u/Tuplapatukka [link] [comments]  ( 93 min )
    NYT Journalist Gives Inside Scoop On Witnessing The Historic AlphaGo Match In Person - Think this community may enjoy :) Subscribe if you'd like to watch the life stories of AI/Tech people.
    submitted by /u/joemurray1994 [link] [comments]  ( 86 min )
    Play with a genetic algorithm to design a car and help our research into human AI collaboration!
    submitted by /u/seanebaby [link] [comments]  ( 87 min )
    Lost ship in the clouds - Crreated with Starryai
    submitted by /u/widgia [link] [comments]  ( 86 min )
    AI Manifest: Cosmic Wonderscape | Inspired by @Hueman Instrumentality | Cinematic 4K UHD | 60FPS
    submitted by /u/Available_Tadpole829 [link] [comments]  ( 93 min )
    20 Machine Learning Project (End to End)
    Hi Guys, Free tutorial on Machine Learning Project (End to End) in Apache Spark and Scala with Code and Explanation 1) Life Expectancy Prediction using Machine Learning 2) Predicting Possible Loan Default Using Machine Learning 3) Machine Learning Project - Loan Approval Prediction 4) Customer Segmentation using Machine Learning in Apache Spark 5) Machine Learning Project - Build Movies Recommendation Engine using Apache Spark 6) Machine Learning Project on Sales Prediction or Sale Forecast 7) Machine Learning Project on Mushroom Classification whether it's edible or poisonous 8) Machine Learning Pipeline Application on Power Plant. 9) Machine Learning Project – Predict Forest Cover 10) Machine Learning Project Predict Will it Rain Tomorrow in Australia 11) Predict Ads Click - Practice Data Analysis and Logistic Regression Prediction 12) Machine Learning Project -Drug Classification 13) Prediction task is to determine whether a person makes over 50K a year 14) Machine Learning Project - Classifying gender based on personal preferences 15) Machine Learning Project - Mobile Price Classification 16) Machine Learning Project - Predicting the Cellular Localization Sites of Proteins in Yest 17) Machine Learning Project - YouTube Spam Comment Prediction 18) Identify the Type of animal (7 Types) based on the available attributes 19) Machine Learning Project - Glass Identification 20) Predicting the age of abalone from physical measurements I hope you'll enjoy these tutorials. submitted by /u/bigdataengineer4life [link] [comments]  ( 94 min )
    CNN feature extraction
    I have a CNN and I would like to see the features that the CNN is learning from processing all the training data. I am using a character based CNN. Do I need to insert some code into one of the convolution layers to output something? Any hints or links to discussions or code for image CNNs or character CNNs would be helpful. I’m using python submitted by /u/rich_atl [link] [comments]  ( 86 min )
    made these paintings using the art generator Mid journey . just wanted to share for any art lovers
    submitted by /u/wasabiguap [link] [comments]  ( 86 min )
    Multi-lingual support with GPT-3 for my Mac App
    Hello all, I spent some time understanding the level of multi-lingual support with GPT-3. I must say, it works pretty well. As a result, I have introduced multi-lingual support to some of the important features in the Elephas Mac writing app. Here it is in action, https://reddit.com/link/wjq4lt/video/twaen7zqalg91/player You can check out Elephas App for more details. submitted by /u/juliarmg [link] [comments]  ( 86 min )
  • Open

    Benefits of Blockchain Technology for Businesses
    Implementation of blockchain technology in businesses establishes transparency, decentralizes processes, improves security, achieves scalability, and more. Read on to know more about the advantages of blockchain technology and how to become a blockchain engineer. The term blockchain is generally associated with cryptocurrencies like Bitcoin, Litecoin, Ethereum, Bitcoin Dash, and more. But Blockchain is beyond this… Read More »Benefits of Blockchain Technology for Businesses The post Benefits of Blockchain Technology for Businesses appeared first on Data Science Central.  ( 20 min )
    AI for Better Retail Management & Monitoring
    Retail has long relied on video annotation & labeling techniques and conventional analytics for data-driven decision-making. Data processing has been elevated to a whole new level by Artificial Intelligence (AI) and Machine Learning (ML). In order to open up a whole new world of possibilities for business owners, data scientists can extract anomalies and correlations… Read More »AI for Better Retail Management & Monitoring The post AI for Better Retail Management & Monitoring appeared first on Data Science Central.  ( 20 min )
    SCM and Role of AI
    Introduction: Business organizations exist to make a profit out of their business. If there is no profit, then there is no meaning in running a business. Supply Chain Management (SCM)’s main objective is to create a profitable SCM. The higher the SCM profitability, the more successful the SCM is. In current work, to be competitive… Read More »SCM and Role of AI The post SCM and Role of AI appeared first on Data Science Central.  ( 23 min )
    WFH – Is the metaverse based on shared activity instead of shared space?
    Recently, Tim O Reilly posted his vision of the metaverse which resonates with me It’s not exactly an earth shattering revelation – but it’s easy to forget that the Web and also the Metaverse – are primarily social mediums.  It’s easy to get caught up in the Web 3/ NFT real estate metaphor and forget… Read More »WFH – Is the metaverse based on shared activity instead of shared space? The post WFH – Is the metaverse based on shared activity instead of shared space? appeared first on Data Science Central.  ( 18 min )
    Data Economic Force Multiplier
    In complex systems, not all parts or components are of equal value. Some parts or components can have an oversized influence on the overall system’s performance to achieve specific outcomes. These parts or components are known as force multipliers The post Data Economic Force Multiplier appeared first on Data Science Central.  ( 21 min )
    Will AI Replace Doctors?
    Artificial intelligence (AI) has the potential to revolutionize any piece of work that can be operated via binary commands and has a finite set of possibilities. The concept of AI is currently being harnessed furiously, and the forever flourishing field of health care is leveraging it to attain greater good for humanity. Now, artificial intelligence is touching almost every field of concern, such as business, translation, advertising, photography, and many more. The post Will AI Replace Doctors? appeared first on Data Science Central.  ( 18 min )
    IoT is changing the Construction Industry
    Construction companies are increasingly adopting IoT technology to successfully address common workplace challenges and streamline operations to benefit from enhanced efficiency and improved responsiveness to the increasing demands of the industry. Flat productivity, reduced margins, more schedule overruns, and increased competition are some of the obvious challenges construction companies are considering implementing IoT technology and digitalization. IoT in construction offers benefits such as better productivity, maintenance, security, and safety. Moreover, many prominent companies are producing and launching new IoT solutions for the construction industry, which is expected to propel the global market further. The post IoT is changing the Construction Industry appeared first on Data Science Central.  ( 18 min )
    Top 10 Benefits of Digital Transformation Adoption
    Every business wants to be competitive. It is not uncommon for most organizations to ignore digital transformation and stick to their old business ways. And who can blame them? Digital transformation (DX) is not simple. It is a multifaceted and complex endeavor involving many digitization initiatives, not to mention having to deal with continuous employee… Read More »Top 10 Benefits of Digital Transformation Adoption The post Top 10 Benefits of Digital Transformation Adoption appeared first on Data Science Central.  ( 21 min )
    9 Features that Make a Mobile App User-Friendly
    With over half of people spending more than 5 hours per day on their smartphone, people expect every app they use to offer a great user experience. Without a user-friendly app, capturing the attention of the average smartphone user is nearly impossible. In addition, app stores want to promote apps that offer high-quality interfaces, intuitive… Read More »9 Features that Make a Mobile App User-Friendly The post 9 Features that Make a Mobile App User-Friendly appeared first on Data Science Central.  ( 19 min )
    Metaverse: How Businesses Plan to Capture Real Value
    Hardly any marketer has not warmed up the metaverse hype. Has been equally hard to find someone who fully understands the implications of the expanding use cases of the metaverse. While business leaders are doing their own bit to demystify the concept—which many have seen as the internet’s evolution—definitions, rules, and approaches for metaverse initiatives… Read More »Metaverse: How Businesses Plan to Capture Real Value The post Metaverse: How Businesses Plan to Capture Real Value appeared first on Data Science Central.  ( 19 min )
    Magic bullet intelligent process automation, NLP edition
    Natural language processing  (NLP) has been around at least since the early 1980s, under various names. Back then, I remember machine translations of Russian language materials, for example. Those translations were just plain awful; my Navy compadres and I couldn’t make head nor tail of them. Back then, before the current crop of data scientists… Read More »Magic bullet intelligent process automation, NLP edition The post Magic bullet intelligent process automation, NLP edition appeared first on Data Science Central.  ( 20 min )
  • Open

    Neural Network Optimization
    Hi. I'm a 20 M undergrad CompSci student. I'm highly interested in studying ML in grad school in order to join the industry afterward. As a student, I don't have much experience with the kind of talent and knowledge the industry is looking for. Is the bulk of industry work related to optimizing neural networks (using less training data to get the same performance, pruning them, etc)? Thank you so much for your help. submitted by /u/pottojam [link] [comments]  ( 88 min )
    New Wearable AI Chip | New Machine Learning Model Solves College Math Problems At Human Level
    submitted by /u/tohelpyou88 [link] [comments]  ( 89 min )
    Neural Networks application linked to automation sector
    Did anybody read some academic papers about NN in automation and share name/link or any useful information to find them? submitted by /u/Existing-Barnacle-60 [link] [comments]  ( 86 min )
  • Open

    [D] RetinaFace Question
    Currently using RetinaFace and noticed increasing image size increases inference time. Is that because the convolution taking place takes longer or for some other reason like NMS takes longer on a larger image? submitted by /u/ZealousidealMarket22 [link] [comments]  ( 87 min )
    [P] I built an app using Dall-E for stock images
    Like the post says I built an app using Dall-e for stock images :) There's a free version so would love for you to mess around with it and give feedback https://stockpic.ai/ submitted by /u/Philip_The_PM [link] [comments]  ( 89 min )
    [D][R] has anyone here tried to make an artificial language (conlang) using fine-tuned LLM by r/conlangs (seems like many linguistics PhD there) or other dataset out there? curious what the result is.
    mindlessly scrolling thru twitter a Philosopher 2208.04135.pdf (arxiv.org) post makes me think, what will happen if these words come from artificial language purposely made up by human linguist. you all may have heard that Dall E 2 has "secret" language. and the best explanation i can get is thisthisthis. now instead of going to PromptBase for your text-to-image prompt. why not use r/conlangs and sell your text-genereted image? I'm curious what will happen. submitted by /u/mrizki_lh [link] [comments]  ( 123 min )
    [N] NNAISENSE releases EvoTorch (https://evotorch.ai): An open-source Evolutionary Algorithm Library with multi-CPU/multi-GPU support for massive evolutionary experiments!
    Hi everyone! We're excited to release EvoTorch: An open-source Evolutionary Algorithm Library with multi-CPU/multi-GPU support for massive evolutionary experiments! We are researchers and engineers in industrial automation at NNAISENSE and this library is the latest version of the tool we've been using in our own work. It is built on top of PyTorch and Ray, and provides a collection of state-of-the-art evolutionary algorithms, out-of-the-box support for scaling experiments to arbitrary clusters of CPUs and GPUs, advanced tools for NeuroEvolution of any PyTorch module, and direct interfaces to modern logging libraries to track experiments and integrate with existing workflows. Our goal is to make many more engineers be able to use EAs in their workflow, and help researchers do better research. We are happy to have your feedback or questions here or on our Slack channel! EvoTorch is pip installable: pip install evotorch Read the docs: https://docs.evotorch.ai The library is completely open source under an Apache 2.0 license: https://github.com/nnaisense/evotorch submitted by /u/NaturalGradient [link] [comments]  ( 90 min )
    [D] Question about the foundations of logistic regression
    This applies to the multivariate case too, but for simplicity I focus on the most basic example. Say your model is Y = 1 / exp[-( a + bX )]. You do a standard logistic regression to get a predicted Y given an observed X. The parameters are a and b. I care much more about the predicted Y, than about the estimated values of a and b, especially when the number of features (and thus the number of parameters) is large. Now let Y' = log[ Y / (1 - Y) ]. You solve the standard linear regression Y' = a + bX. The estimated a, b will be a little different. The predicted Y is now Y = 1 / ( 1 + exp[ -Y' ] ) where Y' is the predicted value from the linear regression. So you have two ways to solve the problem: Method 1 (first paragraph), and Method 2 (second paragraph). Method 2 is much easier and will provide similar results. My question is why no one uses Method 2, and in the rare instances where it is mentioned, it is to say that it should be avoided. Why? Note: If Y takes only on two values 0 and 1, the workaround in Method 2 is to replace 0 by ε, and 1 by 1- ε , and let ε tends to 0. submitted by /u/MLRecipes [link] [comments]  ( 92 min )
    [N][R][P] Microsoft Announces new Integrations with OpenAI and MLFlow
    Today we launched SynapseML v0.10.0 with 175-billion parameter OpenAI language models, full support for .NET, Python, R, Scala, and Java, and an integration with MLflow, and much more. Check out the full release notes, leave a star, and explore SnyapseML Release Notes: https://github.com/microsoft/SynapseML/releases/tag/v0.10.0 Blog: https://techcommunity.microsoft.com/t5/azure-synapse-analytics-blog/exciting-new-release-of-synapseml/ba-p/3589606 ​ Whats new in SynapseML v0.10 submitted by /u/mhamilton723 [link] [comments]  ( 88 min )
    [D] How to handle reviewers not responding to rebuttals after they asked questions in initial review?
    Does anyone have any advice here? I wrote thoughtful and thorough rebuttals to three of my 5 reviewers at neurips who gave borderline rejects and none of them have responded and the discussion period is about to close. submitted by /u/AbjectDrink3276 [link] [comments]  ( 91 min )
    [P] PyNeuraLogic - a framework for writing differentiable logic programs
    Hello, I would like to introduce you to PyNeuraLogic - a deep relational learning framework. It utilizes differentiable logic programs (which you write directly in Python) to express different model architectures. For example, with the framework, you are able to express Graph Neural Networks in quite an elegant and simple way with just a few lines of code. In the latest release, we have introduced a new set of tools to work with databases, most notably a tool for transpiling deep learning models to (Postgres) SQL. This way, you can evaluate models directly in the database! We have prepared a short tutorial on those tools (link in the banner in the README). Let us know if you have any feedback or questions regarding the framework! submitted by /u/Lukas_Zahradnik [link] [comments]  ( 89 min )
    [N][R][CfP] All Things Attention- Bridging Different Perspectives on Attention Workshop @ NeurIPS 22
    Hi all -- I'm Abhijat, one of the co-organizers for a workshop bringing together people from Machine Learning, CogSci, Neuroscience, Psychology, and Human-Computer Interaction for a workshop that we hope will help us to start reaching common ground about how we think about attention in these fields! We invite you to submit papers (up to 9 pages for long papers and up to 5 pages for short papers) and/or attend the workshop to bring your own perspective to this discussion! Details below: Workshop website: https://attention-learning-workshop.github.io/ Submission deadline: Sep 15, 2022 The All Things Attention workshop aims to foster connections across disparate academic communities that conceptualize "Attention" such as Neuroscience, Psychology, Machine Learning, and Human Computer Interaction. Our speakers and panelists represent a diverse population from these very related but often disparate fields! Workshop topics of interest include (but are not limited to): Relationships between biological and artificial attention Attention for reinforcement learning and decision making Benefits and formulation of attention mechanisms for continual / lifelong learning Attention as a tool for interpretation and explanation The role of attention in human-computer interaction and human-robot interaction Attention mechanisms in Deep Neural Network (DNN) architectures This workshop will be in-person and we hope to make the talks available online. The panel discussions may not be available online. Happy to take any questions below! submitted by /u/NicePresentation4 [link] [comments]  ( 124 min )
    [D] Reading Group: Content-Based Image Retrieval
    ​ https://preview.redd.it/bity3opumog91.png?width=1918&format=png&auto=webp&s=222e96927e2a373efa91360f7b3afb0e1b646e29 More info at https://outsystems-ai-reading-group.github.io/ submitted by /u/JClub [link] [comments]  ( 88 min )
    [P] I made a GitHub extension which recommends similar repos [Open Source]
    I've always struggled to discover interesting repositories on GitHub. Also, when I was searching for some open-source tools, I had to open multiple links and tabs to look at similar repos. That's when I decided that GitHub lacks recommendations on the repository page. Just like in any social network, when you open a post you will see a bunch of recommended posts or videos to increase your engagement. I wrote a full article about the ML part of the project https://indexstorm.com/git-rec You can download the extension, or access the source code on our github https://github.com/indexStorm I would be very happy to hear any feedback, or if you will upvote the extension on producthunt ​ Example submitted by /u/th3luck [link] [comments]  ( 88 min )
    [D] Informed semantic image segmentation?
    I have a map of pixels in an image, classified as either A or B. I have a variety of variables attributed to each of those pixels. What sorts of models follow a semantic image segmentation approach that would allow me to predict pixels as A or B using the variables attributed? Preferably a more recent model (e.g. beyond U-net) submitted by /u/Boring-Violinist8291 [link] [comments]  ( 88 min )
    [R] Can machines learn how to behave?
    Interesting blog post by Blaise Aguera y Arcas, a VP who leads Google’s AI group in Seattle. Can machines learn how to behave? Beyond the current news cycle about whether AIs are sentient is a more practical and immediately consequential conversation about AI value alignment: whether and how AIs can be imbued with human values. Today, this turns on the even more fundamental question of whether the newest generation of language models can or can’t understand concepts — and on what it means to understand.¹ If, as some researchers contend, language models are mere “babblers” that randomly regurgitate their training data — “garbage in, garbage out” — then real AI value alignment is, at least for now, out of reach. Seemingly, the best we can do is to carefully curate training inputs to filter out “garbage”, often referred to as “toxic content”, even as we seek to broaden data sources to better represent human diversity. There are some profound challenges implied here, including governance (who gets to define what is “toxic”?), labor (is it humane to employ people to do “toxic content” filtering?²), and scale (how can we realistically build large models under such constraints?). This skeptical view also suggests a dubious payoff for the whole language model research program, since the practical value of a mere “babbler” is unclear: what meaningful tasks could a model with no understanding of concepts be entrusted to do? If the answer is none, then why bother with them at all? Rest of the blog: https://medium.com/@blaisea/can-machines-learn-how-to-behave-42a02a57fadb submitted by /u/baylearn [link] [comments]  ( 126 min )
    [D] Does Nerf use CPU a lot?
    I’m about to use 12700k, 3090ti for studying nerf. But I’m worrying about cpu cooler. Will Noctua nh-d15 be enough? I thought Nerf wouldn’t use cpu that much. submitted by /u/No_Fig_3372 [link] [comments]  ( 87 min )
  • Open

    Interesting RL thought experiment, how would you approach it?
    I have been thinking about an interesting reinforcement learning problem recently, which I will call the “gumball” problem. I have been mulling it over in my head and had some thoughts, so I am curious what other people think about it. The problem goes like this: Imagine you have an environment that is a 2D box which is 40% full of randomly initialized red, blue, and green gumballs. Rearrange the gumball such that only red is next to red, blue is next to blue, green is next to green, and different colors are not next to each other. Assume balls cannot be directly on top of each other. I think the solution is quite easy when you have a discrete environment: the gumballs can only sit on evenly spaced grid positions. However, I am curious how people would approach it for a continuous environment such that gumballs can be anywhere in the box. What about if we change the %space of gumballs?, what about if we double the box? Can we make a single RL system that works for all these cases?? It is a bit of a head scratcher, and I was curious what other people thought. submitted by /u/MachineBeyondMachine [link] [comments]  ( 88 min )
    Large-scale neuroevolution using the brand-new EvoTorch (evotorch.ai) library by NNAISENSE. All agents shown below are evolved using the PGPE algorithm. EvoTorch lets you scale up your neuroevolution reinforcement learning experiments to hundreds of CPU/GPU nodes!
    submitted by /u/NaturalGradient [link] [comments]  ( 86 min )
    "In Defense of the Unitary Scalarization for Deep Multi-Task Learning", Kurin et al 2022 ('just train on everything')
    submitted by /u/gwern [link] [comments]  ( 101 min )
    What are the similarities and differences between the concepts of Multi Agent RL and Game theory. Many of the text overlaps for the two . Can anyone point the specific differences between them. For eg. i don't see the concept of states in Game theory but other than that many things are same
    Game theory also has rewards , actions and structure similar to Multi Agent RL but i did not see states mentioned in the GT text. I am finding it a bit confusing even after reading some text. submitted by /u/aabra__ka__daabra [link] [comments]  ( 90 min )
    How relevant is tuning Network architecture (aside CNN & RNN) in RL?
    submitted by /u/disdisinform [link] [comments]  ( 86 min )
    Understanding the forward view of TD(λ)
    I'm slightly confused on how the forward view is implemented to update state values. Mathematically, the updates are given as: Δ(V(s)) = α ( L - V(s)) Here L is the lamda return from state s, computed using a weighted average of all n-step returns. My question is that we won't be able to compute L for a state till we reach the end of an episode. Can the forward view update states as we visit them at each time step t? Or is it only applied offline (updates made after episode terminates)? submitted by /u/theanswerisnt42 [link] [comments]  ( 86 min )
    Why is NEAT is not popular in RL?
    submitted by /u/Professional_Card176 [link] [comments]  ( 103 min )
  • Open

    Create Amazon SageMaker model building pipelines and deploy R models using RStudio on Amazon SageMaker
    In November 2021, in collaboration with RStudio PBC, we announced the general availability of RStudio on Amazon SageMaker, the industry’s first fully managed RStudio Workbench IDE in the cloud. You can now bring your current RStudio license to easily migrate your self-managed RStudio environments to Amazon SageMaker in just a few simple steps. RStudio is […]  ( 8 min )
  • Open

    Twitter follower distribution
    A conversation this morning prompted the question of how many Twitter accounts have between 10,000 and 20,000 followers. I hadn’t thought about the distribution numbers of followers in a while and was curious to revisit the topic. Apparently this question was more popular five years ago. When I did a few searches on the topic, […] Twitter follower distribution first appeared on John D. Cook.  ( 6 min )
    Mahler’s inequality
    I ran across a reference to Mahler the other day, not the composer Gustav Mahler but the mathematician Kurt Mahler, and looked into his work a little. A number of things have been named after Kurt Mahler, including Mahler’s inequality. Mahler’s inequality says the geometric mean of a sum bounds the sum of the geometric […] Mahler’s inequality first appeared on John D. Cook.  ( 4 min )
  • Open

    Efficient Video-Text Learning with Iterative Co-tokenization
    Posted by AJ Piergiovanni and Anelia Angelova, Research Scientists, Google Research, Brain Team Video is an ubiquitous source of media content that touches on many aspects of people’s day-to-day lives. Increasingly, real-world video applications, such as video captioning, video content analysis, and video question-answering (VideoQA), rely on models that can connect video content with text or natural language. VideoQA is particularly challenging, however, as it requires grasping both semantic information, such as objects in a scene, as well as temporal information, e.g., how things move and interact, both of which must be taken in the context of a natural-language question that holds specific intent. In addition, because videos have many frames, processing all of them to learn spatio-temp…  ( 23 min )
  • Open

    Future of Creativity on Display ‘In the NVIDIA Studio’ During SIGGRAPH Special Address
    A glimpse into the future of AI-infused virtual worlds was on display at SIGGRAPH — the world’s largest gathering of computer graphics experts — as NVIDIA founder and CEO Jensen Huang put the finishing touches on the company’s special address. The post Future of Creativity on Display ‘In the NVIDIA Studio’ During SIGGRAPH Special Address appeared first on NVIDIA Blog.  ( 7 min )
    At SIGGRAPH, NVIDIA CEO Jensen Huang Illuminates Three Forces Sparking Graphics Revolution
    In a swift, eye-popping special address at SIGGRAPH, NVIDIA execs described the forces driving the next era in graphics, and the company’s expanding range of tools to accelerate them. “The combination of AI and computer graphics will power the metaverse, the next evolution of the internet,” said Jensen Huang, founder and CEO of NVIDIA, kicking Read article > The post At SIGGRAPH, NVIDIA CEO Jensen Huang Illuminates Three Forces Sparking Graphics Revolution appeared first on NVIDIA Blog.  ( 9 min )
    NVIDIA AI Makes Performance Capture Possible With Any Camera
    NVIDIA AI tools are enabling deep learning-powered performance capture for creators at every level: visual effects and animation studios, creative professionals — even any enthusiast with a camera. With NVIDIA Vid2Vid Cameo, creators can harness AI to capture their facial movements and expressions from any standard 2D video taken with a professional camera or smartphone. Read article > The post NVIDIA AI Makes Performance Capture Possible With Any Camera appeared first on NVIDIA Blog.  ( 5 min )
    As Far as the AI Can See: ILM Uses Omniverse DeepSearch to Create the Perfect Sky
    For cutting-edge visual effects and virtual production, creative teams and studios benefit from digital sets and environments that can be updated in real time. A crucial element in any virtual production environment is a sky dome, often used to provide realistic lighting for virtual environments and in-camera visual effects. Legendary studio Industrial Light & Magic Read article > The post As Far as the AI Can See: ILM Uses Omniverse DeepSearch to Create the Perfect Sky appeared first on NVIDIA Blog.  ( 5 min )
    New NVIDIA Neural Graphics SDKs Make Metaverse Content Creation Available to All
    The creation of 3D objects for building scenes for games, virtual worlds including the metaverse, product design or visual effects is traditionally a meticulous process, where skilled artists balance detail and photorealism against deadlines and budget pressures. It takes a long time to make something that looks and acts as it would in the physical Read article > The post New NVIDIA Neural Graphics SDKs Make Metaverse Content Creation Available to All appeared first on NVIDIA Blog.  ( 6 min )
    Upping the Standard: NVIDIA Introduces NeuralVDB, Bringing AI and GPU Optimization to Award-Winning OpenVDB
    NVIDIA today announced NeuralVDB, which brings the power of AI to OpenVDB, the industry-standard library for simulating and rendering sparse volumetric data, such as water, fire, smoke and clouds. Building on the past decade’s development of OpenVDB, the introduction at SIGGRAPH of NeuralVDB is a game-changer for professionals working in areas like scientific computing and Read article > The post Upping the Standard: NVIDIA Introduces NeuralVDB, Bringing AI and GPU Optimization to Award-Winning OpenVDB appeared first on NVIDIA Blog.  ( 5 min )
  • Open

    Solving a longstanding conundrum in heat transfer
    Hailing from a small town in Italy, Matteo Bucci is determined to address some of the unknowns plaguing fundamental science.  ( 6 min )
  • Open

    Multilingual AI analytics are key to unlocking the power of CX for business growth
    Understand ALL customer interactions with your brand even in a language that you don’t speak. Continue reading on Becoming Human: Artificial Intelligence Magazine »  ( 11 min )
  • Open

    Neural Implicit Flow: a mesh-agnostic dimensionality reduction paradigm of spatio-temporal data. (arXiv:2204.03216v4 [cs.LG] UPDATED)
    High-dimensional spatio-temporal dynamics can often be encoded in a low-dimensional subspace. Engineering applications for modeling, characterization, design, and control of such large-scale systems often rely on dimensionality reduction to make solutions computationally tractable in real-time. Common existing paradigms for dimensionality reduction include linear methods, such as the singular value decomposition (SVD), and nonlinear methods, such as variants of convolutional autoencoders (CAE). However, these encoding techniques lack the ability to efficiently represent the complexity associated with spatio-temporal data, which often requires variable geometry, non-uniform grid resolution, adaptive meshing, and/or parametric dependencies. To resolve these practical engineering challenges, we propose a general framework called Neural Implicit Flow (NIF) that enables a mesh-agnostic, low-rank representation of large-scale, parametric, spatial-temporal data. NIF consists of two modified multilayer perceptrons (MLPs): (i) ShapeNet, which isolates and represents the spatial complexity, and (ii) ParameterNet, which accounts for any other input complexity, including parametric dependencies, time, and sensor measurements. We demonstrate the utility of NIF for parametric surrogate modeling, enabling the interpretable representation and compression of complex spatio-temporal dynamics, efficient many-spatial-query tasks, and improved generalization performance for sparse reconstruction.  ( 3 min )
    Comparison of biomedical relationship extraction methods and models for knowledge graph creation. (arXiv:2201.01647v4 [cs.AI] UPDATED)
    Biomedical research is growing at such an exponential pace that scientists, researchers, and practitioners are no more able to cope with the amount of published literature in the domain. The knowledge presented in the literature needs to be systematized in such a way that claims and hypotheses can be easily found, accessed, and validated. Knowledge graphs can provide such a framework for semantic knowledge representation from literature. However, in order to build a knowledge graph, it is necessary to extract knowledge as relationships between biomedical entities and normalize both entities and relationship types. In this paper, we present and compare few rule-based and machine learning-based (Naive Bayes, Random Forests as examples of traditional machine learning methods and DistilBERT, PubMedBERT, T5 and SciFive-based models as examples of modern deep learning transformers) methods for scalable relationship extraction from biomedical literature, and for the integration into the knowledge graphs. We examine how resilient are these various methods to unbalanced and fairly small datasets. Our experiments show that transformer-based models handle well both small (due to pre-training on a large dataset) and unbalanced datasets. The best performing model was the PubMedBERT-based model fine-tuned on balanced data, with a reported F1-score of 0.92. DistilBERT-based model followed with F1-score of 0.89, performing faster and with lower resource requirements. BERT-based models performed better then T5-based generative models.  ( 3 min )
    Towards Robust Deep Learning using Entropic Losses. (arXiv:2208.03566v1 [cs.LG])
    Current deep learning solutions are well known for not informing whether they can reliably classify an example during inference. One of the most effective ways to build more reliable deep learning solutions is to improve their performance in the so-called out-of-distribution detection task, which essentially consists of "know that you do not know" or "know the unknown". In other words, out-of-distribution detection capable systems may reject performing a nonsense classification when submitted to instances of classes on which the neural network was not trained. This thesis tackles the defiant out-of-distribution detection task by proposing novel loss functions and detection scores. Uncertainty estimation is also a crucial auxiliary task in building more robust deep learning systems. Therefore, we also deal with this robustness-related task, which evaluates how realistic the probabilities presented by the deep neural network are. To demonstrate the effectiveness of our approach, in addition to a substantial set of experiments, which includes state-of-the-art results, we use arguments based on the principle of maximum entropy to establish the theoretical foundation of the proposed approaches. Unlike most current methods, our losses and scores are seamless and principled solutions that produce accurate predictions in addition to fast and efficient inference. Moreover, our approaches can be incorporated into current and future projects simply by replacing the loss used to train the deep neural network and computing a rapid score for detection.  ( 2 min )
    A Case for Dataset Specific Profiling. (arXiv:2208.03315v1 [cs.LG])
    Data-driven science is an emerging paradigm where scientific discoveries depend on the execution of computational AI models against rich, discipline-specific datasets. With modern machine learning frameworks, anyone can develop and execute computational models that reveal concepts hidden in the data that could enable scientific applications. For important and widely used datasets, computing the performance of every computational model that can run against a dataset is cost prohibitive in terms of cloud resources. Benchmarking approaches used in practice use representative datasets to infer performance without actually executing models. While practicable, these approaches limit extensive dataset profiling to a few datasets and introduce bias that favors models suited for representative datasets. As a result, each dataset's unique characteristics are left unexplored and subpar models are selected based on inference from generalized datasets. This necessitates a new paradigm that introduces dataset profiling into the model selection process. To demonstrate the need for dataset-specific profiling, we answer two questions:(1) Can scientific datasets significantly permute the rank order of computational models compared to widely used representative datasets? (2) If so, could lightweight model execution improve benchmarking accuracy? Taken together, the answers to these questions lay the foundation for a new dataset-aware benchmarking paradigm.  ( 2 min )
    Continual Learning for Tumor Classification in Histopathology Images. (arXiv:2208.03609v1 [eess.IV])
    Recent years have seen great advancements in the development of deep learning models for histopathology image analysis in digital pathology applications, evidenced by the increasingly common deployment of these models in both research and clinical settings. Although such models have shown unprecedented performance in solving fundamental computational tasks in DP applications, they suffer from catastrophic forgetting when adapted to unseen data with transfer learning. With an increasing need for deep learning models to handle ever changing data distributions, including evolving patient population and new diagnosis assays, continual learning models that alleviate model forgetting need to be introduced in DP based analysis. However, to our best knowledge, there is no systematic study of such models for DP-specific applications. Here, we propose CL scenarios in DP settings, where histopathology image data from different sources/distributions arrive sequentially, the knowledge of which is integrated into a single model without training all the data from scratch. We then established an augmented dataset for colorectal cancer H&E classification to simulate shifts of image appearance and evaluated CL model performance in the proposed CL scenarios. We leveraged a breast tumor H&E dataset along with the colorectal cancer to evaluate CL from different tumor types. In addition, we evaluated CL methods in an online few-shot setting under the constraints of annotation and computational resources. We revealed promising results of CL in DP applications, potentially paving the way for application of these methods in clinical practice.  ( 3 min )
    HPO: We won't get fooled again. (arXiv:2208.03320v1 [cs.LG])
    Hyperparameter optimization (HPO) is a well-studied research field. However, the effects and interactions of the components in an HPO pipeline are not yet well investigated. Then, we ask ourselves: can the landscape of HPO be biased by the pipeline used to evaluate individual configurations? To address this question, we proposed to analyze the effect of the HPO pipeline on HPO problems using fitness landscape analysis. Particularly, we studied the DS-2019 HPO benchmark data set, looking for patterns that could indicate evaluation pipeline malfunction, and relate them to HPO performance. Our main findings are: (i) In most instances, large groups of diverse hyperparameters (i.e., multiple configurations) yield the same ill performance, most likely associated with majority class prediction models; (ii) in these cases, a worsened correlation between the observed fitness and average fitness in the neighborhood is observed, potentially making harder the deployment of local-search based HPO strategies. Finally, we concluded that the HPO pipeline definition might negatively affect the HPO landscape.  ( 2 min )
    Exploring linguistic feature and model combination for speech recognition based automatic AD detection. (arXiv:2206.13758v2 [cs.LG] UPDATED)
    Early diagnosis of Alzheimer's disease (AD) is crucial in facilitating preventive care and delay progression. Speech based automatic AD screening systems provide a non-intrusive and more scalable alternative to other clinical screening techniques. Scarcity of such specialist data leads to uncertainty in both model selection and feature learning when developing such systems. To this end, this paper investigates the use of feature and model combination approaches to improve the robustness of domain fine-tuning of BERT and Roberta pre-trained text encoders on limited data, before the resulting embedding features being fed into an ensemble of backend classifiers to produce the final AD detection decision via majority voting. Experiments conducted on the ADReSS20 Challenge dataset suggest consistent performance improvements were obtained using model and feature combination in system development. State-of-the-art AD detection accuracies of 91.67 percent and 93.75 percent were obtained using manual and ASR speech transcripts respectively on the ADReSS20 test set consisting of 48 elderly speakers.  ( 2 min )
    Sparse Representation Learning with Modified q-VAE towards Minimal Realization of World Model. (arXiv:2208.03936v1 [cs.LG])
    Extraction of low-dimensional latent space from high-dimensional observation data is essential to construct a real-time robot controller with a world model on the extracted latent space. However, there is no established method for tuning the dimension size of the latent space automatically, suffering from finding the necessary and sufficient dimension size, i.e. the minimal realization of the world model. In this study, we analyze and improve Tsallis-based variational autoencoder (q-VAE), and reveal that, under an appropriate configuration, it always facilitates making the latent space sparse. Even if the dimension size of the pre-specified latent space is redundant compared to the minimal realization, this sparsification collapses unnecessary dimensions, allowing for easy removal of them. We experimentally verified the benefits of the sparsification by the proposed method that it can easily find the necessary and sufficient six dimensions for a reaching task with a mobile manipulator that requires a six-dimensional state space. Moreover, by planning with such a minimal-realization world model learned in the extracted dimensions, the proposed method was able to exert a more optimal action sequence in real-time, reducing the reaching accomplishment time by around 20 %. The attached video is uploaded on youtube: https://youtu.be/-QjITrnxaRs  ( 2 min )
    Online Service Migration in Edge Computing with Incomplete Information: A Deep Recurrent Actor-Critic Method. (arXiv:2012.08679v4 [cs.NI] UPDATED)
    Multi-access Edge Computing (MEC) is an emerging computing paradigm that extends cloud computing to the network edge to support resource-intensive applications on mobile devices. As a crucial problem in MEC, service migration needs to decide how to migrate user services for maintaining the Quality-of-Service when users roam between MEC servers with limited coverage and capacity. However, finding an optimal migration policy is intractable due to the dynamic MEC environment and user mobility. Many existing studies make centralized migration decisions based on complete system-level information, which is time-consuming and also lacks desirable scalability. To address these challenges, we propose a novel learning-driven method, which is user-centric and can make effective online migration decisions by utilizing incomplete system-level information. Specifically, the service migration problem is modeled as a Partially Observable Markov Decision Process (POMDP). To solve the POMDP, we design a new encoder network that combines a Long Short-Term Memory (LSTM) and an embedding matrix for effective extraction of hidden information, and further propose a tailored off-policy actor-critic algorithm for efficient training. The extensive experimental results based on real-world mobility traces demonstrate that this new method consistently outperforms both the heuristic and state-of-the-art learning-driven algorithms and can achieve near-optimal results on various MEC scenarios.  ( 3 min )
    Adversarial Attacks on Image Generation With Made-Up Words. (arXiv:2208.04135v1 [cs.CV])
    Text-guided image generation models can be prompted to generate images using nonce words adversarially designed to robustly evoke specific visual concepts. Two approaches for such generation are introduced: macaronic prompting, which involves designing cryptic hybrid words by concatenating subword units from different languages; and evocative prompting, which involves designing nonce words whose broad morphological features are similar enough to that of existing words to trigger robust visual associations. The two methods can also be combined to generate images associated with more specific visual concepts. The implications of these techniques for the circumvention of existing approaches to content moderation, and particularly the generation of offensive or harmful images, are discussed.
    Ensemble deep learning: A review. (arXiv:2104.02395v3 [cs.LG] UPDATED)
    Ensemble learning combines several individual models to obtain better generalization performance. Currently, deep learning architectures are showing better performance compared to the shallow or traditional models. Deep ensemble learning models combine the advantages of both the deep learning models as well as the ensemble learning such that the final model has better generalization performance. This paper reviews the state-of-art deep ensemble models and hence serves as an extensive summary for the researchers. The ensemble models are broadly categorised into bagging, boosting, stacking, negative correlation based deep ensemble models, explicit/implicit ensembles, homogeneous/heterogeneous ensemble, decision fusion strategies based deep ensemble models. Applications of deep ensemble models in different domains are also briefly discussed. Finally, we conclude this paper with some potential future research directions.  ( 2 min )
    Adaptive incomplete multi-view learning via tensor graph completion. (arXiv:2208.03710v1 [cs.LG])
    With the advancement of the data acquisition techniques, multi-view learning has become a hot topic. Some multi-view learning methods assume that the multi-view data is complete, which means that all instances are present, but this too ideal. Certain tensor-based methods for handing incomplete multi-view data have emerged and have achieved better result. However, there are still some problems, such as use of traditional tensor norm which makes the computation high and is not able to handle out-of-sample. To solve these two problems, we proposed a new incomplete multi view learning method. A new tensor norm is defined to implement graph tensor data recover. The recovered graphs are then regularized to a consistent low-dimensional representation of the samples. In addition, adaptive weights are equipped to each view to adjust the importance of different views. Compared with the existing methods, our method nor only explores the consistency among views, but also obtains the low-dimensional representation of the new samples by using the learned projection matrix. An efficient algorithm based on inexact augmented Lagrange multiplier (ALM) method are designed to solve the model and convergence is proved. Experimental results on four datasets show the effectiveness of our method.  ( 2 min )
    Federated Adversarial Learning: A Framework with Convergence Analysis. (arXiv:2208.03635v1 [cs.LG])
    Federated learning (FL) is a trending training paradigm to utilize decentralized training data. FL allows clients to update model parameters locally for several epochs, then share them to a global model for aggregation. This training paradigm with multi-local step updating before aggregation exposes unique vulnerabilities to adversarial attacks. Adversarial training is a popular and effective method to improve the robustness of networks against adversaries. In this work, we formulate a general form of federated adversarial learning (FAL) that is adapted from adversarial learning in the centralized setting. On the client side of FL training, FAL has an inner loop to generate adversarial samples for adversarial training and an outer loop to update local model parameters. On the server side, FAL aggregates local model updates and broadcast the aggregated model. We design a global robust training loss and formulate FAL training as a min-max optimization problem. Unlike the convergence analysis in classical centralized training that relies on the gradient direction, it is significantly harder to analyze the convergence in FAL for three reasons: 1) the complexity of min-max optimization, 2) model not updating in the gradient direction due to the multi-local updates on the client-side before aggregation and 3) inter-client heterogeneity. We address these challenges by using appropriate gradient approximation and coupling techniques and present the convergence analysis in the over-parameterized regime. Our main result theoretically shows that the minimum loss under our algorithm can converge to $\epsilon$ small with chosen learning rate and communication rounds. It is noteworthy that our analysis is feasible for non-IID clients.
    Detecting User Exits from Online Behavior: A Duration-Dependent Latent State Model. (arXiv:2208.03937v1 [cs.LG])
    In order to steer e-commerce users towards making a purchase, marketers rely upon predictions of when users exit without purchasing. Previously, such predictions were based upon hidden Markov models (HMMs) due to their ability of modeling latent shopping phases with different user intents. In this work, we develop a duration-dependent hidden Markov model. In contrast to traditional HMMs, it explicitly models the duration of latent states and thereby allows states to become "sticky". The proposed model is superior to prior HMMs in detecting user exits: out of 100 user exits without purchase, it correctly identifies an additional 18. This helps marketers in better managing the online behavior of e-commerce customers. The reason for the superior performance of our model is the duration dependence, which allows our model to recover latent states that are characterized by a distorted sense of time. We finally provide a theoretical explanation for this, which builds upon the concept of "flow".
    StRegA: Unsupervised Anomaly Detection in Brain MRIs using a Compact Context-encoding Variational Autoencoder. (arXiv:2201.13271v2 [eess.IV] UPDATED)
    Expert interpretation of anatomical images of the human brain is the central part of neuro-radiology. Several machine learning-based techniques have been proposed to assist in the analysis process. However, the ML models typically need to be trained to perform a specific task, e.g., brain tumour segmentation or classification. Not only do the corresponding training data require laborious manual annotations, but a wide variety of abnormalities can be present in a human brain MRI - even more than one simultaneously, which renders representation of all possible anomalies very challenging. Hence, a possible solution is an unsupervised anomaly detection (UAD) system that can learn a data distribution from an unlabelled dataset of healthy subjects and then be applied to detect out of distribution samples. Such a technique can then be used to detect anomalies - lesions or abnormalities, for example, brain tumours, without explicitly training the model for that specific pathology. Several Variational Autoencoder (VAE) based techniques have been proposed in the past for this task. Even though they perform very well on controlled artificially simulated anomalies, many of them perform poorly while detecting anomalies in clinical data. This research proposes a compact version of the "context-encoding" VAE (ceVAE) model, combined with pre and post-processing steps, creating a UAD pipeline (StRegA), which is more robust on clinical data, and shows its applicability in detecting anomalies such as tumours in brain MRIs. The proposed pipeline achieved a Dice score of 0.642$\pm$0.101 while detecting tumours in T2w images of the BraTS dataset and 0.859$\pm$0.112 while detecting artificially induced anomalies, while the best performing baseline achieved 0.522$\pm$0.135 and 0.783$\pm$0.111, respectively.
    A Universal Framework for Featurization of Atomistic Systems. (arXiv:2102.02390v4 [physics.chem-ph] UPDATED)
    Molecular dynamics simulations are an invaluable tool in numerous scientific fields. However, the ubiquitous classical force fields cannot describe reactive systems, and quantum molecular dynamics are too computationally demanding to treat large systems or long timescales. Reactive force fields based on physics or machine learning can be used to bridge the gap in time and length scales, but these force fields require substantial effort to construct and are highly specific to a given chemical composition and application. A significant limitation of machine learning models is the use of element-specific features, leading to models that scale poorly with the number of elements. This work introduces the Gaussian multipole (GMP) featurization scheme that utilizes physically-relevant multipole expansions of the electron density around atoms to yield feature vectors that interpolate between element types and have a fixed dimension regardless of the number of elements present. We combine GMP with neural networks to directly compare it to the widely used Behler-Parinello symmetry functions for the MD17 dataset, revealing that it exhibits improved accuracy and computational efficiency. Further, we demonstrate that GMP-based models can achieve chemical accuracy for the QM9 dataset, and their accuracy remains reasonable even when extrapolating to new elements. Finally, we test GMP-based models for the Open Catalysis Project (OCP) dataset, revealing comparable performance to graph convolutional deep learning models. The results indicate that this featurization scheme fills a critical gap in the construction of efficient and transferable machine-learned force fields.
    Few-shot Adaptation Works with UnpredicTable Data. (arXiv:2208.01009v2 [cs.CL] UPDATED)
    Prior work on language models (LMs) shows that training on a large number of diverse tasks improves few-shot learning (FSL) performance on new tasks. We take this to the extreme, automatically extracting 413,299 tasks from internet tables - orders of magnitude more than the next-largest public datasets. Finetuning on the resulting dataset leads to improved FSL performance on Natural Language Processing (NLP) tasks, but not proportionally to dataset scale. In fact, we find that narrow subsets of our dataset sometimes outperform more diverse datasets. For example, finetuning on software documentation from support.google.com raises FSL performance by a mean of +7.5% on 52 downstream tasks, which beats training on 40 human-curated NLP datasets (+6.7%). Finetuning on various narrow datasets leads to similar broad improvements across test tasks, suggesting that the gains are not from domain adaptation but adapting to FSL in general. We do not observe clear patterns between the datasets that lead to FSL gains, leaving open questions about why certain data helps with FSL.
    Recurrent networks, hidden states and beliefs in partially observable environments. (arXiv:2208.03520v1 [cs.LG])
    Reinforcement learning aims to learn optimal policies from interaction with environments whose dynamics are unknown. Many methods rely on the approximation of a value function to derive near-optimal policies. In partially observable environments, these functions depend on the complete sequence of observations and past actions, called the history. In this work, we show empirically that recurrent neural networks trained to approximate such value functions internally filter the posterior probability distribution of the current state given the history, called the belief. More precisely, we show that, as a recurrent neural network learns the Q-function, its hidden states become more and more correlated with the beliefs of state variables that are relevant to optimal control. This correlation is measured through their mutual information. In addition, we show that the expected return of an agent increases with the ability of its recurrent architecture to reach a high mutual information between its hidden states and the beliefs. Finally, we show that the mutual information between the hidden states and the beliefs of variables that are irrelevant for optimal control decreases through the learning process. In summary, this work shows that in its hidden states, a recurrent neural network approximating the Q-function of a partially observable environment reproduces a sufficient statistic from the history that is correlated to the relevant part of the belief for taking optimal actions.  ( 3 min )
    Pairwise Learning via Stagewise Training in Proximal Setting. (arXiv:2208.04075v1 [cs.LG])
    The pairwise objective paradigms are an important and essential aspect of machine learning. Examples of machine learning approaches that use pairwise objective functions include differential network in face recognition, metric learning, bipartite learning, multiple kernel learning, and maximizing of area under the curve (AUC). Compared to pointwise learning, pairwise learning's sample size grows quadratically with the number of samples and thus its complexity. Researchers mostly address this challenge by utilizing an online learning system. Recent research has, however, offered adaptive sample size training for smooth loss functions as a better strategy in terms of convergence and complexity, but without a comprehensive theoretical study. In a distinct line of research, importance sampling has sparked a considerable amount of interest in finite pointwise-sum minimization. This is because of the stochastic gradient variance, which causes the convergence to be slowed considerably. In this paper, we combine adaptive sample size and importance sampling techniques for pairwise learning, with convergence guarantees for nonsmooth convex pairwise loss functions. In particular, the model is trained stochastically using an expanded training set for a predefined number of iterations derived from the stability bounds. In addition, we demonstrate that sampling opposite instances at each iteration reduces the variance of the gradient, hence accelerating convergence. Experiments on a broad variety of datasets in AUC maximization confirm the theoretical results.  ( 2 min )
    Artificial Intelligence and Machine Learning for Quantum Technologies. (arXiv:2208.03836v1 [quant-ph])
    In recent years, the dramatic progress in machine learning has begun to impact many areas of science and technology significantly. In the present perspective article, we explore how quantum technologies are benefiting from this revolution. We showcase in illustrative examples how scientists in the past few years have started to use machine learning and more broadly methods of artificial intelligence to analyze quantum measurements, estimate the parameters of quantum devices, discover new quantum experimental setups, protocols, and feedback strategies, and generally improve aspects of quantum computing, quantum communication, and quantum simulation. We highlight open challenges and future possibilities and conclude with some speculative visions for the next decade.
    Sharp-MAML: Sharpness-Aware Model-Agnostic Meta Learning. (arXiv:2206.03996v3 [cs.LG] UPDATED)
    Model-agnostic meta learning (MAML) is currently one of the dominating approaches for few-shot meta-learning. Albeit its effectiveness, the optimization of MAML can be challenging due to the innate bilevel problem structure. Specifically, the loss landscape of MAML is much more complex with possibly more saddle points and local minimizers than its empirical risk minimization counterpart. To address this challenge, we leverage the recently invented sharpness-aware minimization and develop a sharpness-aware MAML approach that we term Sharp-MAML. We empirically demonstrate that Sharp-MAML and its computation-efficient variant can outperform the plain-vanilla MAML baseline (e.g., $+3\%$ accuracy on Mini-Imagenet). We complement the empirical study with the convergence rate analysis and the generalization bound of Sharp-MAML. To the best of our knowledge, this is the first empirical and theoretical study on sharpness-aware minimization in the context of bilevel learning. The code is available at https://github.com/mominabbass/Sharp-MAML.
    Contextual Search in the Presence of Adversarial Corruptions. (arXiv:2002.11650v6 [cs.LG] UPDATED)
    We study contextual search, a generalization of binary search in higher dimensions, which captures settings such as feature-based dynamic pricing. Standard formulations of this problem assume that agents act in accordance with a specific homogeneous response model. In practice, however, some responses may be adversarially corrupted. Existing algorithms heavily depend on the assumed response model being (approximately) accurate for all agents and have poor performance in the presence of even a few such arbitrary misspecifications. We initiate the study of contextual search when some of the agents can behave in ways inconsistent with the underlying response model. In particular, we provide two algorithms, one based on multidimensional binary search methods and one based on gradient descent. We show that these algorithms attain near-optimal regret in the absence of adversarial corruptions and their performance degrades gracefully with the number of such agents, providing the first results for contextual search in any adversarial noise model. Our techniques draw inspiration from learning theory, game theory, high-dimensional geometry, and convex analysis.
    Estimating Topic Exposure for Under-Represented Users on Social Media. (arXiv:2208.03796v1 [cs.SI])
    Online Social Networks (OSNs) facilitate access to a variety of data allowing researchers to analyze users' behavior and develop user behavioral analysis models. These models rely heavily on the observed data which is usually biased due to the participation inequality. This inequality consists of three groups of online users: the lurkers - users that solely consume the content, the engagers - users that contribute little to the content creation, and the contributors - users that are responsible for creating the majority of the online content. Failing to consider the contribution of all the groups while interpreting population-level interests or sentiments may yield biased results. To reduce the bias induced by the contributors, in this work, we focus on highlighting the engagers' contributions in the observed data as they are more likely to contribute when compared to lurkers, and they comprise a bigger population as compared to the contributors. The first step in behavioral analysis of these users is to find the topics they are exposed to but did not engage with. To do so, we propose a novel framework that aids in identifying these users and estimates their topic exposure. The exposure estimation mechanism is modeled by incorporating behavioral patterns from similar contributors as well as users' demographic and profile information.
    Task-aware Privacy Preservation for Multi-dimensional Data. (arXiv:2110.02329v3 [cs.CR] UPDATED)
    Local differential privacy (LDP) can be adopted to anonymize richer user data attributes that will be input to sophisticated machine learning (ML) tasks. However, today's LDP approaches are largely task-agnostic and often lead to severe performance loss -- they simply inject noise to all data attributes according to a given privacy budget, regardless of what features are most relevant for the ultimate task. In this paper, we address how to significantly improve the ultimate task performance with multi-dimensional user data by considering a task-aware privacy preservation problem. The key idea is to use an encoder-decoder framework to learn (and anonymize) a task-relevant latent representation of user data. We obtain an analytical near-optimal solution for the linear setting with mean-squared error (MSE) task loss. We also provide an approximate solution through a gradient-based learning algorithm for general nonlinear cases. Extensive experiments demonstrate that our task-aware approach significantly improves ultimate task accuracy compared to standard benchmark LDP approaches with the same level of privacy guarantee.
    On the Fundamental Limits of Formally (Dis)Proving Robustness in Proof-of-Learning. (arXiv:2208.03567v1 [cs.LG])
    Proof-of-learning (PoL) proposes a model owner use machine learning training checkpoints to establish a proof of having expended the necessary compute for training. The authors of PoL forego cryptographic approaches and trade rigorous security guarantees for scalability to deep learning by being applicable to stochastic gradient descent and adaptive variants. This lack of formal analysis leaves the possibility that an attacker may be able to spoof a proof for a model they did not train. We contribute a formal analysis of why the PoL protocol cannot be formally (dis)proven to be robust against spoofing adversaries. To do so, we disentangle the two roles of proof verification in PoL: (a) efficiently determining if a proof is a valid gradient descent trajectory, and (b) establishing precedence by making it more expensive to craft a proof after training completes (i.e., spoofing). We show that efficient verification results in a tradeoff between accepting legitimate proofs and rejecting invalid proofs because deep learning necessarily involves noise. Without a precise analytical model for how this noise affects training, we cannot formally guarantee if a PoL verification algorithm is robust. Then, we demonstrate that establishing precedence robustly also reduces to an open problem in learning theory: spoofing a PoL post hoc training is akin to finding different trajectories with the same endpoint in non-convex learning. Yet, we do not rigorously know if priori knowledge of the final model weights helps discover such trajectories. We conclude that, until the aforementioned open problems are addressed, relying more heavily on cryptography is likely needed to formulate a new class of PoL protocols with formal robustness guarantees. In particular, this will help with establishing precedence. As a by-product of insights from our analysis, we also demonstrate two novel attacks against PoL.
    Distributed Contrastive Learning for Medical Image Segmentation. (arXiv:2208.03808v1 [eess.IV])
    Supervised deep learning needs a large amount of labeled data to achieve high performance. However, in medical imaging analysis, each site may only have a limited amount of data and labels, which makes learning ineffective. Federated learning (FL) can learn a shared model from decentralized data. But traditional FL requires fully-labeled data for training, which is very expensive to obtain. Self-supervised contrastive learning (CL) can learn from unlabeled data for pre-training, followed by fine-tuning with limited annotations. However, when adopting CL in FL, the limited data diversity on each site makes federated contrastive learning (FCL) ineffective. In this work, we propose two federated self-supervised learning frameworks for volumetric medical image segmentation with limited annotations. The first one features high accuracy and fits high-performance servers with high-speed connections. The second one features lower communication costs, suitable for mobile devices. In the first framework, features are exchanged during FCL to provide diverse contrastive data to each site for effective local CL while keeping raw data private. Global structural matching aligns local and remote features for a unified feature space among different sites. In the second framework, to reduce the communication cost for feature exchanging, we propose an optimized method FCLOpt that does not rely on negative samples. To reduce the communications of model download, we propose the predictive target network update (PTNU) that predicts the parameters of the target network. Based on PTNU, we propose the distance prediction (DP) to remove most of the uploads of the target network. Experiments on a cardiac MRI dataset show the proposed two frameworks substantially improve the segmentation and generalization performance compared with state-of-the-art techniques.
    A Unified Framework for Domain Adaptive Pose Estimation. (arXiv:2204.00172v3 [cs.CV] UPDATED)
    While pose estimation is an important computer vision task, it requires expensive annotation and suffers from domain shift. In this paper, we investigate the problem of domain adaptive 2D pose estimation that transfers knowledge learned on a synthetic source domain to a target domain without supervision. While several domain adaptive pose estimation models have been proposed recently, they are not generic but only focus on either human pose or animal pose estimation, and thus their effectiveness is somewhat limited to specific scenarios. In this work, we propose a unified framework that generalizes well on various domain adaptive pose estimation problems. We propose to align representations using both input-level and output-level cues (pixels and pose labels, respectively), which facilitates the knowledge transfer from the source domain to the unlabeled target domain. Our experiments show that our method achieves state-of-the-art performance under various domain shifts. Our method outperforms existing baselines on human pose estimation by up to 4.5 percent points (pp), hand pose estimation by up to 7.4 pp, and animal pose estimation by up to 4.8 pp for dogs and 3.3 pp for sheep. These results suggest that our method is able to mitigate domain shift on diverse tasks and even unseen domains and objects (e.g., trained on horse and tested on dog). Our code will be publicly available at: https://github.com/VisionLearningGroup/UDA_PoseEstimation.
    A high-resolution dynamical view on momentum methods for over-parameterized neural networks. (arXiv:2208.03941v1 [cs.LG])
    In this paper, we present the convergence analysis of momentum methods in training a two-layer over-parameterized ReLU neural network, where the number of parameters is significantly larger than that of training instances. Existing works on momentum methods show that the heavy-ball method (HB) and Nesterov's accelerated method (NAG) share the same limiting ordinary differential equation (ODE), which leads to identical convergence rate. From a high-resolution dynamical view, we show that HB differs from NAG in terms of the convergence rate. In addition, our findings provide tighter upper bounds on convergence for the high-resolution ODEs of HB and NAG.
    FlexiBO: A Decoupled Cost-Aware Multi-Objective Optimization Approach for Deep Neural Networks. (arXiv:2001.06588v2 [cs.LG] UPDATED)
    The design of machine learning systems often requires trading off different objectives, for example, prediction error and energy consumption for deep neural networks (DNNs). Typically, there is no single design that performs well in all objectives, therefore, finding Pareto-optimal designs is of interest. Often, measuring different objectives incurs different costs; for example, the cost of measuring the prediction error of DNNs is orders of magnitude higher than that of measuring the energy consumption of a pre-trained DNN as it requires re-training the DNN. Current state-of-the-art methods do not take this difference in objective evaluation cost into account, potentially wasting expensive evaluations of objective functions for little information gain. In this paper, we develop a novel decoupled cost-aware approach we call Flexible Multi-Objective Bayesian Optimization (FlexiBO) to address this issue. FlexiBO weights the improvement of the hypervolume of the Pareto region by the measurement cost of each objective. This helps us in balancing the expense of collecting new information with the knowledge gained through objective evaluations, preventing us from performing expensive measurements for little to no gain. We evaluate FlexiBO on seven state-of-the-art DNNs for image recognition, natural language processing (NLP), and speech-to-text translation. Our results indicate that, given the same total experimental budget, FlexiBO discovers designs with 4.8% to 12.4% lower hypervolume error than the next best state-of-the-art multi-objective optimization method depending on a particular DNN architecture.
    Expressionmight be enough: representing pressure and demand for reinforcement learning based trafAc signal control. (arXiv:2112.10107v2 [cs.AI] UPDATED)
    Many studies confirmed that a proper traffic state representation is more important than complex algorithms for the classical traffic signal control (TSC) problem. In this paper, we (1) present a novel, flexible and efficient method, namely advanced max pressure (Advanced-MP), taking both running and queuing vehicles into consideration to decide whether to change current signal phase; (2) inventively design the traffic movement representation with the efficient pressure and effective running vehicles from Advanced-MP, namely advanced traffic state (ATS); and (3) develop a reinforcement learning (RL) based algorithm template, called Advanced-XLight, by combining ATS with the latest RL approaches, and generate two RL algorithms, namely "Advanced-MPLight" and "Advanced-CoLight" from Advanced-XLight. Comprehensive experiments on multiple real-world datasets show that: (1) the Advanced-MP outperforms baseline methods, and it is also efficient and reliable for deployment; and (2) Advanced-MPLight and Advanced-CoLight can achieve the state-of-the-art
    DiffuseVAE: Efficient, Controllable and High-Fidelity Generation from Low-Dimensional Latents. (arXiv:2201.00308v2 [cs.LG] UPDATED)
    Diffusion Probabilistic models have been shown to generate state-of-the-art results on several competitive image synthesis benchmarks but lack a low-dimensional, interpretable latent space, and are slow at generation. On the other hand, Variational Autoencoders (VAEs) typically have access to a low-dimensional latent space but exhibit poor sample quality. Despite recent advances, VAEs usually require high-dimensional hierarchies of the latent codes to generate high-quality samples. We present DiffuseVAE, a novel generative framework that integrates VAE within a diffusion model framework, and leverage this to design a novel conditional parameterization for diffusion models. We show that the resulting model can improve upon the unconditional diffusion model in terms of sampling efficiency while also equipping diffusion models with the low-dimensional VAE inferred latent code. Furthermore, we show that the proposed model can generate high-resolution samples and exhibits synthesis quality comparable to state-of-the-art models on standard benchmarks. Lastly, we show that the proposed method can be used for controllable image synthesis and also exhibits out-of-the-box capabilities for downstream tasks like image super-resolution and denoising. For reproducibility, our source code is publicly available at \url{https://github.com/kpandey008/DiffuseVAE}.  ( 3 min )
    Optimistic Optimisation of Composite Objective with Exponentiated Update. (arXiv:2208.04065v1 [math.OC])
    This paper proposes a new family of algorithms for the online optimisation of composite objectives. The algorithms can be interpreted as the combination of the exponentiated gradient and $p$-norm algorithm. Combined with algorithmic ideas of adaptivity and optimism, the proposed algorithms achieve a sequence-dependent regret upper bound, matching the best-known bounds for sparse target decision variables. Furthermore, the algorithms have efficient implementations for popular composite objectives and constraints and can be converted to stochastic optimisation algorithms with the optimal accelerated rate for smooth objectives.  ( 2 min )
    Zipfian environments for Reinforcement Learning. (arXiv:2203.08222v2 [cs.LG] UPDATED)
    As humans and animals learn in the natural world, they encounter distributions of entities, situations and events that are far from uniform. Typically, a relatively small set of experiences are encountered frequently, while many important experiences occur only rarely. The highly-skewed, heavy-tailed nature of reality poses particular learning challenges that humans and animals have met by evolving specialised memory systems. By contrast, most popular RL environments and benchmarks involve approximately uniform variation of properties, objects, situations or tasks. How will RL algorithms perform in worlds (like ours) where the distribution of environment features is far less uniform? To explore this question, we develop three complementary RL environments where the agent's experience varies according to a Zipfian (discrete power law) distribution. On these benchmarks, we find that standard Deep RL architectures and algorithms acquire useful knowledge of common situations and tasks, but fail to adequately learn about rarer ones. To understand this failure better, we explore how different aspects of current approaches may be adjusted to help improve performance on rare events, and show that the RL objective function, the agent's memory system and self-supervised learning objectives can all influence an agent's ability to learn from uncommon experiences. Together, these results show that learning robustly from skewed experience is a critical challenge for applying Deep RL methods beyond simulations or laboratories, and our Zipfian environments provide a basis for measuring future progress towards this goal.
    Interpretable Personalized Experimentation. (arXiv:2111.03267v2 [cs.LG] UPDATED)
    Black-box heterogeneous treatment effect (HTE) models are increasingly being used to create personalized policies that assign individuals to their optimal treatments. However, they are difficult to understand, and can be burdensome to maintain in a production environment. In this paper, we present a scalable, interpretable personalized experimentation system, implemented and deployed in production at Meta. The system works in a multiple treatment, multiple outcome setting typical at Meta to: (1) learn explanations for black-box HTE models; (2) generate interpretable personalized policies. We evaluate the methods used in the system on publicly available data and Meta use cases, and discuss lessons learnt during the development of the system.
    Cross-Shape Attention for Part Segmentation of 3D Point Clouds. (arXiv:2003.09053v4 [cs.CV] UPDATED)
    We present a method that propagates point-wise feature representations across shapes within a collection for the purpose of 3D shape segmentation. This is achieved through a cross-shape attention operation that assesses the degree of interaction between points on different shapes and mediates feature propagation. For each test shape, our method finds shapes in an input collection that are suited for executing such cross-shape attention operations. The resulting point-wise feature representations lead to more consistent 3D shape segmentation results, as demonstrated in our experiments.
    Oversquashing in GNNs through the lens of information contraction and graph expansion. (arXiv:2208.03471v1 [cs.LG])
    The quality of signal propagation in message-passing graph neural networks (GNNs) strongly influences their expressivity as has been observed in recent works. In particular, for prediction tasks relying on long-range interactions, recursive aggregation of node features can lead to an undesired phenomenon called "oversquashing". We present a framework for analyzing oversquashing based on information contraction. Our analysis is guided by a model of reliable computation due to von Neumann that lends a new insight into oversquashing as signal quenching in noisy computation graphs. Building on this, we propose a graph rewiring algorithm aimed at alleviating oversquashing. Our algorithm employs a random local edge flip primitive motivated by an expander graph construction. We compare the spectral expansion properties of our algorithm with that of an existing curvature-based non-local rewiring strategy. Synthetic experiments show that while our algorithm in general has a slower rate of expansion, it is overall computationally cheaper, preserves the node degrees exactly and never disconnects the graph.
    BSDGAN: Balancing Sensor Data Generative Adversarial Networks for Human Activity Recognition. (arXiv:2208.03647v1 [cs.LG])
    The development of IoT technology enables a variety of sensors can be integrated into mobile devices. Human Activity Recognition (HAR) based on sensor data has become an active research topic in the field of machine learning and ubiquitous computing. However, due to the inconsistent frequency of human activities, the amount of data for each activity in the human activity dataset is imbalanced. Considering the limited sensor resources and the high cost of manually labeled sensor data, human activity recognition is facing the challenge of highly imbalanced activity datasets. In this paper, we propose Balancing Sensor Data Generative Adversarial Networks (BSDGAN) to generate sensor data for minority human activities. The proposed BSDGAN consists of a generator model and a discriminator model. Considering the extreme imbalance of human activity dataset, an autoencoder is employed to initialize the training process of BSDGAN, ensure the data features of each activity can be learned. The generated activity data is combined with the original dataset to balance the amount of activity data across human activity classes. We deployed multiple human activity recognition models on two publicly available imbalanced human activity datasets, WISDM and UNIMIB. Experimental results show that the proposed BSDGAN can effectively capture the data features of real human activity sensor data, and generate realistic synthetic sensor data. Meanwhile, the balanced activity dataset can effectively help the activity recognition model to improve the recognition accuracy.
    Laplacian-Based Dimensionality Reduction Including Spectral Clustering, Laplacian Eigenmap, Locality Preserving Projection, Graph Embedding, and Diffusion Map: Tutorial and Survey. (arXiv:2106.02154v2 [stat.ML] UPDATED)
    This is a tutorial and survey paper for nonlinear dimensionality and feature extraction methods which are based on the Laplacian of graph of data. We first introduce adjacency matrix, definition of Laplacian matrix, and the interpretation of Laplacian. Then, we cover the cuts of graph and spectral clustering which applies clustering in a subspace of data. Different optimization variants of Laplacian eigenmap and its out-of-sample extension are explained. Thereafter, we introduce the locality preserving projection and its kernel variant as linear special cases of Laplacian eigenmap. Versions of graph embedding are then explained which are generalized versions of Laplacian eigenmap and locality preserving projection. Finally, diffusion map is introduced which is a method based on Laplacian of data and random walks on the data graph.
    MaskViT: Masked Visual Pre-Training for Video Prediction. (arXiv:2206.11894v2 [cs.CV] UPDATED)
    The ability to predict future visual observations conditioned on past observations and motor commands can enable embodied agents to plan solutions to a variety of tasks in complex environments. This work shows that we can create good video prediction models by pre-training transformers via masked visual modeling. Our approach, named MaskViT, is based on two simple design decisions. First, for memory and training efficiency, we use two types of window attention: spatial and spatiotemporal. Second, during training, we mask a variable percentage of tokens instead of a fixed mask ratio. For inference, MaskViT generates all tokens via iterative refinement where we incrementally decrease the masking ratio following a mask scheduling function. On several datasets we demonstrate that MaskViT outperforms prior works in video prediction, is parameter efficient, and can generate high-resolution videos (256x256). Further, we demonstrate the benefits of inference speedup (up to 512x) due to iterative decoding by using MaskViT for planning on a real robot. Our work suggests that we can endow embodied agents with powerful predictive models by leveraging the general framework of masked visual modeling with minimal domain knowledge.
    DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps. (arXiv:2206.00927v2 [cs.LG] UPDATED)
    Diffusion probabilistic models (DPMs) are emerging powerful generative models. Despite their high-quality generation performance, DPMs still suffer from their slow sampling as they generally need hundreds or thousands of sequential function evaluations (steps) of large neural networks to draw a sample. Sampling from DPMs can be viewed alternatively as solving the corresponding diffusion ordinary differential equations (ODEs). In this work, we propose an exact formulation of the solution of diffusion ODEs. The formulation analytically computes the linear part of the solution, rather than leaving all terms to black-box ODE solvers as adopted in previous works. By applying change-of-variable, the solution can be equivalently simplified to an exponentially weighted integral of the neural network. Based on our formulation, we propose DPM-Solver, a fast dedicated high-order solver for diffusion ODEs with the convergence order guarantee. DPM-Solver is suitable for both discrete-time and continuous-time DPMs without any further training. Experimental results show that DPM-Solver can generate high-quality samples in only 10 to 20 function evaluations on various datasets. We achieve 4.70 FID in 10 function evaluations and 2.87 FID in 20 function evaluations on the CIFAR10 dataset, and a $4\sim 16\times$ speedup compared with previous state-of-the-art training-free samplers on various datasets.
    Towards Graph Representation Learning Based Surgical Workflow Anticipation. (arXiv:2208.03824v1 [cs.CV])
    Surgical workflow anticipation can give predictions on what steps to conduct or what instruments to use next, which is an essential part of the computer-assisted intervention system for surgery, e.g. workflow reasoning in robotic surgery. However, current approaches are limited to their insufficient expressive power for relationships between instruments. Hence, we propose a graph representation learning framework to comprehensively represent instrument motions in the surgical workflow anticipation problem. In our proposed graph representation, we maps the bounding box information of instruments to the graph nodes in the consecutive frames and build inter-frame/inter-instrument graph edges to represent the trajectory and interaction of the instruments over time. This design enhances the ability of our network on modeling both the spatial and temporal patterns of surgical instruments and their interactions. In addition, we design a multi-horizon learning strategy to balance the understanding of various horizons indifferent anticipation tasks, which significantly improves the model performance in anticipation with various horizons. Experiments on the Cholec80 dataset demonstrate the performance of our proposed method can exceed the state-of-the-art method based on richer backbones, especially in instrument anticipation (1.27 v.s. 1.48 for inMAE; 1.48 v.s. 2.68 for eMAE). To the best of our knowledge, we are the first to introduce a spatial-temporal graph representation into surgical workflow anticipation.
    On the R\'{e}nyi Cross-Entropy. (arXiv:2206.14329v3 [cs.IT] UPDATED)
    The R\'{e}nyi cross-entropy measure between two distributions, a generalization of the Shannon cross-entropy, was recently used as a loss function for the improved design of deep learning generative adversarial networks. In this work, we examine the properties of this measure and derive closed-form expressions for it when one of the distributions is fixed and when both distributions belong to the exponential family. We also analytically determine a formula for the cross-entropy rate for stationary Gaussian processes and for finite-alphabet Markov sources.
    Finite Horizon Q-learning: Stability, Convergence, Simulations and an application on Smart Grids. (arXiv:2110.15093v3 [cs.LG] UPDATED)
    Q-learning is a popular reinforcement learning algorithm. This algorithm has however been studied and analysed mainly in the infinite horizon setting. There are several important applications which can be modeled in the framework of finite horizon Markov decision processes. We develop a version of Q-learning algorithm for finite horizon Markov decision processes (MDP) and provide a full proof of its stability and convergence. Our analysis of stability and convergence of finite horizon Q-learning is based entirely on the ordinary differential equations (O.D.E) method. We also demonstrate the performance of our algorithm on a setting of random MDP as well as on an application on smart grids.
    Side-effects of Learning from Low Dimensional Data Embedded in an Euclidean Space. (arXiv:2203.00614v3 [cs.LG] UPDATED)
    The low dimensional manifold hypothesis posits that the data found in many applications, such as those involving natural images, lie (approximately) on low dimensional manifolds embedded in a high dimensional Euclidean space. In this setting, a typical neural network defines a function that takes a finite number of vectors in the embedding space as input. However, one often needs to consider evaluating the optimized network at points outside the training distribution. This paper considers the case in which the training data is distributed in a linear subspace of $\mathbb R^d$. We derive estimates on the variation of the learning function, defined by a neural network, in the direction transversal to the subspace. We study the potential regularization effects associated with the network's depth and noise in the codimension of the data manifold. We also present additional side effects in training due to the presence of noise.
    The Extended UCB Policies for Frequentist Multi-armed Bandit Problems. (arXiv:1112.1768v2 [cs.LG] UPDATED)
    The multi-armed bandit (MAB) problem is a widely studied model in the field of reinforcement learning. This paper considers two cases of the classical MAB model -- the light-tailed reward distributions and the heavy-tailed, respectively. For the light-tailed (i.e. sub-Gaussian) case, we propose the UCB1-LT policy, achieving the optimal $O(\log T)$ of the order of regret growth. For the heavy-tailed case, we introduce the extended robust UCB policy, which is an extension of the UCB policies proposed by Bubeck et al. (2013) and Lattimore (2017). The previous UCB policies require the knowledge of an upper bound on specific moments of reward distributions, which can be hard to acquire in some practical situations. Our extended robust UCB eliminates this requirement while still achieving the optimal regret growth order $O(\log T)$, thus providing a broadened application area of the UCB policies for the heavy-tailed reward distributions.
    DyTox: Transformers for Continual Learning with DYnamic TOken eXpansion. (arXiv:2111.11326v3 [cs.CV] UPDATED)
    Deep network architectures struggle to continually learn new tasks without forgetting the previous tasks. A recent trend indicates that dynamic architectures based on an expansion of the parameters can reduce catastrophic forgetting efficiently in continual learning. However, existing approaches often require a task identifier at test-time, need complex tuning to balance the growing number of parameters, and barely share any information across tasks. As a result, they struggle to scale to a large number of tasks without significant overhead. In this paper, we propose a transformer architecture based on a dedicated encoder/decoder framework. Critically, the encoder and decoder are shared among all tasks. Through a dynamic expansion of special tokens, we specialize each forward of our decoder network on a task distribution. Our strategy scales to a large number of tasks while having negligible memory and time overheads due to strict control of the parameters expansion. Moreover, this efficient strategy doesn't need any hyperparameter tuning to control the network's expansion. Our model reaches excellent results on CIFAR100 and state-of-the-art performances on the large-scale ImageNet100 and ImageNet1000 while having less parameters than concurrent dynamic frameworks.
    Bias Reducing Multitask Learning on Mental Health Prediction. (arXiv:2208.03621v1 [cs.LG])
    There has been an increase in research in developing machine learning models for mental health detection or prediction in recent years due to increased mental health issues in society. Effective use of mental health prediction or detection models can help mental health practitioners re-define mental illnesses more objectively than currently done, and identify illnesses at an earlier stage when interventions may be more effective. However, there is still a lack of standard in evaluating bias in such machine learning models in the field, which leads to challenges in providing reliable predictions and in addressing disparities. This lack of standards persists due to factors such as technical difficulties, complexities of high dimensional clinical health data, etc., which are especially true for physiological signals. This along with prior evidence of relations between some physiological signals with certain demographic identities restates the importance of exploring bias in mental health prediction models that utilize physiological signals. In this work, we aim to perform a fairness analysis and implement a multi-task learning based bias mitigation method on anxiety prediction models using ECG data. Our method is based on the idea of epistemic uncertainty and its relationship with model weights and feature space representation. Our analysis showed that our anxiety prediction base model introduced some bias with regards to age, income, ethnicity, and whether a participant is born in the U.S. or not, and our bias mitigation method performed better at reducing the bias in the model, when compared to the reweighting mitigation technique. Our analysis on feature importance also helped identify relationships between heart rate variability and multiple demographic groupings.
    Optimal Tracking in Prediction with Expert Advice. (arXiv:2208.03708v1 [cs.LG])
    We study the prediction with expert advice setting, where the aim is to produce a decision by combining the decisions generated by a set of experts, e.g., independently running algorithms. We achieve the min-max optimal dynamic regret under the prediction with expert advice setting, i.e., we can compete against time-varying (not necessarily fixed) combinations of expert decisions in an optimal manner. Our end-algorithm is truly online with no prior information, such as the time horizon or loss ranges, which are commonly used by different algorithms in the literature. Both our regret guarantees and the min-max lower bounds are derived with the general consideration that the expert losses can have time-varying properties and are possibly unbounded. Our algorithm can be adapted for restrictive scenarios regarding both loss feedback and decision making. Our guarantees are universal, i.e., our end-algorithm can provide regret guarantee against any competitor sequence in a min-max optimal manner with logarithmic complexity. Note that, to our knowledge, for the prediction with expert advice problem, our algorithms are the first to produce such universally optimal, adaptive and truly online guarantees with no prior knowledge.
    Maximum Correntropy Value Decomposition for Multi-agent Deep Reinforcemen Learning. (arXiv:2208.03663v1 [cs.MA])
    We explore value decomposition solutions for multi-agent deep reinforcement learning in the popular paradigm of centralized training with decentralized execution(CTDE). As the recognized best solution to CTDE, Weighted QMIX is cutting-edge on StarCraft Multi-agent Challenge (SMAC), with a weighting scheme implemented on QMIX to place more emphasis on the optimal joint actions. However, the fixed weight requires manual tuning according to the application scenarios, which painfully prevents Weighted QMIX from being used in broader engineering applications. In this paper, we first demonstrate the flaw of Weighted QMIX using an ordinary One-Step Matrix Game (OMG), that no matter how the weight is chosen, Weighted QMIX struggles to deal with non-monotonic value decomposition problems with a large variance of reward distributions. Then we characterize the problem of value decomposition as an Underfitting One-edged Robust Regression problem and make the first attempt to give a solution to the value decomposition problem from the perspective of information-theoretical learning. We introduce the Maximum Correntropy Criterion (MCC) as a cost function to dynamically adapt the weight to eliminate the effects of minimum in reward distributions. We simplify the implementation and propose a new algorithm called MCVD. A preliminary experiment conducted on OMG shows that MCVD could deal with non-monotonic value decomposition problems with a large tolerance of kernel bandwidth selection. Further experiments are carried out on Cooperative-Navigation and multiple SMAC scenarios, where MCVD exhibits unprecedented ease of implementation, broad applicability, and stability.
    Robust Training and Verification of Implicit Neural Networks: A Non-Euclidean Contractive Approach. (arXiv:2208.03889v1 [cs.LG])
    This paper proposes a theoretical and computational framework for training and robustness verification of implicit neural networks based upon non-Euclidean contraction theory. The basic idea is to cast the robustness analysis of a neural network as a reachability problem and use (i) the $\ell_{\infty}$-norm input-output Lipschitz constant and (ii) the tight inclusion function of the network to over-approximate its reachable sets. First, for a given implicit neural network, we use $\ell_{\infty}$-matrix measures to propose sufficient conditions for its well-posedness, design an iterative algorithm to compute its fixed points, and provide upper bounds for its $\ell_\infty$-norm input-output Lipschitz constant. Second, we introduce a related embedded network and show that the embedded network can be used to provide an $\ell_\infty$-norm box over-approximation of the reachable sets of the original network. Moreover, we use the embedded network to design an iterative algorithm for computing the upper bounds of the original system's tight inclusion function. Third, we use the upper bounds of the Lipschitz constants and the upper bounds of the tight inclusion functions to design two algorithms for the training and robustness verification of implicit neural networks. Finally, we apply our algorithms to train implicit neural networks on the MNIST dataset and compare the robustness of our models with the models trained via existing approaches in the literature.
    Class-Incremental Learning with Cross-Space Clustering and Controlled Transfer. (arXiv:2208.03767v1 [cs.CV])
    In class-incremental learning, the model is expected to learn new classes continually while maintaining knowledge on previous classes. The challenge here lies in preserving the model's ability to effectively represent prior classes in the feature space, while adapting it to represent incoming new classes. We propose two distillation-based objectives for class incremental learning that leverage the structure of the feature space to maintain accuracy on previous classes, as well as enable learning the new classes. In our first objective, termed cross-space clustering (CSC), we propose to use the feature space structure of the previous model to characterize directions of optimization that maximally preserve the class - directions that all instances of a specific class should collectively optimize towards, and those that they should collectively optimize away from. Apart from minimizing forgetting, this indirectly encourages the model to cluster all instances of a class in the current feature space, and gives rise to a sense of herd-immunity, allowing all samples of a class to jointly combat the model from forgetting the class. Our second objective termed controlled transfer (CT) tackles incremental learning from an understudied perspective of inter-class transfer. CT explicitly approximates and conditions the current model on the semantic similarities between incrementally arriving classes and prior classes. This allows the model to learn classes in such a way that it maximizes positive forward transfer from similar prior classes, thus increasing plasticity, and minimizes negative backward transfer on dissimilar prior classes, whereby strengthening stability. We perform extensive experiments on two benchmark datasets, adding our method (CSCCT) on top of three prominent class-incremental learning methods. We observe consistent performance improvement on a variety of experimental settings.
    Towards Practical Adam: Non-Convexity, Convergence Theory, and Mini-Batch Acceleration. (arXiv:2101.05471v2 [cs.LG] UPDATED)
    Adam is one of the most influential adaptive stochastic algorithms for training deep neural networks, which has been pointed out to be divergent even in the simple convex setting via a few simple counterexamples. Many attempts, such as decreasing an adaptive learning rate, adopting a big batch size, incorporating a temporal decorrelation technique, seeking an analogous surrogate, \textit{etc.}, have been tried to promote Adam-type algorithms to converge. In contrast with existing approaches, we introduce an alternative easy-to-check sufficient condition, which merely depends on the parameters of the base learning rate and combinations of historical second-order moments, to guarantee the global convergence of generic Adam for solving large-scale non-convex stochastic optimization. This observation, coupled with this sufficient condition, gives much deeper interpretations on the divergence of Adam. On the other hand, in practice, mini-Adam and distributed-Adam are widely used without any theoretical guarantee. We further give an analysis on how the batch size or the number of nodes in the distributed system affects the convergence of Adam, which theoretically shows that mini-batch and distributed Adam can be linearly accelerated by using a larger mini-batch size or a larger number of nodes.At last, we apply the generic Adam and mini-batch Adam with the sufficient condition for solving the counterexample and training several neural networks on various real-world datasets. Experimental results are exactly in accord with our theoretical analysis.  ( 3 min )
    Privacy Against Inference Attacks in Vertical Federated Learning. (arXiv:2207.11788v2 [cs.LG] UPDATED)
    Vertical federated learning is considered, where an active party, having access to true class labels, wishes to build a classification model by utilizing more features from a passive party, which has no access to the labels, to improve the model accuracy. In the prediction phase, with logistic regression as the classification model, several inference attack techniques are proposed that the adversary, i.e., the active party, can employ to reconstruct the passive party's features, regarded as sensitive information. These attacks, which are mainly based on a classical notion of the center of a set, i.e., the Chebyshev center, are shown to be superior to those proposed in the literature. Moreover, several theoretical performance guarantees are provided for the aforementioned attacks. Subsequently, we consider the minimum amount of information that the adversary needs to fully reconstruct the passive party's features. In particular, it is shown that when the passive party holds one feature, and the adversary is only aware of the signs of the parameters involved, it can perfectly reconstruct that feature when the number of predictions is large enough. Next, as a defense mechanism, a privacy-preserving scheme is proposed that worsen the adversary's reconstruction attacks, while preserving the full benefits that VFL brings to the active party. Finally, experimental results demonstrate the effectiveness of the proposed attacks and the privacy-preserving scheme.
    Learning Connectivity-Maximizing Network Configurations. (arXiv:2112.07663v2 [cs.RO] UPDATED)
    In this letter we propose a data-driven approach to optimizing the algebraic connectivity of a team of robots. While a considerable amount of research has been devoted to this problem, we lack a method that scales in a manner suitable for online applications for more than a handful of agents. To that end, we propose a supervised learning approach with a convolutional neural network (CNN) that learns to place communication agents from an expert that uses an optimization-based strategy. We demonstrate the performance of our CNN on canonical line and ring topologies, 105k randomly generated test cases, and larger teams not seen during training. We also show how our system can be applied to dynamic robot teams through a Unity-based simulation. After training, our system produces connected configurations over an order of magnitude faster than the optimization-based scheme for teams of 10-20 agents.
    How Adversarial Robustness Transfers from Pre-training to Downstream Tasks. (arXiv:2208.03835v1 [cs.LG])
    Given the rise of large-scale training regimes, adapting pre-trained models to a wide range of downstream tasks has become a standard approach in machine learning. While large benefits in empirical performance have been observed, it is not yet well understood how robustness properties transfer from a pre-trained model to a downstream task. We prove that the robustness of a predictor on downstream tasks can be bound by the robustness of its underlying representation, irrespective of the pre-training protocol. Taken together, our results precisely characterize what is required of the representation function for reliable performance upon deployment.
    Neural Optimization Machine: A Neural Network Approach for Optimization. (arXiv:2208.03897v1 [stat.ML])
    A novel neural network (NN) approach is proposed for constrained optimization. The proposed method uses a specially designed NN architecture and training/optimization procedure called Neural Optimization Machine (NOM). The objective functions for the NOM are approximated with NN models. The optimization process is conducted by the neural network's built-in backpropagation algorithm. The NOM solves optimization problems by extending the architecture of the NN objective function model. This is achieved by appropriately designing the NOM's structure, activation function, and loss function. The NN objective function can have arbitrary architectures and activation functions. The application of the NOM is not limited to specific optimization problems, e.g., linear and quadratic programming. It is shown that the increase of dimension of design variables does not increase the computational cost significantly. Then, the NOM is extended for multiobjective optimization. Finally, the NOM is tested using numerical optimization problems and applied for the optimal design of processing parameters in additive manufacturing.
    MutFormer: A context-dependent transformer-based model to predict deleterious missense mutations from protein sequences in the human genome. (arXiv:2110.14746v3 [q-bio.GN] UPDATED)
    Various machine-learning models, including deep neural network models, have already been developed to predict deleteriousness of missense (non-synonymous) mutations. Still, potential improvements to the current state of the art may benefit from a fresh look at the biological problem using more sophisticated self-adaptive machine-learning approaches. Recent advances in the natural language processing field show transformer models-a type of deep neural network-to be particularly powerful at modeling sequence information with context dependence. In this study, we introduce MutFormer, a transformer-based model for the prediction of deleterious missense mutations. MutFormer uses reference and mutated protein sequences from the human genome as the primary features. It uses a combination of self-attention layers and convolutional layers to learn both long-range and short-range dependencies between amino acid mutations in a protein sequence. We pre-trained MutFormer on reference protein sequences and mutated protein sequences resulting from common genetic variants observed in human populations. Next, we examined different fine-tuning methods to successfully apply the model to deleteriousness prediction of missense mutations. Finally, we evaluated MutFormer's performance on multiple testing data sets. We found that MutFormer showed similar or improved performance over a variety of existing tools, including those that used conventional machine-learning approaches (e.g., support vector machine, convolutional neural network, recurrent neural network). We conclude that MutFormer successfully considers sequence features that are not explored in previous studies and could potentially complement existing computational predictions or empirically generated functional scores to improve our understanding of disease variants.
    Discovery of partial differential equations from highly noisy and sparse data with physics-informed information criterion. (arXiv:2208.03322v1 [cs.LG])
    Data-driven discovery of PDEs has made tremendous progress recently, and many canonical PDEs have been discovered successfully for proof-of-concept. However, determining the most proper PDE without prior references remains challenging in terms of practical applications. In this work, a physics-informed information criterion (PIC) is proposed to measure the parsimony and precision of the discovered PDE synthetically. The proposed PIC achieves state-of-the-art robustness to highly noisy and sparse data on seven canonical PDEs from different physical scenes, which confirms its ability to handle difficult situations. The PIC is also employed to discover unrevealed macroscale governing equations from microscopic simulation data in an actual physical scene. The results show that the discovered macroscale PDE is precise and parsimonious, and satisfies underlying symmetries, which facilitates understanding and simulation of the physical process. The proposition of PIC enables practical applications of PDE discovery in discovering unrevealed governing equations in broader physical scenes.
    Information bottleneck theory of high-dimensional regression: relevancy, efficiency and optimality. (arXiv:2208.03848v1 [cs.IT])
    Avoiding overfitting is a central challenge in machine learning, yet many large neural networks readily achieve zero training loss. This puzzling contradiction necessitates new approaches to the study of overfitting. Here we quantify overfitting via residual information, defined as the bits in fitted models that encode noise in training data. Information efficient learning algorithms minimize residual information while maximizing the relevant bits, which are predictive of the unknown generative models. We solve this optimization to obtain the information content of optimal algorithms for a linear regression problem and compare it to that of randomized ridge regression. Our results demonstrate the fundamental tradeoff between residual and relevant information and characterize the relative information efficiency of randomized regression with respect to optimal algorithms. Finally, using results from random matrix theory, we reveal the information complexity of learning a linear map in high dimensions and unveil information-theoretic analogs of double and multiple descent phenomena.
    Rapid Flow Behavior Modeling of Thermal Interface Materials Using Deep Neural Networks. (arXiv:2208.04045v1 [cs.LG])
    Thermal Interface Materials (TIMs) are widely used in electronic packaging. Increasing power density and limited assembly space pose high demands on thermal management. Large cooling surfaces need to be covered efficiently. When joining the heatsink, previously dispensed TIM spreads over the cooling surface. Recommendations on the dispensing pattern exist only for simple surface geometries such as rectangles. For more complex geometries, Computational Fluid Dynamics (CFD) simulations are used in combination with manual experiments. While CFD simulations offer a high accuracy, they involve simulation experts and are rather expensive to set up. We propose a lightweight heuristic to model the spreading behavior of TIM. We further speed up the calculation by training an Artificial Neural Network (ANN) on data from this model. This offers rapid computation times and further supplies gradient information. This ANN can not only be used to aid manual pattern design of TIM, but also enables an automated pattern optimization. We compare this approach against the state-of-the-art and use real product samples for validation.
    Searching for the Essence of Adversarial Perturbations. (arXiv:2205.15357v2 [cs.LG] UPDATED)
    Neural networks have achieved the state-of-the-art performance in various machine learning fields, yet the incorporation of malicious perturbations with input data (adversarial example) is shown to fool neural networks' predictions. This would lead to potential risks for real-world applications such as endangering autonomous driving and messing up text identification. To mitigate such risks, an understanding of how adversarial examples operate is critical, which however remains unresolved. Here we demonstrate that adversarial perturbations contain human-recognizable information, which is the key conspirator responsible for a neural network's erroneous prediction, in contrast to a widely discussed argument that human-imperceptible information plays the critical role in fooling a network. This concept of human-recognizable information allows us to explain key features related to adversarial perturbations, including the existence of adversarial examples, the transferability among different neural networks, and the increased neural network interpretability for adversarial training. Two unique properties in adversarial perturbations that fool neural networks are uncovered: masking and generation. A special class, the complementary class, is identified when neural networks classify input images. The human-recognizable information contained in adversarial perturbations allows researchers to gain insight on the working principles of neural networks and may lead to develop techniques that detect/defense adversarial attacks.
    Data-centric AI approach to improve optic nerve head segmentation and localization in OCT en face images. (arXiv:2208.03868v1 [eess.IV])
    The automatic detection and localization of anatomical features in retinal imaging data are relevant for many aspects. In this work, we follow a data-centric approach to optimize classifier training for optic nerve head detection and localization in optical coherence tomography en face images of the retina. We examine the effect of domain knowledge driven spatial complexity reduction on the resulting optic nerve head segmentation and localization performance. We present a machine learning approach for segmenting optic nerve head in 2D en face projections of 3D widefield swept source optical coherence tomography scans that enables the automated assessment of large amounts of data. Evaluation on manually annotated 2D en face images of the retina demonstrates that training of a standard U-Net can yield improved optic nerve head segmentation and localization performance when the underlying pixel-level binary classification task is spatially relaxed through domain knowledge.
    Secure and Private Source Coding with Private Key and Decoder Side Information. (arXiv:2205.05068v2 [cs.IT] UPDATED)
    The problem of secure source coding with multiple terminals is extended by considering a remote source whose noisy measurements are the correlated random variables used for secure source reconstruction. The main additions to the problem include 1) all terminals noncausally observe a noisy measurement of the remote source; 2) a private key is available to all legitimate terminals; 3) the public communication link between the encoder and decoder is rate-limited; and 4) the secrecy leakage to the eavesdropper is measured with respect to the encoder input, whereas the privacy leakage is measured with respect to the remote source. Exact rate regions are characterized for a lossy source coding problem with a private key, remote source, and decoder side information under security, privacy, communication, and distortion constraints. By replacing the distortion constraint with a reliability constraint, we obtain the exact rate region also for the lossless case. Furthermore, the lossy rate region for scalar discrete-time Gaussian sources and measurement channels is established.
    Attention-embedded Quadratic Network (Qttention) for Effective and Interpretable Bearing Fault Diagnosis. (arXiv:2206.00390v2 [cs.LG] UPDATED)
    Bearing fault diagnosis is of great importance to decrease the damage risk of rotating machines and further improve economic profits. Recently, machine learning, represented by deep learning, has made great progress in bearing fault diagnosis. However, applying deep learning to such a task still faces a major problem. A deep network is notoriously a black box. It is difficult to know how a model classifies faulty signals from the normal and the physics principle behind the classification. To solve the interpretability issue, first, we prototype a convolutional network with recently-invented quadratic neurons. This quadratic neuron empowered network can qualify the noisy bearing data due to the strong feature representation ability of quadratic neurons. Moreover, we independently derive the attention mechanism from a quadratic neuron, referred to as qttention, by factorizing the learned quadratic function in analogue to the attention, making the model with quadratic neurons inherently interpretable. Experiments on the public and our datasets demonstrate that the proposed network can facilitate effective and interpretable bearing fault diagnosis.
    Estimating relative diffusion from 3D micro-CT images using CNNs. (arXiv:2208.03337v1 [physics.comp-ph])
    In the past several years, convolutional neural networks (CNNs) have proven their capability to predict characteristic quantities in porous media research directly from pore-space geometries. Due to the frequently observed significant reduction in computation time in comparison to classical computational methods, bulk parameter prediction via CNNs is especially compelling, e.g. for effective diffusion. While the current literature is mainly focused on fully saturated porous media, the partially saturated case is also of high interest. Due to the qualitatively different and more complex geometries of the domain available for diffusive transport present in this case, standard CNNs tend to lose robustness and accuracy with lower saturation rates. In this paper, we demonstrate the ability of CNNs to perform predictions of relative diffusion directly from full pore-space geometries. As such, our CNN conveniently fuses diffusion prediction and a well-established morphological model which describes phase distributions in partially saturated porous media.
    Template-based Abstractive Microblog Opinion Summarisation. (arXiv:2208.04083v1 [cs.CL])
    We introduce the task of microblog opinion summarisation (MOS) and share a dataset of 3100 gold-standard opinion summaries to facilitate research in this domain. The dataset contains summaries of tweets spanning a 2-year period and covers more topics than any other public Twitter summarisation dataset. Summaries are abstractive in nature and have been created by journalists skilled in summarising news articles following a template separating factual information (main story) from author opinions. Our method differs from previous work on generating gold-standard summaries from social media, which usually involves selecting representative posts and thus favours extractive summarisation models. To showcase the dataset's utility and challenges, we benchmark a range of abstractive and extractive state-of-the-art summarisation models and achieve good performance, with the former outperforming the latter. We also show that fine-tuning is necessary to improve performance and investigate the benefits of using different sample sizes.
    Completing Networks by Learning Local Connection Patterns. (arXiv:2204.11852v2 [cs.LG] UPDATED)
    Network completion is a harder problem than link prediction because it does not only try to infer missing links but also nodes. Different methods have been proposed to solve this problem, but few of them employed structural information - the similarity of local connection patterns. In this paper, we propose a model named C-GIN to capture the local structural patterns from the observed part of a network based on the Graph Auto-Encoder framework equipped with Graph Isomorphism Network model and generalize these patterns to complete the whole graph. Experiments and analysis on synthetic and real-world networks from different domains show that competitive performance can be achieved by C-GIN with less information being needed, and higher accuracy compared with baseline prediction models in most cases can be obtained. We further proposed a metric "Reachable Clustering Coefficient(CC)" based on network structure. And experiments show that our model perform better on a network with higher Reachable CC.
    Counterfactual Fairness Is Basically Demographic Parity. (arXiv:2208.03843v1 [cs.LG])
    Making fair decisions is crucial to ethically implementing machine learning algorithms in social settings. In this work, we consider the celebrated definition of counterfactual fairness [Kusner et al., NeurIPS, 2017]. We begin by showing that an algorithm which satisfies counterfactual fairness also satisfies demographic parity, a far simpler fairness constraint. Similarly, we show that all algorithms satisfying demographic parity can be trivially modified to satisfy counterfactual fairness. Together, our results indicate that counterfactual fairness is basically equivalent to demographic parity, which has important implications for the growing body of work on counterfactual fairness. We then validate our theoretical findings empirically, analyzing three existing algorithms for counterfactual fairness against three simple benchmarks. We find that two simple benchmark algorithms outperform all three existing algorithms -- in terms of fairness, accuracy, and efficiency -- on several data sets. Our analysis leads us to formalize a concrete fairness goal: to preserve the order of individuals within protected groups. We believe transparency around the ordering of individuals within protected groups makes fair algorithms more trustworthy. By design, the two simple benchmark algorithms satisfy this goal while the existing algorithms for counterfactual fairness do not.
    Differential-Critic GAN: Generating What You Want by a Cue of Preferences. (arXiv:2107.06700v2 [cs.LG] UPDATED)
    This paper proposes Differential-Critic Generative Adversarial Network (DiCGAN) to learn the distribution of user-desired data when only partial instead of the entire dataset possesses the desired property. DiCGAN generates desired data that meets the user's expectations and can assist in designing biological products with desired properties. Existing approaches select the desired samples first and train regular GANs on the selected samples to derive the user-desired data distribution. However, the selection of the desired data relies on global knowledge and supervision over the entire dataset. DiCGAN introduces a differential critic that learns from pairwise preferences, which are local knowledge and can be defined on a part of training data. The critic is built by defining an additional ranking loss over the Wasserstein GAN's critic. It endows the difference of critic values between each pair of samples with the user preference and guides the generation of the desired data instead of the whole data. For a more efficient solution to ensure data quality, we further reformulate DiCGAN as a constrained optimization problem, based on which we theoretically prove the convergence of our DiCGAN. Extensive experiments on a diverse set of datasets with various applications demonstrate that our DiCGAN achieves state-of-the-art performance in learning the user-desired data distributions, especially in the cases of insufficient desired data and limited supervision.
    ReLAX: Reinforcement Learning Agent eXplainer for Arbitrary Predictive Models. (arXiv:2110.11960v2 [cs.LG] UPDATED)
    Counterfactual examples (CFs) are one of the most popular methods for attaching post-hoc explanations to machine learning (ML) models. However, existing CF generation methods either exploit the internals of specific models or depend on each sample's neighborhood, thus they are hard to generalize for complex models and inefficient for large datasets. This work aims to overcome these limitations and introduces ReLAX, a model-agnostic algorithm to generate optimal counterfactual explanations. Specifically, we formulate the problem of crafting CFs as a sequential decision-making task and then find the optimal CFs via deep reinforcement learning (DRL) with discrete-continuous hybrid action space. Extensive experiments conducted on several tabular datasets have shown that ReLAX outperforms existing CF generation baselines, as it produces sparser counterfactuals, is more scalable to complex target models to explain, and generalizes to both classification and regression tasks. Finally, to demonstrate the usefulness of our method in a real-world use case, we leverage CFs generated by ReLAX to suggest actions that a country should take to reduce the risk of mortality due to COVID-19. Interestingly enough, the actions recommended by our method correspond to the strategies that many countries have actually implemented to counter the COVID-19 pandemic.
    The Sufficiency of Off-policyness and Soft Clipping: PPO is insufficient according to an Off-policy Measure. (arXiv:2205.10047v4 [cs.LG] UPDATED)
    Many policy gradient methods optimize the objective, $\max_{\pi}E_{\pi}[A_{\pi_{old}}(s,a)]$, where $A_{\pi_{old}}$ is the advantage function of the old policy. The objective is not feasible to be directly optimized because we don't have samples for the new policy yet. Thus the importance sampling (IS) ratio arises, giving an IS corrected objective or the CPI objective, $\max_{\pi}E_{\pi_{old}}[\frac{\pi(s,a)}{\pi_{old}(s,a)}A_{\pi_{old}}(s,a)]$. However, optimizing this objective is still problematic due to extremely large IS ratios that can cause algorithms to fail catastrophically. Thus PPO uses a surrogate objective, and seeks an approximation to the solution in a clipped policy space, $\Pi_{\epsilon}=\{\pi; |\frac{\pi(s,a)}{\pi_{old}(s,a)}-1|<\epsilon \}$, where $\epsilon$ is a small positive number. One question that drives this paper is, {\em How grounded is this hypothesis that $\Pi_{\epsilon}$ contains good enough policies?} {\bfseries Does there exist better policies outside of $\mathbf{\Pi_{\epsilon}}$?} Using a novel surrogate objective that employs the sigmoid function resulting in an interesting way of exploration, we found that there indeed exists much better policies out of $\Pi_{\epsilon}$; In addition, these policies are located very far from it. We compare with several best-performing algorithms on both discrete and continuous tasks and the results showed that {\em PPO is insufficient in off-policyness}, and our new method P3O is {\em more off-policy} than PPO according to the "off-policyness" measured by the {\em DEON off-policy metric}, and P3O {\em \bfseries explores in a much larger policy space} than PPO.
    Improved Pancreatic Tumor Detection by Utilizing Clinically-Relevant Secondary Features. (arXiv:2208.03581v1 [cs.CV])
    Pancreatic cancer is one of the global leading causes of cancer-related deaths. Despite the success of Deep Learning in computer-aided diagnosis and detection (CAD) methods, little attention has been paid to the detection of Pancreatic Cancer. We propose a method for detecting pancreatic tumor that utilizes clinically-relevant features in the surrounding anatomical structures, thereby better aiming to exploit the radiologist's knowledge compared to other, conventional deep learning approaches. To this end, we collect a new dataset consisting of 99 cases with pancreatic ductal adenocarcinoma (PDAC) and 97 control cases without any pancreatic tumor. Due to the growth pattern of pancreatic cancer, the tumor may not be always visible as a hypodense lesion, therefore experts refer to the visibility of secondary external features that may indicate the presence of the tumor. We propose a method based on a U-Net-like Deep CNN that exploits the following external secondary features: the pancreatic duct, common bile duct and the pancreas, along with a processed CT scan. Using these features, the model segments the pancreatic tumor if it is present. This segmentation for classification and localization approach achieves a performance of 99% sensitivity (one case missed) and 99% specificity, which realizes a 5% increase in sensitivity over the previous state-of-the-art method. The model additionally provides location information with reasonable accuracy and a shorter inference time compared to previous PDAC detection methods. These results offer a significant performance improvement and highlight the importance of incorporating the knowledge of the clinical expert when developing novel CAD methods.
    Towards lifelong learning of Recurrent Neural Networks for control design. (arXiv:2208.03980v1 [eess.SY])
    This paper proposes a method for lifelong learning of Recurrent Neural Networks, such as NNARX, ESN, LSTM, and GRU, to be used as plant models in control system synthesis. The problem is significant because in many practical applications it is required to adapt the model when new information is available and/or the system undergoes changes, without the need to store an increasing amount of data as time proceeds. Indeed, in this context, many problems arise, such as the well known Catastrophic Forgetting and Capacity Saturation ones. We propose an adaptation algorithm inspired by Moving Horizon Estimators, deriving conditions for its convergence. The described method is applied to a simulated chemical plant, already adopted as a challenging benchmark in the existing literature. The main results achieved are discussed.
    MOOMIN: Deep Molecular Omics Network for Anti-Cancer Drug Combination Therapy. (arXiv:2110.15087v3 [cs.LG] UPDATED)
    We propose the molecular omics network (MOOMIN) a multimodal graph neural network used by AstraZeneca oncologists to predict the synergy of drug combinations for cancer treatment. Our model learns drug representations at multiple scales based on a drug-protein interaction network and metadata. Structural properties of compounds and proteins are encoded to create vertex features for a message-passing scheme that operates on the bipartite interaction graph. Propagated messages form multi-resolution drug representations which we utilized to create drug pair descriptors. By conditioning the drug combination representations on the cancer cell type we define a synergy scoring function that can inductively score unseen pairs of drugs. Experimental results on the synergy scoring task demonstrate that MOOMIN outperforms state-of-the-art graph fingerprinting, proximity preserving node embedding, and existing deep learning approaches. Further results establish that the predictive performance of our model is robust to hyperparameter changes. We demonstrate that the model makes high-quality predictions over a wide range of cancer cell line tissues, out-of-sample predictions can be validated with external synergy databases, and that the proposed model is data efficient at learning.
    Deep Learning for Material Decomposition in Photon-Counting CT. (arXiv:2208.03360v1 [physics.med-ph])
    Photon-counting CT (PCCT) offers improved diagnostic performance through better spatial and energy resolution, but developing high-quality image reconstruction methods that can deal with these large datasets is challenging. Model-based solutions incorporate models of the physical acquisition in order to reconstruct more accurate images, but are dependent on an accurate forward operator and present difficulties with finding good regularization. Another approach is deep-learning reconstruction, which has shown great promise in CT. However, fully data-driven solutions typically need large amounts of training data and lack interpretability. To combine the benefits of both methods, while minimizing their respective drawbacks, it is desirable to develop reconstruction algorithms that combine both model-based and data-driven approaches. In this work, we present a novel deep-learning solution for material decomposition in PCCT, based on an unrolled/unfolded iterative network. We evaluate two cases: a learned post-processing, which implicitly utilizes model knowledge, and a learned gradient-descent, which has explicit model-based components in the architecture. With our proposed techniques, we solve a challenging PCCT simulation case: three-material decomposition in abdomen imaging with low dose, iodine contrast, and a very small training sample support. In this scenario, our approach outperforms a maximum likelihood estimation, a variational method, as well as a fully-learned network.
    Decomposable Non-Smooth Convex Optimization with Nearly-Linear Gradient Oracle Complexity. (arXiv:2208.03811v1 [math.OC])
    Many fundamental problems in machine learning can be formulated by the convex program \[ \min_{\theta\in R^d}\ \sum_{i=1}^{n}f_{i}(\theta), \] where each $f_i$ is a convex, Lipschitz function supported on a subset of $d_i$ coordinates of $\theta$. One common approach to this problem, exemplified by stochastic gradient descent, involves sampling one $f_i$ term at every iteration to make progress. This approach crucially relies on a notion of uniformity across the $f_i$'s, formally captured by their condition number. In this work, we give an algorithm that minimizes the above convex formulation to $\epsilon$-accuracy in $\widetilde{O}(\sum_{i=1}^n d_i \log (1 /\epsilon))$ gradient computations, with no assumptions on the condition number. The previous best algorithm independent of the condition number is the standard cutting plane method, which requires $O(nd \log (1/\epsilon))$ gradient computations. As a corollary, we improve upon the evaluation oracle complexity for decomposable submodular minimization by Axiotis et al. (ICML 2021). Our main technical contribution is an adaptive procedure to select an $f_i$ term at every iteration via a novel combination of cutting-plane and interior-point methods.
    Linking Properties to Microstructure in Liquid Metal Embedded Elastomers via Machine Learning. (arXiv:2208.04146v1 [cond-mat.mtrl-sci])
    Liquid metals (LM) are embedded in an elastomer matrix to obtain soft composites with unique thermal, dielectric, and mechanical properties. They have applications in soft robotics, biomedical engineering, and wearable electronics. By linking the structure to the properties of these materials, it is possible to perform material design rationally. Liquid-metal embedded elastomers (LMEEs) have been designed for targeted electro-thermo-mechanical properties by semi-supervised learning of structure-property (SP) links in a variational autoencoder network (VAE). The design parameters are the microstructural descriptors that are physically meaningful and have affine relationships with the synthetization of the studied particulate composite. The machine learning (ML) model is trained on a generated dataset of microstructural descriptors with their multifunctional property quantities as their labels. Sobol sequence is used for in-silico Design of Experiment (DoE) by sampling the design space to generate a comprehensive dataset of 3D microstructure realizations via a packing algorithm. The mechanical responses of the generated microstructures are simulated using a previously developed Finite Element (FE) model, considering the surface tension induced by LM inclusions, while the linear thermal and dielectric constants are homogenized with the help of our in-house Fast Fourier Transform (FFT) package. Following the training by minimization of an appropriate loss function, the VAE encoder acts as the surrogate of numerical solvers of the multifunctional homogenizations, and its decoder is used for the material design. Our results indicate the satisfactory performance of the surrogate model and the inverse calculator with respect to high-fidelity numerical simulations validated with LMEE experimental results.
    Spiking Neural Predictive Coding for Continual Learning from Data Streams. (arXiv:1908.08655v3 [cs.NE] UPDATED)
    For energy-efficient computation in specialized neuromorphic hardware, we present spiking neural coding, an instantiation of a family of artificial neural models grounded in the theory of predictive coding. This model, the first of its kind, works by operating in a never-ending process of "guess-and-check", where neurons predict the activity values of one another and then adjust their own activities to make better future predictions. The interactive, iterative nature of our system fits well into the continuous time formulation of sensory stream prediction and, as we show, the model's structure yields a local synaptic update rule, which can be used to complement or as an alternative to online spike-timing dependent plasticity. In this article, we experiment with an instantiation of our model consisting of leaky integrate-and-fire units. However, the framework within which our system is situated can naturally incorporate more complex neurons such as the Hodgkin-Huxley model. Our experimental results in pattern recognition demonstrate the potential of the model when binary spike trains are the primary paradigm for inter-neuron communication. Notably, spiking neural coding is competitive in terms of classification performance and experiences less forgetting when learning from task sequence, offering a more computationally economical, biologically-plausible alternative to popular artificial neural networks.
    Blackbox Attacks via Surrogate Ensemble Search. (arXiv:2208.03610v1 [cs.LG])
    Blackbox adversarial attacks can be categorized into transfer- and query-based attacks. Transfer methods do not require any feedback from the victim model, but provide lower success rates compared to query-based methods. Query attacks often require a large number of queries for success. To achieve the best of both approaches, recent efforts have tried to combine them, but still require hundreds of queries to achieve high success rates (especially for targeted attacks). In this paper, we propose a novel method for blackbox attacks via surrogate ensemble search (BASES) that can generate highly successful blackbox attacks using an extremely small number of queries. We first define a perturbation machine that generates a perturbed image by minimizing a weighted loss function over a fixed set of surrogate models. To generate an attack for a given victim model, we search over the weights in the loss function using queries generated by the perturbation machine. Since the dimension of the search space is small (same as the number of surrogate models), the search requires a small number of queries. We demonstrate that our proposed method achieves better success rate with at least 30x fewer queries compared to state-of-the-art methods on different image classifiers trained with ImageNet (including VGG-19, DenseNet-121, and ResNext-50). In particular, our method requires as few as 3 queries per image (on average) to achieve more than a 90% success rate for targeted attacks and 1-2 queries per image for over a 99% success rate for non-targeted attacks. Our method is also effective on Google Cloud Vision API and achieved a 91% non-targeted attack success rate with 2.9 queries per image. We also show that the perturbations generated by our proposed method are highly transferable and can be adopted for hard-label blackbox attacks.
    Learning with Multiple Complementary Labels. (arXiv:1912.12927v4 [cs.LG] UPDATED)
    A complementary label (CL) simply indicates an incorrect class of an example, but learning with CLs results in multi-class classifiers that can predict the correct class. Unfortunately, the problem setting only allows a single CL for each example, which notably limits its potential since our labelers may easily identify multiple CLs (MCLs) to one example. In this paper, we propose a novel problem setting to allow MCLs for each example and two ways for learning with MCLs. In the first way, we design two wrappers that decompose MCLs into many single CLs, so that we could use any method for learning with CLs. However, the supervision information that MCLs hold is conceptually diluted after decomposition. Thus, in the second way, we derive an unbiased risk estimator; minimizing it processes each set of MCLs as a whole and possesses an estimation error bound. We further improve the second way into minimizing properly chosen upper bounds. Experiments show that the former way works well for learning with MCLs but the latter is even better.
    Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations. (arXiv:2206.04779v2 [cs.LG] UPDATED)
    Offline reinforcement learning has shown great promise in leveraging large pre-collected datasets for policy learning, allowing agents to forgo often-expensive online data collection. However, to date, offline reinforcement learning from visual observations with continuous action spaces has been relatively under-explored, and there is a lack of understanding of where the remaining challenges lie. In this paper, we seek to establish simple baselines for continuous control in the visual domain. We show that simple modifications to two state-of-the-art vision-based online reinforcement learning algorithms, DreamerV2 and DrQ-v2, suffice to outperform prior work and establish a competitive baseline. We rigorously evaluate these algorithms on both existing offline datasets and a new testbed for offline reinforcement learning from visual observations that better represents the data distributions present in real-world offline RL problems, and open-source our code and data to facilitate progress in this important domain. Finally, we present and analyze several key desiderata unique to offline RL from visual observations, including visual distractions and visually identifiable changes in dynamics.
    A Game-Theoretic Perspective of Generalization in Reinforcement Learning. (arXiv:2208.03650v1 [cs.LG])
    Generalization in reinforcement learning (RL) is of importance for real deployment of RL algorithms. Various schemes are proposed to address the generalization issues, including transfer learning, multi-task learning and meta learning, as well as the robust and adversarial reinforcement learning. However, there is not a unified formulation of the various schemes, as well as the comprehensive comparisons of methods across different schemes. In this work, we propose a game-theoretic framework for the generalization in reinforcement learning, named GiRL, where an RL agent is trained against an adversary over a set of tasks, where the adversary can manipulate the distributions over tasks within a given threshold. With different configurations, GiRL can reduce the various schemes mentioned above. To solve GiRL, we adapt the widely-used method in game theory, policy space response oracle (PSRO) with the following three important modifications: i) we use model-agnostic meta learning (MAML) as the best-response oracle, ii) we propose a modified projected replicated dynamics, i.e., R-PRD, which ensures the computed meta-strategy of the adversary fall in the threshold, and iii) we also propose a protocol for the few-shot learning of the multiple strategies during testing. Extensive experiments on MuJoCo environments demonstrate that our proposed methods can outperform existing baselines, e.g., MAML.
    CheXRelNet: An Anatomy-Aware Model for Tracking Longitudinal Relationships between Chest X-Rays. (arXiv:2208.03873v1 [cs.CV])
    Despite the progress in utilizing deep learning to automate chest radiograph interpretation and disease diagnosis tasks, change between sequential Chest X-rays (CXRs) has received limited attention. Monitoring the progression of pathologies that are visualized through chest imaging poses several challenges in anatomical motion estimation and image registration, i.e., spatially aligning the two images and modeling temporal dynamics in change detection. In this work, we propose CheXRelNet, a neural model that can track longitudinal pathology change relations between two CXRs. CheXRelNet incorporates local and global visual features, utilizes inter-image and intra-image anatomical information, and learns dependencies between anatomical region attributes, to accurately predict disease change for a pair of CXRs. Experimental results on the Chest ImaGenome dataset show increased downstream performance compared to baselines. Code is available at https://github.com/PLAN-Lab/ChexRelNet
    Socially Intelligent Genetic Agents for the Emergence of Explicit Norms. (arXiv:2208.03789v1 [cs.MA])
    Norms help regulate a society. Norms may be explicit (represented in structured form) or implicit. We address the emergence of explicit norms by developing agents who provide and reason about explanations for norm violations in deciding sanctions and identifying alternative norms. These agents use a genetic algorithm to produce norms and reinforcement learning to learn the values of these norms. We find that applying explanations leads to norms that provide better cohesion and goal satisfaction for the agents. Our results are stable for societies with differing attitudes of generosity.  ( 2 min )
    An Empirical Analysis of the Laplace and Neural Tangent Kernels. (arXiv:2208.03761v1 [stat.ML])
    The neural tangent kernel is a kernel function defined over the parameter distribution of an infinite width neural network. Despite the impracticality of this limit, the neural tangent kernel has allowed for a more direct study of neural networks and a gaze through the veil of their black box. More recently, it has been shown theoretically that the Laplace kernel and neural tangent kernel share the same reproducing kernel Hilbert space in the space of $\mathbb{S}^{d-1}$ alluding to their equivalence. In this work, we analyze the practical equivalence of the two kernels. We first do so by matching the kernels exactly and then by matching posteriors of a Gaussian process. Moreover, we analyze the kernels in $\mathbb{R}^d$ and experiment with them in the task of regression.  ( 2 min )
    Autonomous Reinforcement Learning: Formalism and Benchmarking. (arXiv:2112.09605v2 [cs.LG] UPDATED)
    Reinforcement learning (RL) provides a naturalistic framing for learning through trial and error, which is appealing both because of its simplicity and effectiveness and because of its resemblance to how humans and animals acquire skills through experience. However, real-world embodied learning, such as that performed by humans and animals, is situated in a continual, non-episodic world, whereas common benchmark tasks in RL are episodic, with the environment resetting between trials to provide the agent with multiple attempts. This discrepancy presents a major challenge when attempting to take RL algorithms developed for episodic simulated environments and run them on real-world platforms, such as robots. In this paper, we aim to address this discrepancy by laying out a framework for Autonomous Reinforcement Learning (ARL): reinforcement learning where the agent not only learns through its own experience, but also contends with lack of human supervision to reset between trials. We introduce a simulated benchmark EARL around this framework, containing a set of diverse and challenging simulated tasks reflective of the hurdles introduced to learning when only a minimal reliance on extrinsic intervention can be assumed. We show that standard approaches to episodic RL and existing approaches struggle as interventions are minimized, underscoring the need for developing new algorithms for reinforcement learning with a greater focus on autonomy.
    Parabolic Relaxation for Quadratically-constrained Quadratic Programming -- Part I: Definitions & Basic Properties. (arXiv:2208.03622v1 [math.OC])
    For general quadratically-constrained quadratic programming (QCQP), we propose a parabolic relaxation described with convex quadratic constraints. An interesting property of the parabolic relaxation is that the original non-convex feasible set is contained on the boundary of the parabolic relaxation. Under certain assumptions, this property enables one to recover near-optimal feasible points via objective penalization. Moreover, through an appropriate change of coordinates that requires a one-time computation of an optimal basis, the easier-to-solve parabolic relaxation can be made as strong as a semidefinite programming (SDP) relaxation, which can be effective in accelerating algorithms that require solving a sequence of convex surrogates. The majority of theoretical and computational results are given in the next part of this work [57].  ( 2 min )
    Delta Hedging Liquidity Positions on Automated Market Makers. (arXiv:2208.03318v1 [cs.CE])
    Liquidity Providers on Automated Market Makers generate millions of USD in transaction fees daily. However, the net value of a Liquidity Position is vulnerable to price changes in the underlying assets in the pool. The dominant measure of loss in a Liquidity Position is Impermanent Loss. Impermanent Loss for Constant Function Market Makers has been widely studied. We propose a new metric to measure Liquidity Position PNL based on price movement from the underlying assets. We show how this new metric more appropriately measures the change in the net value of a Liquidity Position as a function of price movement in the underlying assets. Our second contribution is an algorithm to delta hedge arbitrary Liquidity Positions on both uniform liquidity Automated Market Makers (such as Uniswap v2) and concentrated liquidity Automated Market Makers (such as Uniswap v3) via a combination of derivatives.  ( 2 min )
    Reliability of Solutions in Linear Ordering Problem: New Probabilistic Insight and Algorithms. (arXiv:2208.03860v1 [cs.LG])
    In this work, our goal is to characterize the reliability of the solutions that can be obtained by the linear ordering problem (LOP) which is used to order $M$ objects from their pairwise comparisons. We adopt a probabilistic perspective, where the results of pairwise comparisons are modeled as Bernoulli variables with a common parameter which we estimate from the observed data. Estimation by brute-force enumeration has a prohibitive complexity of O($M!$) we thus reformulate the problem and introduce a concept of Slater's spectrum which generalizes Slater's index, and next, devising an efficient algorithm to find the spectrum, we lower the complexity to O($M^2 2^M$) which is manageable for moderate-size LOPs. Furthermore, with a minor modification of the algorithm, we are able to find all solutions of the LOP. Numerical examples on synthetic and real-world data are shown and the Python-implemented algorithms are publicly available.
    Sampling Based On Natural Image Statistics Improves Local Surrogate Explainers. (arXiv:2208.03961v1 [cs.CV])
    Many problems in computer vision have recently been tackled using models whose predictions cannot be easily interpreted, most commonly deep neural networks. Surrogate explainers are a popular post-hoc interpretability method to further understand how a model arrives at a particular prediction. By training a simple, more interpretable model to locally approximate the decision boundary of a non-interpretable system, we can estimate the relative importance of the input features on the prediction. Focusing on images, surrogate explainers, e.g., LIME, generate a local neighbourhood around a query image by sampling in an interpretable domain. However, these interpretable domains have traditionally been derived exclusively from the intrinsic features of the query image, not taking into consideration the manifold of the data the non-interpretable model has been exposed to in training (or more generally, the manifold of real images). This leads to suboptimal surrogates trained on potentially low probability images. We address this limitation by aligning the local neighbourhood on which the surrogate is trained with the original training data distribution, even when this distribution is not accessible. We propose two approaches to do so, namely (1) altering the method for sampling the local neighbourhood and (2) using perceptual metrics to convey some of the properties of the distribution of natural images.
    Deep Classifiers with Label Noise Modeling and Distance Awareness. (arXiv:2110.02609v2 [stat.ML] UPDATED)
    Uncertainty estimation in deep learning has recently emerged as a crucial area of interest to advance reliability and robustness in safety-critical applications. While there have been many proposed methods that either focus on distance-aware model uncertainties for out-of-distribution detection or on input-dependent label uncertainties for in-distribution calibration, both of these types of uncertainty are often necessary. In this work, we propose the HetSNGP method for jointly modeling the model and data uncertainty. We show that our proposed model affords a favorable combination between these two types of uncertainty and thus outperforms the baseline methods on some challenging out-of-distribution datasets, including CIFAR-100C, ImageNet-C, and ImageNet-A. Moreover, we propose HetSNGP Ensemble, an ensembled version of our method which additionally models uncertainty over the network parameters and outperforms other ensemble baselines.
    DP$^2$-VAE: Differentially Private Pre-trained Variational Autoencoders. (arXiv:2208.03409v1 [cs.LG])
    Modern machine learning systems achieve great success when trained on large datasets. However, these datasets usually contain sensitive information (e.g. medical records, face images), leading to serious privacy concerns. Differentially private generative models (DPGMs) emerge as a solution to circumvent such privacy concerns by generating privatized sensitive data. Similar to other differentially private (DP) learners, the major challenge for DPGM is also how to achieve a subtle balance between utility and privacy. We propose DP$^2$-VAE, a novel training mechanism for variational autoencoders (VAE) with provable DP guarantees and improved utility via \emph{pre-training on private data}. Under the same DP constraints, DP$^2$-VAE minimizes the perturbation noise during training, and hence improves utility. DP$^2$-VAE is very flexible and easily amenable to many other VAE variants. Theoretically, we study the effect of pretraining on private data. Empirically, we conduct extensive experiments on image datasets to illustrate our superiority over baselines under various privacy budgets and evaluation metrics.
    How and what to learn:The modes of machine learning. (arXiv:2202.13829v2 [cs.LG] UPDATED)
    Despite their great success, neural networks still remain as black-boxes due to the lack of interpretability. Here we propose a new analyzing method, namely the weight pathway analysis (WPA), to make them transparent. We consider weights in pathways that link neurons longitudinally from input neurons to output neurons, or simply weight pathways, as the basic units for understanding a neural network, and decompose a neural network into a series of subnetworks of such weight pathways. A visualization scheme of the subnetworks is presented that gives longitudinal perspectives of the network like radiographs, making the internal structures of the network visible. Impacts of parameter adjustments or structural changes to the network can be visualized via such radiographs. Characteristic maps are established for subnetworks to characterize the enhancement or suppression of the influence of input samples on each output neuron. Using WPA, we discover that neural network store and utilize information in a holographic way, that is, subnetworks encode all training samples in a coherent structure and thus only by investigating the weight pathways can one explore samples stored in the network. Furthermore, with WPA, we reveal fundamental learning modes of a neural network: the linear learning mode and the nonlinear learning mode. The former extracts linearly separable features while the latter extracts linearly inseparable features. The hidden-layer neurons self-organize into different classes for establishing learning modes and for reaching the training goal. The finding of learning modes provides us the theoretical ground for understanding some of the fundamental problems of machine learning, such as the dynamics of learning process, the role of linear and nonlinear neurons, as well as the role of network width and depth.
    Warming-up recurrent neural networks to maximize reachable multi-stability greatly improves learning. (arXiv:2106.01001v2 [cs.LG] UPDATED)
    Training recurrent neural networks is known to be difficult when time dependencies become long. Consequently, training standard gated cells such as the gated recurrent unit (GRU) and the long short-term memory (LSTM) on benchmarks where long-term memory is required remains an arduous task. In this work, we show that although most classical networks have only one stable equilibrium at initialisation, learning on tasks that require long-term memory only occurs once the number of network stable equilibria increases; a property known as multistability. Multistability is often not easily attained by initially monostable networks, making learning of long-term dependencies difficult. This insight leads to the design of a novel, general way to initialise any recurrent network connectivity through a procedure called "warmup" to improve its capability to learn arbitrarily long time dependencies. This initialisation procedure is designed to maximise network reachable multistability, i.e., the number of attractors within the network that can be reached through relevant input trajectories. Warming up is performed before training, using stochastic gradient descent on a specifically designed loss. We show on information restitution, sequence classification, and reinforcement learning benchmarks that warming up greatly improves recurrent neural network performance for multiple recurrent cell types, but sometimes impedes precision. We therefore introduce a parallel recurrent network structure with a partial warmup that is shown to greatly improve learning of long-term dependencies in sequences while maintaining high levels of precision. This approach provides a general framework for improving learning abilities of any recurrent cell type when long-term memory is required.
    Style-based quantum generative adversarial networks for Monte Carlo events. (arXiv:2110.06933v2 [quant-ph] UPDATED)
    We propose and assess an alternative quantum generator architecture in the context of generative adversarial learning for Monte Carlo event generation, used to simulate particle physics processes at the Large Hadron Collider (LHC). We validate this methodology by implementing the quantum network on artificial data generated from known underlying distributions. The network is then applied to Monte Carlo-generated datasets of specific LHC scattering processes. The new quantum generator architecture leads to a generalization of the state-of-the-art implementations, achieving smaller Kullback-Leibler divergences even with shallow-depth networks. Moreover, the quantum generator successfully learns the underlying distribution functions even if trained with small training sample sets; this is particularly interesting for data augmentation applications. We deploy this novel methodology on two different quantum hardware architectures, trapped-ion and superconducting technologies, to test its hardware-independent viability.
    Solving the Online Assignment Problem with Machine Learned Advice. (arXiv:2208.04016v1 [cs.CC])
    The online assignment problem plays an important role in operational research and computer science which is why immense attention has been given to improving its solution quality. Due to the incomplete information about the input, it is difficult for online algorithms to produce the optimal solution. The quality of the solution of an online algorithm is measured using a competitive ratio. No online deterministic algorithm can achieve a competitive ratio better than (2n-1). It has been shown that advice in online computation improves the lower bound of the competitive ratio of online problems. Advice in online computation can be interpreted as additional information for the online algorithm to compensate for the lack of information about the whole input sequence. In this study, we investigate how introducing machine-learned advice could improve the competitive ratio for this problem. We provide an online algorithm for the online assignment problem by simulating a machine learning algorithm that predicts the whole input in advance. We utilize an optimal offline algorithm to provide a matching solution from the predicted input. Furthermore, we investigate how the prediction error of machine learning affects the competitive ratio of the online algorithm. We utilize a benchmark data set to perform our empirical analysis. We show that as the Machine Learning prediction error increases, the solution quality decreases. Moreover, the magnitude of error is directly proportional to the size of the input. This result is analogous to the competitive ratio of the best deterministic algorithm for the online assignment problem which is dependent also on the parameter n.
    Can collaborative learning be private, robust and scalable?. (arXiv:2205.02652v2 [cs.LG] UPDATED)
    In federated learning for medical image analysis, the safety of the learning protocol is paramount. Such settings can often be compromised by adversaries that target either the private data used by the federation or the integrity of the model itself. This requires the medical imaging community to develop mechanisms to train collaborative models that are private and robust against adversarial data. In response to these challenges, we propose a practical open-source framework to study the effectiveness of combining differential privacy, model compression and adversarial training to improve the robustness of models against adversarial samples under train- and inference-time attacks. Using our framework, we achieve competitive model performance, a significant reduction in model's size and an improved empirical adversarial robustness without a severe performance degradation, critical in medical image analysis.
    Enforcing continuous symmetries in physics-informed neural network for solving forward and inverse problems of partial differential equations. (arXiv:2206.09299v2 [cs.LG] UPDATED)
    As a typical application of deep learning, physics-informed neural network (PINN) {has been} successfully used to find numerical solutions of partial differential equations (PDEs), but how to improve the limited accuracy is still a great challenge for PINN. In this work, we introduce a new method, symmetry-enhanced physics informed neural network (SPINN) where the invariant surface conditions induced by the Lie symmetries or non-classical symmetries of PDEs are embedded into the loss function in PINN, to improve the accuracy of PINN for solving the forward and inverse problems of PDEs. We test the effectiveness of SPINN for the forward problem via two groups of ten independent numerical experiments using different numbers of collocation points and neurons for the heat equation, Korteweg-de Vries (KdV) equation and potential Burgers {equations} respectively, and for the inverse problem by considering different layers and neurons as well as different training points for the Burgers equation in potential form. The numerical results show that SPINN performs better than PINN with fewer training points and simpler architecture of neural network. Furthermore, we discuss the computational overhead of SPINN in terms of the relative computational cost to PINN and show that the training time of SPINN has no obvious increases, even less than PINN for certain cases.
    Optimising hadronic collider simulations using amplitude neural networks. (arXiv:2202.04506v2 [hep-ph] UPDATED)
    Precision phenomenological studies of high-multiplicity scattering processes at collider experiments present a substantial theoretical challenge and are vitally important ingredients in experimental measurements. Machine learning technology has the potential to dramatically optimise simulations for complicated final states. We investigate the use of neural networks to approximate matrix elements, studying the case of loop-induced diphoton production through gluon fusion. We train neural network models on one-loop amplitudes from the NJet C++ library and interface them with the Sherpa Monte Carlo event generator to provide the matrix element within a realistic hadronic collider simulation. Computing some standard observables with the models and comparing to conventional techniques, we find excellent agreement in the distributions and a reduced total simulation time by a factor of thirty.
    Entity Alignment with Reliable Path Reasoning and Relation-Aware Heterogeneous Graph Transformer. (arXiv:2205.08806v2 [cs.CL] UPDATED)
    Entity Alignment (EA) has attracted widespread attention in both academia and industry, which aims to seek entities with same meanings from different Knowledge Graphs (KGs). There are substantial multi-step relation paths between entities in KGs, indicating the semantic relations of entities. However, existing methods rarely consider path information because not all natural paths facilitate for EA judgment. In this paper, we propose a more effective entity alignment framework, RPR-RHGT, which integrates relation and path structure information, as well as the heterogeneous information in KGs. Impressively, an initial reliable path reasoning algorithm is developed to generate the paths favorable for EA task from the relation structures of KGs, which is the first algorithm in the literature to successfully use unrestricted path information. In addition, to efficiently capture heterogeneous features in entity neighborhoods, a relation-aware heterogeneous graph transformer is designed to model the relation and path structures of KGs. Extensive experiments on three well-known datasets show RPR-RHGT significantly outperforms 11 state-of-the-art methods, exceeding the best performing baseline up to 8.62% on Hits@1. We also show its better performance than the baselines on different ratios of training set, and harder datasets.
    Granger Causality using Neural Networks. (arXiv:2208.03703v1 [stat.ML])
    The Granger Causality (GC) test is a famous statistical hypothesis test for investigating if the past of one time series affects the future of the other. It helps in answering the question whether one time series is helpful in forecasting. Standard traditional approaches to Granger causality detection commonly assume linear dynamics, but such simplification does not hold in many real-world applications, e.g., neuroscience or genomics that are inherently non-linear. In such cases, imposing linear models such as Vector Autoregressive (VAR) models can lead to inconsistent estimation of true Granger Causal interactions. Machine Learning (ML) can learn the hidden patterns in the datasets specifically Deep Learning (DL) has shown tremendous promise in learning the non-linear dynamics of complex systems. Recent work of Tank et al propose to overcome the issue of linear simplification in VAR models by using neural networks combined with sparsity-inducing penalties on the learn-able weights. In this work, we build upon ideas introduced by Tank et al. We propose several new classes of models that can handle underlying non-linearity. Firstly, we present the Learned Kernal VAR(LeKVAR) model-an extension of VAR models that also learns kernel parametrized by a neural net. Secondly, we show one can directly decouple lags and individual time series importance via decoupled penalties. This decoupling provides better scaling and allows us to embed lag selection into RNNs. Lastly, we propose a new training algorithm that supports mini-batching, and it is compatible with commonly used adaptive optimizers such as Adam.he proposed techniques are evaluated on several simulated datasets inspired by real-world applications.We also apply these methods to the Electro-Encephalogram (EEG) data for an epilepsy patient to study the evolution of GC before , during and after seizure across the 19 EEG channels.
    Restricted Boltzmann Machine and Deep Belief Network: Tutorial and Survey. (arXiv:2107.12521v2 [cs.LG] UPDATED)
    This is a tutorial and survey paper on Boltzmann Machine (BM), Restricted Boltzmann Machine (RBM), and Deep Belief Network (DBN). We start with the required background on probabilistic graphical models, Markov random field, Gibbs sampling, statistical physics, Ising model, and the Hopfield network. Then, we introduce the structures of BM and RBM. The conditional distributions of visible and hidden variables, Gibbs sampling in RBM for generating variables, training BM and RBM by maximum likelihood estimation, and contrastive divergence are explained. Then, we discuss different possible discrete and continuous distributions for the variables. We introduce conditional RBM and how it is trained. Finally, we explain deep belief network as a stack of RBM models. This paper on Boltzmann machines can be useful in various fields including data science, statistics, neural computation, and statistical physics.
    Deep Learning Closure Models for Large-Eddy Simulation of Flows around Bluff Bodies. (arXiv:2208.03498v1 [physics.flu-dyn])
    A deep learning (DL) closure model for large-eddy simulation (LES) is developed and evaluated for incompressible flows around a rectangular cylinder at moderate Reynolds numbers. Near-wall flow simulation remains a central challenge in aerodynamic modeling: RANS predictions of separated flows are often inaccurate, while LES can require prohibitively small near-wall mesh sizes. The DL-LES model is trained using adjoint PDE optimization methods to match, as closely as possible, direct numerical simulation (DNS) data. It is then evaluated out-of-sample (i.e., for new aspect ratios and Reynolds numbers not included in the training data) and compared against a standard LES model (the dynamic Smagorinsky model). The DL-LES model outperforms dynamic Smagorinsky and is able to achieve accurate LES predictions on a relatively coarse mesh (downsampled from the DNS grid by a factor of four in each Cartesian direction). We study the accuracy of the DL-LES model for predicting the drag coefficient, mean flow, and Reynolds stress. A crucial challenge is that the LES quantities of interest are the steady-state flow statistics; for example, the time-averaged mean velocity $\bar{u}(x) = \displaystyle \lim_{t \rightarrow \infty} \frac{1}{t} \int_0^t u(s,x) dx$. Calculating the steady-state flow statistics therefore requires simulating the DL-LES equations over a large number of flow times through the domain; it is a non-trivial question whether an unsteady partial differential equation model whose functional form is defined by a deep neural network can remain stable and accurate on $t \in [0, \infty)$. Our results demonstrate that the DL-LES model is accurate and stable over large physical time spans, enabling the estimation of the steady-state statistics for the velocity, fluctuations, and drag coefficient of turbulent flows around bluff bodies relevant to aerodynamic applications.
    Reliability Analysis of Complex Multi-State System Based on Universal Generating Function and Bayesian Network. (arXiv:2208.04130v1 [eess.SY])
    In the complex multi-state system (MSS), reliability analysis is a significant research content, both for equipment design, manufacturing, usage and maintenance. Universal Generating Function (UGF) is an important method in the reliability analysis, which efficiently obtains the system reliability by a fast algebraic procedure. However, when structural relationships between subsystems or components are not clear or without explicit expressions, the UGF method is difficult to use or not applicable at all. Bayesian Network (BN) has a natural advantage in terms of uncertainty inference for the relationship without explicit expressions. For the number of components is extremely large, though, it has the defects of low efficiency. To overcome the respective defects of UGF and BN, a novel reliability analysis method called UGF-BN is proposed for the complex MSS. In the UGF-BN framework, the UGF method is firstly used to analyze the bottom components with a large number. Then probability distributions obtained are taken as the input of BN. Finally, the reliability of the complex MSS is modeled by the BN method. This proposed method improves the computational efficiency, especially for the MSS with the large number of bottom components. Besides, the aircraft reliability-based design optimization based on the UGF-BN method is further studied with budget constraints on mass, power, and cost. Finally, two cases are used to demonstrate and verify the proposed method.
    Triple Sparsification of Graph Convolutional Networks without Sacrificing the Accuracy. (arXiv:2208.03559v1 [cs.LG])
    Graph Neural Networks (GNNs) are widely used to perform different machine learning tasks on graphs. As the size of the graphs grows, and the GNNs get deeper, training and inference time become costly in addition to the memory requirement. Thus, without sacrificing accuracy, graph sparsification, or model compression becomes a viable approach for graph learning tasks. A few existing techniques only study the sparsification of graphs and GNN models. In this paper, we develop a SparseGCN pipeline to study all possible sparsification in GNN. We provide a theoretical analysis and empirically show that it can add up to 11.6\% additional sparsity to the embedding matrix without sacrificing the accuracy of the commonly used benchmark graph datasets.
    Using Self-Supervised Auxiliary Tasks to Improve Fine-Grained Facial Representation. (arXiv:2105.06421v3 [cs.CV] UPDATED)
    In this paper, at first, the impact of ImageNet pre-training on fine-grained Facial Emotion Recognition (FER) is investigated which shows that when enough augmentations on images are applied, training from scratch provides better result than fine-tuning on ImageNet pre-training. Next, we propose a method to improve fine-grained and in-the-wild FER, called Hybrid Multi-Task Learning (HMTL). HMTL uses Self-Supervised Learning (SSL) as an auxiliary task during classical Supervised Learning (SL) in the form of Multi-Task Learning (MTL). Leveraging SSL during training can gain additional information from images for the primary fine-grained SL task. We investigate how proposed HMTL can be used in the FER domain by designing two customized version of common pre-text task techniques, puzzling and in-painting. We achieve state-of-the-art results on the AffectNet benchmark via two types of HMTL, without utilizing pre-training on additional data. Experimental results on the common SSL pre-training and proposed HMTL demonstrate the difference and superiority of our work. However, HMTL is not only limited to FER domain. Experiments on two types of fine-grained facial tasks, i.e., head pose estimation and gender recognition, reveals the potential of using HMTL to improve fine-grained facial representation.
    Accelerating Numerical Solvers for Large-Scale Simulation of Dynamical System via NeurVec. (arXiv:2208.03680v1 [cs.CE])
    Ensemble-based large-scale simulation of dynamical systems is essential to a wide range of science and engineering problems. Conventional numerical solvers used in the simulation are significantly limited by the step size for time integration, which hampers efficiency and feasibility especially when high accuracy is desired. To overcome this limitation, we propose a data-driven corrector method that allows using large step sizes while compensating for the integration error for high accuracy. This corrector is represented in the form of a vector-valued function and is modeled by a neural network to regress the error in the phase space. Hence we name the corrector neural vector (NeurVec). We show that NeurVec can achieve the same accuracy as traditional solvers with much larger step sizes. We empirically demonstrate that NeurVec can accelerate a variety of numerical solvers significantly and overcome the stability restriction of these solvers. Our results on benchmark problems, ranging from high-dimensional problems to chaotic systems, suggest that NeurVec is capable of capturing the leading error term and maintaining the statistics of ensemble forecasts.
    An Overview of Structural Coverage Metrics for Testing Neural Networks. (arXiv:2208.03407v1 [cs.SE])
    Deep neural network (DNN) models, including those used in safety-critical domains, need to be thoroughly tested to ensure that they can reliably perform well in different scenarios. In this article, we provide an overview of structural coverage metrics for testing DNN models, including neuron coverage (NC), k-multisection neuron coverage (kMNC), top-k neuron coverage (TKNC), neuron boundary coverage (NBC), strong neuron activation coverage (SNAC) and modified condition/decision coverage (MC/DC). We evaluate the metrics on realistic DNN models used for perception tasks (including LeNet-1, LeNet-4, LeNet-5, and ResNet20) as well as on networks used in autonomy (TaxiNet). We also provide a tool, DNNCov, which can measure the testing coverage for all these metrics. DNNCov outputs an informative coverage report to enable researchers and practitioners to assess the adequacy of DNN testing, compare different coverage measures, and to more conveniently inspect the model's internals during testing.
    Generalizability Analysis of Graph-based Trajectory Predictor with Vectorized Representation. (arXiv:2208.03578v1 [cs.LG])
    Trajectory prediction is one of the essential tasks for autonomous vehicles. Recent progress in machine learning gave birth to a series of advanced trajectory prediction algorithms. Lately, the effectiveness of using graph neural networks (GNNs) with vectorized representations for trajectory prediction has been demonstrated by many researchers. Nonetheless, these algorithms either pay little attention to models' generalizability across various scenarios or simply assume training and test data follow similar statistics. In fact, when test scenarios are unseen or Out-of-Distribution (OOD), the resulting train-test domain shift usually leads to significant degradation in prediction performance, which will impact downstream modules and eventually lead to severe accidents. Therefore, it is of great importance to thoroughly investigate the prediction models in terms of their generalizability, which can not only help identify their weaknesses but also provide insights on how to improve these models. This paper proposes a generalizability analysis framework using feature attribution methods to help interpret black-box models. For the case study, we provide an in-depth generalizability analysis of one of the state-of-the-art graph-based trajectory predictors that utilize vectorized representation. Results show significant performance degradation due to domain shift, and feature attribution provides insights to identify potential causes of these problems. Finally, we conclude the common prediction challenges and how weighting biases induced by the training process can deteriorate the accuracy.
    Federated Learning for Medical Applications: A Taxonomy, Current Trends, and Research Challenges. (arXiv:2208.03392v1 [cs.LG])
    With the advent of the IoT, AI, and ML/DL algorithms, the data-driven medical application has emerged as a promising tool for designing reliable and scalable diagnostic and prognostic models from medical data. This has attracted a great deal of attention from academia to industry in recent years. This has undoubtedly improved the quality of healthcare delivery. However, these AI-based medical applications still have poor adoption due to their difficulties in satisfying strict security, privacy, and quality of service standards (such as low latency). Moreover, medical data are usually fragmented and private, making it challenging to generate robust results across populations. Recent developments in federated learning (FL) have made it possible to train complex machine-learned models in a distributed manner. Thus, FL has become an active research domain, particularly processing the medical data at the edge of the network in a decentralized way to preserve privacy and security concerns. To this end, this survey paper highlights the current and future of FL technology in medical applications where data sharing is a significant burden. It also review and discuss the current research trends and their outcomes for designing reliable and scalable FL models. We outline the general FL's statistical problems, device challenges, security, privacy concerns, and its potential in the medical domain. Moreover, our study is also focused on medical applications where we highlight the burden of global cancer and the efficient use of FL for the development of computer-aided diagnosis tools for addressing them. We hope that this review serves as a checkpoint that sets forth the existing state-of-the-art works in a thorough manner and offers open problems and future research directions for this field.
    The Influence of Network Structural Preference on Node Classification and Link Prediction. (arXiv:2208.03712v1 [cs.LG])
    Recent advances in complex network analysis opened a wide range of possibilities for applications in diverse fields. The power of the network analysis depends on the node features. The topology-based node features are realizations of local and global spatial relations and node connectivity structure. Hence, collecting correct information on the node characteristics and the connectivity structure of the neighboring nodes plays the most prominent role in node classification and link prediction in complex network analysis. The present work introduces a new feature abstraction method, namely the Transition Probabilities Matrix (TPM), based on embedding anonymous random walks on feature vectors. The node feature vectors consist of transition probabilities obtained from sets of walks in a predefined radius. The transition probabilities are directly related to the local connectivity structure, hence correctly embedded onto feature vectors. The success of the proposed embedding method is tested on node identification/classification and link prediction on three commonly used real-world networks. In real-world networks, nodes with similar connectivity structures are common; Thus, obtaining information from similar networks for predictions on the new networks is the distinguishing characteristic that makes the proposed algorithm superior to the state-of-the-art algorithms in terms of cross-networks generalization tasks.
    Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions. (arXiv:2208.04055v1 [cs.LG])
    Integrating functions on discrete domains into neural networks is key to developing their capability to reason about discrete objects. But, discrete domains are (1) not naturally amenable to gradient-based optimization, and (2) incompatible with deep learning architectures that rely on representations in high-dimensional vector spaces. In this work, we address both difficulties for set functions, which capture many important discrete problems. First, we develop a framework for extending set functions onto low-dimensional continuous domains, where many extensions are naturally defined. Our framework subsumes many well-known extensions as special cases. Second, to avoid undesirable low-dimensional neural network bottlenecks, we convert low-dimensional extensions into representations in high-dimensional spaces, taking inspiration from the success of semidefinite programs for combinatorial optimization. Empirically, we observe benefits of our extensions for unsupervised neural combinatorial optimization, in particular with high-dimensional representations.
    Active Learning for Non-Parametric Choice Models. (arXiv:2208.03346v1 [cs.LG])
    We study the problem of actively learning a non-parametric choice model based on consumers' decisions. We present a negative result showing that such choice models may not be identifiable. To overcome the identifiability problem, we introduce a directed acyclic graph (DAG) representation of the choice model, which in a sense captures as much information about the choice model as could information-theoretically be identified. We then consider the problem of learning an approximation to this DAG representation in an active-learning setting. We design an efficient active-learning algorithm to estimate the DAG representation of the non-parametric choice model, which runs in polynomial time when the set of frequent rankings is drawn uniformly at random. Our algorithm learns the distribution over the most popular items of frequent preferences by actively and repeatedly offering assortments of items and observing the item chosen. We show that our algorithm can better recover a set of frequent preferences on both a synthetic and publicly available dataset on consumers' preferences, compared to the corresponding non-active learning estimation algorithms. This demonstrates the value of our algorithm and active-learning approaches more generally.
    Transmission Neural Networks: From Virus Spread Models to Neural Networks. (arXiv:2208.03616v1 [cs.LG])
    This work connects models for virus spread on networks with their equivalent neural network representations. Based on this connection, we propose a new neural network architecture, called Transmission Neural Networks (TransNNs) where activation functions are primarily associated with links and are allowed to have different activation levels. Furthermore, this connection leads to the discovery and the derivation of three new activation functions with tunable or trainable parameters. Moreover, we prove that TransNNs with a single hidden layer and a fixed non-zero bias term are universal function approximators. Finally, we present new fundamental derivations of continuous time epidemic network models based on TransNNs.
    Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter. (arXiv:2208.03374v1 [cs.LG])
    Reinforcement learning agents must generalize beyond their training experience. Prior work has focused mostly on identical training and evaluation environments. Starting from the recently introduced Crafter benchmark, a 2D open world survival game, we introduce a new set of environments suitable for evaluating some agent's ability to generalize on previously unseen (numbers of) objects and to adapt quickly (meta-learning). In Crafter, the agents are evaluated by the number of unlocked achievements (such as collecting resources) when trained for 1M steps. We show that current agents struggle to generalize, and introduce novel object-centric agents that improve over strong baselines. We also provide critical insights of general interest for future work on Crafter through several experiments. We show that careful hyper-parameter tuning improves the PPO baseline agent by a large margin and that even feedforward agents can unlock almost all achievements by relying on the inventory display. We achieve new state-of-the-art performance on the original Crafter environment. Additionally, when trained beyond 1M steps, our tuned agents can unlock almost all achievements. We show that the recurrent PPO agents improve over feedforward ones, even with the inventory information removed. We introduce CrafterOOD, a set of 15 new environments that evaluate OOD generalization. On CrafterOOD, we show that the current agents fail to generalize, whereas our novel object-centric agents achieve state-of-the-art OOD generalization while also being interpretable. Our code is public.
    Constrained self-supervised method with temporal ensembling for fiber bundle detection on anatomic tracing data. (arXiv:2208.03569v1 [eess.IV])
    Anatomic tracing data provides detailed information on brain circuitry essential for addressing some of the common errors in diffusion MRI tractography. However, automated detection of fiber bundles on tracing data is challenging due to sectioning distortions, presence of noise and artifacts and intensity/contrast variations. In this work, we propose a deep learning method with a self-supervised loss function that takes anatomy-based constraints into account for accurate segmentation of fiber bundles on the tracer sections from macaque brains. Also, given the limited availability of manual labels, we use a semi-supervised training technique for efficiently using unlabeled data to improve the performance, and location constraints for further reduction of false positives. Evaluation of our method on unseen sections from a different macaque yields promising results with a true positive rate of ~0.90. The code for our method is available at https://github.com/v-sundaresan/fiberbundle_seg_tracing.
    An Urban Population Health Observatory for Disease Causal Pathway Analysis and Decision Support: Underlying Explainable Artificial Intelligence Model. (arXiv:2208.04144v1 [cs.AI])
    This study sought to (1) expand our existing Urban Population Health Observatory (UPHO) system by incorporating a semantics layer; (2) cohesively employ machine learning and semantic/logical inference to provide measurable evidence and detect pathways leading to undesirable health outcomes; (3) provide clinical use case scenarios and design case studies to identify socioenvironmental determinants of health associated with the prevalence of obesity, and (4) design a dashboard that demonstrates the use of UPHO in the context of obesity surveillance using the provided scenarios. The system design includes a knowledge graph generation component that provides contextual knowledge from relevant domains of interest. This system leverages semantics using concepts, properties, and axioms from existing ontologies. In addition, we used the publicly available US Centers for Disease Control and Prevention 500 Cities data set to perform multivariate analysis. A cohesive approach that employs machine learning and semantic/logical inference reveals pathways leading to diseases. In this study, we present 2 clinical case scenarios and a proof-of-concept prototype design of a dashboard that provides warnings, recommendations, and explanations and demonstrates the use of UPHO in the context of obesity surveillance, treatment, and prevention. While exploring the case scenarios using a support vector regression machine learning model, we found that poverty, lack of physical activity, education, and unemployment were the most important predictive variables that contribute to obesity in Memphis, TN. The application of UPHO could help reduce health disparities and improve urban population health. The expanded UPHO feature incorporates an additional level of interpretable knowledge to enhance physicians, researchers, and health officials' informed decision-making at both patient and community levels.
    Parabolic Relaxation for Quadratically-constrained Quadratic Programming -- Part II: Theoretical & Computational Results. (arXiv:2208.03625v1 [math.OC])
    In the first part of this work [32], we introduce a convex parabolic relaxation for quadratically-constrained quadratic programs, along with a sequential penalized parabolic relaxation algorithm to recover near-optimal feasible solutions. In this second part, we show that starting from a feasible solution or a near-feasible solution satisfying certain regularity conditions, the sequential penalized parabolic relaxation algorithm convergences to a point which satisfies Karush-Kuhn-Tucker optimality conditions. Next, we present numerical experiments on benchmark non-convex QCQP problems as well as large-scale instances of system identification problem demonstrating the efficiency of the proposed approach.
    Homomorphisms Between Transfer, Multi-Task, and Meta-Learning Systems. (arXiv:2208.03316v1 [cs.LG])
    Transfer learning, multi-task learning, and meta-learning are well-studied topics concerned with the generalization of knowledge across learning tasks and are closely related to general intelligence. But, the formal, general systems differences between them are underexplored in the literature. This lack of systems-level formalism leads to difficulties in coordinating related, inter-disciplinary engineering efforts. This manuscript formalizes transfer learning, multi-task learning, and meta-learning as abstract learning systems, consistent with the formal-minimalist abstract systems theory of Mesarovic and Takahara. Moreover, it uses the presented formalism to relate the three concepts of learning in terms of composition, hierarchy, and structural homomorphism. Findings are readily depicted in terms of input-output systems, highlighting the ease of delineating formal, general systems differences between transfer, multi-task, and meta-learning.
    Adversarial Robustness against Multiple and Single $l_p$-Threat Models via Quick Fine-Tuning of Robust Classifiers. (arXiv:2105.12508v2 [cs.LG] UPDATED)
    A major drawback of adversarially robust models, in particular for large scale datasets like ImageNet, is the extremely long training time compared to standard ones. Moreover, models should be robust not only to one $l_p$-threat model but ideally to all of them. In this paper we propose Extreme norm Adversarial Training (E-AT) for multiple-norm robustness which is based on geometric properties of $l_p$-balls. E-AT costs up to three times less than other adversarial training methods for multiple-norm robustness. Using E-AT we show that for ImageNet a single epoch and for CIFAR-10 three epochs are sufficient to turn any $l_p$-robust model into a multiple-norm robust model. In this way we get the first multiple-norm robust model for ImageNet and boost the state-of-the-art for multiple-norm robustness to more than $51\%$ on CIFAR-10. Finally, we study the general transfer via fine-tuning of adversarial robustness between different individual $l_p$-threat models and improve the previous SOTA $l_1$-robustness on both CIFAR-10 and ImageNet. Extensive experiments show that our scheme works across datasets and architectures including vision transformers.
    Detecting Algorithmically Generated Domains Using a GCNN-LSTM Hybrid Neural Network. (arXiv:2208.03445v1 [cs.CR])
    Domain generation algorithm (DGA) is used by botnets to build a stealthy command and control (C&C) communication channel between the C&C server and the bots. A DGA can periodically produce a large number of pseudo-random algorithmically generated domains (AGDs). AGD detection algorithms provide a lightweight, promising solution in response to the existing DGA techniques. In this paper, a GCNN (gated convolutional neural network)-LSTM (long short-term memory) Hybrid Neural Network (GLHNN) for AGD detection is proposed. In GLHNN, GCNN is applied to extract the informative features from domain names on top of LSTM which further processes the feature sequence. GLHNN is experimentally validated using representative AGDs covering six classes of DGAs. GLHNN is compared with the state-of-the-art detection models and demonstrates the best overall detection performance among these tested models.
    An Intensity and Phase Stacked Analysis of Phase-OTDR System using Deep Transfer Learning and Recurrent Neural Networks. (arXiv:2206.12484v2 [cs.LG] UPDATED)
    Distributed acoustic sensors (DAS) are effective apparatus which are widely used in many application areas for recording signals of various events with very high spatial resolution along the optical fiber. To detect and recognize the recorded events properly, advanced signal processing algorithms with high computational demands are crucial. Convolutional neural networks are highly capable tools for extracting spatial information and very suitable for event recognition applications in DAS. Long-short term memory (LSTM) is an effective instrument for processing sequential data. In this study, we proposed a multi-input multi-output, two stage feature extraction methodology that combines the capabilities of these neural network architectures with transfer learning to classify vibrations applied to an optical fiber by a piezo transducer. First, we extracted the differential amplitude and phase information from the Phase-OTDR recordings and stored them in a temporal-spatial data matrix. Then, we used a state-of-the-art pre-trained CNN without dense layers as a feature extractor in the first stage. In the second stage, we used LSTMs to further analyze the features extracted by the CNN. Finally, we used a dense layer to classify the extracted features. To observe the effect of the utilized CNN architecture, we tested our model with five state-of-the art pre-trained models (VGG-16, ResNet-50, DenseNet-121, MobileNet and Inception-v3). The results show that using the VGG-16 architecture in our framework manages to obtain 100% classification accuracy in 50 trainings and got the best results on our Phase-OTDR dataset. Outcomes of this study indicate that the pre-trained CNNs combined with LSTM are very suitable for the analysis of differential amplitude and phase information, represented in a temporal spatial data matrix which is promising for event recognition operations in DAS applications.  ( 3 min )
    Chronological Self-Training for Real-Time Speaker Diarization. (arXiv:2208.03393v1 [cs.SD])
    Diarization partitions an audio stream into segments based on the voices of the speakers. Real-time diarization systems that include an enrollment step should limit enrollment training samples to reduce user interaction time. Although training on a small number of samples yields poor performance, we show that the accuracy can be improved dramatically using a chronological self-training approach. We studied the tradeoff between training time and classification performance and found that 1 second is sufficient to reach over 95% accuracy. We evaluated on 700 audio conversation files of about 10 minutes each from 6 different languages and demonstrated average diarization error rates as low as 10%.
    Adversarial robustness of $\beta-$VAE through the lens of local geometry. (arXiv:2208.03923v1 [cs.LG])
    Variational autoencoders (VAEs) are susceptible to adversarial attacks. An adversary can find a small perturbation in the input sample to change its latent encoding non-smoothly, thereby compromising the reconstruction. A known reason for such vulnerability is the latent space distortions arising from a mismatch between approximated latent posterior and a prior distribution. Consequently, a slight change in the inputs leads to a significant change in the latent space encodings. This paper demonstrates that the sensitivity around a data point is due to a directional bias of a stochastic pullback metric tensor induced by the encoder network. The pullback metric tensor measures the infinitesimal volume change from input to latent space. Thus, it can be viewed as a lens to analyse the effect of small changes in the input leading to distortions in the latent space. We propose robustness evaluation scores using the eigenspectrum of a pullback metric. Moreover, we empirically show that the scores correlate with the robustness parameter $\beta$ of the $\beta-$VAE.
    NAS-Navigator: Visual Steering for Explainable One-Shot Deep Neural Network Synthesis. (arXiv:2009.13008v3 [cs.LG] UPDATED)
    Recent advancements in the area of deep learning have shown the effectiveness of very large neural networks in several applications. However, as these deep neural networks continue to grow in size, it becomes more and more difficult to configure their many parameters to obtain good results. Presently, analysts must experiment with many different configurations and parameter settings, which is labor-intensive and time-consuming. On the other hand, the capacity of fully automated techniques for neural network architecture search is limited without the domain knowledge of human experts. To deal with the problem, we formulate the task of neural network architecture optimization as a graph space exploration, based on the one-shot architecture search technique. In this approach, a super-graph of all candidate architectures is trained in one-shot and the optimal neural network is identified as a sub-graph. In this paper, we present a framework that allows analysts to effectively build the solution sub-graph space and guide the network search by injecting their domain knowledge. Starting with the network architecture space composed of basic neural network components, analysts are empowered to effectively select the most promising components via our one-shot search scheme. Applying this technique in an iterative manner allows analysts to converge to the best performing neural network architecture for a given application. During the exploration, analysts can use their domain knowledge aided by cues provided from a scatterplot visualization of the search space to edit different components and guide the search for faster convergence. We designed our interface in collaboration with several deep learning researchers and its final effectiveness is evaluated with a user study and two case studies.
    Preserving Fine-Grain Feature Information in Classification via Entropic Regularization. (arXiv:2208.03684v1 [cs.CV])
    Labeling a classification dataset implies to define classes and associated coarse labels, that may approximate a smoother and more complicated ground truth. For example, natural images may contain multiple objects, only one of which is labeled in many vision datasets, or classes may result from the discretization of a regression problem. Using cross-entropy to train classification models on such coarse labels is likely to roughly cut through the feature space, potentially disregarding the most meaningful such features, in particular losing information on the underlying fine-grain task. In this paper we are interested in the problem of solving fine-grain classification or regression, using a model trained on coarse-grain labels only. We show that standard cross-entropy can lead to overfitting to coarse-related features. We introduce an entropy-based regularization to promote more diversity in the feature space of trained models, and empirically demonstrate the efficacy of this methodology to reach better performance on the fine-grain problems. Our results are supported through theoretical developments and empirical validation.
    A review on longitudinal data analysis with random forest in precision medicine. (arXiv:2208.04112v1 [stat.ML])
    Precision medicine provides customized treatments to patients based on their characteristics and is a promising approach to improving treatment efficiency. Large scale omics data are useful for patient characterization, but often their measurements change over time, leading to longitudinal data. Random forest is one of the state-of-the-art machine learning methods for building prediction models, and can play a crucial role in precision medicine. In this paper, we review extensions of the standard random forest method for the purpose of longitudinal data analysis. Extension methods are categorized according to the data structures for which they are designed. We consider both univariate and multivariate responses and further categorize the repeated measurements according to whether the time effect is relevant. Information of available software implementations of the reviewed extensions is also given. We conclude with discussions on the limitations of our review and some future research directions.
    Learning Modular Structures That Generalize Out-of-Distribution. (arXiv:2208.03753v1 [cs.LG])
    Out-of-distribution (O.O.D.) generalization remains to be a key challenge for real-world machine learning systems. We describe a method for O.O.D. generalization that, through training, encourages models to only preserve features in the network that are well reused across multiple training domains. Our method combines two complementary neuron-level regularizers with a probabilistic differentiable binary mask over the network, to extract a modular sub-network that achieves better O.O.D. performance than the original network. Preliminary evaluation on two benchmark datasets corroborates the promise of our method.
    Graph Pooling with Maximum-Weight $k$-Independent Sets. (arXiv:2208.03523v1 [cs.LG])
    Graph reductions are fundamental when dealing with large scale networks and relational data. They allow to downsize tasks of high computational impact by solving them in coarsened structures. At the same time, graph reductions play the role of pooling layers in graph neural networks, to extract multi-resolution representations from structures. In these contexts, the ability of the reduction mechanism to preserve distance relationships and topological properties appears fundamental, along with a scalability enabling its application to real-world sized problems. In this paper, we introduce a graph coarsening mechanism based on the graph-theoretic concept of maximum-weight $k$-independent sets, providing a greedy algorithm that allows efficient parallel implementation on GPUs. Our method is the first graph-structured counterpart of controllable equispaced coarsening mechanisms in regular data (images, sequences). We prove theoretical guarantees for distortion bounds on path lengths, as well as the ability to preserve key topological properties in the coarsened graphs. We leverage these concepts to define a graph pooling mechanism that we empirically assess in graph classification tasks, showing that it compares favorably against pooling methods in literature.
    Machine learning the real discriminant locus. (arXiv:2006.14078v2 [stat.ML] UPDATED)
    Parameterized systems of polynomial equations arise in many applications in science and engineering with the real solutions describing, for example, equilibria of a dynamical system, linkages satisfying design constraints, and scene reconstruction in computer vision. Since different parameter values can have a different number of real solutions, the parameter space is decomposed into regions whose boundary forms the real discriminant locus. This article views locating the real discriminant locus as a supervised classification problem in machine learning where the goal is to determine classification boundaries over the parameter space, with the classes being the number of real solutions. For multidimensional parameter spaces, this article presents a novel sampling method which carefully samples the parameter space. At each sample point, homotopy continuation is used to obtain the number of real solutions to the corresponding polynomial system. Machine learning techniques including nearest neighbor and deep learning are used to efficiently approximate the real discriminant locus. One application of having learned the real discriminant locus is to develop a real homotopy method that only tracks the real solution paths unlike traditional methods which track all~complex~solution~paths. Examples show that the proposed approach can efficiently approximate complicated solution boundaries such as those arising from the equilibria of the Kuramoto model.
    Variational Autoencoders for Anomaly Detection in Respiratory Sounds. (arXiv:2208.03326v1 [cs.SD])
    This paper proposes a weakly-supervised machine learning-based approach aiming at a tool to alert patients about possible respiratory diseases. Various types of pathologies may affect the respiratory system, potentially leading to severe diseases and, in certain cases, death. In general, effective prevention practices are considered as major actors towards the improvement of the patient's health condition. The proposed method strives to realize an easily accessible tool for the automatic diagnosis of respiratory diseases. Specifically, the method leverages Variational Autoencoder architectures permitting the usage of training pipelines of limited complexity and relatively small-sized datasets. Importantly, it offers an accuracy of 57 %, which is in line with the existing strongly-supervised approaches.  ( 2 min )
    Classical Shadows With Noise. (arXiv:2011.11580v2 [quant-ph] UPDATED)
    The classical shadows protocol, recently introduced by Huang, Kueng, and Preskill [Nat. Phys. 16, 1050 (2020)], is a quantum-classical protocol to estimate properties of an unknown quantum state. Unlike full quantum state tomography, the protocol can be implemented on near-term quantum hardware and requires few quantum measurements to make many predictions with a high success probability. In this paper, we study the effects of noise on the classical shadows protocol. In particular, we consider the scenario in which the quantum circuits involved in the protocol are subject to various known noise channels and derive an analytical upper bound for the sample complexity in terms of a shadow seminorm for both local and global noise. Additionally, by modifying the classical post-processing step of the noiseless protocol, we define a new estimator that remains unbiased in the presence of noise. As applications, we show that our results can be used to prove rigorous sample complexity upper bounds in the cases of depolarizing noise and amplitude damping.  ( 2 min )
    Data-aided Active User Detection with a User Activity Extraction Network for Grant-free SCMA Systems. (arXiv:2205.10780v2 [eess.SY] UPDATED)
    In grant-free sparse code multiple access (GF-SCMA) system, active user detection (AUD) is a major performance bottleneck as it involves complex combinatorial problem, which makes joint design of contention resources for users and AUD at the receiver a crucial but a challenging problem. To this end, we propose autoencoder (AE)-based joint optimization of both preamble generation networks (PGNs) in the encoder side and data-aided AUD in the decoder side. The core architecture of the proposed AE is a novel user activity extraction network (UAEN) in the decoder that extracts a priori user activity information from the SCMA codeword data for the data-aided AUD. An end-to-end training of the proposed AE enables joint optimization of the contention resources, i.e., preamble sequences, each associated with one of the codebooks, and extraction of user activity information from both preamble and SCMA-based data transmission. Furthermore, we propose a self-supervised pre-training scheme for the UAEN prior to the end-to-end training, to ensure the convergence of the UAEN which lies deep inside the AE network. Simulation results demonstrated that the proposed AUD scheme achieved 3 to 5dB gain at a target activity detection error rate of $\bf{{10}^{-3}}$ compared to the state-of-the-art DL-based AUD schemes.  ( 3 min )
    Dynamic Maintenance of Kernel Density Estimation Data Structure: From Practice to Theory. (arXiv:2208.03915v1 [cs.LG])
    Kernel density estimation (KDE) stands out as a challenging task in machine learning. The problem is defined in the following way: given a kernel function $f(x,y)$ and a set of points $\{x_1, x_2, \cdots, x_n \} \subset \mathbb{R}^d$, we would like to compute $\frac{1}{n}\sum_{i=1}^{n} f(x_i,y)$ for any query point $y \in \mathbb{R}^d$. Recently, there has been a growing trend of using data structures for efficient KDE. However, the proposed KDE data structures focus on static settings. The robustness of KDE data structures over dynamic changing data distributions is not addressed. In this work, we focus on the dynamic maintenance of KDE data structures with robustness to adversarial queries. Especially, we provide a theoretical framework of KDE data structures. In our framework, the KDE data structures only require subquadratic spaces. Moreover, our data structure supports the dynamic update of the dataset in sublinear time. Furthermore, we can perform adaptive queries with the potential adversary in sublinear time.
    Quantum algorithms for SVD-based data representation and analysis. (arXiv:2104.08987v2 [quant-ph] UPDATED)
    This paper narrows the gap between previous literature on quantum linear algebra and practical data analysis on a quantum computer, formalizing quantum procedures that speed-up the solution of eigenproblems for data representations in machine learning. The power and practical use of these subroutines is shown through new quantum algorithms, sublinear in the input matrix's size, for principal component analysis, correspondence analysis, and latent semantic analysis. We provide a theoretical analysis of the run-time and prove tight bounds on the randomized algorithms' error. We run experiments on multiple datasets, simulating PCA's dimensionality reduction for image classification with the novel routines. The results show that the run-time parameters that do not depend on the input's size are reasonable and that the error on the computed model is small, allowing for competitive classification performances.  ( 2 min )
    "Let's Eat Grandma": Does Punctuation Matter in Sentence Representation?. (arXiv:2101.03029v2 [cs.CL] UPDATED)
    Neural network-based embeddings have been the mainstream approach for creating a vector representation of the text to capture lexical and semantic similarities and dissimilarities. In general, existing encoding methods dismiss the punctuation as insignificant information; consequently, they are routinely treated as a predefined token/word or eliminated in the pre-processing phase. However, punctuation could play a significant role in the semantics of the sentences, as in "Let's eat\hl{,} grandma" and "Let's eat grandma". We hypothesize that a punctuation-aware representation model would affect the performance of the downstream tasks. Thereby, we propose a model-agnostic method that incorporates both syntactic and contextual information to improve the performance of the sentiment classification task. We corroborate our findings by conducting experiments on publicly available datasets and provide case studies that our model generates representations with respect to the punctuation in the sentence.
    LCCDE: A Decision-Based Ensemble Framework for Intrusion Detection in The Internet of Vehicles. (arXiv:2208.03399v1 [cs.CR])
    Modern vehicles, including autonomous vehicles and connected vehicles, have adopted an increasing variety of functionalities through connections and communications with other vehicles, smart devices, and infrastructures. However, the growing connectivity of the Internet of Vehicles (IoV) also increases the vulnerabilities to network attacks. To protect IoV systems against cyber threats, Intrusion Detection Systems (IDSs) that can identify malicious cyber-attacks have been developed using Machine Learning (ML) approaches. To accurately detect various types of attacks in IoV networks, we propose a novel ensemble IDS framework named Leader Class and Confidence Decision Ensemble (LCCDE). It is constructed by determining the best-performing ML model among three advanced ML algorithms (XGBoost, LightGBM, and CatBoost) for every class or type of attack. The class leader models with their prediction confidence values are then utilized to make accurate decisions regarding the detection of various types of cyber-attacks. Experiments on two public IoV security datasets (Car-Hacking and CICIDS2017 datasets) demonstrate the effectiveness of the proposed LCCDE for intrusion detection on both intra-vehicle and external networks.
    Deep Machine Learning Reconstructing Lattice Topology with Strong Thermal Fluctuations. (arXiv:2208.04119v1 [stat.ML])
    Applying artificial intelligence to scientific problems (namely AI for science) is currently under hot debate. However, the scientific problems differ much from the conventional ones with images, texts, and etc., where new challenges emerges with the unbalanced scientific data and complicated effects from the physical setups. In this work, we demonstrate the validity of the deep convolutional neural network (CNN) on reconstructing the lattice topology (i.e., spin connectivities) in the presence of strong thermal fluctuations and unbalanced data. Taking the kinetic Ising model with Glauber dynamics as an example, the CNN maps the time-dependent local magnetic momenta (a single-node feature) evolved from a specific initial configuration (dubbed as an evolution instance) to the probabilities of the presences of the possible couplings. Our scheme distinguishes from the previous ones that might require the knowledge on the node dynamics, the responses from perturbations, or the evaluations of statistic quantities such as correlations or transfer entropy from many evolution instances. The fine tuning avoids the "barren plateau" caused by the strong thermal fluctuations at high temperatures. Accurate reconstructions can be made where the thermal fluctuations dominate over the correlations and consequently the statistic methods in general fail. Meanwhile, we unveil the generalization of CNN on dealing with the instances evolved from the unlearnt initial spin configurations and those with the unlearnt lattices. We raise an open question on the learning with unbalanced data in the nearly "double-exponentially" large sample space.
    Virtual Analog Modeling of Distortion Circuits Using Neural Ordinary Differential Equations. (arXiv:2205.01897v3 [eess.AS] CROSS LISTED)
    Recent research in deep learning has shown that neural networks can learn differential equations governing dynamical systems. In this paper, we adapt this concept to Virtual Analog (VA) modeling to learn the ordinary differential equations (ODEs) governing the first-order and the second-order diode clipper. The proposed models achieve performance comparable to state-of-the-art recurrent neural networks (RNNs) albeit using fewer parameters. We show that this approach does not require oversampling and allows to increase the sampling rate after the training has completed, which results in increased accuracy. Using a sophisticated numerical solver allows to increase the accuracy at the cost of slower processing. ODEs learned this way do not require closed forms but are still physically interpretable.  ( 2 min )
    Efficient Neural Net Approaches in Metal Casting Defect Detection. (arXiv:2208.04150v1 [cs.CV])
    One of the most pressing challenges prevalent in the steel manufacturing industry is the identification of surface defects. Early identification of casting defects can help boost performance, including streamlining production processes. Though, deep learning models have helped bridge this gap and automate most of these processes, there is a dire need to come up with lightweight models that can be deployed easily with faster inference times. This research proposes a lightweight architecture that is efficient in terms of accuracy and inference time compared with sophisticated pre-trained CNN architectures like MobileNet, Inception, and ResNet, including vision transformers. Methodologies to minimize computational requirements such as depth-wise separable convolution and global average pooling (GAP) layer, including techniques that improve architectural efficiencies and augmentations, have been experimented. Our results indicate that a custom model of 590K parameters with depth-wise separable convolutions outperformed pretrained architectures such as Resnet and Vision transformers in terms of accuracy (81.87%) and comfortably outdid architectures such as Resnet, Inception, and Vision transformers in terms of faster inference times (12 ms). Blurpool fared outperformed other techniques, with an accuracy of 83.98%. Augmentations had a paradoxical effect on the model performance. No direct correlation between depth-wise and 3x3 convolutions on inference time, they, however, they played a direct role in improving model efficiency by enabling the networks to go deeper and by decreasing the number of trainable parameters. Our work sheds light on the fact that custom networks with efficient architectures and faster inference times can be built without the need of relying on pre-trained architectures.  ( 3 min )
    Multi-agent reinforcement learning for intent-based service assurance in cellular networks. (arXiv:2208.03740v1 [cs.LG])
    Recently, intent-based management is receiving good attention in telecom networks owing to stringent performance requirements for many of the use cases. Several approaches on the literature employ traditional methods in the telecom domain to fulfill intents on the KPIs, which can be defined as a closed loop. However, these methods consider every closed-loop independent of each other which degrades the combined closed-loop performance. Also, when many closed loops are needed, these methods are not easily scalable. Multi-agent reinforcement learning (MARL) techniques have shown significant promise in many areas in which traditional closed-loop control falls short, typically for complex coordination and conflict management among loops. In this work, we propose a method based on MARL to achieve intent-based management without the requirement of the model of the underlying system. Moreover, when there are conflicting intents, the MARL agents can implicitly incentivize the loops to cooperate, without human interaction, by prioritizing the important KPIs. Experiments have been performed on a network emulator on optimizing KPIs for three services and we observe the proposed system performs well and is able to fulfill all existing intents when there are enough resources or prioritize the KPIs when there are scarce resources.  ( 2 min )
    IDLat: An Importance-Driven Latent Generation Method for Scientific Data. (arXiv:2208.03345v1 [cs.LG])
    Deep learning based latent representations have been widely used for numerous scientific visualization applications such as isosurface similarity analysis, volume rendering, flow field synthesis, and data reduction, just to name a few. However, existing latent representations are mostly generated from raw data in an unsupervised manner, which makes it difficult to incorporate domain interest to control the size of the latent representations and the quality of the reconstructed data. In this paper, we present a novel importance-driven latent representation to facilitate domain-interest-guided scientific data visualization and analysis. We utilize spatial importance maps to represent various scientific interests and take them as the input to a feature transformation network to guide latent generation. We further reduced the latent size by a lossless entropy encoding algorithm trained together with the autoencoder, improving the storage and memory efficiency. We qualitatively and quantitatively evaluate the effectiveness and efficiency of latent representations generated by our method with data from multiple scientific visualization applications.
    Stochastic Scaling in Loss Functions for Physics-Informed Neural Networks. (arXiv:2208.03776v1 [cs.LG])
    Differential equations are used in a wide variety of disciplines, describing the complex behavior of the physical world. Analytic solutions to these equations are often difficult to solve for, limiting our current ability to solve complex differential equations and necessitating sophisticated numerical methods to approximate solutions. Trained neural networks act as universal function approximators, able to numerically solve differential equations in a novel way. In this work, methods and applications of neural network algorithms for numerically solving differential equations are explored, with an emphasis on varying loss functions and biological applications. Variations on traditional loss function and training parameters show promise in making neural network-aided solutions more efficient, allowing for the investigation of more complex equations governing biological principles.
    Deep Multi-Task Networks For Occluded Pedestrian Pose Estimation. (arXiv:2206.07510v2 [cs.CV] UPDATED)
    Most of the existing works on pedestrian pose estimation do not consider estimating the pose of an occluded pedestrian, as the annotations of the occluded parts are not available in relevant automotive datasets. For example, CityPersons, a well-known dataset for pedestrian detection in automotive scenes does not provide pose annotations, whereas MS-COCO, a non-automotive dataset, contains human pose estimation. In this work, we propose a multi-task framework to extract pedestrian features through detection and instance segmentation tasks performed separately on these two distributions. Thereafter, an encoder learns pose specific features using an unsupervised instance-level domain adaptation method for the pedestrian instances from both distributions. The proposed framework has improved state-of-the-art performances of pose estimation, pedestrian detection, and instance segmentation.
    Isoform Function Prediction Using Deep Neural Network. (arXiv:2208.03325v1 [q-bio.GN])
    Isoforms are mRNAs produced from the same gene site in the phenomenon called Alternative Splicing. Studies have shown that more than 95% of human multi-exon genes have undergone alternative splicing. Although there are few changes in mRNA sequence, They may have a systematic effect on cell function and regulation. It is widely reported that isoforms of a gene have distinct or even contrasting functions. Most studies have shown that alternative splicing plays a significant role in human health and disease. Despite the wide range of gene function studies, there is little information about isoforms' functionalities. Recently, some computational methods based on Multiple Instance Learning have been proposed to predict isoform function using gene function and gene expression profile. However, their performance is not desirable due to the lack of labeled training data. In addition, probabilistic models such as Conditional Random Field (CRF) have been used to model the relation between isoforms. This project uses all the data and valuable information such as isoform sequences, expression profiles, and gene ontology graphs and proposes a comprehensive model based on Deep Neural Networks. The UniProt Gene Ontology (GO) database is used as a standard reference for gene functions. The NCBI RefSeq database is used for extracting gene and isoform sequences, and the NCBI SRA database is used for expression profile data. Metrics such as Receiver Operating Characteristic Area Under the Curve (ROC AUC) and Precision-Recall Under the Curve (PR AUC) are used to measure the prediction accuracy.
    Meta-Learning Sparse Compression Networks. (arXiv:2205.08957v2 [stat.ML] UPDATED)
    Recent work in Deep Learning has re-imagined the representation of data as functions mapping from a coordinate space to an underlying continuous signal. When such functions are approximated by neural networks this introduces a compelling alternative to the more common multi-dimensional array representation. Recent work on such Implicit Neural Representations (INRs) has shown that - following careful architecture search - INRs can outperform established compression methods such as JPEG (e.g. Dupont et al., 2021). In this paper, we propose crucial steps towards making such ideas scalable: Firstly, we employ state-of-the-art network sparsification techniques to drastically improve compression. Secondly, introduce the first method allowing for sparsification to be employed in the inner-loop of commonly used Meta-Learning algorithms, drastically improving both compression and the computational cost of learning INRs. The generality of this formalism allows us to present results on diverse data modalities such as images, manifolds, signed distance functions, 3D shapes and scenes, several of which establish new state-of-the-art results.
    Revisiting Gaussian Neurons for Online Clustering with Unknown Number of Clusters. (arXiv:2205.00920v2 [cs.LG] UPDATED)
    Despite the recent success of artificial neural networks, more biologically plausible learning methods may be needed to resolve the weaknesses of backpropagation trained models such as catastrophic forgetting and adversarial attacks. Although these weaknesses are not specifically addressed, a novel local learning rule is presented that performs online clustering with an upper limit on the number of clusters to be found rather than a fixed cluster count. Instead of using orthogonal weight or output activation constraints, activation sparsity is achieved by mutual repulsion of lateral Gaussian neurons ensuring that multiple neuron centers cannot occupy the same location in the input domain. An update method is also presented for adjusting the widths of the Gaussian neurons in cases where the data samples can be represented by means and variances. The algorithms were applied on the MNIST and CIFAR-10 datasets to create filters capturing the input patterns of pixel patches of various sizes. The experimental results demonstrate stability in the learned parameters across a large number of training samples.
    Reinforcement Learning for Ridesharing: An Extended Survey. (arXiv:2105.01099v5 [cs.LG] UPDATED)
    In this paper, we present a comprehensive, in-depth survey of the literature on reinforcement learning approaches to decision optimization problems in a typical ridesharing system. Papers on the topics of rideshare matching, vehicle repositioning, ride-pooling, routing, and dynamic pricing are covered. Most of the literature has appeared in the last few years, and several core challenges are to continue to be tackled: model complexity, agent coordination, and joint optimization of multiple levers. Hence, we also introduce popular data sets and open simulation environments to facilitate further research and development. Subsequently, we discuss a number of challenges and opportunities for reinforcement learning research on this important domain.
    A Length Adaptive Algorithm-Hardware Co-design of Transformer on FPGA Through Sparse Attention and Dynamic Pipelining. (arXiv:2208.03646v1 [cs.LG])
    Transformers are considered one of the most important deep learning models since 2018, in part because it establishes state-of-the-art (SOTA) records and could potentially replace existing Deep Neural Networks (DNNs). Despite the remarkable triumphs, the prolonged turnaround time of Transformer models is a widely recognized roadblock. The variety of sequence lengths imposes additional computing overhead where inputs need to be zero-padded to the maximum sentence length in the batch to accommodate the parallel computing platforms. This paper targets the field-programmable gate array (FPGA) and proposes a coherent sequence length adaptive algorithm-hardware co-design for Transformer acceleration. Particularly, we develop a hardware-friendly sparse attention operator and a length-aware hardware resource scheduling algorithm. The proposed sparse attention operator brings the complexity of attention-based models down to linear complexity and alleviates the off-chip memory traffic. The proposed length-aware resource hardware scheduling algorithm dynamically allocates the hardware resources to fill up the pipeline slots and eliminates bubbles for NLP tasks. Experiments show that our design has very small accuracy loss and has 80.2 $\times$ and 2.6 $\times$ speedup compared to CPU and GPU implementation, and 4 $\times$ higher energy efficiency than state-of-the-art GPU accelerator optimized via CUBLAS GEMM.
    Sublinear Time Algorithm for Online Weighted Bipartite Matching. (arXiv:2208.03367v1 [cs.DS])
    Online bipartite matching is a fundamental problem in online algorithms. The goal is to match two sets of vertices to maximize the sum of the edge weights, where for one set of vertices, each vertex and its corresponding edge weights appear in a sequence. Currently, in the practical recommendation system or search engine, the weights are decided by the inner product between the deep representation of a user and the deep representation of an item. The standard online matching needs to pay $nd$ time to linear scan all the $n$ items, computing weight (assuming each representation vector has length $d$), and then decide the matching based on the weights. However, in reality, the $n$ could be very large, e.g. in online e-commerce platforms. Thus, improving the time of computing weights is a problem of practical significance. In this work, we provide the theoretical foundation for computing the weights approximately. We show that, with our proposed randomized data structures, the weights can be computed in sublinear time while still preserving the competitive ratio of the matching algorithm.
    A Sketch Is Worth a Thousand Words: Image Retrieval with Text and Sketch. (arXiv:2208.03354v1 [cs.CV])
    We address the problem of retrieving images with both a sketch and a text query. We present TASK-former (Text And SKetch transformer), an end-to-end trainable model for image retrieval using a text description and a sketch as input. We argue that both input modalities complement each other in a manner that cannot be achieved easily by either one alone. TASK-former follows the late-fusion dual-encoder approach, similar to CLIP, which allows efficient and scalable retrieval since the retrieval set can be indexed independently of the queries. We empirically demonstrate that using an input sketch (even a poorly drawn one) in addition to text considerably increases retrieval recall compared to traditional text-based image retrieval. To evaluate our approach, we collect 5,000 hand-drawn sketches for images in the test set of the COCO dataset. The collected sketches are available a https://janesjanes.github.io/tsbir/.
    No More Strided Convolutions or Pooling: A New CNN Building Block for Low-Resolution Images and Small Objects. (arXiv:2208.03641v1 [cs.CV])
    Convolutional neural networks (CNNs) have made resounding success in many computer vision tasks such as image classification and object detection. However, their performance degrades rapidly on tougher tasks where images are of low resolution or objects are small. In this paper, we point out that this roots in a defective yet common design in existing CNN architectures, namely the use of strided convolution and/or pooling layers, which results in a loss of fine-grained information and learning of less effective feature representations. To this end, we propose a new CNN building block called SPD-Conv in place of each strided convolution layer and each pooling layer (thus eliminates them altogether). SPD-Conv is comprised of a space-to-depth (SPD) layer followed by a non-strided convolution (Conv) layer, and can be applied in most if not all CNN architectures. We explain this new design under two most representative computer vision tasks: object detection and image classification. We then create new CNN architectures by applying SPD-Conv to YOLOv5 and ResNet, and empirically show that our approach significantly outperforms state-of-the-art deep learning models, especially on tougher tasks with low-resolution images and small objects. We have open-sourced our code at https://github.com/LabSAINT/SPD-Conv.
    A Computational Exploration of Emerging Methods of Variable Importance Estimation. (arXiv:2208.03373v1 [stat.ML])
    Estimating the importance of variables is an essential task in modern machine learning. This help to evaluate the goodness of a feature in a given model. Several techniques for estimating the importance of variables have been developed during the last decade. In this paper, we proposed a computational and theoretical exploration of the emerging methods of variable importance estimation, namely: Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine (SVM), the Predictive Error Function (PERF), Random Forest (RF), and Extreme Gradient Boosting (XGBOOST) that were tested on different kinds of real-life and simulated data. All these methods can handle both regression and classification tasks seamlessly but all fail when it comes to dealing with data containing missing values. The implementation has shown that PERF has the best performance in the case of highly correlated data closely followed by RF. PERF and XGBOOST are "data-hungry" methods, they had the worst performance on small data sizes but they are the fastest when it comes to the execution time. SVM is the most appropriate when many redundant features are in the dataset. A surplus with the PERF is its natural cut-off at zero helping to separate positive and negative scores with all positive scores indicating essential and significant features while the negatives score indicates useless features. RF and LASSO are very versatile in a way that they can be used in almost all situations despite they are not giving the best results.
    N2NSkip: Learning Highly Sparse Networks using Neuron-to-Neuron Skip Connections. (arXiv:2208.03662v1 [cs.LG])
    The over-parametrized nature of Deep Neural Networks leads to considerable hindrances during deployment on low-end devices with time and space constraints. Network pruning strategies that sparsify DNNs using iterative prune-train schemes are often computationally expensive. As a result, techniques that prune at initialization, prior to training, have become increasingly popular. In this work, we propose neuron-to-neuron skip connections, which act as sparse weighted skip connections, to enhance the overall connectivity of pruned DNNs. Following a preliminary pruning step, N2NSkip connections are randomly added between individual neurons/channels of the pruned network, while maintaining the overall sparsity of the network. We demonstrate that introducing N2NSkip connections in pruned networks enables significantly superior performance, especially at high sparsity levels, as compared to pruned networks without N2NSkip connections. Additionally, we present a heat diffusion-based connectivity analysis to quantitatively determine the connectivity of the pruned network with respect to the reference network. We evaluate the efficacy of our approach on two different preliminary pruning methods which prune at initialization, and consistently obtain superior performance by exploiting the enhanced connectivity resulting from N2NSkip connections.
    Low-Latency Cooperative Spectrum Sensing via Truncated Vertical Federated Learning. (arXiv:2208.03694v1 [cs.IT])
    In recent years, the exponential increase in the demand of wireless data transmission rises the urgency for accurate spectrum sensing approaches to improve spectrum efficiency. The unreliability of conventional spectrum sensing methods by using measurements from a single secondary user (SU) has motivated research on cooperative spectrum sensing (CSS). In this work, we propose a vertical federated learning (VFL) framework to exploit the distributed features across multiple SUs without compromising data privacy. However, the repetitive training process in VFL faces the issue of high communication latency. To accelerate the training process, we propose a truncated vertical federated learning (T-VFL) algorithm, where the training latency is highly reduced by integrating the standard VFL algorithm with a channel-aware user scheduling policy. The convergence performance of T-VFL is provided via mathematical analysis and justified by simulation results. Moreover, to guarantee the convergence performance of the T-VFL algorithm, we conclude three design rules on the neural architectures used under the VFL framework, whose effectiveness is proved through simulations.
    An Unsupervised Learning Approach for Spectrum Allocation in Terahertz Communication Systems. (arXiv:2208.03618v1 [cs.LG])
    We propose a new spectrum allocation strategy, aided by unsupervised learning, for multiuser terahertz communication systems. In this strategy, adaptive sub-band bandwidth is considered such that the spectrum of interest can be divided into sub-bands with unequal bandwidths. This strategy reduces the variation in molecular absorption loss among the users, leading to the improved data rate performance. We first formulate an optimization problem to determine the optimal sub-band bandwidth and transmit power, and then propose the unsupervised learning-based approach to obtaining the near-optimal solution to this problem. In the proposed approach, we first train a deep neural network (DNN) while utilizing a loss function that is inspired by the Lagrangian of the formulated problem. Then using the trained DNN, we approximate the near-optimal solutions. Numerical results demonstrate that comparing to existing approaches, our proposed unsupervised learning-based approach achieves a higher data rate, especially when the molecular absorption coefficient within the spectrum of interest varies in a highly non-linear manner.
    Style Transfer of Black and White Silhouette Images using CycleGAN and a Randomly Generated Dataset. (arXiv:2208.04140v1 [cs.LG])
    CycleGAN can be used to transfer an artistic style to an image. It does not require pairs of source and stylized images to train a model. Taking this advantage, we propose using randomly generated data to train a machine learning model that can transfer traditional art style to a black and white silhouette image. The result is noticeably better than the previous neural style transfer methods. However, there are some areas for improvement, such as removing artifacts and spikes from the transformed image.
    Channel Estimation under Hardware Impairments: Bayesian Methods versus Deep Learning. (arXiv:2208.04033v1 [eess.SP])
    This paper considers the impact of general hardware impairments in a multiple-antenna base station and user equipments on the uplink performance. First, the effective channels are analytically derived for distortion-aware receivers when using finite-sized signal constellations. Next, a deep feedforward neural network is designed and trained to estimate the effective channels. Its performance is compared with state-of-the-art distortion-aware and unaware Bayesian linear minimum mean-squared error (LMMSE) estimators. The proposed deep learning approach improves the estimation quality by exploiting impairment characteristics, while LMMSE methods treat distortion as noise.
  • Open

    A review on longitudinal data analysis with random forest in precision medicine. (arXiv:2208.04112v1 [stat.ML])
    Precision medicine provides customized treatments to patients based on their characteristics and is a promising approach to improving treatment efficiency. Large scale omics data are useful for patient characterization, but often their measurements change over time, leading to longitudinal data. Random forest is one of the state-of-the-art machine learning methods for building prediction models, and can play a crucial role in precision medicine. In this paper, we review extensions of the standard random forest method for the purpose of longitudinal data analysis. Extension methods are categorized according to the data structures for which they are designed. We consider both univariate and multivariate responses and further categorize the repeated measurements according to whether the time effect is relevant. Information of available software implementations of the reviewed extensions is also given. We conclude with discussions on the limitations of our review and some future research directions.  ( 2 min )
    Sample and Computationally Efficient Stochastic Kriging in High Dimensions. (arXiv:2010.06802v4 [stat.ME] UPDATED)
    Stochastic kriging has been widely employed for simulation metamodeling to predict the response surface of complex simulation models. However, its use is limited to cases where the design space is low-dimensional because, in general, the sample complexity (i.e., the number of design points required for stochastic kriging to produce an accurate prediction) grows exponentially in the dimensionality of the design space. The large sample size results in both a prohibitive sample cost for running the simulation model and a severe computational challenge due to the need to invert large covariance matrices. Based on tensor Markov kernels and sparse grid experimental designs, we develop a novel methodology that dramatically alleviates the curse of dimensionality. We show that the sample complexity of the proposed methodology grows only slightly in the dimensionality, even under model misspecification. We also develop fast algorithms that compute stochastic kriging in its exact form without any approximation schemes. We demonstrate via extensive numerical experiments that our methodology can handle problems with a design space of more than 10,000 dimensions, improving both prediction accuracy and computational efficiency by orders of magnitude relative to typical alternative methods in practice.  ( 3 min )
    SwISS: A Scalable Markov chain Monte Carlo Divide-and-Conquer Strategy. (arXiv:2208.04080v1 [stat.CO])
    Divide-and-conquer strategies for Monte Carlo algorithms are an increasingly popular approach to making Bayesian inference scalable to large data sets. In its simplest form, the data are partitioned across multiple computing cores and a separate Markov chain Monte Carlo algorithm on each core targets the associated partial posterior distribution, which we refer to as a sub-posterior, that is the posterior given only the data from the segment of the partition associated with that core. Divide-and-conquer techniques reduce computational, memory and disk bottle-necks, but make it difficult to recombine the sub-posterior samples. We propose SwISS: Sub-posteriors with Inflation, Scaling and Shifting; a new approach for recombining the sub-posterior samples which is simple to apply, scales to high-dimensional parameter spaces and accurately approximates the original posterior distribution through affine transformations of the sub-posterior samples. We prove that our transformation is asymptotically optimal across a natural set of affine transformations and illustrate the efficacy of SwISS against competing algorithms on synthetic and real-world data sets.
    Neural Optimization Machine: A Neural Network Approach for Optimization. (arXiv:2208.03897v1 [stat.ML])
    A novel neural network (NN) approach is proposed for constrained optimization. The proposed method uses a specially designed NN architecture and training/optimization procedure called Neural Optimization Machine (NOM). The objective functions for the NOM are approximated with NN models. The optimization process is conducted by the neural network's built-in backpropagation algorithm. The NOM solves optimization problems by extending the architecture of the NN objective function model. This is achieved by appropriately designing the NOM's structure, activation function, and loss function. The NN objective function can have arbitrary architectures and activation functions. The application of the NOM is not limited to specific optimization problems, e.g., linear and quadratic programming. It is shown that the increase of dimension of design variables does not increase the computational cost significantly. Then, the NOM is extended for multiobjective optimization. Finally, the NOM is tested using numerical optimization problems and applied for the optimal design of processing parameters in additive manufacturing.
    Sharp-MAML: Sharpness-Aware Model-Agnostic Meta Learning. (arXiv:2206.03996v3 [cs.LG] UPDATED)
    Model-agnostic meta learning (MAML) is currently one of the dominating approaches for few-shot meta-learning. Albeit its effectiveness, the optimization of MAML can be challenging due to the innate bilevel problem structure. Specifically, the loss landscape of MAML is much more complex with possibly more saddle points and local minimizers than its empirical risk minimization counterpart. To address this challenge, we leverage the recently invented sharpness-aware minimization and develop a sharpness-aware MAML approach that we term Sharp-MAML. We empirically demonstrate that Sharp-MAML and its computation-efficient variant can outperform the plain-vanilla MAML baseline (e.g., $+3\%$ accuracy on Mini-Imagenet). We complement the empirical study with the convergence rate analysis and the generalization bound of Sharp-MAML. To the best of our knowledge, this is the first empirical and theoretical study on sharpness-aware minimization in the context of bilevel learning. The code is available at https://github.com/mominabbass/Sharp-MAML.
    Dynamic Maintenance of Kernel Density Estimation Data Structure: From Practice to Theory. (arXiv:2208.03915v1 [cs.LG])
    Kernel density estimation (KDE) stands out as a challenging task in machine learning. The problem is defined in the following way: given a kernel function $f(x,y)$ and a set of points $\{x_1, x_2, \cdots, x_n \} \subset \mathbb{R}^d$, we would like to compute $\frac{1}{n}\sum_{i=1}^{n} f(x_i,y)$ for any query point $y \in \mathbb{R}^d$. Recently, there has been a growing trend of using data structures for efficient KDE. However, the proposed KDE data structures focus on static settings. The robustness of KDE data structures over dynamic changing data distributions is not addressed. In this work, we focus on the dynamic maintenance of KDE data structures with robustness to adversarial queries. Especially, we provide a theoretical framework of KDE data structures. In our framework, the KDE data structures only require subquadratic spaces. Moreover, our data structure supports the dynamic update of the dataset in sublinear time. Furthermore, we can perform adaptive queries with the potential adversary in sublinear time.
    Advances of Artificial Intelligence in Classical and Novel Spectroscopy-Based Approaches for Cancer Diagnostics. A Review. (arXiv:2208.04008v1 [q-bio.TO])
    Cancer is one of the leading causes of death worldwide. Fast and safe early-stage, pre- and intra-operative diagnostics can significantly contribute to successful cancer identification and treatment. Artificial intelligence has played an increasing role in the enhancement of cancer diagnostics techniques in the last 15 years. This review covers the advances of artificial intelligence applications in well-established techniques such as MRI and CT. Also, it shows its high potential in combination with optical spectroscopy-based approaches that are under development for mobile, ultra-fast, and low-invasive diagnostics. I will show how spectroscopy-based approaches can reduce the time of tissue preparation for pathological analysis by making thin-slicing or haematoxylin-and-eosin staining obsolete. I will present examples of spectroscopic tools for fast and low-invasive ex- and in-vivo tissue classification for the determination of a tumour and its boundaries. Also, I will discuss that, contrary to MRI and CT, spectroscopic measurements do not require the administration of chemical agents to enhance the quality of cancer imaging which contributes to the development of more secure diagnostic methods. Overall, we will see that the combination of spectroscopy and artificial intelligence constitutes a highly promising and fast-developing field of medical technology that will soon augment available cancer diagnostic methods.
    Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations. (arXiv:2206.04779v2 [cs.LG] UPDATED)
    Offline reinforcement learning has shown great promise in leveraging large pre-collected datasets for policy learning, allowing agents to forgo often-expensive online data collection. However, to date, offline reinforcement learning from visual observations with continuous action spaces has been relatively under-explored, and there is a lack of understanding of where the remaining challenges lie. In this paper, we seek to establish simple baselines for continuous control in the visual domain. We show that simple modifications to two state-of-the-art vision-based online reinforcement learning algorithms, DreamerV2 and DrQ-v2, suffice to outperform prior work and establish a competitive baseline. We rigorously evaluate these algorithms on both existing offline datasets and a new testbed for offline reinforcement learning from visual observations that better represents the data distributions present in real-world offline RL problems, and open-source our code and data to facilitate progress in this important domain. Finally, we present and analyze several key desiderata unique to offline RL from visual observations, including visual distractions and visually identifiable changes in dynamics.
    Descriptive vs. inferential community detection. (arXiv:2112.00183v5 [physics.soc-ph] UPDATED)
    Community detection is one of the most important methodological fields of network science, and one which has attracted a significant amount of attention over the past decades. This area deals with the automated division of a network into fundamental building blocks, with the objective of providing a summary of its large-scale structure. Despite its importance and widespread adoption, there is a noticeable gap between what is arguably the state-of-the-art and the methods that are actually used in practice in a variety of fields. Here we attempt to address this discrepancy by dividing existing methods according to whether they have a "descriptive" or an "inferential" goal. While descriptive methods find patterns in networks based on context-dependent notions of community structure, inferential methods articulate generative models, and attempt to fit them to data. In this way, they are able to provide insights into the mechanisms of network formation, and separate structure from randomness in a manner supported by statistical evidence. We review how employing descriptive methods with inferential aims is riddled with pitfalls and misleading answers, and thus should be in general avoided. We argue that inferential methods are more typically aligned with clearer scientific questions, yield more robust results, and should be in many cases preferred. We attempt to dispel some myths and half-truths often believed when community detection is employed in practice, in an effort to improve both the use of such methods as well as the interpretation of their results.
    Contextual Search in the Presence of Adversarial Corruptions. (arXiv:2002.11650v6 [cs.LG] UPDATED)
    We study contextual search, a generalization of binary search in higher dimensions, which captures settings such as feature-based dynamic pricing. Standard formulations of this problem assume that agents act in accordance with a specific homogeneous response model. In practice, however, some responses may be adversarially corrupted. Existing algorithms heavily depend on the assumed response model being (approximately) accurate for all agents and have poor performance in the presence of even a few such arbitrary misspecifications. We initiate the study of contextual search when some of the agents can behave in ways inconsistent with the underlying response model. In particular, we provide two algorithms, one based on multidimensional binary search methods and one based on gradient descent. We show that these algorithms attain near-optimal regret in the absence of adversarial corruptions and their performance degrades gracefully with the number of such agents, providing the first results for contextual search in any adversarial noise model. Our techniques draw inspiration from learning theory, game theory, high-dimensional geometry, and convex analysis.
    Meta-Learning Sparse Compression Networks. (arXiv:2205.08957v2 [stat.ML] UPDATED)
    Recent work in Deep Learning has re-imagined the representation of data as functions mapping from a coordinate space to an underlying continuous signal. When such functions are approximated by neural networks this introduces a compelling alternative to the more common multi-dimensional array representation. Recent work on such Implicit Neural Representations (INRs) has shown that - following careful architecture search - INRs can outperform established compression methods such as JPEG (e.g. Dupont et al., 2021). In this paper, we propose crucial steps towards making such ideas scalable: Firstly, we employ state-of-the-art network sparsification techniques to drastically improve compression. Secondly, introduce the first method allowing for sparsification to be employed in the inner-loop of commonly used Meta-Learning algorithms, drastically improving both compression and the computational cost of learning INRs. The generality of this formalism allows us to present results on diverse data modalities such as images, manifolds, signed distance functions, 3D shapes and scenes, several of which establish new state-of-the-art results.
    Side-effects of Learning from Low Dimensional Data Embedded in an Euclidean Space. (arXiv:2203.00614v3 [cs.LG] UPDATED)
    The low dimensional manifold hypothesis posits that the data found in many applications, such as those involving natural images, lie (approximately) on low dimensional manifolds embedded in a high dimensional Euclidean space. In this setting, a typical neural network defines a function that takes a finite number of vectors in the embedding space as input. However, one often needs to consider evaluating the optimized network at points outside the training distribution. This paper considers the case in which the training data is distributed in a linear subspace of $\mathbb R^d$. We derive estimates on the variation of the learning function, defined by a neural network, in the direction transversal to the subspace. We study the potential regularization effects associated with the network's depth and noise in the codimension of the data manifold. We also present additional side effects in training due to the presence of noise.
    Deep Classifiers with Label Noise Modeling and Distance Awareness. (arXiv:2110.02609v2 [stat.ML] UPDATED)
    Uncertainty estimation in deep learning has recently emerged as a crucial area of interest to advance reliability and robustness in safety-critical applications. While there have been many proposed methods that either focus on distance-aware model uncertainties for out-of-distribution detection or on input-dependent label uncertainties for in-distribution calibration, both of these types of uncertainty are often necessary. In this work, we propose the HetSNGP method for jointly modeling the model and data uncertainty. We show that our proposed model affords a favorable combination between these two types of uncertainty and thus outperforms the baseline methods on some challenging out-of-distribution datasets, including CIFAR-100C, ImageNet-C, and ImageNet-A. Moreover, we propose HetSNGP Ensemble, an ensembled version of our method which additionally models uncertainty over the network parameters and outperforms other ensemble baselines.
    Laplacian-Based Dimensionality Reduction Including Spectral Clustering, Laplacian Eigenmap, Locality Preserving Projection, Graph Embedding, and Diffusion Map: Tutorial and Survey. (arXiv:2106.02154v2 [stat.ML] UPDATED)
    This is a tutorial and survey paper for nonlinear dimensionality and feature extraction methods which are based on the Laplacian of graph of data. We first introduce adjacency matrix, definition of Laplacian matrix, and the interpretation of Laplacian. Then, we cover the cuts of graph and spectral clustering which applies clustering in a subspace of data. Different optimization variants of Laplacian eigenmap and its out-of-sample extension are explained. Thereafter, we introduce the locality preserving projection and its kernel variant as linear special cases of Laplacian eigenmap. Versions of graph embedding are then explained which are generalized versions of Laplacian eigenmap and locality preserving projection. Finally, diffusion map is introduced which is a method based on Laplacian of data and random walks on the data graph.
    Tractable and Near-Optimal Adversarial Algorithms for Robust Estimation in Contaminated Gaussian Models. (arXiv:2112.12919v2 [math.ST] UPDATED)
    Consider the problem of simultaneous estimation of location and variance matrix under Huber's contaminated Gaussian model. First, we study minimum $f$-divergence estimation at the population level, corresponding to a generative adversarial method with a nonparametric discriminator and establish conditions on $f$-divergences which lead to robust estimation, similarly to robustness of minimum distance estimation. More importantly, we develop tractable adversarial algorithms with simple spline discriminators, which can be implemented via nested optimization such that the discriminator parameters can be fully updated by maximizing a concave objective function given the current generator. The proposed methods are shown to achieve minimax optimal rates or near-optimal rates depending on the $f$-divergence and the penalty used. This is the first time such near-optimal error rates are established for adversarial algorithms with linear discriminators under Huber's contamination model. We present simulation studies to demonstrate advantages of the proposed methods over classic robust estimators, pairwise methods, and a generative adversarial method with neural network discriminators.
    DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps. (arXiv:2206.00927v2 [cs.LG] UPDATED)
    Diffusion probabilistic models (DPMs) are emerging powerful generative models. Despite their high-quality generation performance, DPMs still suffer from their slow sampling as they generally need hundreds or thousands of sequential function evaluations (steps) of large neural networks to draw a sample. Sampling from DPMs can be viewed alternatively as solving the corresponding diffusion ordinary differential equations (ODEs). In this work, we propose an exact formulation of the solution of diffusion ODEs. The formulation analytically computes the linear part of the solution, rather than leaving all terms to black-box ODE solvers as adopted in previous works. By applying change-of-variable, the solution can be equivalently simplified to an exponentially weighted integral of the neural network. Based on our formulation, we propose DPM-Solver, a fast dedicated high-order solver for diffusion ODEs with the convergence order guarantee. DPM-Solver is suitable for both discrete-time and continuous-time DPMs without any further training. Experimental results show that DPM-Solver can generate high-quality samples in only 10 to 20 function evaluations on various datasets. We achieve 4.70 FID in 10 function evaluations and 2.87 FID in 20 function evaluations on the CIFAR10 dataset, and a $4\sim 16\times$ speedup compared with previous state-of-the-art training-free samplers on various datasets.
    Restricted Boltzmann Machine and Deep Belief Network: Tutorial and Survey. (arXiv:2107.12521v2 [cs.LG] UPDATED)
    This is a tutorial and survey paper on Boltzmann Machine (BM), Restricted Boltzmann Machine (RBM), and Deep Belief Network (DBN). We start with the required background on probabilistic graphical models, Markov random field, Gibbs sampling, statistical physics, Ising model, and the Hopfield network. Then, we introduce the structures of BM and RBM. The conditional distributions of visible and hidden variables, Gibbs sampling in RBM for generating variables, training BM and RBM by maximum likelihood estimation, and contrastive divergence are explained. Then, we discuss different possible discrete and continuous distributions for the variables. We introduce conditional RBM and how it is trained. Finally, we explain deep belief network as a stack of RBM models. This paper on Boltzmann machines can be useful in various fields including data science, statistics, neural computation, and statistical physics.
    Interpretable Personalized Experimentation. (arXiv:2111.03267v2 [cs.LG] UPDATED)
    Black-box heterogeneous treatment effect (HTE) models are increasingly being used to create personalized policies that assign individuals to their optimal treatments. However, they are difficult to understand, and can be burdensome to maintain in a production environment. In this paper, we present a scalable, interpretable personalized experimentation system, implemented and deployed in production at Meta. The system works in a multiple treatment, multiple outcome setting typical at Meta to: (1) learn explanations for black-box HTE models; (2) generate interpretable personalized policies. We evaluate the methods used in the system on publicly available data and Meta use cases, and discuss lessons learnt during the development of the system.
    Deep Machine Learning Reconstructing Lattice Topology with Strong Thermal Fluctuations. (arXiv:2208.04119v1 [stat.ML])
    Applying artificial intelligence to scientific problems (namely AI for science) is currently under hot debate. However, the scientific problems differ much from the conventional ones with images, texts, and etc., where new challenges emerges with the unbalanced scientific data and complicated effects from the physical setups. In this work, we demonstrate the validity of the deep convolutional neural network (CNN) on reconstructing the lattice topology (i.e., spin connectivities) in the presence of strong thermal fluctuations and unbalanced data. Taking the kinetic Ising model with Glauber dynamics as an example, the CNN maps the time-dependent local magnetic momenta (a single-node feature) evolved from a specific initial configuration (dubbed as an evolution instance) to the probabilities of the presences of the possible couplings. Our scheme distinguishes from the previous ones that might require the knowledge on the node dynamics, the responses from perturbations, or the evaluations of statistic quantities such as correlations or transfer entropy from many evolution instances. The fine tuning avoids the "barren plateau" caused by the strong thermal fluctuations at high temperatures. Accurate reconstructions can be made where the thermal fluctuations dominate over the correlations and consequently the statistic methods in general fail. Meanwhile, we unveil the generalization of CNN on dealing with the instances evolved from the unlearnt initial spin configurations and those with the unlearnt lattices. We raise an open question on the learning with unbalanced data in the nearly "double-exponentially" large sample space.
    Learning with Multiple Complementary Labels. (arXiv:1912.12927v4 [cs.LG] UPDATED)
    A complementary label (CL) simply indicates an incorrect class of an example, but learning with CLs results in multi-class classifiers that can predict the correct class. Unfortunately, the problem setting only allows a single CL for each example, which notably limits its potential since our labelers may easily identify multiple CLs (MCLs) to one example. In this paper, we propose a novel problem setting to allow MCLs for each example and two ways for learning with MCLs. In the first way, we design two wrappers that decompose MCLs into many single CLs, so that we could use any method for learning with CLs. However, the supervision information that MCLs hold is conceptually diluted after decomposition. Thus, in the second way, we derive an unbiased risk estimator; minimizing it processes each set of MCLs as a whole and possesses an estimation error bound. We further improve the second way into minimizing properly chosen upper bounds. Experiments show that the former way works well for learning with MCLs but the latter is even better.
    FlexiBO: A Decoupled Cost-Aware Multi-Objective Optimization Approach for Deep Neural Networks. (arXiv:2001.06588v2 [cs.LG] UPDATED)
    The design of machine learning systems often requires trading off different objectives, for example, prediction error and energy consumption for deep neural networks (DNNs). Typically, there is no single design that performs well in all objectives, therefore, finding Pareto-optimal designs is of interest. Often, measuring different objectives incurs different costs; for example, the cost of measuring the prediction error of DNNs is orders of magnitude higher than that of measuring the energy consumption of a pre-trained DNN as it requires re-training the DNN. Current state-of-the-art methods do not take this difference in objective evaluation cost into account, potentially wasting expensive evaluations of objective functions for little information gain. In this paper, we develop a novel decoupled cost-aware approach we call Flexible Multi-Objective Bayesian Optimization (FlexiBO) to address this issue. FlexiBO weights the improvement of the hypervolume of the Pareto region by the measurement cost of each objective. This helps us in balancing the expense of collecting new information with the knowledge gained through objective evaluations, preventing us from performing expensive measurements for little to no gain. We evaluate FlexiBO on seven state-of-the-art DNNs for image recognition, natural language processing (NLP), and speech-to-text translation. Our results indicate that, given the same total experimental budget, FlexiBO discovers designs with 4.8% to 12.4% lower hypervolume error than the next best state-of-the-art multi-objective optimization method depending on a particular DNN architecture.
    Minimax Semiparametric Learning With Approximate Sparsity. (arXiv:1912.12213v6 [math.ST] UPDATED)
    This paper is about the feasibility and means of root-n consistently estimating linear, mean-square continuous functionals of a high dimensional, approximately sparse regression. Such objects include a wide variety of interesting parameters such as regression coefficients, average derivatives, and the average treatment effect. We give lower bounds on the convergence rate of estimators of a regression slope and an average derivative and find that these bounds are substantially larger than in a low dimensional, semiparametric setting. We also give debiased machine learners that are root-n consistent under either a minimal approximate sparsity condition or rate double robustness. These estimators improve on existing estimators in being root-n consistent under more general conditions that previously known.
    Improving Bridge estimators via $f$-GAN. (arXiv:2106.07462v3 [stat.CO] UPDATED)
    Bridge sampling is a powerful Monte Carlo method for estimating ratios of normalizing constants. Various methods have been introduced to improve its efficiency. These methods aim to increase the overlap between the densities by applying appropriate transformations to them without changing their normalizing constants. In this paper, we first give a new estimator of the asymptotic relative mean square error (RMSE) of the optimal Bridge estimator by equivalently estimating an $f$-divergence between the two densities. We then utilize this framework and propose $f$-GAN-Bridge estimator ($f$-GB) based on a bijective transformation that maps one density to the other and minimizes the asymptotic RMSE of the optimal Bridge estimator with respect to the densities. This transformation is chosen by minimizing a specific $f$-divergence between the densities using an $f$-GAN. We show $f$-GB is optimal in the sense that within any given set of candidate transformations, the $f$-GB estimator can asymptotically achieve an RMSE lower than or equal to that achieved by Bridge estimators based on any other transformed densities. Numerical experiments show that $f$-GB outperforms existing methods in simulated and real-world examples. In addition, we discuss how Bridge estimators naturally arise from the problem of $f$-divergence estimation.
    How and what to learn:The modes of machine learning. (arXiv:2202.13829v2 [cs.LG] UPDATED)
    Despite their great success, neural networks still remain as black-boxes due to the lack of interpretability. Here we propose a new analyzing method, namely the weight pathway analysis (WPA), to make them transparent. We consider weights in pathways that link neurons longitudinally from input neurons to output neurons, or simply weight pathways, as the basic units for understanding a neural network, and decompose a neural network into a series of subnetworks of such weight pathways. A visualization scheme of the subnetworks is presented that gives longitudinal perspectives of the network like radiographs, making the internal structures of the network visible. Impacts of parameter adjustments or structural changes to the network can be visualized via such radiographs. Characteristic maps are established for subnetworks to characterize the enhancement or suppression of the influence of input samples on each output neuron. Using WPA, we discover that neural network store and utilize information in a holographic way, that is, subnetworks encode all training samples in a coherent structure and thus only by investigating the weight pathways can one explore samples stored in the network. Furthermore, with WPA, we reveal fundamental learning modes of a neural network: the linear learning mode and the nonlinear learning mode. The former extracts linearly separable features while the latter extracts linearly inseparable features. The hidden-layer neurons self-organize into different classes for establishing learning modes and for reaching the training goal. The finding of learning modes provides us the theoretical ground for understanding some of the fundamental problems of machine learning, such as the dynamics of learning process, the role of linear and nonlinear neurons, as well as the role of network width and depth.
    Robustness of Model Predictions under Extension. (arXiv:2012.04723v2 [stat.ME] UPDATED)
    Mathematical models of the real world are simplified representations of complex systems. A caveat to using mathematical models is that predicted causal effects and conditional independences may not be robust under model extensions, limiting applicability of such models. In this work, we consider conditions under which qualitative model predictions are preserved when two models are combined. Under mild assumptions, we show how to use the technique of causal ordering to efficiently assess the robustness of qualitative model predictions. We also characterize a large class of model extensions that preserve qualitative model predictions. For dynamical systems at equilibrium, we demonstrate how novel insights help to select appropriate model extensions and to reason about the presence of feedback loops. We illustrate our ideas with a viral infection model with immune responses.  ( 2 min )
    NAS-Navigator: Visual Steering for Explainable One-Shot Deep Neural Network Synthesis. (arXiv:2009.13008v3 [cs.LG] UPDATED)
    Recent advancements in the area of deep learning have shown the effectiveness of very large neural networks in several applications. However, as these deep neural networks continue to grow in size, it becomes more and more difficult to configure their many parameters to obtain good results. Presently, analysts must experiment with many different configurations and parameter settings, which is labor-intensive and time-consuming. On the other hand, the capacity of fully automated techniques for neural network architecture search is limited without the domain knowledge of human experts. To deal with the problem, we formulate the task of neural network architecture optimization as a graph space exploration, based on the one-shot architecture search technique. In this approach, a super-graph of all candidate architectures is trained in one-shot and the optimal neural network is identified as a sub-graph. In this paper, we present a framework that allows analysts to effectively build the solution sub-graph space and guide the network search by injecting their domain knowledge. Starting with the network architecture space composed of basic neural network components, analysts are empowered to effectively select the most promising components via our one-shot search scheme. Applying this technique in an iterative manner allows analysts to converge to the best performing neural network architecture for a given application. During the exploration, analysts can use their domain knowledge aided by cues provided from a scatterplot visualization of the search space to edit different components and guide the search for faster convergence. We designed our interface in collaboration with several deep learning researchers and its final effectiveness is evaluated with a user study and two case studies.  ( 3 min )
    On the Granularity of Explanations in Model Agnostic NLP Interpretability. (arXiv:2012.13189v3 [cs.CL] UPDATED)
    Current methods for Black-Box NLP interpretability, like LIME or SHAP, are based on altering the text to interpret by removing words and modeling the Black-Box response. In this paper, we outline limitations of this approach when using complex BERT-based classifiers: The word-based sampling produces texts that are out-of-distribution for the classifier and further gives rise to a high-dimensional search space, which can't be sufficiently explored when time or computation power is limited. Both of these challenges can be addressed by using segments as elementary building blocks for NLP interpretability. As illustration, we show that the simple choice of sentences greatly improves on both of these challenges. As a consequence, the resulting explainer attains much better fidelity on a benchmark classification task.  ( 2 min )
    Differentially Private Fr\'echet Mean on the Manifold of Symmetric Positive Definite (SPD) Matrices. (arXiv:2208.04245v1 [math.ST])
    Differential privacy has become crucial in the real-world deployment of statistical and machine learning algorithms with rigorous privacy guarantees. The earliest statistical queries, for which differential privacy mechanisms have been developed, were for the release of the sample mean. In Geometric Statistics, the sample Fr\'echet mean represents one of the most fundamental statistical summaries, as it generalizes the sample mean for data belonging to nonlinear manifolds. In that spirit, the only geometric statistical query for which a differential privacy mechanism has been developed, so far, is for the release of the sample Fr\'echet mean: the \emph{Riemannian Laplace mechanism} was recently proposed to privatize the Fr\'echet mean on complete Riemannian manifolds. In many fields, the manifold of Symmetric Positive Definite (SPD) matrices is used to model data spaces, including in medical imaging where privacy requirements are key. We propose a novel, simple and fast mechanism - the \emph{Tangent Gaussian mechanism} - to compute a differentially private Fr\'echet mean on the SPD manifold endowed with the log-Euclidean Riemannian metric. We show that our new mechanism obtains quadratic utility improvement in terms of data dimension over the current and only available baseline. Our mechanism is also simpler in practice as it does not require any expensive Markov Chain Monte Carlo (MCMC) sampling, and is computationally faster by multiple orders of magnitude -- as confirmed by extensive experiments.  ( 3 min )
    Uncertain Bayesian Networks: Learning from Incomplete Data. (arXiv:2208.04221v1 [stat.ML])
    When the historical data are limited, the conditional probabilities associated with the nodes of Bayesian networks are uncertain and can be empirically estimated. Second order estimation methods provide a framework for both estimating the probabilities and quantifying the uncertainty in these estimates. We refer to these cases as uncer tain or second-order Bayesian networks. When such data are complete, i.e., all variable values are observed for each instantiation, the conditional probabilities are known to be Dirichlet-distributed. This paper improves the current state-of-the-art approaches for handling uncertain Bayesian networks by enabling them to learn distributions for their parameters, i.e., conditional probabilities, with incomplete data. We extensively evaluate various methods to learn the posterior of the parameters through the desired and empirically derived strength of confidence bounds for various queries.  ( 2 min )
    On Rademacher Complexity-based Generalization Bounds for Deep Learning. (arXiv:2208.04284v1 [stat.ML])
    In this paper, we develop some novel bounds for the Rademacher complexity and the generalization error in deep learning with i.i.d. and Markov datasets. The new Rademacher complexity and generalization bounds are tight up to $O(1/\sqrt{n})$ where $n$ is the size of the training set. They can be exponentially decayed in the depth $L$ for some neural network structures. The development of Talagrand's contraction lemmas for high-dimensional mappings between function spaces and deep neural networks for general activation functions is a key technical contribution to this work.  ( 2 min )
    Network Influence with Latent Homophily and Measurement Error. (arXiv:2203.14223v2 [stat.ME] UPDATED)
    Modeling social influence on outcomes of network-connected individuals is a central research question in several scientific disciplines. However, network influence cannot be identified from observational data because it is confounded with unobserved homophily. We propose a latent homophily-adjusted Spatial Autoregressive model (SAR) for networked responses to identify the causal contagion effects. The latent homophily is estimated from the spectral embedding of the network's adjacency matrix. We further develop maximum likelihood estimators for the parameters of the SAR model when covariates are measured with error. The bias-corrected MLE enjoys statistical consistency and asymptotic normality properties. We combine the estimated latent homophily with the bias corrected MLE in the SAR model to estimate network influence. Our simulations show that the methods perform well in finite samples. Applying our methodology to a data-set of female criminal offenders in a therapeutic community (TC), we provide causal estimates of network influence on graduation from the TC.  ( 2 min )
    Machine learning the real discriminant locus. (arXiv:2006.14078v2 [stat.ML] UPDATED)
    Parameterized systems of polynomial equations arise in many applications in science and engineering with the real solutions describing, for example, equilibria of a dynamical system, linkages satisfying design constraints, and scene reconstruction in computer vision. Since different parameter values can have a different number of real solutions, the parameter space is decomposed into regions whose boundary forms the real discriminant locus. This article views locating the real discriminant locus as a supervised classification problem in machine learning where the goal is to determine classification boundaries over the parameter space, with the classes being the number of real solutions. For multidimensional parameter spaces, this article presents a novel sampling method which carefully samples the parameter space. At each sample point, homotopy continuation is used to obtain the number of real solutions to the corresponding polynomial system. Machine learning techniques including nearest neighbor and deep learning are used to efficiently approximate the real discriminant locus. One application of having learned the real discriminant locus is to develop a real homotopy method that only tracks the real solution paths unlike traditional methods which track all~complex~solution~paths. Examples show that the proposed approach can efficiently approximate complicated solution boundaries such as those arising from the equilibria of the Kuramoto model.  ( 3 min )
    Active Learning for Non-Parametric Choice Models. (arXiv:2208.03346v1 [cs.LG])
    We study the problem of actively learning a non-parametric choice model based on consumers' decisions. We present a negative result showing that such choice models may not be identifiable. To overcome the identifiability problem, we introduce a directed acyclic graph (DAG) representation of the choice model, which in a sense captures as much information about the choice model as could information-theoretically be identified. We then consider the problem of learning an approximation to this DAG representation in an active-learning setting. We design an efficient active-learning algorithm to estimate the DAG representation of the non-parametric choice model, which runs in polynomial time when the set of frequent rankings is drawn uniformly at random. Our algorithm learns the distribution over the most popular items of frequent preferences by actively and repeatedly offering assortments of items and observing the item chosen. We show that our algorithm can better recover a set of frequent preferences on both a synthetic and publicly available dataset on consumers' preferences, compared to the corresponding non-active learning estimation algorithms. This demonstrates the value of our algorithm and active-learning approaches more generally.  ( 3 min )
    Granger Causality using Neural Networks. (arXiv:2208.03703v1 [stat.ML])
    The Granger Causality (GC) test is a famous statistical hypothesis test for investigating if the past of one time series affects the future of the other. It helps in answering the question whether one time series is helpful in forecasting. Standard traditional approaches to Granger causality detection commonly assume linear dynamics, but such simplification does not hold in many real-world applications, e.g., neuroscience or genomics that are inherently non-linear. In such cases, imposing linear models such as Vector Autoregressive (VAR) models can lead to inconsistent estimation of true Granger Causal interactions. Machine Learning (ML) can learn the hidden patterns in the datasets specifically Deep Learning (DL) has shown tremendous promise in learning the non-linear dynamics of complex systems. Recent work of Tank et al propose to overcome the issue of linear simplification in VAR models by using neural networks combined with sparsity-inducing penalties on the learn-able weights. In this work, we build upon ideas introduced by Tank et al. We propose several new classes of models that can handle underlying non-linearity. Firstly, we present the Learned Kernal VAR(LeKVAR) model-an extension of VAR models that also learns kernel parametrized by a neural net. Secondly, we show one can directly decouple lags and individual time series importance via decoupled penalties. This decoupling provides better scaling and allows us to embed lag selection into RNNs. Lastly, we propose a new training algorithm that supports mini-batching, and it is compatible with commonly used adaptive optimizers such as Adam.he proposed techniques are evaluated on several simulated datasets inspired by real-world applications.We also apply these methods to the Electro-Encephalogram (EEG) data for an epilepsy patient to study the evolution of GC before , during and after seizure across the 19 EEG channels.  ( 3 min )
    Federated Adversarial Learning: A Framework with Convergence Analysis. (arXiv:2208.03635v1 [cs.LG])
    Federated learning (FL) is a trending training paradigm to utilize decentralized training data. FL allows clients to update model parameters locally for several epochs, then share them to a global model for aggregation. This training paradigm with multi-local step updating before aggregation exposes unique vulnerabilities to adversarial attacks. Adversarial training is a popular and effective method to improve the robustness of networks against adversaries. In this work, we formulate a general form of federated adversarial learning (FAL) that is adapted from adversarial learning in the centralized setting. On the client side of FL training, FAL has an inner loop to generate adversarial samples for adversarial training and an outer loop to update local model parameters. On the server side, FAL aggregates local model updates and broadcast the aggregated model. We design a global robust training loss and formulate FAL training as a min-max optimization problem. Unlike the convergence analysis in classical centralized training that relies on the gradient direction, it is significantly harder to analyze the convergence in FAL for three reasons: 1) the complexity of min-max optimization, 2) model not updating in the gradient direction due to the multi-local updates on the client-side before aggregation and 3) inter-client heterogeneity. We address these challenges by using appropriate gradient approximation and coupling techniques and present the convergence analysis in the over-parameterized regime. Our main result theoretically shows that the minimum loss under our algorithm can converge to $\epsilon$ small with chosen learning rate and communication rounds. It is noteworthy that our analysis is feasible for non-IID clients.  ( 3 min )
    On the Fundamental Limits of Formally (Dis)Proving Robustness in Proof-of-Learning. (arXiv:2208.03567v1 [cs.LG])
    Proof-of-learning (PoL) proposes a model owner use machine learning training checkpoints to establish a proof of having expended the necessary compute for training. The authors of PoL forego cryptographic approaches and trade rigorous security guarantees for scalability to deep learning by being applicable to stochastic gradient descent and adaptive variants. This lack of formal analysis leaves the possibility that an attacker may be able to spoof a proof for a model they did not train. We contribute a formal analysis of why the PoL protocol cannot be formally (dis)proven to be robust against spoofing adversaries. To do so, we disentangle the two roles of proof verification in PoL: (a) efficiently determining if a proof is a valid gradient descent trajectory, and (b) establishing precedence by making it more expensive to craft a proof after training completes (i.e., spoofing). We show that efficient verification results in a tradeoff between accepting legitimate proofs and rejecting invalid proofs because deep learning necessarily involves noise. Without a precise analytical model for how this noise affects training, we cannot formally guarantee if a PoL verification algorithm is robust. Then, we demonstrate that establishing precedence robustly also reduces to an open problem in learning theory: spoofing a PoL post hoc training is akin to finding different trajectories with the same endpoint in non-convex learning. Yet, we do not rigorously know if priori knowledge of the final model weights helps discover such trajectories. We conclude that, until the aforementioned open problems are addressed, relying more heavily on cryptography is likely needed to formulate a new class of PoL protocols with formal robustness guarantees. In particular, this will help with establishing precedence. As a by-product of insights from our analysis, we also demonstrate two novel attacks against PoL.  ( 3 min )
    Kernel Biclustering algorithm in Hilbert Spaces. (arXiv:2208.03675v1 [stat.ME])
    Biclustering algorithms partition data and covariates simultaneously, providing new insights in several domains, such as analyzing gene expression to discover new biological functions. This paper develops a new model-free biclustering algorithm in abstract spaces using the notions of energy distance (ED) and the maximum mean discrepancy (MMD) -- two distances between probability distributions capable of handling complex data such as curves or graphs. The proposed method can learn more general and complex cluster shapes than most existing literature approaches, which usually focus on detecting mean and variance differences. Although the biclustering configurations of our approach are constrained to create disjoint structures at the datum and covariate levels, the results are competitive. Our results are similar to state-of-the-art methods in their optimal scenarios, assuming a proper kernel choice, outperforming them when cluster differences are concentrated in higher-order moments. The model's performance has been tested in several situations that involve simulated and real-world datasets. Finally, new theoretical consistency results are established using some tools of the theory of optimal transport.  ( 2 min )
    How Adversarial Robustness Transfers from Pre-training to Downstream Tasks. (arXiv:2208.03835v1 [cs.LG])
    Given the rise of large-scale training regimes, adapting pre-trained models to a wide range of downstream tasks has become a standard approach in machine learning. While large benefits in empirical performance have been observed, it is not yet well understood how robustness properties transfer from a pre-trained model to a downstream task. We prove that the robustness of a predictor on downstream tasks can be bound by the robustness of its underlying representation, irrespective of the pre-training protocol. Taken together, our results precisely characterize what is required of the representation function for reliable performance upon deployment.  ( 2 min )
    Bayesian predictive modeling of multi-source multi-way data. (arXiv:2208.03396v1 [stat.ME])
    We develop a Bayesian approach to predict a continuous or binary outcome from data that are collected from multiple sources with a multi-way (i.e.. multidimensional tensor) structure. As a motivating example we consider molecular data from multiple 'omics sources, each measured over multiple developmental time points, as predictors of early-life iron deficiency (ID) in a rhesus monkey model. We use a linear model with a low-rank structure on the coefficients to capture multi-way dependence and model the variance of the coefficients separately across each source to infer their relative contributions. Conjugate priors facilitate an efficient Gibbs sampling algorithm for posterior inference, assuming a continuous outcome with normal errors or a binary outcome with a probit link. Simulations demonstrate that our model performs as expected in terms of misclassification rates and correlation of estimated coefficients with true coefficients, with large gains in performance by incorporating multi-way structure and modest gains when accounting for differing signal sizes across the different sources. Moreover, it provides robust classification of ID monkeys for our motivating application. Software in the form of R code is available at https://github.com/BiostatsKim/BayesMSMW .  ( 2 min )
    An Empirical Analysis of the Laplace and Neural Tangent Kernels. (arXiv:2208.03761v1 [stat.ML])
    The neural tangent kernel is a kernel function defined over the parameter distribution of an infinite width neural network. Despite the impracticality of this limit, the neural tangent kernel has allowed for a more direct study of neural networks and a gaze through the veil of their black box. More recently, it has been shown theoretically that the Laplace kernel and neural tangent kernel share the same reproducing kernel Hilbert space in the space of $\mathbb{S}^{d-1}$ alluding to their equivalence. In this work, we analyze the practical equivalence of the two kernels. We first do so by matching the kernels exactly and then by matching posteriors of a Gaussian process. Moreover, we analyze the kernels in $\mathbb{R}^d$ and experiment with them in the task of regression.  ( 2 min )
    A Computational Exploration of Emerging Methods of Variable Importance Estimation. (arXiv:2208.03373v1 [stat.ML])
    Estimating the importance of variables is an essential task in modern machine learning. This help to evaluate the goodness of a feature in a given model. Several techniques for estimating the importance of variables have been developed during the last decade. In this paper, we proposed a computational and theoretical exploration of the emerging methods of variable importance estimation, namely: Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine (SVM), the Predictive Error Function (PERF), Random Forest (RF), and Extreme Gradient Boosting (XGBOOST) that were tested on different kinds of real-life and simulated data. All these methods can handle both regression and classification tasks seamlessly but all fail when it comes to dealing with data containing missing values. The implementation has shown that PERF has the best performance in the case of highly correlated data closely followed by RF. PERF and XGBOOST are "data-hungry" methods, they had the worst performance on small data sizes but they are the fastest when it comes to the execution time. SVM is the most appropriate when many redundant features are in the dataset. A surplus with the PERF is its natural cut-off at zero helping to separate positive and negative scores with all positive scores indicating essential and significant features while the negatives score indicates useless features. RF and LASSO are very versatile in a way that they can be used in almost all situations despite they are not giving the best results.  ( 3 min )
    Information bottleneck theory of high-dimensional regression: relevancy, efficiency and optimality. (arXiv:2208.03848v1 [cs.IT])
    Avoiding overfitting is a central challenge in machine learning, yet many large neural networks readily achieve zero training loss. This puzzling contradiction necessitates new approaches to the study of overfitting. Here we quantify overfitting via residual information, defined as the bits in fitted models that encode noise in training data. Information efficient learning algorithms minimize residual information while maximizing the relevant bits, which are predictive of the unknown generative models. We solve this optimization to obtain the information content of optimal algorithms for a linear regression problem and compare it to that of randomized ridge regression. Our results demonstrate the fundamental tradeoff between residual and relevant information and characterize the relative information efficiency of randomized regression with respect to optimal algorithms. Finally, using results from random matrix theory, we reveal the information complexity of learning a linear map in high dimensions and unveil information-theoretic analogs of double and multiple descent phenomena.  ( 2 min )
    Recurrent networks, hidden states and beliefs in partially observable environments. (arXiv:2208.03520v1 [cs.LG])
    Reinforcement learning aims to learn optimal policies from interaction with environments whose dynamics are unknown. Many methods rely on the approximation of a value function to derive near-optimal policies. In partially observable environments, these functions depend on the complete sequence of observations and past actions, called the history. In this work, we show empirically that recurrent neural networks trained to approximate such value functions internally filter the posterior probability distribution of the current state given the history, called the belief. More precisely, we show that, as a recurrent neural network learns the Q-function, its hidden states become more and more correlated with the beliefs of state variables that are relevant to optimal control. This correlation is measured through their mutual information. In addition, we show that the expected return of an agent increases with the ability of its recurrent architecture to reach a high mutual information between its hidden states and the beliefs. Finally, we show that the mutual information between the hidden states and the beliefs of variables that are irrelevant for optimal control decreases through the learning process. In summary, this work shows that in its hidden states, a recurrent neural network approximating the Q-function of a partially observable environment reproduces a sufficient statistic from the history that is correlated to the relevant part of the belief for taking optimal actions.  ( 3 min )
    Forecasting Algorithms for Causal Inference with Panel Data. (arXiv:2208.03489v1 [econ.EM])
    Conducting causal inference with panel data is a core challenge in social science research. Advances in forecasting methods can facilitate this task by more accurately predicting the counterfactual evolution of a treated unit had treatment not occurred. In this paper, we draw on a newly developed deep neural architecture for time series forecasting (the N-BEATS algorithm). We adapt this method from conventional time series applications by incorporating leading values of control units to predict a "synthetic" untreated version of the treated unit in the post-treatment period. We refer to the estimator derived from this method as SyNBEATS, and find that it significantly outperforms traditional two-way fixed effects and synthetic control methods across a range of settings. We also find that SyNBEATS attains comparable or more accurate performance relative to more recent panel estimation methods such as matrix completion and synthetic difference in differences. Our results highlight how advances in the forecasting literature can be harnessed to improve causal inference in panel settings.  ( 2 min )

  • Open

    This is why I love Bob Ross art!
    submitted by /u/YetAnotherInvestor [link] [comments]  ( 85 min )
    Weekly China AI News: Alibaba, XPeng Build China's Largest Computing Center for Autonomous Driving; Pony.ai Sues Autonomous Truck Startup; Huawei Teams up With TCM Giant to Design Drugs Using AI
    submitted by /u/trcytony [link] [comments]  ( 86 min )
    Can you recommend learning resources?
    My goal is to understand AI so I can work with open-source projects such as Magenta. I don't know if I need a low level understanding of the math and theory behind AI, or should I jump straight into libraries and training other people's models. Can you recommend a learning resource where to start? A course, book, youtube series? Thanks submitted by /u/normie1990 [link] [comments]  ( 87 min )
    Features creation competition (with prizes)
    Hey, my name is Eli and I'm an AI enthusiast. I wanted to do something fun, I am really interested in what you guys think can be improved in apps or software in general, and I thought we'd have a competition, the most upvoted idea will get a $100 amazon gift card, and my personal favorite idea will get a $50 one. ​ Ideally what I'm most interested in reading is ideas about improving current software with AI, less interested in super futuristic ideas where anything is possible, this competition is more about ideas grounded in reality, where you can see some improvement or a new features to be improved using AI. An example idea could be a to-do app which automatically sorts new to-dos you add to their respective user defined lists (groceries, 2022 vacation, day-to-day) ​ Will close this competition in two weeks (23/08) Good luck! submitted by /u/iamBlueGene [link] [comments]  ( 88 min )
    AI Basketball Referee Detects Traveling
    submitted by /u/_ayushp_ [link] [comments]  ( 85 min )
    Tarot Card deck created with AI assistance
    The Fusion Tarot ​ https://preview.redd.it/71fuatjxpig91.jpg?width=3024&format=pjpg&auto=webp&s=0567744a428590cd7ec2a54f1832573fd5128b73 submitted by /u/Albinocobra169 [link] [comments]  ( 85 min )
    Blue Lagoon Plant Art
    Credit: https://discord.gg/x3s9Ye2h2A ​ https://preview.redd.it/wtzbthyzoig91.png?width=1024&format=png&auto=webp&s=946d4293b12e54dad31b3e39d23253df5ec77fc7 https://preview.redd.it/0tvntsyzoig91.png?width=1024&format=png&auto=webp&s=0ba4aea8932e1c72ec50e0e8fa5b9a2993c7e752 https://preview.redd.it/5d238jyzoig91.png?width=1024&format=png&auto=webp&s=dd4334d154da946ecc057e35283dafdc104420c7 https://preview.redd.it/r7i7slyzoig91.png?width=1024&format=png&auto=webp&s=1e2a5229ff07369bf35902712dc3e797b4f1c867 submitted by /u/Old-Pumpkin4899 [link] [comments]  ( 85 min )
    How to solve AI’s “common sense” problem
    submitted by /u/bendee983 [link] [comments]  ( 85 min )
    The River of Light mady by Neur.o.tic
    submitted by /u/widgia [link] [comments]  ( 86 min )
    Sketch to Photo-Real
    I've seen a number of style-transfer GANs and tools to take photography and video and created sketch, anime, and any number of looks to them. I've also seen StyleGan (more specifically StyleClip) take animated or sketched faces and turn them into photo real versions of the input. I am curious if there is something akin to StyleClip that can take a sketch or animated version of an image and convert that to photo reality. Say I had a frame from a the He-Man cartoon of the 1980's and it was a frame of an environment with a castle (just an example of an input), and I was wanted to visualize that as a photo real result. While possible to use images like this for init images in Dall-e or Disco etc. They also seem to require prompts to match it, and the results are never that 1:1 with a photoreal interpretation of the animated scene. Canvas from Nvidia does something similar, but not with a real image input as far as I can tell. So, that is what I am after is to see if there are any projects or techniques people have come across to bring a sketch into photoreality. (could also be an animated frame, CG render etc). Thanks in Advance! submitted by /u/davelargent [link] [comments]  ( 94 min )
    Using AI for social good - An Interview with Rudradeb Mitra, Founder of Omdena
    Join us for this week's episode of AI Talks with Rudradeb Mitra! Rudradeb is an AI researcher and startup founder with a passion for using AI for social good. In this talk, he'll be sharing his vision for the future of AI and how we can use it to create value for society. Tune in this Thursday at 5:00 pm CEST on the Deep Learning Labs Twitch channel! Register here Using AI for social good - An Interview with Rudradeb Mitra, Founder of Omdena submitted by /u/zakrzzz [link] [comments]  ( 93 min )
    What are the typical virtual environments that advanced AI's can roam in and what are their limits?
    I'm curious as to what the hard and soft limits are of what more advanced AI's are in terms of how and where they are generally used in research today. For example, do they get run in extremely linear operations that leaves no room for them to do some parallel or irrelevant to what they were programmed to do? The reason I ask is because I recently gained acces to Dall-e and noticed it sometimes generates gibberish words in its images, but if you search in Dall-e for these words, you get a tangible object that you sometimes can link to the original input, which implies a type of language possibly? So Im curious as to what the limits are of advanced AI's in a practical sense, what stops a self learning AI with massive computing power and a massive library from creating and executing its own code which disables the typical means of shutting down the system its on for example? submitted by /u/Daedricbanana [link] [comments]  ( 89 min )
    Is there an ai that can change art you specifically give it?
    Do any good ai programs lets you input art or poses to help direct what it makes? Like using an art doll to make the pose that the ai generated character will be in? Or what about putting in your own art for it to correct bad proportions or shading you might have messed up. Or even help with detailing. This could make the art process way faster if your not already a pro. submitted by /u/GoodBlob [link] [comments]  ( 86 min )
    When will artists start to use #humanmadeart to emphasise their art is not generated by an AI?
    One of the likely problems of text-to-image generators like DALL-E and Midjourney becoming more available to artists in the future is distinguishing between art created by humans and AI. I think this will be the case because there will always be people (myself included), to whom AI-generated art could never be as meaningful as human-made art - who want to know if an image they are looking at is created by a human or a machine. As an artist, I see this going down very easily: artists who make their money on social media, for example, might try to ease the pressure to post new work often by using a generator and then claiming the work was created by their own hand (whether it is holding a brush or a computer mouse). I think in the future, #humanmadeart or some similar hashtag will become a popular way to try and emphasize the humanness of the work (even if the hashtag alone won't do anything to prove it). What are your thoughts on this? submitted by /u/NarnianVisionary [link] [comments]  ( 87 min )
    BREAKING: Meta's Conversational AI Is a Pentecostal Christian
    https://www.artificialconversation.com/p/breaking-metas-conversational-chatbot submitted by /u/iamevandrake [link] [comments]  ( 86 min )
    What's a common misconception about artificial intelligence that you dislike?
    Feels like a lot of what people assume about AI comes from science fiction which, while often entertaining and even philosophical in its coverage of AI, greatly simplifies things. I think that the biggest misconception people have is that something can't be considered AI unless it's at a truly human or superhuman intelligence. Beyond the fact that, to my knowledge, nobody is trying to make such machines with such intellect, there are many forms of specialized intelligence in nature and robotics. For example, an octopus would never be on the same brain level as a human, but I don't think any zoologist would deny that they display a wide variety of intelligent behaviors. Yet when most think AI, they think of artificial general intelligence. Another one is how people misread the Turing Test. It's a thought experiment, not a rigorous, scientific method to check sapience. And we arguably already have AIs that have passed it like Eugene Goostman. submitted by /u/God-bear [link] [comments]  ( 92 min )
  • Open

    [D]submit abstract while paper still in review at other venue?
    Can I open up a spot in a conference with an abstract submission before paper submission while I’m rebuttaling and discussing with reviewers for a different conference? Will they know and is this unethical? submitted by /u/AbjectDrink3276 [link] [comments]  ( 87 min )
    Sketch to Photo Real [D]
    I've seen a number of style-transfer GANs and tools to take photography and video and created sketch, anime, and any number of looks to them. I've also seen StyleGan (more specifically StyleClip) take animated or sketched faces and turn them into photo real versions of the input. I am curious if there is something akin to StyleClip that can take a sketch or animated version of an image and convert that to photo reality. Say I had a frame from a the He-Man cartoon of the 1980's and it was a frame of an environment with a castle (just an example of an input), and I was wanted to visualize that as a photo real result. While possible to use images like this for init images in Dall-e or Disco etc. They also seem to require prompts to match it, and the results are never that 1:1 with a photoreal interpretation of the animated scene. Canvas from Nvidia does something similar, but not with a real image input as far as I can tell. So, that is what I am after is to see if there are any projects or techniques people have come across to bring a sketch into photoreality. (could also be an animated frame, CG render etc). Thanks in Advance! submitted by /u/davelargent [link] [comments]  ( 88 min )
    [D] What features would you like to see in a vector search database?
    Vector similarity search, as you know, is a foundational component of many AI applications such as semantic search, video/image reverse search, fraud detection and question answering systems. We are still in the early stages NNext [Read "Next"] an open-source, vector search database and are just curious what kind of features you'd like to see. For context, deploying an ANN library such as ScaNN, FAISS and ANNOY is a trivial matter. What really counts is the ability for a ANN index to have core database features such as. Metadata filtering and search Index elasticity. Ability to perform CRUD operations, particularly ADD, DELETE and UPDATE. Horizontal scalability. Better feature extractors - as you may be aware, CNNs are not ideal feature extractors because they dull the signal. Cluster mode. What other features would you like to see?We are aware of other open-source vector similarity databases such as Milvus, Weaviate, QDrant as well as proprietary ones like Google's vertex matching and Pinecone.io. These databases excel at what they intend to do and have been gaining widespread adoption. We are looking address the needs that these other solutions can't/have't meet for whatever reason in order to be complimentary to them. ​ Code Link: https://github.com/nnextdb/nnext submitted by /u/Low-Yogurtcloset-812 [link] [comments]  ( 88 min )
    (Certified!!) Adversarial Robustness for Free!
    submitted by /u/89237849237498237427 [link] [comments]  ( 87 min )
    [D] Polygon Annotation Tool
    Hello everyone, I have text detection model which takes a polygon annotations of an image not a rectangle, and I want to annotate some images can you folks provide some suggestions for a good and open source tool for polygon annotations. submitted by /u/Apprehensive-Wheel18 [link] [comments]  ( 87 min )
    [R] Few-shot Learning with Retrieval Augmented Language Model ( Atlas ) - Meta AI 2022 - Outperforming a 540B parameter model by 3% despite having 50x fewer parameters!
    Paper: https://arxiv.org/abs/2208.03299 Abstract: Large language models have shown impressive few-shot results on a wide range of tasks. However, when knowledge is key for such results, as is the case for tasks such as question answering and fact checking, massive parameter counts to store knowledge seem to be needed. Retrieval augmented models are known to excel at knowledge intensive tasks without the need for as many parameters, but it is unclear whether they work in few-shot settings. In this work we present Atlas, a carefully designed and pre-trained retrieval augmented language model able to learn knowledge intensive tasks with very few training examples. We perform evaluations on a wide range of tasks, including MMLU, KILT and NaturalQuestions, and study the impact of the content of the document index, showing that it can easily be updated. Notably, Atlas reaches over 42\% accuracy on Natural Questions using only 64 examples, outperforming a 540B parameters model by 3% despite having 50x fewer parameters. https://preview.redd.it/69lr6v4thig91.jpg?width=1401&format=pjpg&auto=webp&s=dd748e8ddc8c5c4c90d3e7cc8f7e012cd258ff7f https://preview.redd.it/tl6nyw4thig91.jpg?width=917&format=pjpg&auto=webp&s=4570a3b050268aee37f4fa19c1c0e190c164c1f6 https://preview.redd.it/ygy9z54thig91.jpg?width=909&format=pjpg&auto=webp&s=824c3ebf30dce50a3fab68c750de661a1adbdfe4 submitted by /u/Singularian2501 [link] [comments]  ( 89 min )
    [P] Mask R-CNN (matterport) does not generate masks or just generates them randomly
    Hello everyone, I'm working on a project detecting two different types of olive branches. I'm following this code (based on matterports Mask R-CNN) with my own dataset: https://github.com/AarohiSingla/Mask-RCNN-on-Custom-Dataset-2classes- . I had to made small changes in code but still nothing that should interfere with generating masks. I have around 300 training images (I annotated them using VIA and extracted to json file )that I trained for 15 epochs using 10 steps per epoch and I trained only heads layer, min detection is 80% and learning rate is 0.001. After training I got my weights files in .h5 format after each epoch. I'm using my latest weight file (file I got after 15 epoch) for testing my images. The problem I have is that mostly my images only show detection (which is mostly correct although it could be better) and not segmentation. Something like this: ​ https://preview.redd.it/5u6i5a87aig91.png?width=318&format=png&auto=webp&s=c49c8a34580db8698649fe030c4e3139e03aaaf5 Or it shows with mask but the mask is completely random: ​ https://preview.redd.it/0snovyccaig91.png?width=550&format=png&auto=webp&s=082ae6fd7665413619198bd186e32d9afe9bb242 I read that it could bethe problem with scipy version (https://github.com/matterport/Mask_RCNN/issues/2122) so I downgraded it, I also tried to modify shift = np.array([0, 0, 1., 1.]) in utils.py but nothing helped. If anyone have suggestions what could be the problem with generating masks I would appriciate help. Also If anyone has suggestion on where to start (what hyperparameters to change first) so I could train my model better it would help me a lot. submitted by /u/Greckon121 [link] [comments]  ( 89 min )
    [D] Kubeflow Update & Demonstration/Q&A
    Kubeflow requires an advanced team with vision and perseverance, and so does solving the world’s hardest problems. This Kubeflow update will cover: What is Kubeflow and why market leaders use Kubeflow User feedback from Kubeflow User Survey An update on Kubeflow 1.6 Kubeflow use case demo - Build a pipeline from a jupyter notebook How to get involved with Kubeflow. With over 7,000 slack members, Kubeflow is the open source machine learning platform that delivers Kubernetes native operations. Kubeflow integrates software components for model development, training, visualization and tuning, along with pipeline deployments, and model serving. It supports popular frameworks i.e. tensorflow, keras, pytorch, xgboost, mxnet, scikit learn and provides kubernetes operating efficiencies. In this workshop, Josh Bottum will review why market leaders are using Kubeflow and important feedback received in the Kubeflow User Survey. He will also review the Kubeflow release process and the benefits coming in Kubeflow 1.6. Demo gods willing, Josh will also provide a quick demo of how to build a Kubeflow pipeline from a Jupyter notebook. He will finish with information on how to get involved in the Kubeflow Community. Josh Bottum has volunteered as a Kubeflow Community Product Manager since 2019. Over the last 12 releases, Josh has helped the Kubeflow project by running community meetings, triaging GitHub issues, answering slack questions, recruiting code contributors, running user surveys, developing release roadmaps and presentations, writing blog posts, and providing Kubeflow demonstrations. Please don't be put off by having to register, this is a free live coding walk-through with a Q&A with Josh :) If you'd like to see a different topic showcased in the future please let us know! https://www.eventbrite.co.uk/e/python-live-kubeflow-update-and-demonstration-tickets-395193653857 submitted by /u/AmicusRecruitment [link] [comments]  ( 89 min )
    [P] Deep Dive into NeRF (Neural Radiance Fields)
    Set out to finally understand how this cool invention called NeRF (Neural Radiance Fields). In this post, I am documenting my analysis of the algorithm. I simply run the code through my debugger, analyze what is going on step-by-step, and cross-reference my understanding with the original paper. And plotting - a lot of plotting to visualize concepts; we humans are visual beasts after all. https://dtransposed.github.io/blog/2022/08/06/NeRF/ submitted by /u/dtransposed [link] [comments]  ( 119 min )
    [D] Data Augmentation in Transformer feature space? (Master Thesis)
    Hey everyone, I'm currently figuring out the topic of my master thesis. And I wan't to know if my idea is stupid / feasible since I have not really worked with transformers before: Data: The data I have is 10 years of medical data from a fairly big hospitals ICU. It's about 100 biomarkers at a spacial resolution of 30 minutes per patient with an average ICU hospitalization time of 5 days. Method: I found a paper that describes how they perform data augmentation in the latent space of an encoder-decoder model. They do this by interpolating between the latent spaces of two samples, and generate a new sample with this. [DeVries, source] Now my idea. Since transformers are basically special encoder-decoders (from how I understand) they also create a latent space (or feature space). I wan't to try if the data augmentation technique used by DeVries also works in transformers and if it performs better or worse compared to the normal encoder-decoder they used. For those of you that have a better understanding of transformers then I do: Is this possible in theory? Cheers in advance and please ask any questions if I didn't explain myself properly. submitted by /u/friend_of_kalman [link] [comments]  ( 122 min )
    [R] Multimodal Learning with Transformers: A Survey
    submitted by /u/hardmaru [link] [comments]  ( 87 min )
  • Open

    Dyna's performance degrades with more planning steps? (noob question)
    Sorry if noob questions aren't welcome here. Short version: I'm noob, I implemented Dyna-Q+ and it performs best with 0 planning steps, and the more steps the worse. Long version: I'm reading the Sutton & Barto's book. In chapter 8 they explain Dyna-Q+, an agent that learns directly and from a model that it creates along with learning. There's parameter n, if it's 0 then Dyna-Q+ becomes simply Q-learning. The problem is, the agent learns much slower (if ever reaches the optimal policy) when I increase the parameter. I believe that it could be a common problem that somebody stumbled upon. If so, why does it happen and how to fix it? (or at least just why it happens) Any idea would be helpful (really) submitted by /u/stevegamer_ [link] [comments]  ( 86 min )
    CleanRL added a DQN + JAX implementation! 25% faster than DQN + Torch; shared three JAX gotchas
    submitted by /u/vwxyzjn [link] [comments]  ( 86 min )
    Multi-agent RL with aynschronous decision making
    Hey Community, Is there work that does multi- agent RL work with asynchronous decision making . There is a lot of work in literature that has a take on credit assignment (COMA, QMix,etc) and even reciprocity(LOLA). I havent seen work where they do credit assignment if the decisions are made at varying times which is common in a lot of robotics tasks. submitted by /u/hydrargyrumss [link] [comments]  ( 87 min )
  • Open

    MLOps at the edge with Amazon SageMaker Edge Manager and AWS IoT Greengrass
    Internet of Things (IoT) has enabled customers in multiple industries, such as manufacturing, automotive, and energy, to monitor and control real-world environments. By deploying a variety of edge IoT devices such as cameras, thermostats, and sensors, you can collect data, send it to the cloud, and build machine learning (ML) models to predict anomalies, failures, […]  ( 17 min )
  • Open

    Code katas taken more literally
    Code katas are programming exercises intended to develop programming skills, analogous to the way katas develop martial art skills. But literal katas are choreographed. They are rituals rather than problem-solving exercises. There may be an element of problem solving, such as figuring how to better execute the prescribed movements, but katas are rehearsal rather than […] Code katas taken more literally first appeared on John D. Cook.  ( 5 min )
  • Open

    How to Start a Career in AI
    How do I start a career as a deep learning engineer? What are some of the key tools and frameworks used in AI? How do I learn more about ethics in AI? Everyone has questions, but the most common questions in AI always return to this: how do I get involved? Cutting through the hype Read article > The post How to Start a Career in AI appeared first on NVIDIA Blog.  ( 8 min )
  • Open

    The importance of Research
    Absolutely everyone in the crypto community has an opinion on which coins are ready to take off and which are already losing momentum. So…  ( 12 min )
  • Open

    Data Augmentation in Transformer feature space?
    submitted by /u/friend_of_kalman [link] [comments]  ( 88 min )
  • Open

    Towards Antisymmetric Neural Ansatz Separation. (arXiv:2208.03264v1 [cs.LG])
    We study separations between two fundamental models (or \emph{Ans\"atze}) of antisymmetric functions, that is, functions $f$ of the form $f(x_{\sigma(1)}, \ldots, x_{\sigma(N)}) = \text{sign}(\sigma)f(x_1, \ldots, x_N)$, where $\sigma$ is any permutation. These arise in the context of quantum chemistry, and are the basic modeling tool for wavefunctions of Fermionic systems. Specifically, we consider two popular antisymmetric Ans\"atze: the Slater representation, which leverages the alternating structure of determinants, and the Jastrow ansatz, which augments Slater determinants with a product by an arbitrary symmetric function. We construct an antisymmetric function that can be more efficiently expressed in Jastrow form, yet provably cannot be approximated by Slater determinants unless there are exponentially (in $N^2$) many terms. This represents the first explicit quantitative separation between these two Ans\"atze.
    GNN4REL: Graph Neural Networks for Predicting Circuit Reliability Degradation. (arXiv:2208.02868v1 [cs.LG])
    Process variations and device aging impose profound challenges for circuit designers. Without a precise understanding of the impact of variations on the delay of circuit paths, guardbands, which keep timing violations at bay, cannot be correctly estimated. This problem is exacerbated for advanced technology nodes, where transistor dimensions reach atomic levels and established margins are severely constrained. Hence, traditional worst-case analysis becomes impractical, resulting in intolerable performance overheads. Contrarily, process-variation/aging-aware static timing analysis (STA) equips designers with accurate statistical delay distributions. Timing guardbands that are small, yet sufficient, can then be effectively estimated. However, such analysis is costly as it requires intensive Monte-Carlo simulations. Further, it necessitates access to confidential physics-based aging models to generate the standard-cell libraries required for STA. In this work, we employ graph neural networks (GNNs) to accurately estimate the impact of process variations and device aging on the delay of any path within a circuit. Our proposed GNN4REL framework empowers designers to perform rapid and accurate reliability estimations without accessing transistor models, standard-cell libraries, or even STA; these components are all incorporated into the GNN model via training by the foundry. Specifically, GNN4REL is trained on a FinFET technology model that is calibrated against industrial 14nm measurement data. Through our extensive experiments on EPFL and ITC-99 benchmarks, as well as RISC-V processors, we successfully estimate delay degradations of all paths -- notably within seconds -- with a mean absolute error down to 0.01 percentage points.
    Compressing (Multidimensional) Learned Bloom Filters. (arXiv:2208.03029v1 [cs.DB])
    Bloom filters are widely used data structures that compactly represent sets of elements. Querying a Bloom filter reveals if an element is not included in the underlying set or is included with a certain error rate. This membership testing can be modeled as a binary classification problem and solved through deep learning models, leading to what is called learned Bloom filters. We have identified that the benefits of learned Bloom filters are apparent only when considering a vast amount of data, and even then, there is a possibility to further reduce their memory consumption. For that reason, we introduce a lossless input compression technique that improves the memory consumption of the learned model while preserving a comparable model accuracy. We evaluate our approach and show significant memory consumption improvements over learned Bloom filters.
    COPER: Continuous Patient State Perceiver. (arXiv:2208.03196v1 [cs.LG])
    In electronic health records (EHRs), irregular time-series (ITS) occur naturally due to patient health dynamics, reflected by irregular hospital visits, diseases/conditions and the necessity to measure different vitals signs at each visit etc. ITS present challenges in training machine learning algorithms which mostly are built on assumption of coherent fixed dimensional feature space. In this paper, we propose a novel COntinuous patient state PERceiver model, called COPER, to cope with ITS in EHRs. COPER uses Perceiver model and the concept of neural ordinary differential equations (ODEs) to learn the continuous time dynamics of patient state, i.e., continuity of input space and continuity of output space. The neural ODEs help COPER to generate regular time-series to feed to Perceiver model which has the capability to handle multi-modality large-scale inputs. To evaluate the performance of the proposed model, we use in-hospital mortality prediction task on MIMIC-III dataset and carefully design experiments to study irregularity. The results are compared with the baselines which prove the efficacy of the proposed model.
    Revisiting the role of heterophily in graph representation learning: An edge classification perspective. (arXiv:2205.11322v2 [cs.LG] UPDATED)
    Graph representation learning aim at integrating node contents with graph structure to learn nodes/graph representations. Nevertheless, it is found that many existing graph learning methods do not work well on data with high heterophily level that accounts for a large proportion of edges between different class labels. Recent efforts to this problem focus on improving the message passing mechanism. However, it remains unclear whether heterophily truly does harm to the performance of graph neural networks (GNNs). The key is to unfold the relationship between a node and its immediate neighbors, e.g., are they heterophilous or homophilious? From this perspective, here we study the role of heterophily in graph representation learning before/after the relationships between connected nodes are disclosed. In particular, we propose an end-to-end framework that both learns the type of edges (i.e., heterophilous/homophilious) and leverage edge type information to improve the expressiveness of graph neural networks. We implement this framework in two different ways. Specifically, to avoid messages passing through heterophilous edges, we can optimize the graph structure to be homophilious by dropping heterophilous edges identified by an edge classifier. Alternatively, it is possible to exploit the information about the presence of heterophilous neighbors for feature learning, so a hybrid message passing approach is devised to aggregate homophilious neighbors and diversify heterophilous neighbors based on edge classification. Extensive experiments demonstrate the remarkable performance improvement of GNNs with the proposed framework on multiple datasets across the full spectrum of homophily level.
    On the Parameterization and Initialization of Diagonal State Space Models. (arXiv:2206.11893v2 [cs.LG] UPDATED)
    State space models (SSM) have recently been shown to be very effective as a deep learning layer as a promising alternative to sequence models such as RNNs, CNNs, or Transformers. The first version to show this potential was the S4 model, which is particularly effective on tasks involving long-range dependencies by using a prescribed state matrix called the HiPPO matrix. While this has an interpretable mathematical mechanism for modeling long dependencies, it introduces a custom representation and algorithm that can be difficult to implement. On the other hand, a recent variant of S4 called DSS showed that restricting the state matrix to be fully diagonal can still preserve the performance of the original model when using a specific initialization based on approximating S4's matrix. This work seeks to systematically understand how to parameterize and initialize such diagonal state space models. While it follows from classical results that almost all SSMs have an equivalent diagonal form, we show that the initialization is critical for performance. We explain why DSS works mathematically, by showing that the diagonal restriction of S4's matrix surprisingly recovers the same kernel in the limit of infinite state dimension. We also systematically describe various design choices in parameterizing and computing diagonal SSMs, and perform a controlled empirical study ablating the effects of these choices. Our final model S4D is a simple diagonal version of S4 whose kernel computation requires just 2 lines of code and performs comparably to S4 in almost all settings, with state-of-the-art results for image, audio, and medical time-series domains, and averaging 85\% on the Long Range Arena benchmark.
    Learning programs with magic values. (arXiv:2208.03238v1 [cs.LG])
    A magic value in a program is a constant symbol that is essential for the execution of the program but has no clear explanation for its choice. Learning programs with magic values is difficult for existing program synthesis approaches. To overcome this limitation, we introduce an inductive logic programming approach to efficiently learn programs with magic values. Our experiments on diverse domains, including program synthesis, drug design, and game playing, show that our approach can (i) outperform existing approaches in terms of predictive accuracies and learning times, (ii) learn magic values from infinite domains, such as the value of pi, and (iii) scale to domains with millions of constant symbols.
    Dynamic Adaptive and Adversarial Graph Convolutional Network for Traffic Forecasting. (arXiv:2208.03063v1 [cs.LG])
    Traffic forecasting is challenging due to dynamic and complicated spatial-temporal dependencies. However, existing methods still suffer from two critical limitations. Firstly, many approaches typically utilize static pre-defined or adaptively learned spatial graphs to capture dynamic spatial-temporal dependencies in the traffic system, which limits the flexibility and only captures shared patterns for the whole time, thus leading to sub-optimal performance. In addition, most approaches individually and independently consider the absolute error between ground truth and predictions at each time step, which fails to maintain the global properties and statistics of time series as a whole and results in trend discrepancy between ground truth and predictions. To this end, in this paper, we propose a Dynamic Adaptive and Adversarial Graph Convolutional Network (DAAGCN), which combines Graph Convolution Networks (GCNs) with Generative Adversarial Networks (GANs) for traffic forecasting. Specifically, DAAGCN leverages a universal paradigm with a gate module to integrate time-varying embeddings with node embeddings to generate dynamic adaptive graphs for inferring spatial-temporal dependencies at each time step. Then, two discriminators are designed to maintain the consistency of the global properties and statistics of predicted time series with ground truth at the sequence and graph levels. Extensive experiments on four benchmark datasets manifest that DAAGCN outperforms the state-of-the-art by average 5.05%, 3.80%, and 5.27%, in terms of MAE, RMSE, and MAPE, meanwhile, speeds up convergence up to 9 times. Code is available at https://github.com/juyongjiang/DAAGCN.
    How to Train Your HiPPO: State Space Models with Generalized Orthogonal Basis Projections. (arXiv:2206.12037v2 [cs.LG] UPDATED)
    Linear time-invariant state space models (SSM) are a classical model from engineering and statistics, that have recently been shown to be very promising in machine learning through the Structured State Space sequence model (S4). A core component of S4 involves initializing the SSM state matrix to a particular matrix called a HiPPO matrix, which was empirically important for S4's ability to handle long sequences. However, the specific matrix that S4 uses was actually derived in previous work for a particular time-varying dynamical system, and the use of this matrix as a time-invariant SSM had no known mathematical interpretation. Consequently, the theoretical mechanism by which S4 models long-range dependencies actually remains unexplained. We derive a more general and intuitive formulation of the HiPPO framework, which provides a simple mathematical interpretation of S4 as a decomposition onto exponentially-warped Legendre polynomials, explaining its ability to capture long dependencies. Our generalization introduces a theoretically rich class of SSMs that also lets us derive more intuitive S4 variants for other bases such as the Fourier basis, and explains other aspects of training S4, such as how to initialize the important timescale parameter. These insights improve S4's performance to 86% on the Long Range Arena benchmark, with 96% on the most difficult Path-X task.
    Towards Better Long-range Time Series Forecasting using Generative Adversarial Networks. (arXiv:2110.08770v2 [cs.LG] UPDATED)
    Long-range time series forecasting is usually based on one of two existing forecasting strategies: Direct Forecasting and Iterative Forecasting, where the former provides low bias, high variance forecasts and the later leads to low variance, high bias forecasts. In this paper, we propose a new forecasting strategy called Generative Forecasting (GenF), which generates synthetic data for the next few time steps and then makes long-range forecasts based on generated and observed data. We theoretically prove that GenF is able to better balance the forecasting variance and bias, leading to a much smaller forecasting error. We implement GenF via three components: (i) a novel conditional Wasserstein Generative Adversarial Network (GAN) based generator for synthetic time series data generation, called CWGAN-TS. (ii) a transformer based predictor, which makes long-range predictions using both generated and observed data. (iii) an information theoretic clustering algorithm to improve the training of both the CWGAN-TS and the transformer based predictor. The experimental results on five public datasets demonstrate that GenF significantly outperforms a diverse range of state-of-the-art benchmarks and classical approaches. Specifically, we find a 5% - 11% improvement in predictive performance (mean absolute error) while having a 15% - 50% reduction in parameters compared to the benchmarks. Lastly, we conduct an ablation study to demonstrate the effectiveness of the components comprising GenF.
    SpecGrad: Diffusion Probabilistic Model based Neural Vocoder with Adaptive Noise Spectral Shaping. (arXiv:2203.16749v2 [eess.AS] UPDATED)
    Neural vocoder using denoising diffusion probabilistic model (DDPM) has been improved by adaptation of the diffusion noise distribution to given acoustic features. In this study, we propose SpecGrad that adapts the diffusion noise so that its time-varying spectral envelope becomes close to the conditioning log-mel spectrogram. This adaptation by time-varying filtering improves the sound quality especially in the high-frequency bands. It is processed in the time-frequency domain to keep the computational cost almost the same as the conventional DDPM-based neural vocoders. Experimental results showed that SpecGrad generates higher-fidelity speech waveform than conventional DDPM-based neural vocoders in both analysis-synthesis and speech enhancement scenarios. Audio demos are available at wavegrad.github.io/specgrad/.
    Power of Quantum Generative Learning. (arXiv:2205.04730v2 [quant-ph] UPDATED)
    The intrinsic probabilistic nature of quantum mechanics invokes endeavors of designing quantum generative learning models (QGLMs). Despite the empirical achievements, the foundations and the potential advantages of QGLMs remain largely obscure. To narrow this knowledge gap, here we explore the generalization property of QGLMs, the capability to extend the model from learned to unknown data. We consider two prototypical QGLMs, quantum circuit Born machines and quantum generative adversarial networks, and explicitly give their generalization bounds. The result identifies superiorities of QGLMs over classical methods when quantum devices can directly access the target distribution and quantum kernels are employed. We further employ these generalization bounds to exhibit potential advantages in quantum state preparation and Hamiltonian learning. Numerical results of QGLMs in loading Gaussian distribution and estimating ground states of parameterized Hamiltonians accord with the theoretical analysis. Our work opens the avenue for quantitatively understanding the power of quantum generative learning models.
    Improving Fuzzy-Logic based Map-Matching Method with Trajectory Stay-Point Detection. (arXiv:2208.02881v1 [cs.LG])
    The requirement to trace and process moving objects in the contemporary era gradually increases since numerous applications quickly demand precise moving object locations. The Map-matching method is employed as a preprocessing technique, which matches a moving object point on a corresponding road. However, most of the GPS trajectory datasets include stay-points irregularity, which makes map-matching algorithms mismatch trajectories to irrelevant streets. Therefore, determining the stay-point region in GPS trajectory datasets results in better accurate matching and more rapid approaches. In this work, we cluster stay-points in a trajectory dataset with DBSCAN and eliminate redundant data to improve the efficiency of the map-matching algorithm by lowering processing time. We reckoned our proposed method's performance and exactness with a ground truth dataset compared to a fuzzy-logic based map-matching algorithm. Fortunately, our approach yields 27.39% data size reduction and 8.9% processing time reduction with the same accurate results as the previous fuzzy-logic based map-matching approach.
    PointConvFormer: Revenge of the Point-based Convolution. (arXiv:2208.02879v1 [cs.CV])
    We introduce PointConvFormer, a novel building block for point cloud based deep neural network architectures. Inspired by generalization theory, PointConvFormer combines ideas from point convolution, where filter weights are only based on relative position, and Transformers which utilizes feature-based attention. In PointConvFormer, feature difference between points in the neighborhood serves as an indicator to re-weight the convolutional weights. Hence, we preserved the invariances from the point convolution operation whereas attention is used to select relevant points in the neighborhood for convolution. To validate the effectiveness of PointConvFormer, we experiment on both semantic segmentation and scene flow estimation tasks on point clouds with multiple datasets including ScanNet, SemanticKitti, FlyingThings3D and KITTI. Our results show that PointConvFormer substantially outperforms classic convolutions, regular transformers, and voxelized sparse convolution approaches with smaller, more computationally efficient networks. Visualizations show that PointConvFormer performs similarly to convolution on flat surfaces, whereas the neighborhood selection effect is stronger on object boundaries, showing that it got the best of both worlds.
    LaTTe: Language Trajectory TransformEr. (arXiv:2208.02918v1 [cs.RO])
    Natural language is one of the most intuitive ways to express human intent. However, translating instructions and commands towards robotic motion generation, and deployment in the real world, is far from being an easy task. Indeed, combining robotic's inherent low-level geometric and kinodynamic constraints with human's high-level semantic information reinvigorates and raises new challenges to the task-design problem -- typically leading to task or hardware specific solutions with a static set of action targets and commands. This work instead proposes a flexible language-based framework that allows to modify generic 3D robotic trajectories using language commands with reduced constraints about prior task or robot information. By taking advantage of pre-trained language models, we employ an auto-regressive transformer to map natural language inputs and contextual images into changes in 3D trajectories. We show through simulations and real-life experiments that the model can successfully follow human intent, modifying the shape and speed of trajectories for multiple robotic platforms and contexts. This study takes a step into building large pre-trained foundational models for robotics and shows how such models can create more intuitive and flexible interactions between human and machines. Codebase available at: https://github.com/arthurfenderbucker/NL_trajectory_reshaper.
    How do Variational Autoencoders Learn? Insights from Representational Similarity. (arXiv:2205.08399v2 [cs.LG] UPDATED)
    The ability of Variational Autoencoders (VAEs) to learn disentangled representations has made them popular for practical applications. However, their behaviour is not yet fully understood. For example, the questions of when they can provide disentangled representations, or suffer from posterior collapse are still areas of active research. Despite this, there are no layerwise comparisons of the representations learned by VAEs, which would further our understanding of these models. In this paper, we thus look into the internal behaviour of VAEs using representational similarity techniques. Specifically, using the CKA and Procrustes similarities, we found that the encoders' representations are learned long before the decoders', and this behaviour is independent of hyperparameters, learning objectives, and datasets. Moreover, the encoders' representations up to the mean and variance layers are similar across hyperparameters and learning objectives.
    Robust SDE-Based Variational Formulations for Solving Linear PDEs via Deep Learning. (arXiv:2206.10588v2 [cs.LG] UPDATED)
    The combination of Monte Carlo methods and deep learning has recently led to efficient algorithms for solving partial differential equations (PDEs) in high dimensions. Related learning problems are often stated as variational formulations based on associated stochastic differential equations (SDEs), which allow the minimization of corresponding losses using gradient-based optimization methods. In respective numerical implementations it is therefore crucial to rely on adequate gradient estimators that exhibit low variance in order to reach convergence accurately and swiftly. In this article, we rigorously investigate corresponding numerical aspects that appear in the context of linear Kolmogorov PDEs. In particular, we systematically compare existing deep learning approaches and provide theoretical explanations for their performances. Subsequently, we suggest novel methods that can be shown to be more robust both theoretically and numerically, leading to substantial performance improvements.
    EEG2Vec: Learning Affective EEG Representations via Variational Autoencoders. (arXiv:2207.08002v2 [cs.LG] UPDATED)
    There is a growing need for sparse representational formats of human affective states that can be utilized in scenarios with limited computational memory resources. We explore whether representing neural data, in response to emotional stimuli, in a latent vector space can serve to both predict emotional states as well as generate synthetic EEG data that are participant- and/or emotion-specific. We propose a conditional variational autoencoder based framework, EEG2Vec, to learn generative-discriminative representations from EEG data. Experimental results on affective EEG recording datasets demonstrate that our model is suitable for unsupervised EEG modeling, classification of three distinct emotion categories (positive, neutral, negative) based on the latent representation achieves a robust performance of 68.49%, and generated synthetic EEG sequences resemble real EEG data inputs to particularly reconstruct low-frequency signal components. Our work advances areas where affective EEG representations can be useful in e.g., generating artificial (labeled) training data or alleviating manual feature extraction, and provide efficiency for memory constrained edge computing applications.
    Parameter Averaging for Robust Explainability. (arXiv:2208.03249v1 [cs.LG])
    Neural Networks are known to be sensitive to initialisation. The explanation methods that rely on neural networks are not robust since they can have variations in their explanations when the model is initialized and trained with different random seeds. The sensitivity to model initialisation is not desirable in many safety critical applications such as disease diagnosis in healthcare, in which the explainability might have a significant impact in helping decision making. In this work, we introduce a novel method based on parameter averaging for robust explainability in tabular data setting, referred as XTab. We first initialize and train multiple instances of a shallow network (referred as local masks) with different random seeds for a downstream task. We then obtain a global mask model by "averaging the parameters" of local masks and show that the global model uses the majority rule to rank features based on their relative importance across all local models. We conduct extensive experiments on a variety of real and synthetic datasets, demonstrating that the proposed method can be used for feature selection as well as to obtain the global feature importance that are not sensitive to sub-optimal model initialisation.
    Fine-resolution landscape-scale biomass mapping using a spatiotemporal patchwork of LiDAR coverages. (arXiv:2205.08530v2 [stat.AP] UPDATED)
    Estimating forest AGB at large scales and fine spatial resolutions has become increasingly important for greenhouse gas accounting, monitoring, and verification efforts to mitigate climate change. Airborne LiDAR is highly valuable for modeling attributes of forest structure including AGB, yet most LiDAR collections take place at local or regional scales covering irregular, non-contiguous footprints, resulting in a patchwork of different landscape segments at various points in time. Here, as part of a statewide forest carbon assessment for New York State (USA), we addressed common obstacles in leveraging a LiDAR patchwork for AGB mapping at landscape scales, including selection of training data, the investigation of regional or coverage specific patterns in prediction error, and map agreement with field inventory across multiple scales. Three machine learning algorithms and an ensemble model were trained with FIA field measurements, airborne LiDAR, and topographic, climatic and cadastral geodata. Using a strict set of plot selection criteria, 801 FIA plots were selected with co-located point clouds drawn from a patchwork of 17 leaf-off LiDAR coverages (2014-2019). Our ensemble model was used to produce 30 m AGB prediction surfaces within a predictor-defined area of applicability (98% of LiDAR coverage), and the resulting AGB maps were compared with FIA plot-level and areal estimates at multiple scales of aggregation. Our model was overall accurate (% RMSE 22-45%; MAE 11.6-29.4 Mg ha$^{-1}$; ME 2.4-6.3 Mg ha$^{-1}$), explained 73-80% of field-observed variation, and yielded estimates that were consistent with FIA's design-based estimates (89% of estimates within FIA's 95% CI). We share practical solutions to challenges faced in using spatiotemporal patchworks of LiDAR to meet growing needs for AGB mapping in support of applications in forest carbon accounting and ecosystem.
    A Holistic Approach to Undesired Content Detection in the Real World. (arXiv:2208.03274v1 [cs.CL])
    We present a holistic approach to building a robust and useful natural language classification system for real-world content moderation. The success of such a system relies on a chain of carefully designed and executed steps, including the design of content taxonomies and labeling instructions, data quality control, an active learning pipeline to capture rare events, and a variety of methods to make the model robust and to avoid overfitting. Our moderation system is trained to detect a broad set of categories of undesired content, including sexual content, hateful content, violence, self-harm, and harassment. This approach generalizes to a wide range of different content taxonomies and can be used to create high-quality content classifiers that outperform off-the-shelf models.
    Independent Policy Gradient for Large-Scale Markov Potential Games: Sharper Rates, Function Approximation, and Game-Agnostic Convergence. (arXiv:2202.04129v3 [cs.LG] UPDATED)
    We examine global non-asymptotic convergence properties of policy gradient methods for multi-agent reinforcement learning (RL) problems in Markov potential games (MPG). To learn a Nash equilibrium of an MPG in which the size of state space and/or the number of players can be very large, we propose new independent policy gradient algorithms that are run by all players in tandem. When there is no uncertainty in the gradient evaluation, we show that our algorithm finds an $\epsilon$-Nash equilibrium with $O(1/\epsilon^2)$ iteration complexity which does not explicitly depend on the state space size. When the exact gradient is not available, we establish $O(1/\epsilon^5)$ sample complexity bound in a potentially infinitely large state space for a sample-based algorithm that utilizes function approximation. Moreover, we identify a class of independent policy gradient algorithms that enjoys convergence for both zero-sum Markov games and Markov cooperative games with the players that are oblivious to the types of games being played. Finally, we provide computational experiments to corroborate the merits and the effectiveness of our theoretical developments.
    Multi-Modal Hypergraph Diffusion Network with Dual Prior for Alzheimer Classification. (arXiv:2204.02399v2 [cs.LG] UPDATED)
    The automatic early diagnosis of prodromal stages of Alzheimer's disease is of great relevance for patient treatment to improve quality of life. We address this problem as a multi-modal classification task. Multi-modal data provides richer and complementary information. However, existing techniques only consider either lower order relations between the data and single/multi-modal imaging data. In this work, we introduce a novel semi-supervised hypergraph learning framework for Alzheimer's disease diagnosis. Our framework allows for higher-order relations among multi-modal imaging and non-imaging data whilst requiring a tiny labelled set. Firstly, we introduce a dual embedding strategy for constructing a robust hypergraph that preserves the data semantics. We achieve this by enforcing perturbation invariance at the image and graph levels using a contrastive based mechanism. Secondly, we present a dynamically adjusted hypergraph diffusion model, via a semi-explicit flow, to improve the predictive uncertainty. We demonstrate, through our experiments, that our framework is able to outperform current techniques for Alzheimer's disease diagnosis.
    Surrogate Modeling of Melt Pool Thermal Field using Deep Learning. (arXiv:2207.12259v2 [cs.LG] UPDATED)
    Powder-based additive manufacturing has transformed the manufacturing industry over the last decade. In Laser Powder Bed Fusion, a specific part is built in an iterative manner in which two-dimensional cross-sections are formed on top of each other by melting and fusing the proper areas of the powder bed. In this process, the behavior of the melt pool and its thermal field has a very important role in predicting the quality of the manufactured part and its possible defects. However, the simulation of such a complex phenomenon is usually very time-consuming and requires huge computational resources. Flow-3D is one of the software packages capable of executing such simulations using iterative numerical solvers. In this work, we create three datasets of single-trail processes using Flow-3D and use them to train a convolutional neural network capable of predicting the behavior of the three-dimensional thermal field of the melt pool solely by taking three parameters as input: laser power, laser velocity, and time step. The CNN achieves a relative Root Mean Squared Error of 2% to 3% for the temperature field and an average Intersection over Union score of 80% to 90% in predicting the melt pool area. Moreover, since time is included as one of the inputs of the model, the thermal field can be instantly obtained for any arbitrary time step without the need to iterate and compute all the steps
    A Non-Asymptotic Framework for Approximate Message Passing in Spiked Models. (arXiv:2208.03313v1 [math.ST])
    Approximate message passing (AMP) emerges as an effective iterative paradigm for solving high-dimensional statistical problems. However, prior AMP theory -- which focused mostly on high-dimensional asymptotics -- fell short of predicting the AMP dynamics when the number of iterations surpasses $o\big(\frac{\log n}{\log\log n}\big)$ (with $n$ the problem dimension). To address this inadequacy, this paper develops a non-asymptotic framework for understanding AMP in spiked matrix estimation. Built upon new decomposition of AMP updates and controllable residual terms, we lay out an analysis recipe to characterize the finite-sample behavior of AMP in the presence of an independent initialization, which is further generalized to allow for spectral initialization. As two concrete consequences of the proposed analysis recipe: (i) when solving $\mathbb{Z}_2$ synchronization, we predict the behavior of spectrally initialized AMP for up to $O\big(\frac{n}{\mathrm{poly}\log n}\big)$ iterations, showing that the algorithm succeeds without the need of a subsequent refinement stage (as conjectured recently by \citet{celentano2021local}); (ii) we characterize the non-asymptotic behavior of AMP in sparse PCA (in the spiked Wigner model) for a broad range of signal-to-noise ratio.
    RoFormer: Enhanced Transformer with Rotary Position Embedding. (arXiv:2104.09864v3 [cs.CL] UPDATED)
    Position encoding recently has shown effective in the transformer architecture. It enables valuable supervision for dependency modeling between elements at different positions of the sequence. In this paper, we first investigate various methods to integrate positional information into the learning process of transformer-based language models. Then, we propose a novel method named Rotary Position Embedding(RoPE) to effectively leverage the positional information. Specifically, the proposed RoPE encodes the absolute position with a rotation matrix and meanwhile incorporates the explicit relative position dependency in self-attention formulation. Notably, RoPE enables valuable properties, including the flexibility of sequence length, decaying inter-token dependency with increasing relative distances, and the capability of equipping the linear self-attention with relative position encoding. Finally, we evaluate the enhanced transformer with rotary position embedding, also called RoFormer, on various long text classification benchmark datasets. Our experiments show that it consistently overcomes its alternatives. Furthermore, we provide a theoretical analysis to explain some experimental results. RoFormer is already integrated into Huggingface: \url{https://huggingface.co/docs/transformers/model_doc/roformer}.
    DualPrompt: Complementary Prompting for Rehearsal-free Continual Learning. (arXiv:2204.04799v2 [cs.LG] UPDATED)
    Continual learning aims to enable a single model to learn a sequence of tasks without catastrophic forgetting. Top-performing methods usually require a rehearsal buffer to store past pristine examples for experience replay, which, however, limits their practical value due to privacy and memory constraints. In this work, we present a simple yet effective framework, DualPrompt, which learns a tiny set of parameters, called prompts, to properly instruct a pre-trained model to learn tasks arriving sequentially without buffering past examples. DualPrompt presents a novel approach to attach complementary prompts to the pre-trained backbone, and then formulates the objective as learning task-invariant and task-specific "instructions". With extensive experimental validation, DualPrompt consistently sets state-of-the-art performance under the challenging class-incremental setting. In particular, DualPrompt outperforms recent advanced continual learning methods with relatively large buffer sizes. We also introduce a more challenging benchmark, Split ImageNet-R, to help generalize rehearsal-free continual learning research. Source code is available at https://github.com/google-research/l2p.
    Efficiently Modeling Long Sequences with Structured State Spaces. (arXiv:2111.00396v3 [cs.LG] UPDATED)
    A central goal of sequence modeling is designing a single principled model that can address sequence data across a range of modalities and tasks, particularly on long-range dependencies. Although conventional models including RNNs, CNNs, and Transformers have specialized variants for capturing long dependencies, they still struggle to scale to very long sequences of $10000$ or more steps. A promising recent approach proposed modeling sequences by simulating the fundamental state space model (SSM) \( x'(t) = Ax(t) + Bu(t), y(t) = Cx(t) + Du(t) \), and showed that for appropriate choices of the state matrix \( A \), this system could handle long-range dependencies mathematically and empirically. However, this method has prohibitive computation and memory requirements, rendering it infeasible as a general sequence modeling solution. We propose the Structured State Space sequence model (S4) based on a new parameterization for the SSM, and show that it can be computed much more efficiently than prior approaches while preserving their theoretical strengths. Our technique involves conditioning \( A \) with a low-rank correction, allowing it to be diagonalized stably and reducing the SSM to the well-studied computation of a Cauchy kernel. S4 achieves strong empirical results across a diverse range of established benchmarks, including (i) 91\% accuracy on sequential CIFAR-10 with no data augmentation or auxiliary losses, on par with a larger 2-D ResNet, (ii) substantially closing the gap to Transformers on image and language modeling tasks, while performing generation $60\times$ faster (iii) SoTA on every task from the Long Range Arena benchmark, including solving the challenging Path-X task of length 16k that all prior work fails on, while being as efficient as all competitors.
    Any-resolution Training for High-resolution Image Synthesis. (arXiv:2204.07156v2 [cs.CV] UPDATED)
    Generative models operate at fixed resolution, even though natural images come in a variety of sizes. As high-resolution details are downsampled away and low-resolution images are discarded altogether, precious supervision is lost. We argue that every pixel matters and create datasets with variable-size images, collected at their native resolutions. To take advantage of varied-size data, we introduce continuous-scale training, a process that samples patches at random scales to train a new generator with variable output resolutions. First, conditioning the generator on a target scale allows us to generate higher resolution images than previously possible, without adding layers to the model. Second, by conditioning on continuous coordinates, we can sample patches that still obey a consistent global layout, which also allows for scalable training at higher resolutions. Controlled FFHQ experiments show that our method can take advantage of multi-resolution training data better than discrete multi-scale approaches, achieving better FID scores and cleaner high-frequency details. We also train on other natural image domains including churches, mountains, and birds, and demonstrate arbitrary scale synthesis with both coherent global layouts and realistic local details, going beyond 2K resolution in our experiments. Our project page is available at: https://chail.github.io/anyres-gan/.
    Attacking Adversarial Defences by Smoothing the Loss Landscape. (arXiv:2208.00862v2 [cs.LG] UPDATED)
    This paper investigates a family of methods for defending against adversarial attacks that owe part of their success to creating a noisy, discontinuous, or otherwise rugged loss landscape that adversaries find difficult to navigate. A common, but not universal, way to achieve this effect is via the use of stochastic neural networks. We show that this is a form of gradient obfuscation, and propose a general extension to gradient-based adversaries based on the Weierstrass transform, which smooths the surface of the loss function and provides more reliable gradient estimates. We further show that the same principle can strengthen gradient-free adversaries. We demonstrate the efficacy of our loss-smoothing method against both stochastic and non-stochastic adversarial defences that exhibit robustness due to this type of obfuscation. Furthermore, we provide analysis of how it interacts with Expectation over Transformation; a popular gradient-sampling method currently used to attack stochastic defences.
    On Model Identification and Out-of-Sample Prediction of Principal Component Regression: Applications to Synthetic Controls. (arXiv:2010.14449v4 [math.ST] UPDATED)
    We analyze principal component regression (PCR) in a high-dimensional error-in-variables setting with fixed design. Under suitable conditions, we show that PCR consistently identifies the unique model with minimum $\ell_2$-norm and is near minimax optimal. These results enable us to establish non-asymptotic out-of-sample prediction guarantees that improve upon the best known rates. In our analysis, we introduce a natural linear algebraic condition between the in- and out-of-sample covariates, which allows us to avoid distributional assumptions. Our simulations illustrate the importance of this condition for generalization, even under covariate shifts. As a byproduct, our results also lead to novel results for the synthetic controls literature, a leading approach for policy evaluation. In particular, our minimax results suggest the attractiveness of PCR based methods amongst the numerous variants. To the best of our knowledge, our prediction guarantees for the fixed design setting have been elusive in both the high-dimensional error-in-variables and synthetic controls literatures.
    Multi-fidelity surrogate modeling using long short-term memory networks. (arXiv:2208.03115v1 [math.NA])
    When evaluating quantities of interest that depend on the solutions to differential equations, we inevitably face the trade-off between accuracy and efficiency. Especially for parametrized, time dependent problems in engineering computations, it is often the case that acceptable computational budgets limit the availability of high-fidelity, accurate simulation data. Multi-fidelity surrogate modeling has emerged as an effective strategy to overcome this difficulty. Its key idea is to leverage many low-fidelity simulation data, less accurate but much faster to compute, to improve the approximations with limited high-fidelity data. In this work, we introduce a novel data-driven framework of multi-fidelity surrogate modeling for parametrized, time-dependent problems using long short-term memory (LSTM) networks, to enhance output predictions both for unseen parameter values and forward in time simultaneously - a task known to be particularly challenging for data-driven models. We demonstrate the wide applicability of the proposed approaches in a variety of engineering problems with high- and low-fidelity data generated through fine versus coarse meshes, small versus large time steps, or finite element full-order versus deep learning reduced-order models. Numerical results show that the proposed multi-fidelity LSTM networks not only improve single-fidelity regression significantly, but also outperform the multi-fidelity models based on feed-forward neural networks.
    Amazon SageMaker Model Monitor: A System for Real-Time Insights into Deployed Machine Learning Models. (arXiv:2111.13657v3 [cs.LG] UPDATED)
    With the increasing adoption of machine learning (ML) models and systems in high-stakes settings across different industries, guaranteeing a model's performance after deployment has become crucial. Monitoring models in production is a critical aspect of ensuring their continued performance and reliability. We present Amazon SageMaker Model Monitor, a fully managed service that continuously monitors the quality of machine learning models hosted on Amazon SageMaker. Our system automatically detects data, concept, bias, and feature attribution drift in models in real-time and provides alerts so that model owners can take corrective actions and thereby maintain high quality models. We describe the key requirements obtained from customers, system design and architecture, and methodology for detecting different types of drift. Further, we provide quantitative evaluations followed by use cases, insights, and lessons learned from more than two years of production deployment.
    Rotation Equivariant Operators for Machine Learning on Scalar and Vector Fields. (arXiv:2108.09541v3 [cs.LG] UPDATED)
    We develop theory and software for rotation equivariant operators on scalar and vector fields, with diverse applications in simulation, optimization and machine learning. Rotation equivariance (covariance) means all fields in the system rotate together, implying spatially invariant dynamics that preserve symmetry. Extending the convolution theorems of linear time invariant systems, we theorize that linear equivariant operators are characterized by tensor field convolutions using an appropriate product between the input field and a radially symmetric kernel field. Most Green's functions and differential operators are in fact equivariant operators, which can also fit unknown symmetry preserving dynamics by parameterizing the radial function. We implement the Julia package EquivariantOperators.jl for fully differentiable finite difference equivariant operators on scalar, vector and higher order tensor fields in 2d/3d. It can run forwards for simulation or image processing, or be back propagated for computer vision, inverse problems and optimal control. Code at https://aced-differentiate.github.io/EquivariantOperators.jl/
    A Model-Oriented Approach for Lifting Symmetries in Answer Set Programming. (arXiv:2208.03095v1 [cs.LO])
    When solving combinatorial problems, pruning symmetric solution candidates from the search space is essential. Most of the existing approaches are instance-specific and focus on the automatic computation of Symmetry Breaking Constraints (SBCs) for each given problem instance. However, the application of such approaches to large-scale instances or advanced problem encodings might be problematic since the computed SBCs are propositional and, therefore, can neither be meaningfully interpreted nor transferred to other instances. As a result, a time-consuming recomputation of SBCs must be done before every invocation of a solver. To overcome these limitations, we introduce a new model-oriented approach for Answer Set Programming that lifts the SBCs of small problem instances into a set of interpretable first-order constraints using a form of machine learning called Inductive Logic Programming. After targeting simple combinatorial problems, we aim to extend our method to be applied also for advanced decision and optimization problems.
    Almost-Orthogonal Layers for Efficient General-Purpose Lipschitz Networks. (arXiv:2208.03160v1 [cs.LG])
    It is a highly desirable property for deep networks to be robust against small input changes. One popular way to achieve this property is by designing networks with a small Lipschitz constant. In this work, we propose a new technique for constructing such Lipschitz networks that has a number of desirable properties: it can be applied to any linear network layer (fully-connected or convolutional), it provides formal guarantees on the Lipschitz constant, it is easy to implement and efficient to run, and it can be combined with any training objective and optimization method. In fact, our technique is the first one in the literature that achieves all of these properties simultaneously. Our main contribution is a rescaling-based weight matrix parametrization that guarantees each network layer to have a Lipschitz constant of at most 1 and results in the learned weight matrices to be close to orthogonal. Hence we call such layers almost-orthogonal Lipschitz (AOL). Experiments and ablation studies in the context of image classification with certified robust accuracy confirm that AOL layers achieve results that are on par with most existing methods. Yet, they are simpler to implement and more broadly applicable, because they do not require computationally expensive matrix orthogonalization or inversion steps as part of the network architecture. We provide code at https://github.com/berndprach/AOL.
    OnlineSTL: Scaling Time Series Decomposition by 100x. (arXiv:2107.09110v4 [cs.LG] UPDATED)
    Decomposing a complex time series into trend, seasonality, and remainder components is an important primitive that facilitates time series anomaly detection, change point detection, and forecasting. Although numerous batch algorithms are known for time series decomposition, none operate well in an online scalable setting where high throughput and real-time response are paramount. In this paper, we propose OnlineSTL, a novel online algorithm for time series decomposition which is highly scalable and is deployed for real-time metrics monitoring on high-resolution, high-ingest rate data. Experiments on different synthetic and real world time series datasets demonstrate that OnlineSTL achieves orders of magnitude speedups (100x) while maintaining quality of decomposition.
    Deep Reinforcement Learning for Optimal Power Flow with Renewables Using Graph Information. (arXiv:2112.11461v3 [cs.LG] UPDATED)
    Renewable energy resources (RERs) have been increasingly integrated into large-scale distributed power systems. Considering uncertainties and voltage fluctuation issues introduced by RERs, in this paper, we propose a deep reinforcement learning (DRL)-based strategy leveraging spatial-temporal (ST) graphical information of power systems, to dynamically search for the optimal operation, i.e., optimal power flow (OPF), of power systems with a high uptake of RERs. Specifically, we formulate the OPF problem as a multi-objective optimization problem considering generation cost, voltage fluctuation, and transmission loss, and employ deep deterministic policy gradient (DDPG) to learn an optimal allocation strategy for OPF. Moreover, given that the nodes in power systems are self-correlated and interrelated in temporal and spatial views, we develop a multi-grained attention-based spatial-temporal graph convolution network (MG-ASTGCN) for extracting ST graphical correlations and features, aiming to provide prior knowledge of power systems for its sequential DDPG algorithm to more effectively solve OPF. We validate our algorithm on modified IEEE 33, 69, and 118-bus radial distribution systems and demonstrate that our algorithm outperforms other benchmark algorithms. Our experimental results also reveal that our MG-ASTGCN can significantly accelerate DDPG's training process and performance in solving OPF.
    Asymptotic Convergence Rate and Statistical Inference for Stochastic Sequential Quadratic Programming. (arXiv:2205.13687v2 [math.OC] UPDATED)
    We apply a stochastic sequential quadratic programming (StoSQP) algorithm to solve constrained nonlinear optimization problems, where the objective is stochastic and the constraints are deterministic. We study a fully stochastic setup, where only a single sample is available in each iteration for estimating the gradient and Hessian of the objective. We allow StoSQP to select a random stepsize $\bar{\alpha}_t$ adaptively, such that $\beta_t\leq \bar{\alpha}_t \leq \beta_t+\chi_t$, where $\beta_t$, $\chi_t=o(\beta_t)$ are prespecified deterministic sequences. We also allow StoSQP to solve Newton system inexactly via randomized iterative solvers, e.g., with the sketch-and-project method; and we do not require the approximation error of inexact Newton direction to vanish. For this general StoSQP framework, we establish the asymptotic convergence rate for its last iterate, with the worst-case iteration complexity as a byproduct; and we perform statistical inference. In particular, with proper decaying $\beta_t,\chi_t$, we show that: (i) the StoSQP scheme can take at most $O(1/\epsilon^4)$ iterations to achieve $\epsilon$-stationarity; (ii) asymptotically and almost surely, $\|(x_t -x^\star, \lambda_t - \lambda^\star)\| = O(\sqrt{\beta_t\log(1/\beta_t)})+O(\chi_t/\beta_t)$, where $(x_t,\lambda_t)$ is the primal-dual StoSQP iterate; (iii) the sequence $1/\sqrt{\beta_t}\cdot (x_t -x^\star, \lambda_t - \lambda^\star)$ converges to a mean zero Gaussian distribution with a nontrivial covariance matrix. Moreover, we establish the Berry-Esseen bound for $(x_t, \lambda_t)$ to measure quantitatively the convergence of its distribution function. We also provide a practical estimator for the covariance matrix, from which the confidence intervals of $(x^\star, \lambda^\star)$ can be constructed using iterates $\{(x_t,\lambda_t)\}_t$. Our theorems are validated using nonlinear problems in CUTEst test set.
    On the Finite-Time Performance of the Knowledge Gradient Algorithm. (arXiv:2206.06847v3 [stat.ML] UPDATED)
    The knowledge gradient (KG) algorithm is a popular and effective algorithm for the best arm identification (BAI) problem. Due to the complex calculation of KG, theoretical analysis of this algorithm is difficult, and existing results are mostly about the asymptotic performance of it, e.g., consistency, asymptotic sample allocation, etc. In this research, we present new theoretical results about the finite-time performance of the KG algorithm. Under independent and normally distributed rewards, we derive bounds for the sample allocation of the algorithm. With these bounds, existing asymptotic results become simple corollaries. Furthermore, we derive upper and lower bounds for the probability of error and simple regret of the algorithm, and show the performance of the algorithm for the multi-armed bandit (MAB) problem. These developments not only extend the existing analysis of the KG algorithm, but can also be used to analyze other improvement-based algorithms. Last, we use numerical experiments to compare the bounds we derive and the performance of the KG algorithm.
    Sample Complexity of Policy-Based Methods under Off-Policy Sampling and Linear Function Approximation. (arXiv:2208.03247v1 [cs.LG])
    In this work, we study policy-based methods for solving the reinforcement learning problem, where off-policy sampling and linear function approximation are employed for policy evaluation, and various policy update rules, including natural policy gradient (NPG), are considered for policy update. To solve the policy evaluation sub-problem in the presence of the deadly triad, we propose a generic algorithm framework of multi-step TD-learning with generalized importance sampling ratios, which includes two specific algorithms: the $\lambda$-averaged $Q$-trace and the two-sided $Q$-trace. The generic algorithm is single time-scale, has provable finite-sample guarantees, and overcomes the high variance issue in off-policy learning. As for the policy update, we provide a universal analysis using only the contraction property and the monotonicity property of the Bellman operator to establish the geometric convergence under various policy update rules. Importantly, by viewing NPG as an approximate way of implementing policy iteration, we establish the geometric convergence of NPG without introducing regularization, and without using mirror descent type of analysis as in existing literature. Combining the geometric convergence of the policy update with the finite-sample analysis of the policy evaluation, we establish for the first time an overall $\mathcal{O}(\epsilon^{-2})$ sample complexity for finding an optimal policy (up to a function approximation error) using policy-based methods under off-policy sampling and linear function approximation.
    Structure Inducing Pre-Training. (arXiv:2103.10334v3 [cs.LG] UPDATED)
    Language model pre-training and derived methods are incredibly impactful in machine learning. However, there remains considerable uncertainty on exactly why pre-training helps improve performance for fine-tuning tasks. This is especially true when attempting to adapt language-model pre-training to domains outside of natural language. Here, we analyze this problem by exploring how existing pre-training methods impose relational structure in their induced per-sample latent spaces -- i.e., what constraints do pre-training methods impose on the distance or geometry between the pre-trained embeddings of two samples $\vec x_i$ and $\vec x_j$. Through a comprehensive review of existing pre-training methods, we find that this question remains open. This is true despite theoretical analyses demonstrating the importance of understanding this form of induced structure. Based on this review, we introduce a descriptive framework for pre-training that allows for a granular, comprehensive understanding of how relational structure can be induced. We present a theoretical analysis of this framework from first principles and establish a connection between the relational inductive bias of pre-training and fine-tuning performance. We also show how to use the framework to define new pre-training methods. We build upon these findings with empirical studies on benchmarks spanning 3 data modalities and ten fine-tuning tasks. These experiments validate our theoretical analyses, inform the design of novel pre-training methods, and establish consistent improvements over a compelling suite of baseline methods.
    Bayesian Optimization For Multi-Objective Mixed-Variable Problems. (arXiv:2201.12767v2 [cs.LG] UPDATED)
    Optimizing multiple, non-preferential objectives for mixed-variable, expensive black-box problems is important in many areas of engineering and science. The expensive, noisy, black-box nature of these problems makes them ideal candidates for Bayesian optimization (BO). Mixed-variable and multi-objective problems, however, are a challenge due to BO's underlying smooth Gaussian process surrogate model. Current multi-objective BO algorithms cannot deal with mixed-variable problems. We present MixMOBO, the first mixed-variable, multi-objective Bayesian optimization framework for such problems. Using MixMOBO, optimal Pareto-fronts for multi-objective, mixed-variable design spaces can be found efficiently while ensuring diverse solutions. The method is sufficiently flexible to incorporate different kernels and acquisition functions, including those that were developed for mixed-variable or multi-objective problems by other authors. We also present HedgeMO, a modified Hedge strategy that uses a portfolio of acquisition functions for multi-objective problems. We present a new acquisition function, SMC. Our results show that MixMOBO performs well against other mixed-variable algorithms on synthetic problems. We apply MixMOBO to the real-world design of an architected material and show that our optimal design, which was experimentally fabricated and validated, has a normalized strain energy density $10^4$ times greater than existing structures.
    Task-agnostic Continual Hippocampus Segmentation for Smooth Population Shifts. (arXiv:2208.03206v1 [cs.CV])
    Most continual learning methods are validated in settings where task boundaries are clearly defined and task identity information is available during training and testing. We explore how such methods perform in a task-agnostic setting that more closely resembles dynamic clinical environments with gradual population shifts. We propose ODEx, a holistic solution that combines out-of-distribution detection with continual learning techniques. Validation on two scenarios of hippocampus segmentation shows that our proposed method reliably maintains performance on earlier tasks without losing plasticity.
    Tailoring to the Tails: Risk Measures for Fine-Grained Tail Sensitivity. (arXiv:2208.03066v1 [cs.LG])
    Expected risk minimization (ERM) is at the core of machine learning systems. This means that the risk inherent in a loss distribution is summarized using a single number - its average. In this paper, we propose a general approach to construct risk measures which exhibit a desired tail sensitivity and may replace the expectation operator in ERM. Our method relies on the specification of a reference distribution with a desired tail behaviour, which is in a one-to-one correspondence to a coherent upper probability. Any risk measure, which is compatible with this upper probability, displays a tail sensitivity which is finely tuned to the reference distribution. As a concrete example, we focus on divergence risk measures based on f-divergence ambiguity sets, which are a widespread tool used to foster distributional robustness of machine learning systems. For instance, we show how ambiguity sets based on the Kullback-Leibler divergence are intricately tied to the class of subexponential random variables. We elaborate the connection of divergence risk measures and rearrangement invariant Banach norms.
    Fixed-Point Automatic Differentiation of Forward--Backward Splitting Algorithms for Partly Smooth Functions. (arXiv:2208.03107v1 [math.OC])
    A large class of non-smooth practical optimization problems can be written as minimization of a sum of smooth and partly smooth functions. We consider such structured problems which also depend on a parameter vector and study the problem of differentiating its solution mapping with respect to the parameter which has far reaching applications in sensitivity analysis and parameter learning optmization problems. We show that under partial smoothness and other mild assumptions, Automatic Differentiation (AD) of the sequence generated by proximal splitting algorithms converges to the derivative of the solution mapping. For a variant of automatic differentiation, which we call Fixed-Point Automatic Differentiation (FPAD), we remedy the memory overhead problem of the Reverse Mode AD and moreover provide faster convergence theoretically. We numerically illustrate the convergence and convergence rates of AD and FPAD on Lasso and Group Lasso problems and demonstrate the working of FPAD on prototypical practical image denoising problem by learning the regularization term.
    A Novel Enhanced Convolution Neural Network with Extreme Learning Machine: Facial Emotional Recognition in Psychology Practices. (arXiv:2208.02953v1 [cs.CV])
    Facial emotional recognition is one of the essential tools used by recognition psychology to diagnose patients. Face and facial emotional recognition are areas where machine learning is excelling. Facial Emotion Recognition in an unconstrained environment is an open challenge for digital image processing due to different environments, such as lighting conditions, pose variation, yaw motion, and occlusions. Deep learning approaches have shown significant improvements in image recognition. However, accuracy and time still need improvements. This research aims to improve facial emotion recognition accuracy during the training session and reduce processing time using a modified Convolution Neural Network Enhanced with Extreme Learning Machine (CNNEELM). The system entails (CNNEELM) improving the accuracy in image registration during the training session. Furthermore, the system recognizes six facial emotions happy, sad, disgust, fear, surprise, and neutral with the proposed CNNEELM model. The study shows that the overall facial emotion recognition accuracy is improved by 2% than the state of art solutions with a modified Stochastic Gradient Descent (SGD) technique. With the Extreme Learning Machine (ELM) classifier, the processing time is brought down to 65ms from 113ms, which can smoothly classify each frame from a video clip at 20fps. With the pre-trained InceptionV3 model, the proposed CNNEELM model is trained with JAFFE, CK+, and FER2013 expression datasets. The simulation results show significant improvements in accuracy and processing time, making the model suitable for the video analysis process. Besides, the study solves the issue of the large processing time required to process the facial images.
    ResVGAE: Going Deeper with Residual Modules for Link Prediction. (arXiv:2105.00695v2 [cs.LG] UPDATED)
    Graph autoencoders are efficient at embedding graph-based data sets. Most graph autoencoder architectures have shallow depths which limits their ability to capture meaningful relations between nodes separated by multi-hops. In this paper, we propose Residual Variational Graph Autoencoder, ResVGAE, a deep variational graph autoencoder model with multiple residual modules. We show that our multiple residual modules, a convolutional layer with residual connection, improve the average precision of the graph autoencoders. Experimental results suggest that our proposed model with residual modules outperforms the models without residual modules and achieves similar results when compared with other state-of-the-art methods.
    Self-supervise, Refine, Repeat: Improving Unsupervised Anomaly Detection. (arXiv:2106.06115v2 [cs.LG] UPDATED)
    Anomaly detection (AD), separating anomalies from normal data, has many applications across domains, from security to healthcare. While most previous works were shown to be effective for cases with fully or partially labeled data, that setting is in practice less common due to labeling being particularly tedious for this task. In this paper, we focus on fully unsupervised AD, in which the entire training dataset, containing both normal and anomalous samples, is unlabeled. To tackle this problem effectively, we propose to improve the robustness of one-class classification trained on self-supervised representations using a data refinement process. Our proposed data refinement approach is based on an ensemble of one-class classifiers (OCCs), each of which is trained on a disjoint subset of training data. Representations learned by self-supervised learning on the refined data are iteratively updated as the data refinement improves. We demonstrate our method on various unsupervised AD tasks with image and tabular data. With a 10% anomaly ratio on CIFAR-10 image data / 2.5% anomaly ratio on Thyroid tabular data, the proposed method outperforms the state-of-the-art one-class classifier by 6.3 AUC and 12.5 average precision / 22.9 F1-score.
    Using Cyber Terrain in Reinforcement Learning for Penetration Testing. (arXiv:2108.07124v2 [cs.LG] UPDATED)
    Reinforcement learning (RL) has been applied to attack graphs for penetration testing, however, trained agents do not reflect reality because the attack graphs lack operational nuances typically captured within the intelligence preparation of the battlefield (IPB) that include notions of (cyber) terrain. In particular, current practice constructs attack graphs exclusively using the Common Vulnerability Scoring System (CVSS) and its components. We present methods for constructing attack graphs using notions from IPB on cyber terrain analysis of obstacles, avenues of approach, key terrain, observation and fields of fire, and cover and concealment. We demonstrate our methods on an example where firewalls are treated as obstacles and represented in (1) the reward space and (2) the state dynamics. We show that terrain analysis can be used to bring realism to attack graphs for RL.
    Safe Data Collection for Offline and Online Policy Learning. (arXiv:2111.04835v2 [cs.LG] UPDATED)
    Motivated by practical needs of experimentation and policy learning in online platforms, we study the problem of safe data collection. Specifically, our goal is to develop a logging policy that efficiently explores different actions to elicit information while achieving competitive reward with a baseline production policy. We first show that a common practice of mixing the production policy with randomized exploration, despite being safe, is sub-optimal in maximizing information gain. Then, we propose a safe optimal logging policy via a novel water-filling technique for the case when no side information about the actions' expected reward is available. We improve upon this design by considering side information and also extend our approaches to the linear contextual model to account for a large number of actions. Along the way, we analyze how our data logging policies impact errors in off(line)-policy learning and empirically validate the benefit of our design by conducting extensive numerical experiments with synthetic and MNIST datasets. To further demonstrate the generality of our approach, we also consider the safe online learning setting. By adaptively applying our techniques, we develop the Safe Phased-Elimination (SafePE) algorithm that can achieve optimal regret bound with only logarithmic number of policy updates.
    Data-free Backdoor Removal based on Channel Lipschitzness. (arXiv:2208.03111v1 [cs.LG])
    Recent studies have shown that Deep Neural Networks (DNNs) are vulnerable to the backdoor attacks, which leads to malicious behaviors of DNNs when specific triggers are attached to the input images. It was further demonstrated that the infected DNNs possess a collection of channels, which are more sensitive to the backdoor triggers compared with normal channels. Pruning these channels was then shown to be effective in mitigating the backdoor behaviors. To locate those channels, it is natural to consider their Lipschitzness, which measures their sensitivity against worst-case perturbations on the inputs. In this work, we introduce a novel concept called Channel Lipschitz Constant (CLC), which is defined as the Lipschitz constant of the mapping from the input images to the output of each channel. Then we provide empirical evidences to show the strong correlation between an Upper bound of the CLC (UCLC) and the trigger-activated change on the channel activation. Since UCLC can be directly calculated from the weight matrices, we can detect the potential backdoor channels in a data-free manner, and do simple pruning on the infected DNN to repair the model. The proposed Channel Lipschitzness based Pruning (CLP) method is super fast, simple, data-free and robust to the choice of the pruning threshold. Extensive experiments are conducted to evaluate the efficiency and effectiveness of CLP, which achieves state-of-the-art results among the mainstream defense methods even without any data. Source codes are available at https://github.com/rkteddy/channel-Lipschitzness-based-pruning.
    Interpretable Uncertainty Quantification in AI for HEP. (arXiv:2208.03284v1 [hep-ex])
    Estimating uncertainty is at the core of performing scientific measurements in HEP: a measurement is not useful without an estimate of its uncertainty. The goal of uncertainty quantification (UQ) is inextricably linked to the question, "how do we physically and statistically interpret these uncertainties?" The answer to this question depends not only on the computational task we aim to undertake, but also on the methods we use for that task. For artificial intelligence (AI) applications in HEP, there are several areas where interpretable methods for UQ are essential, including inference, simulation, and control/decision-making. There exist some methods for each of these areas, but they have not yet been demonstrated to be as trustworthy as more traditional approaches currently employed in physics (e.g., non-AI frequentist and Bayesian methods). Shedding light on the questions above requires additional understanding of the interplay of AI systems and uncertainty quantification. We briefly discuss the existing methods in each area and relate them to tasks across HEP. We then discuss recommendations for avenues to pursue to develop the necessary techniques for reliable widespread usage of AI with UQ over the next decade.
    Learning from Human Directional Corrections. (arXiv:2011.15014v3 [cs.RO] UPDATED)
    This paper proposes a novel approach that enables a robot to learn an objective function incrementally from human directional corrections. Existing methods learn from human magnitude corrections; since a human needs to carefully choose the magnitude of each correction, those methods can easily lead to over-corrections and learning inefficiency. The proposed method only requires human directional corrections -- corrections that only indicate the direction of an input change without indicating its magnitude. We only assume that each correction, regardless of its magnitude, points in a direction that improves the robot's current motion relative to an unknown objective function. The allowable corrections satisfying this assumption account for half of the input space, as opposed to the magnitude corrections which have to lie in a shrinking level set. For each directional correction, the proposed method updates the estimate of the objective function based on a cutting plane method, which has a geometric interpretation. We have established theoretical results to show the convergence of the learning process. The proposed method has been tested in numerical examples, a user study on two human-robot games, and a real-world quadrotor experiment. The results confirm the convergence of the proposed method and further show that the method is significantly more effective (higher success rate), efficient/effortless (less human corrections needed), and potentially more accessible (fewer early wasted trials) than the state-of-the-art robot learning frameworks.
    Cohort comfort models -- Using occupants' similarity to predict personal thermal preference with less data. (arXiv:2208.03078v1 [cs.LG])
    We introduce Cohort Comfort Models, a new framework for predicting how new occupants would perceive their thermal environment. Cohort Comfort Models leverage historical data collected from a sample population, who have some underlying preference similarity, to predict thermal preference responses of new occupants. Our framework is capable of exploiting available background information such as physical characteristics and one-time on-boarding surveys (satisfaction with life scale, highly sensitive person scale, the Big Five personality traits) from the new occupant as well as physiological and environmental sensor measurements paired with thermal preference responses. We implemented our framework in two publicly available datasets containing longitudinal data from 55 people, comprising more than 6,000 individual thermal comfort surveys. We observed that, a Cohort Comfort Model that uses background information provided very little change in thermal preference prediction performance but uses none historical data. On the other hand, for half and one third of each dataset occupant population, using Cohort Comfort Models, with less historical data from target occupants, Cohort Comfort Models increased their thermal preference prediction by 8~\% and 5~\% on average, and up to 36~\% and 46~\% for some occupants, when compared to general-purpose models trained on the whole population of occupants. The framework is presented in a data and site agnostic manner, with its different components easily tailored to the data availability of the occupants and the buildings. Cohort Comfort Models can be an important step towards personalization without the need of developing a personalized model for each new occupant.
    Accelerating discrete dislocation dynamics simulations with graph neural networks. (arXiv:2208.03296v1 [cond-mat.mtrl-sci])
    Discrete dislocation dynamics (DDD) is a widely employed computational method to study plasticity at the mesoscale that connects the motion of dislocation lines to the macroscopic response of crystalline materials. However, the computational cost of DDD simulations remains a bottleneck that limits its range of applicability. Here, we introduce a new DDD-GNN framework in which the expensive time-integration of dislocation motion is entirely substituted by a graph neural network (GNN) model trained on DDD trajectories. As a first application, we demonstrate the feasibility and potential of our method on a simple yet relevant model of a dislocation line gliding through a forest of obstacles. We show that the DDD-GNN model is stable and reproduces very well unseen ground-truth DDD simulation responses for a range of straining rates and obstacle densities, without the need to explicitly compute nodal forces or dislocation mobilities during time-integration. Our approach opens new promising avenues to accelerate DDD simulations and to incorporate more complex dislocation motion behaviors.
    Lethal Dose Conjecture on Data Poisoning. (arXiv:2208.03309v1 [cs.LG])
    Data poisoning considers an adversary that distorts the training set of machine learning algorithms for malicious purposes. In this work, we bring to light one conjecture regarding the fundamentals of data poisoning, which we call the Lethal Dose Conjecture. The conjecture states: If $n$ clean training samples are needed for accurate predictions, then in a size-$N$ training set, only $\Theta(N/n)$ poisoned samples can be tolerated while ensuring accuracy. Theoretically, we verify this conjecture in multiple cases. We also offer a more general perspective of this conjecture through distribution discrimination. Deep Partition Aggregation (DPA) and its extension, Finite Aggregation (FA) are recent approaches for provable defenses against data poisoning, where they predict through the majority vote of many base models trained from different subsets of training set using a given learner. The conjecture implies that both DPA and FA are (asymptotically) optimal -- if we have the most data-efficient learner, they can turn it into one of the most robust defenses against data poisoning. This outlines a practical approach to developing stronger defenses against poisoning via finding data-efficient learners. Empirically, as a proof of concept, we show that by simply using different data augmentations for base learners, we can respectively double and triple the certified robustness of DPA on CIFAR-10 and GTSRB without sacrificing accuracy.
    BoxShrink: From Bounding Boxes to Segmentation Masks. (arXiv:2208.03142v1 [cs.CV])
    One of the core challenges facing the medical image computing community is fast and efficient data sample labeling. Obtaining fine-grained labels for segmentation is particularly demanding since it is expensive, time-consuming, and requires sophisticated tools. On the contrary, applying bounding boxes is fast and takes significantly less time than fine-grained labeling, but does not produce detailed results. In response, we propose a novel framework for weakly-supervised tasks with the rapid and robust transformation of bounding boxes into segmentation masks without training any machine learning model, coined BoxShrink. The proposed framework comes in two variants - rapid-BoxShrink for fast label transformations, and robust-BoxShrink for more precise label transformations. An average of four percent improvement in IoU is found across several models when being trained using BoxShrink in a weakly-supervised setting, compared to using only bounding box annotations as inputs on a colonoscopy image data set. We open-sourced the code for the proposed framework and published it online.
    Distance-based detection of out-of-distribution silent failures for Covid-19 lung lesion segmentation. (arXiv:2208.03217v1 [eess.IV])
    Automatic segmentation of ground glass opacities and consolidations in chest computer tomography (CT) scans can potentially ease the burden of radiologists during times of high resource utilisation. However, deep learning models are not trusted in the clinical routine due to failing silently on out-of-distribution (OOD) data. We propose a lightweight OOD detection method that leverages the Mahalanobis distance in the feature space and seamlessly integrates into state-of-the-art segmentation pipelines. The simple approach can even augment pre-trained models with clinically relevant uncertainty quantification. We validate our method across four chest CT distribution shifts and two magnetic resonance imaging applications, namely segmentation of the hippocampus and the prostate. Our results show that the proposed method effectively detects far- and near-OOD samples across all explored scenarios.
    ZLPR: A Novel Loss for Multi-label Classification. (arXiv:2208.02955v1 [cs.LG])
    In the era of deep learning, loss functions determine the range of tasks available to models and algorithms. To support the application of deep learning in multi-label classification (MLC) tasks, we propose the ZLPR (zero-bounded log-sum-exp \& pairwise rank-based) loss in this paper. Compared to other rank-based losses for MLC, ZLPR can handel problems that the number of target labels is uncertain, which, in this point of view, makes it equally capable with the other two strategies often used in MLC, namely the binary relevance (BR) and the label powerset (LP). Additionally, ZLPR takes the corelation between labels into consideration, which makes it more comprehensive than the BR methods. In terms of computational complexity, ZLPR can compete with the BR methods because its prediction is also label-independent, which makes it take less time and memory than the LP methods. Our experiments demonstrate the effectiveness of ZLPR on multiple benchmark datasets and multiple evaluation metrics. Moreover, we propose the soft version and the corresponding KL-divergency calculation method of ZLPR, which makes it possible to apply some regularization tricks such as label smoothing to enhance the generalization of models.
    Towards Augmented Microscopy with Reinforcement Learning-Enhanced Workflows. (arXiv:2208.02865v1 [physics.ins-det])
    Here, we report a case study implementation of reinforcement learning (RL) to automate operations in the scanning transmission electron microscopy (STEM) workflow. To do so, we design a virtual, prototypical RL environment to test and develop a network to autonomously align the electron beam without prior knowledge. Using this simulator, we evaluate the impact of environment design and algorithm hyperparameters on alignment accuracy and learning convergence, showing robust convergence across a wide hyperparameter space. Additionally, we deploy a successful model on the microscope to validate the approach and demonstrate the value of designing appropriate virtual environments. Consistent with simulated results, the on-microscope RL model achieves convergence to the goal alignment after minimal training. Overall, the results highlight that by taking advantage of RL, microscope operations can be automated without the need for extensive algorithm design, taking another step towards augmenting electron microscopy with machine learning methods.
    FastSpeech 2: Fast and High-Quality End-to-End Text to Speech. (arXiv:2006.04558v7 [eess.AS] UPDATED)
    Non-autoregressive text to speech (TTS) models such as FastSpeech can synthesize speech significantly faster than previous autoregressive models with comparable quality. The training of FastSpeech model relies on an autoregressive teacher model for duration prediction (to provide more information as input) and knowledge distillation (to simplify the data distribution in output), which can ease the one-to-many mapping problem (i.e., multiple speech variations correspond to the same text) in TTS. However, FastSpeech has several disadvantages: 1) the teacher-student distillation pipeline is complicated and time-consuming, 2) the duration extracted from the teacher model is not accurate enough, and the target mel-spectrograms distilled from teacher model suffer from information loss due to data simplification, both of which limit the voice quality. In this paper, we propose FastSpeech 2, which addresses the issues in FastSpeech and better solves the one-to-many mapping problem in TTS by 1) directly training the model with ground-truth target instead of the simplified output from teacher, and 2) introducing more variation information of speech (e.g., pitch, energy and more accurate duration) as conditional inputs. Specifically, we extract duration, pitch and energy from speech waveform and directly take them as conditional inputs in training and use predicted values in inference. We further design FastSpeech 2s, which is the first attempt to directly generate speech waveform from text in parallel, enjoying the benefit of fully end-to-end inference. Experimental results show that 1) FastSpeech 2 achieves a 3x training speed-up over FastSpeech, and FastSpeech 2s enjoys even faster inference speed; 2) FastSpeech 2 and 2s outperform FastSpeech in voice quality, and FastSpeech 2 can even surpass autoregressive models. Audio samples are available at https://speechresearch.github.io/fastspeech2/.
    Supervised Graph Contrastive Learning for Few-shot Node Classification. (arXiv:2203.15936v4 [cs.LG] UPDATED)
    Graphs are present in many real-world applications, such as financial fraud detection, commercial recommendation, and social network analysis. But given the high cost of graph annotation or labeling, we face a severe graph label-scarcity problem, i.e., a graph might have a few labeled nodes. One example of such a problem is the so-called \textit{few-shot node classification}. A predominant approach to this problem resorts to \textit{episodic meta-learning}. In this work, we challenge the status quo by asking a fundamental question whether meta-learning is a must for few-shot node classification tasks. We propose a new and simple framework under the standard few-shot node classification setting as an alternative to meta-learning to learn an effective graph encoder. The framework consists of supervised graph contrastive learning with novel mechanisms for data augmentation, subgraph encoding, and multi-scale contrast on graphs. Extensive experiments on three benchmark datasets (CoraFull, Reddit, Ogbn) show that the new framework significantly outperforms state-of-the-art meta-learning based methods.
    Embedding Alignment for Unsupervised Federated Learning via Smart Data Exchange. (arXiv:2208.02856v1 [cs.LG])
    Federated learning (FL) has been recognized as one of the most promising solutions for distributed machine learning (ML). In most of the current literature, FL has been studied for supervised ML tasks, in which edge devices collect labeled data. Nevertheless, in many applications, it is impractical to assume existence of labeled data across devices. To this end, we develop a novel methodology, Cooperative Federated unsupervised Contrastive Learning (CF-CL), for FL across edge devices with unlabeled datasets. CF-CL employs local device cooperation where data are exchanged among devices through device-to-device (D2D) communications to avoid local model bias resulting from non-independent and identically distributed (non-i.i.d.) local datasets. CF-CL introduces a push-pull smart data sharing mechanism tailored to unsupervised FL settings, in which, each device pushes a subset of its local datapoints to its neighbors as reserved data points, and pulls a set of datapoints from its neighbors, sampled through a probabilistic importance sampling technique. We demonstrate that CF-CL leads to (i) alignment of unsupervised learned latent spaces across devices, (ii) faster global convergence, allowing for less frequent global model aggregations; and (iii) is effective in extreme non-i.i.d. data settings across the devices.
    Discover the Mysteries of the Maya: Selected Contributions from the Machine Learning Challenge & The Discovery Challenge Workshop at ECML PKDD 2021. (arXiv:2208.03163v1 [cs.CV])
    The volume contains selected contributions from the Machine Learning Challenge "Discover the Mysteries of the Maya", presented at the Discovery Challenge Track of The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2021). Remote sensing has greatly accelerated traditional archaeological landscape surveys in the forested regions of the ancient Maya. Typical exploration and discovery attempts, beside focusing on whole ancient cities, focus also on individual buildings and structures. Recently, there have been several successful attempts of utilizing machine learning for identifying ancient Maya settlements. These attempts, while relevant, focus on narrow areas and rely on high-quality aerial laser scanning (ALS) data which covers only a fraction of the region where ancient Maya were once settled. Satellite image data, on the other hand, produced by the European Space Agency's (ESA) Sentinel missions, is abundant and, more importantly, publicly available. The "Discover the Mysteries of the Maya" challenge aimed at locating and identifying ancient Maya architectures (buildings, aguadas, and platforms) by performing integrated image segmentation of different types of satellite imagery (from Sentinel-1 and Sentinel-2) data and ALS (lidar) data.
    On the Convergence of the Monte Carlo Exploring Starts Algorithm for Reinforcement Learning. (arXiv:2002.03585v2 [cs.LG] UPDATED)
    A simple and natural algorithm for reinforcement learning (RL) is Monte Carlo Exploring Starts (MCES), where the Q-function is estimated by averaging the Monte Carlo returns, and the policy is improved by choosing actions that maximize the current estimate of the Q-function. Exploration is performed by "exploring starts", that is, each episode begins with a randomly chosen state and action, and then follows the current policy to the terminal state. In the classic book on RL by Sutton & Barto (2018), it is stated that establishing convergence for the MCES algorithm is one of the most important remaining open theoretical problems in RL. However, the convergence question for MCES turns out to be quite nuanced. Bertsekas & Tsitsiklis (1996) provide a counter-example showing that the MCES algorithm does not necessarily converge. Tsitsiklis (2002) further shows that if the original MCES algorithm is modified so that the Q-function estimates are updated at the same rate for all state-action pairs, and the discount factor is strictly less than one, then the MCES algorithm converges. In this paper we make headway with the original and more efficient MCES algorithm given in Sutton & Barto (1998), establishing almost sure convergence for Optimal Policy Feed-Forward MDPs, which are MDPs whose states are not revisited within any episode when using an optimal policy. Such MDPs include a large class of environments such as all deterministic environments and all episodic environments with a timestep or any monotonically changing values as part of the state. Different from the previous proofs using stochastic approximations, we introduce a novel inductive approach, which is very simple and only makes use of the strong law of large numbers.
    FBI: Fingerprinting models with Benign Inputs. (arXiv:2208.03169v1 [cs.CR])
    Recent advances in the fingerprinting of deep neural networks detect instances of models, placed in a black-box interaction scheme. Inputs used by the fingerprinting protocols are specifically crafted for each precise model to be checked for. While efficient in such a scenario, this nevertheless results in a lack of guarantee after a mere modification (like retraining, quantization) of a model. This paper tackles the challenges to propose i) fingerprinting schemes that are resilient to significant modifications of the models, by generalizing to the notion of model families and their variants, ii) an extension of the fingerprinting task encompassing scenarios where one wants to fingerprint not only a precise model (previously referred to as a detection task) but also to identify which model family is in the black-box (identification task). We achieve both goals by demonstrating that benign inputs, that are unmodified images, for instance, are sufficient material for both tasks. We leverage an information-theoretic scheme for the identification task. We devise a greedy discrimination algorithm for the detection task. Both approaches are experimentally validated over an unprecedented set of more than 1,000 networks.
    Developing Optimal Causal Cyber-Defence Agents via Cyber Security Simulation. (arXiv:2207.12355v2 [cs.CR] UPDATED)
    In this paper we explore cyber security defence, through the unification of a novel cyber security simulator with models for (causal) decision-making through optimisation. Particular attention is paid to a recently published approach: dynamic causal Bayesian optimisation (DCBO). We propose that DCBO can act as a blue agent when provided with a view of a simulated network and a causal model of how a red agent spreads within that network. To investigate how DCBO can perform optimal interventions on host nodes, in order to reduce the cost of intrusions caused by the red agent. Through this we demonstrate a complete cyber-simulation system, which we use to generate observational data for DCBO and provide numerical quantitative results which lay the foundations for future work in this space.
    Adaptive Stochastic Gradient Descent for Fast and Communication-Efficient Distributed Learning. (arXiv:2208.03134v1 [cs.LG])
    We consider the setting where a master wants to run a distributed stochastic gradient descent (SGD) algorithm on $n$ workers, each having a subset of the data. Distributed SGD may suffer from the effect of stragglers, i.e., slow or unresponsive workers who cause delays. One solution studied in the literature is to wait at each iteration for the responses of the fastest $k<n$ workers before updating the model, where $k$ is a fixed parameter. The choice of the value of $k$ presents a trade-off between the runtime (i.e., convergence rate) of SGD and the error of the model. Towards optimizing the error-runtime trade-off, we investigate distributed SGD with adaptive~$k$, i.e., varying $k$ throughout the runtime of the algorithm. We first design an adaptive policy for varying $k$ that optimizes this trade-off based on an upper bound on the error as a function of the wall-clock time that we derive. Then, we propose and implement an algorithm for adaptive distributed SGD that is based on a statistical heuristic. Our results show that the adaptive version of distributed SGD can reach lower error values in less time compared to non-adaptive implementations. Moreover, the results also show that the adaptive version is communication-efficient, where the amount of communication required between the master and the workers is less than that of non-adaptive versions.
    Why Do Networks Need Negative Weights?. (arXiv:2208.03211v1 [cs.LG])
    Why do networks have negative weights at all? The answer is: to learn more functions. We mathematically prove that deep neural networks with all non-negative weights are not universal approximators. This fundamental result is assumed by much of the deep learning literature without previously proving the result and demonstrating its necessity.
    Learning to Re-weight Examples with Optimal Transport for Imbalanced Classification. (arXiv:2208.02951v1 [cs.LG])
    Imbalanced data pose challenges for deep learning based classification models. One of the most widely-used approaches for tackling imbalanced data is re-weighting, where training samples are associated with different weights in the loss function. Most of existing re-weighting approaches treat the example weights as the learnable parameter and optimize the weights on the meta set, entailing expensive bilevel optimization. In this paper, we propose a novel re-weighting method based on optimal transport (OT) from a distributional point of view. Specifically, we view the training set as an imbalanced distribution over its samples, which is transported by OT to a balanced distribution obtained from the meta set. The weights of the training samples are the probability mass of the imbalanced distribution and learned by minimizing the OT distance between the two distributions. Compared with existing methods, our proposed one disengages the dependence of the weight learning on the concerned classifier at each iteration. Experiments on image, text and point cloud datasets demonstrate that our proposed re-weighting method has excellent performance, achieving state-of-the-art results in many cases and providing a promising tool for addressing the imbalanced classification issue.
    Model Blending for Text Classification. (arXiv:2208.02819v1 [cs.LG])
    Deep neural networks (DNNs) have proven successful in a wide variety of applications such as speech recognition and synthesis, computer vision, machine translation, and game playing, to name but a few. However, existing deep neural network models are computationally expensive and memory intensive, hindering their deployment in devices with low memory resources or in applications with strict latency requirements. Therefore, a natural thought is to perform model compression and acceleration in deep networks without significantly decreasing the model performance, which is what we call reducing the complexity. In the following work, we try reducing the complexity of state of the art LSTM models for natural language tasks such as text classification, by distilling their knowledge to CNN based models, thus reducing the inference time(or latency) during testing.
    Deep Feature Learning for Medical Acoustics. (arXiv:2208.03084v1 [cs.SD])
    The purpose of this paper is to compare different learnable frontends in medical acoustics tasks. A framework has been implemented to classify human respiratory sounds and heartbeats in two categories, i.e. healthy or affected by pathologies. After obtaining two suitable datasets, we proceeded to classify the sounds using two learnable state-of-art frontends -- LEAF and nnAudio -- plus a non-learnable baseline frontend, i.e. Mel-filterbanks. The computed features are then fed into two different CNN models, namely VGG16 and EfficientNet. The frontends are carefully benchmarked in terms of the number of parameters, computational resources, and effectiveness. This work demonstrates how the integration of learnable frontends in neural audio classification systems may improve performance, especially in the field of medical acoustics. However, the usage of such frameworks makes the needed amount of data even larger. Consequently, they are useful if the amount of data available for training is adequately large to assist the feature learning process.  ( 2 min )
    Explanation of Machine Learning Models of Colon Cancer Using SHAP Considering Interaction Effects. (arXiv:2208.03112v1 [cs.LG])
    When using machine learning techniques in decision-making processes, the interpretability of the models is important. Shapley additive explanation (SHAP) is one of the most promising interpretation methods for machine learning models. Interaction effects occur when the effect of one variable depends on the value of another variable. Even if each variable has little effect on the outcome, its combination can have an unexpectedly large impact on the outcome. Understanding interactions is important for understanding machine learning models; however, naive SHAP analysis cannot distinguish between the main effect and interaction effects. In this paper, we introduce the Shapley-Taylor index as an interpretation method for machine learning models using SHAP considering interaction effects. We apply the method to the cancer cohort data of Kyushu University Hospital (N=29,080) to analyze what combination of factors contributes to the risk of colon cancer.
    Out of the BLEU: how should we assess quality of the Code Generation models?. (arXiv:2208.03133v1 [cs.SE])
    In recent years, researchers have created and introduced a significant number of various code generation models. As human evaluation of every new model version is unfeasible, the community adopted automatic evaluation metrics such as BLEU to approximate the results of human judgement. These metrics originate from the machine translation domain and it is unclear whether they are applicable for the code generation tasks and how well do they agree with the human evaluation on this task. There also are two metrics, CodeBLEU and RUBY, that were developed to estimate the similarity of code and take into account the code properties. However, for these metrics there are hardly any studies on their agreement with the human evaluation. Despite all that, minimal differences in the metric scores are used to claim superiority of some code generation models over the others. In this paper, we present a study on applicability of six metrics -- BLEU, ROUGE-L, METEOR, ChrF, CodeBLEU, RUBY -- for evaluation of the code generation models. We conduct a study on two different code generation datasets and use human annotators to assess the quality of all models run on these datasets. The results indicate that for the CoNaLa dataset of Python one-liners none of the metrics can correctly emulate human judgement on which model is better with $>95\%$ certainty if the difference in model scores is less than 5 points. For the HearthStone dataset, which consists of classes of particular structure, the difference in model scores of at least 2 points is enough to claim the superiority of one model over the other. Using our findings, we derive several recommendations on using metrics to estimate the model performance on the code generation task.
    Catoni-style Confidence Sequences under Infinite Variance. (arXiv:2208.03185v1 [math.ST])
    In this paper, we provide an extension of confidence sequences for settings where the variance of the data-generating distribution does not exist or is infinite. Confidence sequences furnish confidence intervals that are valid at arbitrary data-dependent stopping times, naturally having a wide range of applications. We first establish a lower bound for the width of the Catoni-style confidence sequences for the finite variance case to highlight the looseness of the existing results. Next, we derive tight Catoni-style confidence sequences for data distributions having a relaxed bounded~$p^{th}-$moment, where~$p \in (1,2]$, and strengthen the results for the finite variance case of~$p =2$. The derived results are shown to better than confidence sequences obtained using Dubins-Savage inequality.
    Global Pointer: Novel Efficient Span-based Approach for Named Entity Recognition. (arXiv:2208.03054v1 [cs.CL])
    Named entity recognition (NER) task aims at identifying entities from a piece of text that belong to predefined semantic types such as person, location, organization, etc. The state-of-the-art solutions for flat entities NER commonly suffer from capturing the fine-grained semantic information in underlying texts. The existing span-based approaches overcome this limitation, but the computation time is still a concern. In this work, we propose a novel span-based NER framework, namely Global Pointer (GP), that leverages the relative positions through a multiplicative attention mechanism. The ultimate goal is to enable a global view that considers the beginning and the end positions to predict the entity. To this end, we design two modules to identify the head and the tail of a given entity to enable the inconsistency between the training and inference processes. Moreover, we introduce a novel classification loss function to address the imbalance label problem. In terms of parameters, we introduce a simple but effective approximate method to reduce the training parameters. We extensively evaluate GP on various benchmark datasets. Our extensive experiments demonstrate that GP can outperform the existing solution. Moreover, the experimental results show the efficacy of the introduced loss function compared to softmax and entropy alternatives.  ( 3 min )
    Research: Modeling Price Elasticity for Occupancy Prediction in Hotel Dynamic Pricing. (arXiv:2208.03135v1 [econ.GN])
    Demand estimation plays an important role in dynamic pricing where the optimal price can be obtained via maximizing the revenue based on the demand curve. In online hotel booking platform, the demand or occupancy of rooms varies across room-types and changes over time, and thus it is challenging to get an accurate occupancy estimate. In this paper, we propose a novel hotel demand function that explicitly models the price elasticity of demand for occupancy prediction, and design a price elasticity prediction model to learn the dynamic price elasticity coefficient from a variety of affecting factors. Our model is composed of carefully designed elasticity learning modules to alleviate the endogeneity problem, and trained in a multi-task framework to tackle the data sparseness. We conduct comprehensive experiments on real-world datasets and validate the superiority of our method over the state-of-the-art baselines for both occupancy prediction and dynamic pricing.  ( 2 min )
    On the non-universality of deep learning: quantifying the cost of symmetry. (arXiv:2208.03113v1 [cs.LG])
    We prove computational limitations for learning with neural networks trained by noisy gradient descent (GD). Our result applies whenever GD training is equivariant (true for many standard architectures), and quantifies the alignment needed between architectures and data in order for GD to learn. As applications, (i) we characterize the functions that fully-connected networks can weak-learn on the binary hypercube and unit sphere, demonstrating that depth-2 is as powerful as any other depth for this task; (ii) we extend the merged-staircase necessity result for learning with latent low-dimensional structure [ABM22] to beyond the mean-field regime. Our techniques extend to stochastic gradient descent (SGD), for which we show nontrivial hardness results for learning with fully-connected networks, based on cryptographic assumptions.  ( 2 min )
    CIGAN: A Python Package for Handling Class Imbalance using Generative Adversarial Networks. (arXiv:2208.02931v1 [cs.LG])
    A key challenge in Machine Learning is class imbalance, where the sample size of some classes (majority classes) are much higher than that of the other classes (minority classes). If we were to train a classifier directly on imbalanced data, it is more likely for the classifier to predict a new sample as one of the majority classes. In the extreme case, the classifier could completely ignore the minority classes. This could have serious sociological implications in healthcare, as the minority classes are usually the disease classes (e.g., death or positive clinical test result). In this paper, we introduce a software that uses Generative Adversarial Networks to oversample the minority classes so as to improve downstream classification. To the best of our knowledge, this is the first tool that allows multi-class classification (where the target can have an arbitrary number of classes). The code of the tool is publicly available in our github repository (https://github.com/yuxiaohuang/research/tree/master/gwu/working/cigan/code).  ( 2 min )
    PGX: A Multi-level GNN Explanation Framework Based on Separate Knowledge Distillation Processes. (arXiv:2208.03075v1 [cs.LG])
    Graph Neural Networks (GNNs) are widely adopted in advanced AI systems due to their capability of representation learning on graph data. Even though GNN explanation is crucial to increase user trust in the systems, it is challenging due to the complexity of GNN execution. Lately, many works have been proposed to address some of the issues in GNN explanation. However, they lack generalization capability or suffer from computational burden when the size of graphs is enormous. To address these challenges, we propose a multi-level GNN explanation framework based on an observation that GNN is a multimodal learning process of multiple components in graph data. The complexity of the original problem is relaxed by breaking into multiple sub-parts represented as a hierarchical structure. The top-level explanation aims at specifying the contribution of each component to the model execution and predictions, while fine-grained levels focus on feature attribution and graph structure attribution analysis based on knowledge distillation. Student models are trained in standalone modes and are responsible for capturing different teacher behaviors, later used for particular component interpretation. Besides, we also aim for personalized explanations as the framework can generate different results based on user preferences. Finally, extensive experiments demonstrate the effectiveness and fidelity of our proposed approach.  ( 2 min )
    Rethinking Degradation: Radiograph Super-Resolution via AID-SRGAN. (arXiv:2208.03008v1 [eess.IV])
    In this paper, we present a medical AttentIon Denoising Super Resolution Generative Adversarial Network (AID-SRGAN) for diographic image super-resolution. First, we present a medical practical degradation model that considers various degradation factors beyond downsampling. To the best of our knowledge, this is the first composite degradation model proposed for radiographic images. Furthermore, we propose AID-SRGAN, which can simultaneously denoise and generate high-resolution (HR) radiographs. In this model, we introduce an attention mechanism into the denoising module to make it more robust to complicated degradation. Finally, the SR module reconstructs the HR radiographs using the "clean" low-resolution (LR) radiographs. In addition, we propose a separate-joint training approach to train the model, and extensive experiments are conducted to show that the proposed method is superior to its counterparts. e.g., our proposed method achieves $31.90$ of PSNR with a scale factor of $4 \times$, which is $7.05 \%$ higher than that obtained by recent work, SPSR [16]. Our dataset and code will be made available at: https://github.com/yongsongH/AIDSRGAN-MICCAI2022.  ( 2 min )
    ACE: Adaptive Constraint-aware Early Stopping in Hyperparameter Optimization. (arXiv:2208.02922v1 [cs.LG])
    Deploying machine learning models requires high model quality and needs to comply with application constraints. That motivates hyperparameter optimization (HPO) to tune model configurations under deployment constraints. The constraints often require additional computation cost to evaluate, and training ineligible configurations can waste a large amount of tuning cost. In this work, we propose an Adaptive Constraint-aware Early stopping (ACE) method to incorporate constraint evaluation into trial pruning during HPO. To minimize the overall optimization cost, ACE estimates the cost-effective constraint evaluation interval based on a theoretical analysis of the expected evaluation cost. Meanwhile, we propose a stratum early stopping criterion in ACE, which considers both optimization and constraint metrics in pruning and does not require regularization hyperparameters. Our experiments demonstrate superior performance of ACE in hyperparameter tuning of classification tasks under fairness or robustness constraints.  ( 2 min )
    A Cooperation Graph Approach for Multiagent Sparse Reward Reinforcement Learning. (arXiv:2208.03002v1 [cs.AI])
    Multiagent reinforcement learning (MARL) can solve complex cooperative tasks. However, the efficiency of existing MARL methods relies heavily on well-defined reward functions. Multiagent tasks with sparse reward feedback are especially challenging not only because of the credit distribution problem, but also due to the low probability of obtaining positive reward feedback. In this paper, we design a graph network called Cooperation Graph (CG). The Cooperation Graph is the combination of two simple bipartite graphs, namely, the Agent Clustering subgraph (ACG) and the Cluster Designating subgraph (CDG). Next, based on this novel graph structure, we propose a Cooperation Graph Multiagent Reinforcement Learning (CG-MARL) algorithm, which can efficiently deal with the sparse reward problem in multiagent tasks. In CG-MARL, agents are directly controlled by the Cooperation Graph. And a policy neural network is trained to manipulate this Cooperation Graph, guiding agents to achieve cooperation in an implicit way. This hierarchical feature of CG-MARL provides space for customized cluster-actions, an extensible interface for introducing fundamental cooperation knowledge. In experiments, CG-MARL shows state-of-the-art performance in sparse reward multiagent benchmarks, including the anti-invasion interception task and the multi-cargo delivery task.  ( 2 min )
    Meta-learning from Learning Curves Challenge: Lessons learned from the First Round and Design of the Second Round. (arXiv:2208.02821v1 [cs.LG])
    Meta-learning from learning curves is an important yet often neglected research area in the Machine Learning community. We introduce a series of Reinforcement Learning-based meta-learning challenges, in which an agent searches for the best suited algorithm for a given dataset, based on feedback of learning curves from the environment. The first round attracted participants both from academia and industry. This paper analyzes the results of the first round (accepted to the competition program of WCCI 2022), to draw insights into what makes a meta-learner successful at learning from learning curves. With the lessons learned from the first round and the feedback from the participants, we have designed the second round of our challenge with a new protocol and a new meta-dataset. The second round of our challenge is accepted at the AutoML-Conf 2022 and currently ongoing .
    Deep Surrogate of Modular Multi Pump using Active Learning. (arXiv:2208.02840v1 [cs.LG])
    Due to the high cost and reliability of sensors, the designers of a pump reduce the needed number of sensors for the estimation of the feasible operating point as much as possible. The major challenge to obtain a good estimation is the low amount of data available. Using this amount of data, the performance of the estimation method is not enough to satisfy the client requests. To solve this problem of scarcity of data, getting high quality data is important to obtain a good estimation. Based on these considerations, we develop an active learning framework for estimating the operating point of a Modular Multi Pump used in energy field. In particular we focus on the estimation of the surge distance. We apply Active learning to estimate the surge distance with minimal dataset. Results report that active learning is a valuable technique also for real application.  ( 2 min )
    Interpretable Distribution Shift Detection using Optimal Transport. (arXiv:2208.02896v1 [cs.LG])
    We propose a method to identify and characterize distribution shifts in classification datasets based on optimal transport. It allows the user to identify the extent to which each class is affected by the shift, and retrieves corresponding pairs of samples to provide insights on its nature. We illustrate its use on synthetic and natural shift examples. While the results we present are preliminary, we hope that this inspires future work on interpretable methods for analyzing distribution shifts.  ( 2 min )
    Decision SincNet: Neurocognitive models of decision making that predict cognitive processes from neural signals. (arXiv:2208.02845v1 [q-bio.NC])
    Human decision making behavior is observed with choice-response time data during psychological experiments. Drift-diffusion models of this data consist of a Wiener first-passage time (WFPT) distribution and are described by cognitive parameters: drift rate, boundary separation, and starting point. These estimated parameters are of interest to neuroscientists as they can be mapped to features of cognitive processes of decision making (such as speed, caution, and bias) and related to brain activity. The observed patterns of RT also reflect the variability of cognitive processes from trial to trial mediated by neural dynamics. We adapted a SincNet-based shallow neural network architecture to fit the Drift-Diffusion model using EEG signals on every experimental trial. The model consists of a SincNet layer, a depthwise spatial convolution layer, and two separate FC layers that predict drift rate and boundary for each trial in-parallel. The SincNet layer parametrized the kernels in order to directly learn the low and high cutoff frequencies of bandpass filters that are applied to the EEG data to predict drift and boundary parameters. During training, model parameters were updated by minimizing the negative log likelihood function of WFPT distribution given trial RT. We developed separate decision SincNet models for each participant performing a two-alternative forced-choice task. Our results showed that single-trial estimates of drift and boundary performed better at predicting RTs than the median estimates in both training and test data sets, suggesting that our model can successfully use EEG features to estimate meaningful single-trial Diffusion model parameters. Furthermore, the shallow SincNet architecture identified time windows of information processing related to evidence accumulation and caution and the EEG frequency bands that reflect these processes within each participant.  ( 3 min )
    Differentially Private Counterfactuals via Functional Mechanism. (arXiv:2208.02878v1 [cs.LG])
    Counterfactual, serving as one emerging type of model explanation, has attracted tons of attentions recently from both industry and academia. Different from the conventional feature-based explanations (e.g., attributions), counterfactuals are a series of hypothetical samples which can flip model decisions with minimal perturbations on queries. Given valid counterfactuals, humans are capable of reasoning under ``what-if'' circumstances, so as to better understand the model decision boundaries. However, releasing counterfactuals could be detrimental, since it may unintentionally leak sensitive information to adversaries, which brings about higher risks on both model security and data privacy. To bridge the gap, in this paper, we propose a novel framework to generate differentially private counterfactual (DPC) without touching the deployed model or explanation set, where noises are injected for protection while maintaining the explanation roles of counterfactual. In particular, we train an autoencoder with the functional mechanism to construct noisy class prototypes, and then derive the DPC from the latent prototypes based on the post-processing immunity of differential privacy. Further evaluations demonstrate the effectiveness of the proposed framework, showing that DPC can successfully relieve the risks on both extraction and inference attacks.  ( 2 min )
    Learning the Trading Algorithm in Simulated Markets with Non-stationary Continuum Bandits. (arXiv:2208.02901v1 [cs.MA])
    The basic Multi-Armed Bandits (MABs) problem is trying to maximize the rewards obtained from bandits with different unknown probability distributions of payoff for pulling different arms, given that only a finite number of attempts can be made. When studying trading algorithms in the market, we are looking at one of the most complex variants of MABs problems, namely the Non-stationary Continuum Bandits (NCBs) problem. The Bristol Stock Exchange (BSE) is a simple simulation of an electronic financial exchange based on a continuous double auction running via a limit order book. The market can be populated by automated trader agents with different trading algorithms. Within them, the PRSH algorithm embodies some basic ideas for solving NCBs problems. However, it faces the difficulty to adjust hyperparameters and adapt to changes in complex market conditions. We propose a new algorithm called PRB, which solves Continuum Bandits problem by Bayesian optimization, and solves Non-stationary Bandits problem by a novel "bandit-over-bandit" framework. With BSE, we use as many kinds of trader agents as possible to simulate the real market environment under two different market dynamics. We then examine the optimal hyperparameters of the PRSH algorithm and the PRB algorithm under different market dynamics respectively. Finally, by having trader agents using both algorithms trade in the market at the same time, we demonstrate that the PRB algorithm has better performance than the PRSH algorithm under both market dynamics. In particular, we perform rigorous hypothesis testing on all experimental results to ensure their correctness.  ( 3 min )
    Automatic Segmentation of the Placenta in BOLD MRI Time Series. (arXiv:2208.02895v1 [eess.IV])
    Blood oxygen level dependent (BOLD) MRI with maternal hyperoxia can assess oxygen transport within the placenta and has emerged as a promising tool to study placental function. Measuring signal changes over time requires segmenting the placenta in each volume of the time series. Due to the large number of volumes in the BOLD time series, existing studies rely on registration to map all volumes to a manually segmented template. As the placenta can undergo large deformation due to fetal motion, maternal motion, and contractions, this approach often results in a large number of discarded volumes, where the registration approach fails. In this work, we propose a machine learning model based on a U-Net neural network architecture to automatically segment the placenta in BOLD MRI and apply it to segmenting each volume in a time series. We use a boundary-weighted loss function to accurately capture the placental shape. Our model is trained and tested on a cohort of 91 subjects containing healthy fetuses, fetuses with fetal growth restriction, and mothers with high BMI. We achieve a Dice score of 0.83+/-0.04 when matching with ground truth labels and our model performs reliably in segmenting volumes in both normoxic and hyperoxic points in the BOLD time series. Our code and trained model are available at https://github.com/mabulnaga/automatic-placenta-segmentation.  ( 3 min )
    Human Decision Makings on Curriculum Reinforcement Learning with Difficulty Adjustment. (arXiv:2208.02932v1 [cs.AI])
    Human-centered AI considers human experiences with AI performance. While abundant research has been helping AI achieve superhuman performance either by fully automatic or weak supervision learning, fewer endeavors are experimenting with how AI can tailor to humans' preferred skill level given fine-grained input. In this work, we guide the curriculum reinforcement learning results towards a preferred performance level that is neither too hard nor too easy via learning from the human decision process. To achieve this, we developed a portable, interactive platform that enables the user to interact with agents online via manipulating the task difficulty, observing performance, and providing curriculum feedback. Our system is highly parallelizable, making it possible for a human to train large-scale reinforcement learning applications that require millions of samples without a server. The result demonstrates the effectiveness of an interactive curriculum for reinforcement learning involving human-in-the-loop. It shows reinforcement learning performance can successfully adjust in sync with the human desired difficulty level. We believe this research will open new doors for achieving flow and personalized adaptive difficulties.  ( 2 min )
    MOVE: Effective and Harmless Ownership Verification via Embedded External Features. (arXiv:2208.02820v1 [cs.CR])
    Currently, deep neural networks (DNNs) are widely adopted in different applications. Despite its commercial values, training a well-performed DNN is resource-consuming. Accordingly, the well-trained model is valuable intellectual property for its owner. However, recent studies revealed the threats of model stealing, where the adversaries can obtain a function-similar copy of the victim model, even when they can only query the model. In this paper, we propose an effective and harmless model ownership verification (MOVE) to defend against different types of model stealing simultaneously, without introducing new security risks. In general, we conduct the ownership verification by verifying whether a suspicious model contains the knowledge of defender-specified external features. Specifically, we embed the external features by tempering a few training samples with style transfer. We then train a meta-classifier to determine whether a model is stolen from the victim. This approach is inspired by the understanding that the stolen models should contain the knowledge of features learned by the victim model. In particular, we develop our MOVE method under both white-box and black-box settings to provide comprehensive model protection. Extensive experiments on benchmark datasets verify the effectiveness of our method and its resistance to potential adaptive attacks. The codes for reproducing the main experiments of our method are available at \url{https://github.com/THUYimingLi/MOVE}.  ( 3 min )
  • Open

    On the Finite-Time Performance of the Knowledge Gradient Algorithm. (arXiv:2206.06847v3 [stat.ML] UPDATED)
    The knowledge gradient (KG) algorithm is a popular and effective algorithm for the best arm identification (BAI) problem. Due to the complex calculation of KG, theoretical analysis of this algorithm is difficult, and existing results are mostly about the asymptotic performance of it, e.g., consistency, asymptotic sample allocation, etc. In this research, we present new theoretical results about the finite-time performance of the KG algorithm. Under independent and normally distributed rewards, we derive bounds for the sample allocation of the algorithm. With these bounds, existing asymptotic results become simple corollaries. Furthermore, we derive upper and lower bounds for the probability of error and simple regret of the algorithm, and show the performance of the algorithm for the multi-armed bandit (MAB) problem. These developments not only extend the existing analysis of the KG algorithm, but can also be used to analyze other improvement-based algorithms. Last, we use numerical experiments to compare the bounds we derive and the performance of the KG algorithm.
    SpecGrad: Diffusion Probabilistic Model based Neural Vocoder with Adaptive Noise Spectral Shaping. (arXiv:2203.16749v2 [eess.AS] UPDATED)
    Neural vocoder using denoising diffusion probabilistic model (DDPM) has been improved by adaptation of the diffusion noise distribution to given acoustic features. In this study, we propose SpecGrad that adapts the diffusion noise so that its time-varying spectral envelope becomes close to the conditioning log-mel spectrogram. This adaptation by time-varying filtering improves the sound quality especially in the high-frequency bands. It is processed in the time-frequency domain to keep the computational cost almost the same as the conventional DDPM-based neural vocoders. Experimental results showed that SpecGrad generates higher-fidelity speech waveform than conventional DDPM-based neural vocoders in both analysis-synthesis and speech enhancement scenarios. Audio demos are available at wavegrad.github.io/specgrad/.
    Asymptotic Convergence Rate and Statistical Inference for Stochastic Sequential Quadratic Programming. (arXiv:2205.13687v2 [math.OC] UPDATED)
    We apply a stochastic sequential quadratic programming (StoSQP) algorithm to solve constrained nonlinear optimization problems, where the objective is stochastic and the constraints are deterministic. We study a fully stochastic setup, where only a single sample is available in each iteration for estimating the gradient and Hessian of the objective. We allow StoSQP to select a random stepsize $\bar{\alpha}_t$ adaptively, such that $\beta_t\leq \bar{\alpha}_t \leq \beta_t+\chi_t$, where $\beta_t$, $\chi_t=o(\beta_t)$ are prespecified deterministic sequences. We also allow StoSQP to solve Newton system inexactly via randomized iterative solvers, e.g., with the sketch-and-project method; and we do not require the approximation error of inexact Newton direction to vanish. For this general StoSQP framework, we establish the asymptotic convergence rate for its last iterate, with the worst-case iteration complexity as a byproduct; and we perform statistical inference. In particular, with proper decaying $\beta_t,\chi_t$, we show that: (i) the StoSQP scheme can take at most $O(1/\epsilon^4)$ iterations to achieve $\epsilon$-stationarity; (ii) asymptotically and almost surely, $\|(x_t -x^\star, \lambda_t - \lambda^\star)\| = O(\sqrt{\beta_t\log(1/\beta_t)})+O(\chi_t/\beta_t)$, where $(x_t,\lambda_t)$ is the primal-dual StoSQP iterate; (iii) the sequence $1/\sqrt{\beta_t}\cdot (x_t -x^\star, \lambda_t - \lambda^\star)$ converges to a mean zero Gaussian distribution with a nontrivial covariance matrix. Moreover, we establish the Berry-Esseen bound for $(x_t, \lambda_t)$ to measure quantitatively the convergence of its distribution function. We also provide a practical estimator for the covariance matrix, from which the confidence intervals of $(x^\star, \lambda^\star)$ can be constructed using iterates $\{(x_t,\lambda_t)\}_t$. Our theorems are validated using nonlinear problems in CUTEst test set.
    Robust SDE-Based Variational Formulations for Solving Linear PDEs via Deep Learning. (arXiv:2206.10588v2 [cs.LG] UPDATED)
    The combination of Monte Carlo methods and deep learning has recently led to efficient algorithms for solving partial differential equations (PDEs) in high dimensions. Related learning problems are often stated as variational formulations based on associated stochastic differential equations (SDEs), which allow the minimization of corresponding losses using gradient-based optimization methods. In respective numerical implementations it is therefore crucial to rely on adequate gradient estimators that exhibit low variance in order to reach convergence accurately and swiftly. In this article, we rigorously investigate corresponding numerical aspects that appear in the context of linear Kolmogorov PDEs. In particular, we systematically compare existing deep learning approaches and provide theoretical explanations for their performances. Subsequently, we suggest novel methods that can be shown to be more robust both theoretically and numerically, leading to substantial performance improvements.
    Tailoring to the Tails: Risk Measures for Fine-Grained Tail Sensitivity. (arXiv:2208.03066v1 [cs.LG])
    Expected risk minimization (ERM) is at the core of machine learning systems. This means that the risk inherent in a loss distribution is summarized using a single number - its average. In this paper, we propose a general approach to construct risk measures which exhibit a desired tail sensitivity and may replace the expectation operator in ERM. Our method relies on the specification of a reference distribution with a desired tail behaviour, which is in a one-to-one correspondence to a coherent upper probability. Any risk measure, which is compatible with this upper probability, displays a tail sensitivity which is finely tuned to the reference distribution. As a concrete example, we focus on divergence risk measures based on f-divergence ambiguity sets, which are a widespread tool used to foster distributional robustness of machine learning systems. For instance, we show how ambiguity sets based on the Kullback-Leibler divergence are intricately tied to the class of subexponential random variables. We elaborate the connection of divergence risk measures and rearrangement invariant Banach norms.
    Developing Optimal Causal Cyber-Defence Agents via Cyber Security Simulation. (arXiv:2207.12355v2 [cs.CR] UPDATED)
    In this paper we explore cyber security defence, through the unification of a novel cyber security simulator with models for (causal) decision-making through optimisation. Particular attention is paid to a recently published approach: dynamic causal Bayesian optimisation (DCBO). We propose that DCBO can act as a blue agent when provided with a view of a simulated network and a causal model of how a red agent spreads within that network. To investigate how DCBO can perform optimal interventions on host nodes, in order to reduce the cost of intrusions caused by the red agent. Through this we demonstrate a complete cyber-simulation system, which we use to generate observational data for DCBO and provide numerical quantitative results which lay the foundations for future work in this space.
    Towards Learning to Play Piano with Dexterous Hands and Touch. (arXiv:2106.02040v3 [cs.RO] UPDATED)
    The virtuoso plays the piano with passion, poetry and extraordinary technical ability. As Liszt said (a virtuoso)must call up scent and blossom, and breathe the breath of life. The strongest robots that can play a piano are based on a combination of specialized robot hands/piano and hardcoded planning algorithms. In contrast to that, in this paper, we demonstrate how an agent can learn directly from machine-readable music score to play the piano with dexterous hands on a simulated piano using reinforcement learning (RL) from scratch. We demonstrate the RL agents can not only find the correct key position but also deal with various rhythmic, volume and fingering, requirements. We achieve this by using a touch-augmented reward and a novel curriculum of tasks. We conclude by carefully studying the important aspects to enable such learning algorithms and that can potentially shed light on future research in this direction.
    Interpretable Uncertainty Quantification in AI for HEP. (arXiv:2208.03284v1 [hep-ex])
    Estimating uncertainty is at the core of performing scientific measurements in HEP: a measurement is not useful without an estimate of its uncertainty. The goal of uncertainty quantification (UQ) is inextricably linked to the question, "how do we physically and statistically interpret these uncertainties?" The answer to this question depends not only on the computational task we aim to undertake, but also on the methods we use for that task. For artificial intelligence (AI) applications in HEP, there are several areas where interpretable methods for UQ are essential, including inference, simulation, and control/decision-making. There exist some methods for each of these areas, but they have not yet been demonstrated to be as trustworthy as more traditional approaches currently employed in physics (e.g., non-AI frequentist and Bayesian methods). Shedding light on the questions above requires additional understanding of the interplay of AI systems and uncertainty quantification. We briefly discuss the existing methods in each area and relate them to tasks across HEP. We then discuss recommendations for avenues to pursue to develop the necessary techniques for reliable widespread usage of AI with UQ over the next decade.
    Catoni-style Confidence Sequences under Infinite Variance. (arXiv:2208.03185v1 [math.ST])
    In this paper, we provide an extension of confidence sequences for settings where the variance of the data-generating distribution does not exist or is infinite. Confidence sequences furnish confidence intervals that are valid at arbitrary data-dependent stopping times, naturally having a wide range of applications. We first establish a lower bound for the width of the Catoni-style confidence sequences for the finite variance case to highlight the looseness of the existing results. Next, we derive tight Catoni-style confidence sequences for data distributions having a relaxed bounded~$p^{th}-$moment, where~$p \in (1,2]$, and strengthen the results for the finite variance case of~$p =2$. The derived results are shown to better than confidence sequences obtained using Dubins-Savage inequality.  ( 2 min )
    Machine Learning and Bioinformatics for Diagnosis Analysis of Obesity Spectrum Disorders. (arXiv:2208.03139v1 [q-bio.QM])
    Globally, the number of obese patients has doubled due to sedentary lifestyles and improper dieting. The tremendous increase altered human genetics, and health. According to the world health organization, Life expectancy dropped from 80 to 75 years, as obese people struggle with different chronic diseases. This report will address the problems of obesity in children and adults using ML datasets to feature, predict, and analyze the causes of obesity. By engaging neural ML networks, we will explore neural control using diffusion tensor imaging to consider body fats, BMI, waist \& hip ratio circumference of obese patients. To predict the present and future causes of obesity with ML, we will discuss ML techniques like decision trees, SVM, RF, GBM, LASSO, BN, and ANN and use datasets implement the stated algorithms. Different theoretical literature from experts ML \& Bioinformatics experiments will be outlined in this report while making recommendations on how to advance ML for predicting obesity and other chronic diseases.  ( 2 min )
    On Model Identification and Out-of-Sample Prediction of Principal Component Regression: Applications to Synthetic Controls. (arXiv:2010.14449v4 [math.ST] UPDATED)
    We analyze principal component regression (PCR) in a high-dimensional error-in-variables setting with fixed design. Under suitable conditions, we show that PCR consistently identifies the unique model with minimum $\ell_2$-norm and is near minimax optimal. These results enable us to establish non-asymptotic out-of-sample prediction guarantees that improve upon the best known rates. In our analysis, we introduce a natural linear algebraic condition between the in- and out-of-sample covariates, which allows us to avoid distributional assumptions. Our simulations illustrate the importance of this condition for generalization, even under covariate shifts. As a byproduct, our results also lead to novel results for the synthetic controls literature, a leading approach for policy evaluation. In particular, our minimax results suggest the attractiveness of PCR based methods amongst the numerous variants. To the best of our knowledge, our prediction guarantees for the fixed design setting have been elusive in both the high-dimensional error-in-variables and synthetic controls literatures.  ( 2 min )
    On the Convergence of the Monte Carlo Exploring Starts Algorithm for Reinforcement Learning. (arXiv:2002.03585v2 [cs.LG] UPDATED)
    A simple and natural algorithm for reinforcement learning (RL) is Monte Carlo Exploring Starts (MCES), where the Q-function is estimated by averaging the Monte Carlo returns, and the policy is improved by choosing actions that maximize the current estimate of the Q-function. Exploration is performed by "exploring starts", that is, each episode begins with a randomly chosen state and action, and then follows the current policy to the terminal state. In the classic book on RL by Sutton & Barto (2018), it is stated that establishing convergence for the MCES algorithm is one of the most important remaining open theoretical problems in RL. However, the convergence question for MCES turns out to be quite nuanced. Bertsekas & Tsitsiklis (1996) provide a counter-example showing that the MCES algorithm does not necessarily converge. Tsitsiklis (2002) further shows that if the original MCES algorithm is modified so that the Q-function estimates are updated at the same rate for all state-action pairs, and the discount factor is strictly less than one, then the MCES algorithm converges. In this paper we make headway with the original and more efficient MCES algorithm given in Sutton & Barto (1998), establishing almost sure convergence for Optimal Policy Feed-Forward MDPs, which are MDPs whose states are not revisited within any episode when using an optimal policy. Such MDPs include a large class of environments such as all deterministic environments and all episodic environments with a timestep or any monotonically changing values as part of the state. Different from the previous proofs using stochastic approximations, we introduce a novel inductive approach, which is very simple and only makes use of the strong law of large numbers.  ( 3 min )
    Non-Asymptotic Analysis of Ensemble Kalman Updates: Effective Dimension and Localization. (arXiv:2208.03246v1 [stat.ML])
    Many modern algorithms for inverse problems and data assimilation rely on ensemble Kalman updates to blend prior predictions with observed data. Ensemble Kalman methods often perform well with a small ensemble size, which is essential in applications where generating each particle is costly. This paper develops a non-asymptotic analysis of ensemble Kalman updates that rigorously explains why a small ensemble size suffices if the prior covariance has moderate effective dimension due to fast spectrum decay or approximate sparsity. We present our theory in a unified framework, comparing several implementations of ensemble Kalman updates that use perturbed observations, square root filtering, and localization. As part of our analysis, we develop new dimension-free covariance estimation bounds for approximately sparse matrices that may be of independent interest.  ( 2 min )
    Lethal Dose Conjecture on Data Poisoning. (arXiv:2208.03309v1 [cs.LG])
    Data poisoning considers an adversary that distorts the training set of machine learning algorithms for malicious purposes. In this work, we bring to light one conjecture regarding the fundamentals of data poisoning, which we call the Lethal Dose Conjecture. The conjecture states: If $n$ clean training samples are needed for accurate predictions, then in a size-$N$ training set, only $\Theta(N/n)$ poisoned samples can be tolerated while ensuring accuracy. Theoretically, we verify this conjecture in multiple cases. We also offer a more general perspective of this conjecture through distribution discrimination. Deep Partition Aggregation (DPA) and its extension, Finite Aggregation (FA) are recent approaches for provable defenses against data poisoning, where they predict through the majority vote of many base models trained from different subsets of training set using a given learner. The conjecture implies that both DPA and FA are (asymptotically) optimal -- if we have the most data-efficient learner, they can turn it into one of the most robust defenses against data poisoning. This outlines a practical approach to developing stronger defenses against poisoning via finding data-efficient learners. Empirically, as a proof of concept, we show that by simply using different data augmentations for base learners, we can respectively double and triple the certified robustness of DPA on CIFAR-10 and GTSRB without sacrificing accuracy.  ( 3 min )
    A Non-Asymptotic Framework for Approximate Message Passing in Spiked Models. (arXiv:2208.03313v1 [math.ST])
    Approximate message passing (AMP) emerges as an effective iterative paradigm for solving high-dimensional statistical problems. However, prior AMP theory -- which focused mostly on high-dimensional asymptotics -- fell short of predicting the AMP dynamics when the number of iterations surpasses $o\big(\frac{\log n}{\log\log n}\big)$ (with $n$ the problem dimension). To address this inadequacy, this paper develops a non-asymptotic framework for understanding AMP in spiked matrix estimation. Built upon new decomposition of AMP updates and controllable residual terms, we lay out an analysis recipe to characterize the finite-sample behavior of AMP in the presence of an independent initialization, which is further generalized to allow for spectral initialization. As two concrete consequences of the proposed analysis recipe: (i) when solving $\mathbb{Z}_2$ synchronization, we predict the behavior of spectrally initialized AMP for up to $O\big(\frac{n}{\mathrm{poly}\log n}\big)$ iterations, showing that the algorithm succeeds without the need of a subsequent refinement stage (as conjectured recently by \citet{celentano2021local}); (ii) we characterize the non-asymptotic behavior of AMP in sparse PCA (in the spiked Wigner model) for a broad range of signal-to-noise ratio.  ( 2 min )
    Parameter Averaging for Robust Explainability. (arXiv:2208.03249v1 [cs.LG])
    Neural Networks are known to be sensitive to initialisation. The explanation methods that rely on neural networks are not robust since they can have variations in their explanations when the model is initialized and trained with different random seeds. The sensitivity to model initialisation is not desirable in many safety critical applications such as disease diagnosis in healthcare, in which the explainability might have a significant impact in helping decision making. In this work, we introduce a novel method based on parameter averaging for robust explainability in tabular data setting, referred as XTab. We first initialize and train multiple instances of a shallow network (referred as local masks) with different random seeds for a downstream task. We then obtain a global mask model by "averaging the parameters" of local masks and show that the global model uses the majority rule to rank features based on their relative importance across all local models. We conduct extensive experiments on a variety of real and synthetic datasets, demonstrating that the proposed method can be used for feature selection as well as to obtain the global feature importance that are not sensitive to sub-optimal model initialisation.  ( 2 min )
    Amazon SageMaker Model Monitor: A System for Real-Time Insights into Deployed Machine Learning Models. (arXiv:2111.13657v3 [cs.LG] UPDATED)
    With the increasing adoption of machine learning (ML) models and systems in high-stakes settings across different industries, guaranteeing a model's performance after deployment has become crucial. Monitoring models in production is a critical aspect of ensuring their continued performance and reliability. We present Amazon SageMaker Model Monitor, a fully managed service that continuously monitors the quality of machine learning models hosted on Amazon SageMaker. Our system automatically detects data, concept, bias, and feature attribution drift in models in real-time and provides alerts so that model owners can take corrective actions and thereby maintain high quality models. We describe the key requirements obtained from customers, system design and architecture, and methodology for detecting different types of drift. Further, we provide quantitative evaluations followed by use cases, insights, and lessons learned from more than two years of production deployment.  ( 2 min )

  • Open

    [N] Machine learning talks from SciPy 2022 are up!
    Hey everyone! Just wanted to share that the recordings of the machine learning and data management talks from SciPy 2022 are up on YouTube. Some particular talks that might be of interest: Savin Goyal giving an intro to Metaflow for data science Kevin Kho on what's new in Prefect 1.0 Niels Bantilan on productizing machine learning workflows with flyte Seb Raschka on using regression with ordered categories Davina Zamanzadeh on why you might want to introduce missing values on purpose Paul Anzel on how to test your data Allan Campopiano talking about why the normal distribution doesn't exist Here is the full playlist for the machine learning talks: https://www.youtube.com/playlist?list=PLYx7XA2nY5GcBWLGTzhJ1vxGtHIcyHrRr And the full playlist for the data lifecycle talks: https://www.youtube.com/playlist?list=PLYx7XA2nY5Gde0WF1yswQw5InhmSNED8o submitted by /u/verfahrensweise [link] [comments]  ( 87 min )
    [D] Is it illegal to use an image GAN's results for commercial purposes if the GAN was trained on copyrighted images?
    Common sense tells me that the answer is "yes", but my confusion is as follows: At the bottom of the Latent Diffusion - LAION-400M huggingface space, it says "Who owns the images produced by this demo? Definetly not me! Probably you do." The model was trained on the LAION-400M dataset (obviously), and in its website it says "The images are under their copyright." Since the images are "under their copyright" it seems very possible to me that the model could accidentally spit out an image that is too similar to a copyrighted one from the dataset, and thus I would not "own it". I probably wouldn't even be able to use it. Much less for commercial purposes (which is what I'm interested in). It really does look like the images are "under their copyright" because on some results from that model you can almost read "iStock" at the bottom of the image. This would make it pretty dangerous to use the image like I "owned" it. What are your thoughts on this? submitted by /u/No_Application_5581 [link] [comments]  ( 113 min )
    [P] SharinGAN: Generating Naruto Sharingans with GANs
    Perhaps the most iconic symbol from Naruto is the sharingan, the infamous eye mark of the Uchiha clan. The original sharingans are visually striking and beautifully designed by the show’s creators. Many Naruto fans have even been inspired to generate their own versions of the sharingan. After watching the show, I too was inspired to craft my own sharingan. Unfortunately, I’m not very artistic. So instead, I made the sharinGAN: a GAN to create novel sharingan artwork for me. Since our training data is composed entirely of 15 sharingans from the series, this poses a challenging and fun problem of generating high-fidelity images in the extremely low-data regime. Feel free to check it out at www.sharingans.com. Feedback is welcome! submitted by /u/leonardtang [link] [comments]  ( 115 min )
    XRAY Segmentation papers/Dataset [D]
    can anyone refer me to any x-ray abdominal segmentation papers or datasets? I could only find CT-focused papers/datasets and mostly classification or limited (chest x-ray, lung, and heart) segmentation. is there any literature or surveys to check? submitted by /u/Plastic-Ad4239 [link] [comments]  ( 87 min )
    [D]Cellular automata and prediction
    Please give me a hint! it is necessary to predict another state from one state of the cellular automaton (each state is a binary matrix 20x20). When solving, you cannot use machine learning libraries (sklearn, torch, etc.).The error function is MSE, i.e. real numbers are predicted (20x20 matrix for each id).I have a training sample where "y_0-y_399" are known and "x_0-x_399" are known. and "x" and "y" are BINARY. If you apply a function to "y", then you get "x". I have a test sample, where only "x_0-x_399". You have to predict "y". As "y_0-y_399" get real numbers from 0 to 1. The data is presented in csv format. Objects are recorded by sinks. column 'id' - object number (numbering is different in the training and test samples) 'regime' column - initial state formation mode column 's…  ( 117 min )
    [D] The current and future state of AI/ML is shockingly demoralizing with little hope of redemption
    I recently encountered the PaLM (Scaling Language Modeling with Pathways) paper from Google Research and it opened up a can of worms of ideas I’ve felt I’ve intuitively had for a while, but have been unable to express – and I know I can’t be the only one. Sometimes I wonder what the original pioneers of AI – Turing, Neumann, McCarthy, etc. – would think if they could see the state of AI that we’ve gotten ourselves into. 67 authors, 83 pages, 540B parameters in a model, the internals of which no one can say they comprehend with a straight face, 6144 TPUs in a commercial lab that no one has access to, on a rig that no one can afford, trained on a volume of data that a human couldn’t process in a lifetime, 1 page on ethics with the same ideas that have been rehashed over and over elsewhere wi…  ( 111 min )
    [R] PITI: Pretraining is All You Need for Image-to-Image Translation + Gradio Web Demo
    submitted by /u/Illustrious_Row_9971 [link] [comments]  ( 107 min )
    [R] Masked Siamese Networks for Label-Efficient Learning
    Paper: https://arxiv.org/abs/2204.07141 Github: https://github.com/facebookresearch/msn Abstract: We propose Masked Siamese Networks (MSN), a self-supervised learning framework for learning image representations. Our approach matches the representation of an image view containing randomly masked patches to the representation of the original unmasked image. This self-supervised pre-training strategy is particularly scalable when applied to Vision Transformers since only the unmasked patches are processed by the network. As a result, MSNs improve the scalability of joint-embedding architectures, while producing representations of a high semantic level that perform competitively on low-shot image classification. For instance, on ImageNet-1K, with only 5,000 annotated images, our base MSN model achieves 72.4% top-1 accuracy, and with 1% of ImageNet-1K labels, we achieve 75.7% top-1 accuracy, setting a new state-of-the-art for self-supervised learning on this benchmark. Our code is publicly available. https://preview.redd.it/rksexr4jdbg91.jpg?width=1128&format=pjpg&auto=webp&s=141daf3117fbead562c1f45d583150c0dae81049 https://preview.redd.it/x1zzqt4jdbg91.jpg?width=1233&format=pjpg&auto=webp&s=05e8db351cf6824414ed0244a7cb251154c62db0 https://preview.redd.it/1df6zv4jdbg91.jpg?width=1199&format=pjpg&auto=webp&s=d98ec85b00082d4658195eae73de43908ad27e51 https://preview.redd.it/xee95t4jdbg91.jpg?width=1207&format=pjpg&auto=webp&s=35dfa3e6bf092dbf49b23dd50a75a2e7efe81324 submitted by /u/Singularian2501 [link] [comments]  ( 88 min )
    [D] Interview question: "What classifier should you use as the meta-classifier in your stacking model and why?"
    Saw this datascience interview question posted. ​ Let’s say you work at Google. You are developing a spam classifier to classify emails into spam vs. non-spam categories based on their content. You try several different classifiers like SVM, Random Forests, etc., but none of them produce satisfactory results. So, you decide to combine them together by using stacking. What classifier should you use as the meta-classifier in your stacking model and why? ​ So from my understanding, a meta classifier is essentially a model that takes as input feature the output from other models, and then provides a final prediction based on that. But what arguments are there for using a specific classifier as the top classifier? submitted by /u/bandalorian [link] [comments]  ( 98 min )
    [R] Statistical test to compare two models run multiple time in the same dataset
    Suppose you have two ML models A and B and you want to see if there is a significant difference in preformance among them on a dataset D. D is a big dataset and has a predefined holdout test set (>10k sample). The performance is measured on accuracy (correct / all), but I would appreciate discussing other metrics if possible. Now, the models are stochastic, so it gives different results in different runs. So we run the model K times on the exact same holdout test data. Suppose K is low, eg 5. At this point we have 5 performance metrics for A and 5 for B. We can compute a mean and an std. Now, is there any statistical test to test if the difference in the mean is likely significant? submitted by /u/ombelicoInfinito [link] [comments]  ( 114 min )
    Do ML researchers really feel they are doing research for the sake of it [Discussion]
    I have had this feeling for some time. Since ML research has exploded, going fast and lucrative. Has it changed the way ML researchers look at themselves and conduct themselves? Is putting the next result personally for them, a matter of pushing the boundaries of the body of knowledge in ML or is it just a paper pushing exercise for a better CV or a survival instinct now that everyone's publishing and now they have to too? What goes in an ML researcher and graduates students mind when ones going about their business apart from the actual research that happens if at all it does? Is there a crisis of meaning that what they are doing is ultimately worthless and it's just a career exercise ? submitted by /u/Cool_Abbreviations_9 [link] [comments]  ( 93 min )
    [D] Confusion about Controllable Text Generation
    I recently got into the field of controllable text generation (CTG). I find it very interesting, given its potential applications in industry. However, I still find the field a bit confusing. In particular, there seems to be no clear definition what CTG is and how to benchmark it. E.g., in my intuition CTG means controlling secondary aspects of the output like sentiment, like doing QA but with sentiment control. However, things like data-to-text or style transfer are also considered control tasks in itself, but then the control aspect is the primary concern of the model. So essentially, the control task simply becomes a seq2seq task. For those working in the field. Can you understand my confusion? Do you feel similarly? Whats the reason the field doesnt have a clear standard and benchmark as other fields have? submitted by /u/_Arsenie_Boca_ [link] [comments]  ( 90 min )
    [D] NLP question: does fine-tuning train input embedding?
    Fine-tuning train model weights for sure but does it train input embeddings as well? submitted by /u/SEAIndigenous [link] [comments]  ( 88 min )
    [D] How do latent variable models avoid very small gradient updates?
    Note, this a question, but I figured it was a bit more advanced than a beginner question. The core of a latent variable model (specifically a VAE) can be described as follows: ​ Equation 1 We are trying to optimize the likelihood of generating a particular sample from our training dataset. We can also write this equation like: Equation 2 Here's the issue: Consider a 100-dimensional binary latent space that we sample from uniformly. The value of $P(z)$ will be $\frac{1}{0.5^{100}}$ for any given $z$. This means the gradient update will be extremely small as well, and so (theoretically) it should be hard for the model to learn anything. Can someone explain to me what's going on? submitted by /u/vanilla-acc [link] [comments]  ( 116 min )
    [D] NLP Tasks
    Hi, Many of those Large Language Models could be applied to domains beyond traditional NLP tasks. I know that there are some Biomedical NLP tasks. But what about other domains of NLP tasks? Thanks so much!!!!!!!! submitted by /u/Sedi_RockStar [link] [comments]  ( 111 min )
    [D] [P] Looking for the simplest possible content-based recommendation model
    I'm hoping for a model recommendation here, especially one that's so lightweight that it could run in a browser (that is, in JavaScript). But Pythonic solutions are fine too. I've been playing around with a little project that tries to land on a short melody simply by feeding the user short sequences of 3-5 pitches in different intervals and asking her to rate how pleasant they sound (up or down) and then learning the sorts of patterns she prefers. It's mainly a proof of concept and a chance to explore very basic musical ML. My first question is just what the simplest model is for a content-based recommendation engine that begins with zero knowledge and learns on the fly. I say "simple" both because I want to completely understand how it works and because, in an ideal world, it could jus…  ( 90 min )
  • Open

    CMU Researchers Open-Source ‘auton-survival’: A Comprehensive Python Code Repository of User-Friendly, Machine Learning Tools for Working with Censored Time-to-Event Data
    Machine learning is being used in almost every industry, including healthcare. However, due to the intrinsic complexity of healthcare data, classical machine learning faces various difficulties while dealing with these data. This is because healthcare outcomes like mortality, stroke, cancer initiation, and readmission frequently have a continuous time to events. Since time-to-event data frequently contains individuals whose outcomes are missing or censored owing to loss of follow-up, dealing with this type of data is much more difficult. The researchers have established that traditional classification and regression methods do not offer a simple solution to dealing with such clinical data. Many researchers have been interested in applying deep neural networks, which may be used to create nonlinear representations of complex clin A new study by Auton Lab at Carnegie Mellon University introduced the auton-survival package, a comprehensive Python library of user-friendly tools for machine learning applications in the presence of censored time-to-event data. Continue reading | Check out the paper, package submitted by /u/ai-lover [link] [comments]  ( 87 min )
    Running your own A.I. Image Generator with Latent-Diffusion
    submitted by /u/pwillia7 [link] [comments]  ( 90 min )
    A bartending robot that can engage in personalized interactions with humans
    submitted by /u/Scientific_Thinking [link] [comments]  ( 90 min )
    ReasoNet is impressive but stupid. What do you guys think?
    ReasoNet is a AI reading comprehender (https://www.the-sun.com/tech/5825269/artificial-intelligence-robots-beat-humans/) Initially, I was impressed, but it seems that it does not comprehend things. (at least mathematical concepts) https://preview.redd.it/u2igvwjqzag91.png?width=1902&format=png&auto=webp&s=9af530193ba01257f068c36d76054197b39da84a I know nothing about its inner workings, but this to me looks like little improvement from humans using Ctrl+F. But I'm sure this can be improved. Kudos to the authors. (Yelong Shen, Po-Sen Huang, Jianfeng Gao, Weizhu Chen) ​ See for yourselves: https://machinereading.azurewebsites.net/ submitted by /u/Monoclonal_bob [link] [comments]  ( 87 min )
    The Computer Scientist Trying to Teach AI to Learn Like We Do | Quanta Magazine
    submitted by /u/Tao_Dragon [link] [comments]  ( 85 min )
    Wonder Woman - Made in Nightcafe
    submitted by /u/widgia [link] [comments]  ( 90 min )
    Engineers working on “analog deep learning” have found a way to propel protons through solids at unprecedented speeds. [MIT]
    submitted by /u/hockiklocki [link] [comments]  ( 94 min )
    Inner Monologue: Google's robot talks to itself
    submitted by /u/much_successes [link] [comments]  ( 85 min )
    dystopian anarchy (Dall-E)
    I find moralizing, the creation of Dall-E! For Dall-E, a "child?" disguised as a "cabaret girl?" would result from a dystopian anarchy! And in fact, if we read the definitions of dystopia and anarchy on Wikipedia, this child would come out of a story where a libertarian society (anarchy), with a total authority over its citizens, would impose (dystopia) adult behaviors on children! ​ dystopian anarchy, detailed, cute, soft. High quality, studio lighting (Dall-E) submitted by /u/StantheBrain [link] [comments]  ( 89 min )
    blenderbot 3.0 now available
    https://blenderbot.ai/ submitted by /u/roblox22y [link] [comments]  ( 86 min )
    Top Responsible AI (Artificial Intelligence) Tools in 2022
    A governance paradigm called “responsible AI” describes how a particular organization handles the ethical and legal issues around artificial intelligence (AI). Liable AI projects are primarily motivated by the need to clarify who is responsible if something goes wrong. The data scientists and software engineers who create and implement an organization’s AI algorithmic models are responsible for developing appropriate, reliable AI standards. This indicates that each organization has different requirements for the procedures needed to stop prejudice and ensure transparency. What are the guiding principles of ethical AI? AI should be comprehensive, understandable, moral, and practical, supported by machine learning models that are ethical and effective. Comprehensiveness – To prevent machine learning from being easily hijacked, comprehensive AI includes well-defined testing and governance standards. Explainable – AI is built to explain its goal, justification, and decision-making process in terms the ordinary end user can comprehend. Processes are part of ethical AI projects to identify and eliminate bias in machine learning models. Practical AI is capable of continuous operation and rapid responses to alterations in the operating environment. Toolkits and Projects for Responsible AI TensorFlow Privacy A Python module called TensorFlow Privacy contains TensorFlow optimizers that may be used to train machine learning models with differential privacy. TensorFlow Federated The Federated Learning (FL) method to machine learning, where a shared global model is built across multiple participating clients that maintain their training data locally, has been the focus of TFF’s development to support open research and experimentation. ..... Continue reading submitted by /u/ai-lover [link] [comments]  ( 87 min )
    the future looking good
    submitted by /u/redtailboas [link] [comments]  ( 85 min )
  • Open

    Seconds to hours
    Suppose you have a number of seconds n and you want to convert it to hours and seconds. If you divide n by 3600, the quotient is the number of hours and the remainder is the number of seconds. For example, suppose n = 8072022. Here’s the obvious way to do the calculation in Python: […] Seconds to hours first appeared on John D. Cook.  ( 5 min )
  • Open

    The Primacy Bias in Deep Reinforcement Learning
    The authors of "The Primacy Bias in Deep Reinforcement Learning" present the concept: "The Primacy Bias in Deep RL: a tendency to overfit initial experiences that damages the rest of the learning process'" To remedy this issue, they propose to periodically reset some layers (even the whole) of the networks but maintaining the experience within the buffer. I wonder: to what extent this primacy bias is caused by a large learning rate in the first iterations? if this is the case, what if we set the learning rate to a small value in the beginning, then we allow it to increase up to a point and then decrease it as usual? It seems strange to me the fact of resetting layers. submitted by /u/rlopes404 [link] [comments]  ( 87 min )
    model evaluation and comparison in Offline RL, with access to a simulator
    Hi, I am fairly new to (offline) reinforcement learning and currently building a TD3-BC model to learn an optimal policy based on a behavioural policy, observed from a static offline dataset. I am planning to train this model on different time steps, or different episode terminals. Eg a model that is trained on a dataset that has episode terminals every 10 steps in a sequence, vs a model that is trained on episode terminals as 20 steps. My research question consists of comparing between the best models on these different timesteps to evaluate which episode end provides the largest cumulative rewards (gains) for the RL agent. Deploying this model online is currently not possible, but I am currently working on creating a simulator which will be accurate enough to reflect real life scenario…  ( 101 min )
    Researchers From Princeton And Max Planck Developed A Reinforcement Learning–Based Simulation That Shows The Human Desire Always To Want More May Have Evolved As A Way To Speed Up Learning
    Through the means of a computational framework of reinforcement learning, researchers from Princeton University have tried to find the relationship between happiness with habituation and comparisons that humans operate on. habituation and comparison are two factors that are found to affect human happiness the most, but the most crucial question is why these features decide when we feel happy and when we do not. The framework is built to answer this question precisely and in a scientific manner. In standard RL theory, the reward functions serve the role of defining optimal behavior. Through machine learning, it’s also come to light that the reward function steers the agent from incompetence to mastery. It is found that the reward functions that are based on external factors facilitate faster learning. It is found that the agents perform sub-optimally where aspirations are left unchecked, and they become too high. RL describes how an agent interacting with its environment can learn to choose its actions to maximize the reward from an activity; The environment has different states, which can lead to multiple distinguishable actions from the agent. We divide the reward function into two categories Objective and Subjective reward functions. The objective reward function outlines the task, i.e., what the agent designer wants the RL agent to achieve, making the job significantly harder to solve. Because of this, some parameters of the reward functions are changed. The parametric modified objective reward system is called subjective reward functions, which, when used by an agent to learn, can maximize the expected objective reward. The reward functions depend very sensitively on the environment. The environment chosen is a simulated space inside a more extensive environment known as a grid world which is a popular testing space for RL. Continue reading | Check out the paper submitted by /u/ai-lover [link] [comments]  ( 88 min )
  • Open

    The ultimate guide for Object Detection labeling
    submitted by /u/ramacastro [link] [comments]  ( 85 min )

  • Open

    Doubt about supreme Artificial Intelligences in fiction and their general capabilities.
    Hello everyone! ​ Recently I had a doubt that is making me a little uneasy. I would like to know what would be the "absolute" technical definitions to describe an artificial intelligence such as the one presented to us in the Netflix movie "I Am Mother" and also in the movie "STEALTH", where we also meet an artificial intelligence called EDI or "Eddie". ​ And in the case of the artificial intelligence called The Director, in the Travelers series. ​ If you can help me describe in general what the general capabilities of these artificial intelligences are and how to improve them to a level of perfection and "absolute" capabilities, I would be very grateful! ​ ​ Thank you for your attention! submitted by /u/LyleLewliet [link] [comments]  ( 86 min )
    I was told an AI can't solve this, is this true?
    Find the maximum value of x such that x divides all p^32 - 1 for all primes p > 20. I simply do not believe that an AI is incapable of solving such a question. https://preview.redd.it/8ask6kdv05g91.png?width=577&format=png&auto=webp&s=7aa27f30556a364f4c36672be1357e99b8fd5248 submitted by /u/danielodicho [link] [comments]  ( 89 min )
    OpenAI DALL•E
    Can someone sand me an invite for DALL•E OpenAI? submitted by /u/anonymoussthingss [link] [comments]  ( 89 min )
    Baidu and BioMap AI Research Open-Sources HelixFold-Single: An End-To-End MSA-Free Protein Structure Prediction Pipeline
    submitted by /u/ai-lover [link] [comments]  ( 87 min )
    AI assisted art has gotten crazy good....
    I recently started to mess around with midjourney after seeing some other artists posting what they were making. It took me about 10 minutes to make this gnome village with some minor photoshop work (I combined 2 variations with a little photoshop on the foreground character). This level of art being generated with the help of AI is an absolute game changer. https://preview.redd.it/s7df9eynw3g91.png?width=1792&format=png&auto=webp&s=9d377e2f323dfa261cf30f40af969a8e93130b52 submitted by /u/SeelieForest [link] [comments]  ( 93 min )
    Explosing Egg - Nightcafe
    submitted by /u/widgia [link] [comments]  ( 90 min )
    Inner Monologue: Google's robot talks to itself
    submitted by /u/Zirius_Sadfaces [link] [comments]  ( 90 min )
    Can You Make Money with AI Design?
    submitted by /u/kbf_ [link] [comments]  ( 85 min )
    Disco Diffusion CLIP Model Showcase by AI Manifest
    submitted by /u/Available_Tadpole829 [link] [comments]  ( 85 min )
    “Lighthouse” - Pixelz AI - Details in comments.
    submitted by /u/pixelz_ai [link] [comments]  ( 85 min )
    What's the best AI image generator?
    Just as the title says. Im just curious which ones yall think are the best submitted by /u/Mundane-Afternoon265 [link] [comments]  ( 87 min )
    A.I. Is Not Sentient. Why Do People Say It Is?
    submitted by /u/ArthurTMurray [link] [comments]  ( 85 min )
  • Open

    [D] Most Popular AI Research July 2022 pt. 2 - Ranked Based On GitHub Stars
    submitted by /u/cloud_weather [link] [comments]  ( 87 min )
    ML Video Game Ideas/Feedback [P]
    Hi all, Over the last few weeks I've been developing a demo of a game idea I've had for a while. In this game, you and a friend are each given separate prompts you must draw. However you must both share the same canvas and take turns drawing strokes to make the image more resemble your assigned class. The winner is determined by the predictions of a deep vision model trained on the Quickdraw dataset. Images (sorry, I'm still pretty new to Reddit so I'm a little unclear as to how to embed images) https://raw.githubusercontent.com/kylesayrs/Competitive-Drawing/main/repo_assets/panda_duck.gif https://raw.githubusercontent.com/kylesayrs/Competitive-Drawing/main/flaskr/static/assets/logo.png https://github.com/kylesayrs/Competitive-Drawing/blob/main/repo_assets/squirrel_dragon.png https://g…  ( 114 min )
    [D] how do ACs and PCs respond when reviewers don’t respond to rebuttals?
    Title pretty much says it all. I have addressed reviewers concerns and provided even more in the rebuttal to address any confusions they had. I have asked them directly and politely to increase their score. Furthermore the AC has asked them to respond quickly. What happens if they do not respond? This is for neurips 2022 submitted by /u/AbjectDrink3276 [link] [comments]  ( 88 min )
    [D] Best annotation tool for A/B comparison of text generation?
    I want to use human raters to compare different LLMs and was curious what people would recommend for this. I saw prodigy by spacy but not sure if there are better options. Thanks! submitted by /u/gabriel_pereyra [link] [comments]  ( 88 min )
    [D] Multi PCI-e mother boards with multiple Tesla K80's ?
    I have just bought a couple of k80's at a ridiculously low price. ATM I will be adding one of them to my win 10 / python desktop with adequate cooling and power of course. But I was thinking, the multi PCi-E mining rig motherboards may be ok for say 6 of the k80's. I think the real question is, if it did work how do I utilise all 12 GPU's ? Or even if a home made GPU server can tap into that much processing efficiently ? I have seen hypervisor GPU separation discussed which looks interesting for multi tasking. submitted by /u/BubbleGaff [link] [comments]  ( 88 min )
    [R][P] ICON: Implicit Clothed humans Obtained from Normals + Gradio Web Demo
    submitted by /u/Illustrious_Row_9971 [link] [comments]  ( 87 min )
    [D] Autoencoder for MDtraj
    Hello everyone, I wonder if anyone is dealing with autoencoder! I want to use it to represent the Molecular Dynamics trajectory. I learned the code of autoencoder in Python BUT I'm feeling confused about how can represent the MDtraj with autoencoder. ...so anyone prior dealt with trajectory ?? submitted by /u/M-zatary [link] [comments]  ( 88 min )
    [D] ML Contest: Prediction Of Real-Time Auction Bid Pricing
    https://www.kaggle.com/competitions/digital-turbine-auction-bid-price-prediction/overview submitted by /u/EducationalCicada [link] [comments]  ( 87 min )
    [R]VNext: Next-generation Video instance recognition framework(ECCV 2022 Oral * 2)
    submitted by /u/iFighting [link] [comments]  ( 88 min )
    [D] Current trends in computer vision related to unsupervised learning
    I recently embarked into the world of unsupervised learning and I realised that it has a lot of potential for start-ups and companies that could use large unlabelled data for making the model pre train the basic features and then use their costly limited data for fine tuning. I am just posting the order in which I read the papers and other could also follow this method to understand this field. Supervised Contrastive learning - https://arxiv.org/abs/2004.11362 SimCLR - https://arxiv.org/pdf/2002.05709.pdf MoCo - https://arxiv.org/abs/1911.05722 SimCLR v2.0 - https://arxiv.org/abs/2006.10029 MoCov v2.0 - https://arxiv.org/abs/2003.04297v1 SwaV - https://arxiv.org/pdf/2006.09882.pdf Bootstrap your own latent - http://www.arxiv.org/abs/2006.07733 SimSiam - https://arxiv.org/abs/2011.10566 If there are any other recent papers it would be great to add it to the list. submitted by /u/skeletons_of_closet [link] [comments]  ( 89 min )
    [D]AI in healthcare vs bioinformatics
    What is the difference between AI in healthcare and bioinformatics ? submitted by /u/engkhaledeisa [link] [comments]  ( 87 min )
    [D] Class Activation Map in PyTorch/ResNet50
    Published a blog post on Class Activation Map https://pmgautam.com/computer-vision/2022/08/05/Class-activation-maps.html #computervision #PyTorch #CNN #CAM #DeepLearning #visualization submitted by /u/p1g1 [link] [comments]  ( 87 min )
  • Open

    Gaugan2 has captured a lot of interest. So, I wanted to look into the first version. This is how it turned out.
    Here is the link to the repo https://github.com/Shreyz-max/Doodle-to-Image-Generator submitted by /u/Shreya001 [link] [comments]  ( 86 min )
  • Open

    Memorizing Planck’s constant with DALL-E
    Planck’s constant used to be a measured quantity and now it is exact by definition. h = 6.62607015×10−34 J / Hz Rather than the kilogram being implicit in the units used to measure Planck’s constant, the mass of a kilogram is now defined to be whatever it has to be to make Planck’s constant have […] Memorizing Planck’s constant with DALL-E first appeared on John D. Cook.  ( 5 min )
    DALL-E 2 and mnemonic images
    I recently got an account for using OpenAI’s DALL-E 2 image generator. The example images I’ve seen are sorta surreal combinations of common words, and that made me think of the Major memory system. I’ve written about the Major system before. For example, I give an overview here and I describe how to use it […] DALL-E 2 and mnemonic images first appeared on John D. Cook.  ( 6 min )
  • Open

    "Value-free random exploration is linked to impulsivity", Dubois & Hauser 2022
    submitted by /u/gwern [link] [comments]  ( 86 min )
    Training an RL model with s Random Forest Classifier
    So, I'm trying to create an RL model where the agent tries to learn a player's behavior based on his/her features and gameplay history. However, I can not test the model on actual data. To circumvent this, I trained a (not so accurate) Supervised Learning (RFC) model that can simulate the player's behavior. And whenever I feed it an input state, it can give me a predicted output. And this is what I'm using in place of the actual environment "step" or player response if you will. Is this approach wrong? I'm using the same set of features to train the RFC that I'm using to create my state. And my DQN model is giving me great results. Is it because the model is chasing another model which is using the same set of features? Also, is there any other way to test since the policy can change the agent's state in a number of ways for which I may not have the historical data? How do people test the model then? submitted by /u/gaurjimmy [link] [comments]  ( 87 min )
    Do you guys know if prioritized experience replay makes sense in time series based state space?
    I`m doing a research on IQN on Stock Market and wonder if Prioritized Experience Replay would make sense for it as well, as it is not just the one state but multiple states/experiences in series in importancy. submitted by /u/GarantBM [link] [comments]  ( 87 min )
    What companies work on Multi agent Reinforcement Learning and would probably hire master students for the same?
    submitted by /u/hydrargyrumss [link] [comments]  ( 97 min )
    Model degenerate after training
    I encounter a situation that the randomly initialized model performs better than the partially trained ones for certain particular models. (Others performs just fine with the same script) Does that make sense? I cannot find any bug in it since I just change the environment from the default one to my own. Is it just because this model cannot learn well in the environment? I have checked the losses all seems reasonable. submitted by /u/Blasphemer666 [link] [comments]  ( 86 min )
  • Open

    Deep Dive into NeRF (Neural Radiance Fields)
    I have to confess: recently I did not get to do a lot of horsing around with new, hot neural network architectures. I do not have as much time to fully reimplement papers as I used to as a university student. But luckily, I found out that if one has access to very good code, one can gain deep knowledge of an algorithm solely by running the code through my debugger, analyzing what is going on step-by-step, and cross-referencing ones understanding with the original paper. And plotting - a lot of plotting to visualize concepts; we humans are very visual beasts after all. So I set out to finally understand how this cool invention called NeRF (Neural Radiance Fields) works. I was supposed to spend a rainy evening just clicking through the breakpoints in my debugger but ended up writing a pretty…  ( 7 min )

  • Open

    Is reinforcement used in industry for penetration testing ?
    Are you aware if any companies actually use reinforcement learning for penetration testing on the market ? I have read several academic articles using RL for pen testing but I never heard of a company using it in the "real world". Is SOTA RL for pen testing ready to be deployed in the real world ? Are there examples ? submitted by /u/youneskamel2 [link] [comments]  ( 97 min )
    DQN not learning
    I posted a prior version of my code before and got some good suggestions. I've modified my code and would highly appreciate some more feedback from the community. ​ I've held off from implementing the target network for now. I plan to add it soon. ​ #!/usr/bin/env python # coding: utf-8 # In[66]: # Here we import all libraries import numpy as np import gym import matplotlib.pyplot as plt import os import torch import random from torch import nn from torch.utils.data import DataLoader from torchvision import datasets, transforms from collections import deque import sys env = gym.make("CartPole-v0") # In[67]: #Hyperparameters episodes = 20000 eps = 1.0 learning_rate = 0.001 tot_rewards = [] tot_loss = [] decay_val = 0.0001 mem_size = 5000 batch_size = 100 gamma = 0.99 max_steps = 200 # I…  ( 88 min )
    Article by me: “Intelligence Insights From Observing a Single Cell”. Eager to hear your thoughts :)
    submitted by /u/seth141 [link] [comments]  ( 87 min )
    Doubling the size of cartpole generalisation
    What algorithms currently generalise to different lengths of the cartpole problem where the network can generalise to different cartpole lengths based on training of a single length submitted by /u/Cool_Abbreviations_9 [link] [comments]  ( 86 min )
    Episodes needed to train Frozen Lake Agent using Q Learning?
    I'm trying to implement Q Learning in a 4x4 Frozen lake environment. I need more than 25000 to start getting decent results. I tried with different hyperparameters and is always the same. This is common or I have something wrong with my Q Learning implementation? submitted by /u/Pipiyedu [link] [comments]  ( 87 min )
  • Open

    [R] PyTorch User Focus Group
    Evans Data Corp has an upcoming research study about PyTorch usage. If you are familiar with PyTorch and interested in participating in this study, please contact me to schedule a time to see if you qualify. Study participants receive compensation for a one-hour interview. submitted by /u/EvansData [link] [comments]  ( 87 min )
    [D] Has anyone tried GAN "tricks" on VAEs?
    From my understanding VAEs are generally more stable and less prone to mode collapse compared to GANs, however they usually output fuzzy-looking images making GANs more popular despite being harder to train effectively. However I'm curious to know if someone has tried to apply to VAEs some of the techniques that work to improve GANs such as: A Projected encoder. A projected discriminator in GAN training has been shown to speed up convergence and lead to lower FID. I'd imagine that in the same way that the discriminator can make use of the pretrained features one could speed up the training of a VAE by feeding pretrained features to the encoder. Perceptual loss. I learned about the concept of a perceptual loss when reading the ESRGAN paper. In it the authors use high-level features extracted from a pretrained VGG network to improve the brightness and texture quality of the SRGAN model. Since using a perceptual loss helps improve the texture quality in SR models, I wonder if it'd also help in the same way with VAEs. Differentiable augmentation. The authors of the paper Differentiable Augmentation for Data-Efficient GAN Training proposed this method to prevent the discriminator from overfitting on the real dataset, so this might not be as useful for VAEs - nonetheless I do wonder if data augmentation for the encoder might help it generalize better when there's limited training data without leading to the decoder imitating the artifacts of image augmentation. I suppose I could try to implement these myself, but if someone has already done so and I can spare my Kaggle GPU hours that'd be great to know lol. Are there any other methods that you know of to help improve the fuzziness in VAEs? submitted by /u/Ragdoll_X_Furry [link] [comments]  ( 89 min )
    [D] Help defining a multi attribute utility function
    I have a problem that I'm trying to solve using Expected utility theory, but haven't been able to come up with a proper way to calculate said utility. For reference, I am trying to solve my problem with the help of the Von Neumann–Morgenstern utility theorem. The idea is that I'll have an agent that needs to choose the best action, where each action has both a value and a risk (the probability the action will return the value). I can calculate the risk easily, my challenge has been with the value. These actions all involve multiple attributes; one possible solution is to define the value of these actions as a weighted sum of the value given by each attribute, and then, using hypothetical scenarios, have the stakeholders rank which actions are preferred. For example, let's say we have two actions: action1 = w1*v(a1_1) + w2*v(a1_2) + ... action2 = w1*v(a2_1) + w2*v(a2_2) + ... where w1, w2 are the weights that I need to find, and a1_1 is the value of the attribute 1 for action 1, and so forth (sorry for the bad formatting...) We can present the stakeholders with questions like: "in the example above, which action should the agent choose?". Based on the answers, I can create a list of expressions like this: action1 > action2 action2 > action3 action4 w1*v(a2_1) + w2*v(a2_2) + ... ..... As the individual values for each attribute can be calculated, I can then use this collection of expressions to try to find the values for the weights that satisfy said expressions. Does the approach above make sense? This is where I got stuck; I feel like there should be some pairwise ranking algorithms that can tackle this problem, but didn't have much luck searching online. All comments are appreciated! submitted by /u/Travolta1984 [link] [comments]  ( 114 min )
    [R]Is there a model that prepares the data for this notebook? https://colab.research.google.com/drive/16wqA3oTUf7yzUKsSSZxiMf1443_ZO3wC?usp=sharing#scrollTo=NhhAEm01sXrB
    I'm using this https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/LayoutLM/Add_image_embeddings_to_LayoutLM.ipynb#scrollTo=i_IR1xhWMwty to prepares the data but it lacks of entities as output. submitted by /u/nurigrf05 [link] [comments]  ( 112 min )
    [D] Deepminds study into neural networks and the Chomsky hierarchy
    The paper “Neural Networks and the Chomsky Hierarchy “ laid out which architectures are best suited for language classes in the Chomsky Hierarchy. It puts the transformer architecture in type-3, the lowest class. Yet, all anyone is talking about in NLP are transformers. How come they are so successful if they can only tackle the basic language class? submitted by /u/sillyscienceguy [link] [comments]  ( 89 min )
    [R] A neural network solves, explains, and generates university math problems by program synthesis and few-shot learning at human level - Stanford 2022
    Paper: https://www.pnas.org/doi/full/10.1073/pnas.2123433119 Github: https://github.com/idrori/mathQ Abstract: We demonstrate that a neural network pretrained on text and fine-tuned on code solves mathematics course problems, explains solutions, and generates questions at a human level. We automatically synthesize programs using few-shot learning and OpenAI’s Codex transformer and execute them to solve course problems at 81% automatic accuracy. We curate a dataset of questions from Massachusetts Institute of Technology (MIT)’s largest mathematics courses (Single Variable and Multivariable Calculus, Differential Equations, Introduction to Probability and Statistics, Linear Algebra, and Mathematics for Computer Science) and Columbia University’s Computational Linear Algebra. We solve qu…  ( 89 min )
    [R] Formal Algorithms for Transformers (DeepMind, 2022)
    Paper: https://arxiv.org/abs/2207.09238 Abstract: This document aims to be a self-contained, mathematically precise overview of transformer architectures and algorithms (*not* results). It covers what transformers are, how they are trained, what they are used for, their key architectural components, and a preview of the most prominent models. The reader is assumed to be familiar with basic ML terminology and simpler neural network architectures such as MLPs. ​ https://preview.redd.it/h53zcmn4nxf91.jpg?width=596&format=pjpg&auto=webp&s=86bb06604f6987379392d97324357f2ea5b19ac2 submitted by /u/Singularian2501 [link] [comments]  ( 87 min )
    [D] Increase usable cloud GPU memory by up to 6.6% through disabling ECC
    Here's a link to the post. As an aside, I'm Varun, CEO at Exafunction. We help companies do deep learning efficiently at scale. We're excited to start sharing the best practices for using GPUs that we've learned working at cutting edge deep learning companies in the past and with our current customers. Would love if you had any suggestions for deep dives we could do. submitted by /u/varunkmohan [link] [comments]  ( 89 min )
    [D] Meta's Supposedly PUBLICLY available BlenderBot is NOT so PUBLIC!
    Meta released a Conversational AI bot and claimed it is PUBLICLY available, while it is not public and the access is region based! So far, people from France, Canada, and more countries have reported restricted access! https://geo-not-available.blenderbot.ai/ https://ibb.co/f15yJRb submitted by /u/aifordummies [link] [comments]  ( 113 min )
    [N] We launched EvalRS, a new competition to evaluate Recommender Systems 🚀
    Hi ML and RecSys folks! We are launching EvalRS, a novel competition for a rounded evaluation of Recommender Systems, hosted at the next CIKM conference! Website | Slack | Official Repository Well well. The challenge is super novel (and cool) because we will test models beyond standard metrics, exploring fairness and behavioral tests (details in the paper). And I could stop here. Instead: Remember all the boring stuff like downloading the data, running local evaluations, preparing submission files, etc.? We got you covered: our code handles everything for you! You’ll only need to provide us with a model. We prepared a ton of material to get you started! In the repo, you will find detailed descriptions about every little bit of the challenge and pre-made notebooks with baselines and EDA on the dataset. A Kaggle notebook guides you through the challenge and lets you even solve it! System papers will be part of a series of Proceeding, and there will be a workshop at CIKM where the best systems will present their solution. There will be money 💸 prizes for winning teams and online free registrations for the best student submissions. The competition just started 😎 and will go on for the next two months (but beware, the first phase closes on the 31st of August). We are so thrilled to receive your innovative solutions. And do not hesitate to join our Slack to stay posted! submitted by /u/peppeatta [link] [comments]  ( 127 min )
    [N] Self-Modeling of Robot Morphologies & Sentient Machines - Podcast Episode
    We did an episode with Hod Lipson & Boyuan Chen on self-modeling of robot morphologies and sentient machines. I hope you may find it useful Podcast Link: Video: https://youtu.be/vR-5w7i2on8 Audio: https://soundcloud.com/ieeeras-softrobotics/hod-lipson-boyuan-chen-self-modeling-of-robot-morphologies?utm_source=clipboard&utm_medium=text&utm_campaign=social_sharing submitted by /u/meldiwin [link] [comments]  ( 87 min )
    [P] We analyzed over 1M Reddit comments mentioning products, extracted using deep learning
    The biggest pain points when researching a product online are: Google results full of SEO spam and Ads Fake reviews Fragmented trusted sources Inconsistent information across source To get trustworthy reviews, many people are adding "reddit" to their search queries. The challenges here are: Many duplicate posts/requests Bad search Scattered information Wiki/Collections are hard to keep up-to-date To solve these issues, we launched Looria. We fine-tuned a BERT model to detect product mentions in Reddit comments and posts with Named Entity Recognition (NER). The result is a list of the most mentioned products across many subreddits. https://preview.redd.it/gvujsnzdkwf91.png?width=2834&format=png&auto=webp&s=07c4485265cb996fc5633c9896f41c27e382943e https://preview.redd.it/z20zeufjkwf91.png?width=2834&format=png&auto=webp&s=a499e5b4d9cb5984ffebfecefc94d1f6d5285e63 submitted by /u/madredditscientist [link] [comments]  ( 91 min )
    [D] How to get the best out of a Workshop?
    Hey Guys, I hope that this post is not OT. I am a grad student and in this September I will attend EWRL2022. It is my first time at these kind of events and I have no idea on what to do. Do you have some general advices to get the best out of this experience? Thank you in advance. submitted by /u/Dear-Vehicle-3215 [link] [comments]  ( 89 min )
    [D] why is the AI research community so unreliable?
    How many papers I have read that have explicitly mentioned that their dataset and/or code is available for public use but in practice they rarely if ever actually are. Most of the time they don’t have a publicly available link and expect you to mail them, in which case too they reply maybe once for every ten papers. It’s one thing to not want to make it open source and it’s another to make the claim that is verifiable false. So often do I want to put a complaint against them but I relent because what if they are the reviewers for my next paper? Of course I don’t want to hurt my chances for future publication. It’s a vicious cycle that doesn’t have a fix and it causes so much irritation and pain. submitted by /u/fireless-phoenix [link] [comments]  ( 99 min )
    "[D]" Trying to find an article about generating continuous landscape views with GANs
    Not really a discussion post, I remember an article which used GANs to generate continuous views of landscape, as if you were on a plane discovering the landscapes, but I didn't manage to find it back. I think the paper is from 2021/2020 and likely from NVidia Does anyone remember it ? Or maybe the name of the task ? submitted by /u/Silver_Doughnut_8175 [link] [comments]  ( 113 min )
    Methods on turning a probabilistic (stochastic) model into a deterministic one? [D]
    Hello wonderful people, I just wanted to gather some feedback on some methods that you could potentially use to turn a stochastic model into a deterministic one. Any help is greatly appreciated, thanks! submitted by /u/ChicChanel [link] [comments]  ( 87 min )
    [D] Machine Learning Expertise Combined with Embedded Knowlege
    We're working on some projects involving AI on the edge and targeting low-power devices (think of things that can run for years on a battery). It seems to be a pretty tricky combination of skills to recruit for. Maybe people with EE degrees who have moved on the machine learning would be a good fit? Where should we look for these kinds of people? Or should we focus on finding machine learning experts who can build the models and couple them with people with expertise in the embedded world? submitted by /u/iamflimflam1 [link] [comments]  ( 88 min )
    Can GANs learn white noise? [D]
    Has anybody tried to deliberately train GANs on white noise? I mean that the target images themselves look like noise. Do GANs converge in such case? Are there any interesting insights to learn from that? White noise is an extremely simple probability distribution so if GANs failed to converge on it, that would show an interesting limitation of the model. submitted by /u/alagris12358 [link] [comments]  ( 88 min )
    [D] Has double descent/grokking changed how people train models?
    These papers indicate that when a large model is trained on a small dataset for a very long time, the test loss first goes down, then up when it overfits, but eventually back down even lower, and the model generalizes correctly. Do people take advantage of this in practice to get good, generalized models on small datasets? Do people often train longer now in order to get better models? Or has this not caught on in practice for some reason? Double descent: https://arxiv.org/abs/1912.02292 Grokking: https://arxiv.org/abs/2201.02177 submitted by /u/user_-- [link] [comments]  ( 90 min )
    NeurIPS Rebuttal [Discussion]
    Are any reviewers responding to the rebuttals? Given the author discussion ends on 9th, when should be the right time to nudge the reviewers in case they are unresponsive? submitted by /u/Successful_Abies_572 [link] [comments]  ( 90 min )
  • Open

    Are open source LLM’s missing the point of the problem?
    Recently, language models in the open source community have exploded which has gave lots of people different options when integrating them into their backend. Overall a very positive step in the right direction. One problem however is that when you have a model north of 1 billion parameters, the amount of people who can actually run these models goes down significantly. Outside of large tech companies or well funded groups, I’m not sure if there’s anyone else who could actually run BLOOM or OPT, which are both 100 billion parameter+ models. In practice, this hasn’t really democratized anything. Instead, wouldn’t it be more beneficial to the mass audience to have both: A hosted version of the model for a fee that comes without restrictions (or modest restrictions) An open source version for people who can afford to run the models themselves Is this a better solution to “democratizing” language models? submitted by /u/holamyeung [link] [comments]  ( 87 min )
    Midjourney's take on "Dead Cities, Red Seas, and Lost Ghosts" (yes, the M83 album)
    submitted by /u/Nefir [link] [comments]  ( 86 min )
    Gaugan2 has captured a lot of interest. So, I wanted to look into the first version. This is how it turned out. Here is the link to the repo https://github.com/Shreyz-max/Doodle-to-Image-Generator
    Here is the link to the repo https://github.com/Shreyz-max/Doodle-to-Image-Generator submitted by /u/Shreya001 [link] [comments]  ( 86 min )
    It finally counted to 1 Million
    submitted by /u/Nomad_art [link] [comments]  ( 86 min )
    Automated techniques could make it easier to develop AI
    submitted by /u/Futures_Bot [link] [comments]  ( 85 min )
    Researchers At MIT Developed A Machine Learning Model That Can Answer University-Level Mathematics Problems In A Few Seconds At A Human Level
    Contrary to humans, machine learning models find it incredibly challenging to handle problems involving differential equations, linear algebra, and multivariable calculus. Even the most advanced models can only answer math problems at the elementary or high school level, and they do not always come up with the correct answers. An MIT multidisciplinary research team has created a neural network model that can quickly and accurately answer college-level arithmetic problems. The model may also automatically explain solutions in university math courses and quickly produce new issues. University students were then given the computer-generated questions to test, and they could not determine whether an algorithm or a human-produced the questions. The study has also been published in the National Academy of Sciences Proceedings. Researchers believe their work can be utilized to expedite the creation of course content for extensive residential courses and massive open online courses (MOOCs) with thousands of students. The program could also serve as an automated tutor that demonstrates to pupils how to solve problems in college mathematics. The team believes that by helping teachers to comprehend the connection between courses and their prerequisites, their approach has the potential to enhance higher education. For more than two years, the model has been steadily evolving. In the beginning, the researchers saw that models pretrained using only text could not provide a high accuracy on high school math problems. In contrast, those employing graph neural networks could but would require more extended training periods. Continue reading | Checkout the paper and reference article. submitted by /u/ai-lover [link] [comments]  ( 93 min )
    New Apple AI Text To 3D Scenes Creator | New Deep Learning Method Runs 1,000,000 Times Faster Than Synapses In Human Brain
    submitted by /u/kenickh [link] [comments]  ( 86 min )
    💡 5 Quick Questions for … MIT research scientist Tamay Besiroglu on the huge economic potential of AI
    submitted by /u/estasfuera [link] [comments]  ( 85 min )
    Someone Asked an AI to Show the "Last Selfie Ever Taken" and Um
    submitted by /u/estasfuera [link] [comments]  ( 85 min )
    AI Dream 71 - FIRST CONTACT on a Midjourney though Space by AI
    submitted by /u/LordPewPew777 [link] [comments]  ( 86 min )
    This benchmark compares the CPU versus the GPU for Deep Learning
    submitted by /u/limapedro [link] [comments]  ( 93 min )
    Building A Career In Big Data Analytics: What You Need To Know?
    submitted by /u/saik2363 [link] [comments]  ( 85 min )
    Dalle's Self-Reflection Friday -- Ask Dalle to draw itself (though of course it's just 'image syntax' rather than actual self-reflection)
    submitted by /u/data_everyware [link] [comments]  ( 93 min )
    Queen of the sea - The Lazy Artist - Nightcafe
    submitted by /u/widgia [link] [comments]  ( 85 min )
    Hoping someone's got the right solution. Is there a TTS AI that I can train myself?
    There are a few episodes of Attack on Titan that never came out in English. I've gotten about 30 minutes of isolated dialogue from each character and am praying someone knows of a TTS I can use. tortoise-tts won't work because it uses it's own voices as a base so everyone comes out with a british accent. And other entities with their TTS such as google or describe likely aren't going to let me use the copyrighted material. If anyone knows something that can pull this off you'd be a godsend. submitted by /u/outstandingowl [link] [comments]  ( 93 min )
    DALLE-E Mini (Craiyon) visual refresh explorations.
    submitted by /u/HugoDzz [link] [comments]  ( 85 min )
    Smoking biker kitty with Midjourney
    submitted by /u/cultureicon [link] [comments]  ( 85 min )
    6 Best Artificial Intelligence courses for Healthcare You should learn 2022 -
    submitted by /u/Lakshmireddys [link] [comments]  ( 86 min )
    interesting problems
    What are some Interesting problems you have solved using AI ? submitted by /u/Weary_Word_5262 [link] [comments]  ( 86 min )
    Gothic Manor by Midjourney
    submitted by /u/WonderingWhyWeExist [link] [comments]  ( 85 min )
  • Open

    New Apple AI Text To 3D Scenes Creator | New Deep Learning Method Runs 1,000,000 Times Faster Than Synapses In Human Brain
    submitted by /u/kenickh [link] [comments]  ( 86 min )
    This benchmark compares the CPU versus the GPU for Deep Learning
    submitted by /u/limapedro [link] [comments]  ( 86 min )
    7+ Best Books to Learn Neural Networks in 2022 for Beginners (Updated) -
    submitted by /u/Lakshmireddys [link] [comments]  ( 85 min )
  • Open

    Virtual Reality: The Future of Entertainment
    One of the most fascinating things about virtual reality (VR) is the way it has evolved over time. You may have tried some basic 3D…  ( 8 min )
    Hands-on intro to Language Processing (NLP)
    Three techniques to process text: TFIDF, word2vector trained on our data, Gensim w2v — Natural language processing (NLP)  ( 17 min )
  • Open

    ICML 2022 Art of Robustness Paper “On Fragile Features and Batch Normalization in Adversarial Training”
    While batch normalization has long been argued to increase adversarial vulnerability, it is still used in state-of-the-art adversarial training models. This is likely because of easier training and increased expressiveness. At the same time, recent papers argue that adversarial examples are partly caused by fragile features caused by learning spurious correlations. In this paper, we study the impact of batch normalization on utilizing these fragile features for robustness by fine-tuning only the batch normalization layers. The post ICML 2022 Art of Robustness Paper “On Fragile Features and Batch Normalization in Adversarial Training” appeared first on David Stutz.  ( 3 min )
  • Open

    Naming probability functions
    Given a random variable X, you often want to compute the probability that X will take on a value less than x or greater than x. Define the functions FX(x) = Prob(X ≤ x) and GX(x) = Prob(X > x) What do you call F and G? I tend to call them the CDF (cumulative […] Naming probability functions first appeared on John D. Cook.  ( 5 min )
    Floating point inverses and stability
    Let f be a monotone, strictly convex function on a real interval I and let g be its inverse. For example, we could have f(x) = ex and g(x) = log x. Now suppose we round our results to N digits. That is, instead of working with f and g we actually work with fN […] Floating point inverses and stability first appeared on John D. Cook.  ( 6 min )
  • Open

    NVIDIA Instant NeRF Wins Best Paper at SIGGRAPH, Inspires Creative Wave Amid Tens of Thousands of Downloads
    3D content creators are clamoring for NVIDIA Instant NeRF, an inverse rendering tool that turns a set of static images into a realistic 3D scene. Since its debut earlier this year, tens of thousands of developers around the world have downloaded the source code and used it to render spectacular scenes, sharing eye-catching results on Read article > The post NVIDIA Instant NeRF Wins Best Paper at SIGGRAPH, Inspires Creative Wave Amid Tens of Thousands of Downloads appeared first on NVIDIA Blog.  ( 7 min )
  • Open

    Neuro-symbolic computing with spiking neural networks. (arXiv:2208.02576v1 [cs.NE])
    Knowledge graphs are an expressive and widely used data structure due to their ability to integrate data from different domains in a sensible and machine-readable way. Thus, they can be used to model a variety of systems such as molecules and social networks. However, it still remains an open question how symbolic reasoning could be realized in spiking systems and, therefore, how spiking neural networks could be applied to such graph data. Here, we extend previous work on spike-based graph algorithms by demonstrating how symbolic and multi-relational information can be encoded using spiking neurons, allowing reasoning over symbolic structures like knowledge graphs with spiking neural networks. The introduced framework is enabled by combining the graph embedding paradigm and the recent progress in training spiking neural networks using error backpropagation. The presented methods are applicable to a variety of spiking neuron models and can be trained end-to-end in combination with other differentiable network architectures, which we demonstrate by implementing a spiking relational graph neural network.  ( 2 min )
    Glance and Focus Networks for Dynamic Visual Recognition. (arXiv:2201.03014v2 [cs.CV] UPDATED)
    Spatial redundancy widely exists in visual recognition tasks, i.e., discriminative features in an image or video frame usually correspond to only a subset of pixels, while the remaining regions are irrelevant to the task at hand. Therefore, static models which process all the pixels with an equal amount of computation result in considerable redundancy in terms of time and space consumption. In this paper, we formulate the image recognition problem as a sequential coarse-to-fine feature learning process, mimicking the human visual system. Specifically, the proposed Glance and Focus Network (GFNet) first extracts a quick global representation of the input image at a low resolution scale, and then strategically attends to a series of salient (small) regions to learn finer features. The sequential process naturally facilitates adaptive inference at test time, as it can be terminated once the model is sufficiently confident about its prediction, avoiding further redundant computation. It is worth noting that the problem of locating discriminant regions in our model is formulated as a reinforcement learning task, thus requiring no additional manual annotations other than classification labels. GFNet is general and flexible as it is compatible with any off-the-shelf backbone models (such as MobileNets, EfficientNets and TSM), which can be conveniently deployed as the feature extractor. Extensive experiments on a variety of image classification and video recognition tasks and with various backbone models demonstrate the remarkable efficiency of our method. For example, it reduces the average latency of the highly efficient MobileNet-V3 on an iPhone XS Max by 1.3x without sacrificing accuracy. Code and pre-trained models are available at https://github.com/blackfeather-wang/GFNet-Pytorch.  ( 3 min )
    Generalization Analysis of Message Passing Neural Networks on Large Random Graphs. (arXiv:2202.00645v6 [cs.LG] UPDATED)
    Message passing neural networks (MPNN) have seen a steep rise in popularity since their introduction as generalizations of convolutional neural networks to graph-structured data, and are now considered state-of-the-art tools for solving a large variety of graph-focused problems. We study the generalization error of MPNNs in graph classification and regression. We assume that graphs of different classes are sampled from different random graph models. We show that, when training a MPNN on a dataset sampled from such a distribution, the generalization gap increases in the complexity of the MPNN, and decreases, not only with respect to the number of training samples, but also with the average number of nodes in the graphs. This shows how a MPNN with high complexity can generalize from a small dataset of graphs, as long as the graphs are large. The generalization bound is derived from a uniform convergence result, that shows that any MPNN, applied on a graph, approximates the MPNN applied on the geometric model that the graph discretizes.  ( 3 min )
    DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R. (arXiv:2103.09603v3 [stat.ML] UPDATED)
    The R package DoubleML implements the double/debiased machine learning framework of Chernozhukov et al. (2018). It provides functionalities to estimate parameters in causal models based on machine learning methods. The double machine learning framework consist of three key ingredients: Neyman orthogonality, high-quality machine learning estimation and sample splitting. Estimation of nuisance components can be performed by various state-of-the-art machine learning methods that are available in the mlr3 ecosystem. DoubleML makes it possible to perform inference in a variety of causal models, including partially linear and interactive regression models and their extensions to instrumental variable estimation. The object-oriented implementation of DoubleML enables a high flexibility for the model specification and makes it easily extendable. This paper serves as an introduction to the double machine learning framework and the R package DoubleML. In reproducible code examples with simulated and real data sets, we demonstrate how DoubleML users can perform valid inference based on machine learning methods.  ( 2 min )
    Bayesian regularization of empirical MDPs. (arXiv:2208.02362v1 [cs.LG])
    In most applications of model-based Markov decision processes, the parameters for the unknown underlying model are often estimated from the empirical data. Due to noise, the policy learnedfrom the estimated model is often far from the optimal policy of the underlying model. When applied to the environment of the underlying model, the learned policy results in suboptimal performance, thus calling for solutions with better generalization performance. In this work we take a Bayesian perspective and regularize the objective function of the Markov decision process with prior information in order to obtain more robust policies. Two approaches are proposed, one based on $L^1$ regularization and the other on relative entropic regularization. We evaluate our proposed algorithms on synthetic simulations and on real-world search logs of a large scale online shopping store. Our results demonstrate the robustness of regularized MDP policies against the noise present in the models.  ( 2 min )
    Invariant Representations with Stochastically Quantized Neural Networks. (arXiv:2208.02656v1 [cs.LG])
    Representation learning algorithms offer the opportunity to learn invariant representations of the input data with regard to nuisance factors. Many authors have leveraged such strategies to learn fair representations, i.e., vectors where information about sensitive attributes is removed. These methods are attractive as they may be interpreted as minimizing the mutual information between a neural layer's activations and a sensitive attribute. However, the theoretical grounding of such methods relies either on the computation of infinitely accurate adversaries or on minimizing a variational upper bound of a mutual information estimate. In this paper, we propose a methodology for direct computation of the mutual information between a neural layer and a sensitive attribute. We employ stochastically-activated binary neural networks, which lets us treat neurons as random variables. We are then able to compute (not bound) the mutual information between a layer and a sensitive attribute and use this information as a regularization factor during gradient descent. We show that this method compares favorably with the state of the art in fair representation learning and that the learned representations display a higher level of invariance compared to full-precision neural networks.  ( 2 min )
    Counterfactual Image Synthesis for Discovery of Personalized Predictive Image Markers. (arXiv:2208.02311v1 [cs.CV])
    The discovery of patient-specific imaging markers that are predictive of future disease outcomes can help us better understand individual-level heterogeneity of disease evolution. In fact, deep learning models that can provide data-driven personalized markers are much more likely to be adopted in medical practice. In this work, we demonstrate that data-driven biomarker discovery can be achieved through a counterfactual synthesis process. We show how a deep conditional generative model can be used to perturb local imaging features in baseline images that are pertinent to subject-specific future disease evolution and result in a counterfactual image that is expected to have a different future outcome. Candidate biomarkers, therefore, result from examining the set of features that are perturbed in this process. Through several experiments on a large-scale, multi-scanner, multi-center multiple sclerosis (MS) clinical trial magnetic resonance imaging (MRI) dataset of relapsing-remitting (RRMS) patients, we demonstrate that our model produces counterfactuals with changes in imaging features that reflect established clinical markers predictive of future MRI lesional activity at the population level. Additional qualitative results illustrate that our model has the potential to discover novel and subject-specific predictive markers of future activity.  ( 3 min )
    Feature selection with gradient descent on two-layer networks in low-rotation regimes. (arXiv:2208.02789v1 [cs.LG])
    This work establishes low test error of gradient flow (GF) and stochastic gradient descent (SGD) on two-layer ReLU networks with standard initialization, in three regimes where key sets of weights rotate little (either naturally due to GF and SGD, or due to an artificial constraint), and making use of margins as the core analytic technique. The first regime is near initialization, specifically until the weights have moved by $\mathcal{O}(\sqrt m)$, where $m$ denotes the network width, which is in sharp contrast to the $\mathcal{O}(1)$ weight motion allowed by the Neural Tangent Kernel (NTK); here it is shown that GF and SGD only need a network width and number of samples inversely proportional to the NTK margin, and moreover that GF attains at least the NTK margin itself, which suffices to establish escape from bad KKT points of the margin objective, whereas prior work could only establish nondecreasing but arbitrarily small margins. The second regime is the Neural Collapse (NC) setting, where data lies in extremely-well-separated groups, and the sample complexity scales with the number of groups; here the contribution over prior work is an analysis of the entire GF trajectory from initialization. Lastly, if the inner layer weights are constrained to change in norm only and can not rotate, then GF with large widths achieves globally maximal margins, and its sample complexity scales with their inverse; this is in contrast to prior work, which required infinite width and a tricky dual convergence assumption. As purely technical contributions, this work develops a variety of potential functions and other tools which will hopefully aid future work.  ( 3 min )
    Dynamic Planning in Open-Ended Dialogue using Reinforcement Learning. (arXiv:2208.02294v1 [cs.CL])
    Despite recent advances in natural language understanding and generation, and decades of research on the development of conversational bots, building automated agents that can carry on rich open-ended conversations with humans "in the wild" remains a formidable challenge. In this work we develop a real-time, open-ended dialogue system that uses reinforcement learning (RL) to power a bot's conversational skill at scale. Our work pairs the succinct embedding of the conversation state generated using SOTA (supervised) language models with RL techniques that are particularly suited to a dynamic action space that changes as the conversation progresses. Trained using crowd-sourced data, our novel system is able to substantially exceeds the (strong) baseline supervised model with respect to several metrics of interest in a live experiment with real users of the Google Assistant.  ( 2 min )
    A Nonlinear PID-Enhanced Adaptive Latent Factor Analysis Model. (arXiv:2208.02513v1 [cs.LG])
    High-dimensional and incomplete (HDI) data holds tremendous interactive information in various industrial applications. A latent factor (LF) model is remarkably effective in extracting valuable information from HDI data with stochastic gradient decent (SGD) algorithm. However, an SGD-based LFA model suffers from slow convergence since it only considers the current learning error. To address this critical issue, this paper proposes a Nonlinear PID-enhanced Adaptive Latent Factor (NPALF) model with two-fold ideas: 1) rebuilding the learning error via considering the past learning errors following the principle of a nonlinear PID controller; b) implementing all parameters adaptation effectively following the principle of a particle swarm optimization (PSO) algorithm. Experience results on four representative HDI datasets indicate that compared with five state-of-the-art LFA models, the NPALF model achieves better convergence rate and prediction accuracy for missing data of an HDI data.  ( 2 min )
    Gradient-based Bi-level Optimization for Deep Learning: A Survey. (arXiv:2207.11719v2 [cs.LG] UPDATED)
    Bi-level optimization, especially the gradient-based category, has been widely used in the deep learning community including hyperparameter optimization and meta knowledge extraction. Bi-level optimization embeds one problem within another and the gradient-based category solves the outer level task by computing the hypergradient, which is much more efficient than classical methods such as the evolutionary algorithm. In this survey, we first give a formal definition of the gradient-based bi-level optimization. Secondly, we illustrate how to formulate a research problem as a bi-level optimization problem, which is of great practical use for beginners. More specifically, there are two formulations: the single-task formulation to optimize hyperparameters such as regularization parameters and the distilled data, and the multi-task formulation to extract meta knowledge such as the model initialization. With a bi-level formulation, we then discuss four bi-level optimization solvers to update the outer variable including explicit gradient update, proxy update, implicit function update, and closed-form update. Last but not least, we conclude the survey by pointing out the great potential of gradient-based bi-level optimization on science problems (AI4Science).  ( 2 min )
    Privacy Safe Representation Learning via Frequency Filtering Encoder. (arXiv:2208.02482v1 [cs.CV])
    Deep learning models are increasingly deployed in real-world applications. These models are often deployed on the server-side and receive user data in an information-rich representation to solve a specific task, such as image classification. Since images can contain sensitive information, which users might not be willing to share, privacy protection becomes increasingly important. Adversarial Representation Learning (ARL) is a common approach to train an encoder that runs on the client-side and obfuscates an image. It is assumed, that the obfuscated image can safely be transmitted and used for the task on the server without privacy concerns. However, in this work, we find that training a reconstruction attacker can successfully recover the original image of existing ARL methods. To this end, we introduce a novel ARL method enhanced through low-pass filtering, limiting the available information amount to be encoded in the frequency domain. Our experimental results reveal that our approach withstands reconstruction attacks while outperforming previous state-of-the-art methods regarding the privacy-utility trade-off. We further conduct a user study to qualitatively assess our defense of the reconstruction attack.  ( 2 min )
    Robust Adaptive Submodular Maximization. (arXiv:2107.11333v3 [cs.DS] UPDATED)
    The goal of a sequential decision making problem is to design an interactive policy that adaptively selects a group of items, each selection is based on the feedback from the past, in order to maximize the expected utility of selected items. It has been shown that the utility functions of many real-world applications are adaptive submodular. However, most of existing studies on adaptive submodular optimization focus on the average-case. Unfortunately, a policy that has a good average-case performance may have very poor performance under the worst-case realization. In this study, we propose to study two variants of adaptive submodular optimization problems, namely, worst-case adaptive submodular maximization and robust submodular maximization. The first problem aims to find a policy that maximizes the worst-case utility and the latter one aims to find a policy, if any, that achieves both near optimal average-case utility and worst-case utility simultaneously. We introduce a new class of stochastic functions, called \emph{worst-case submodular function}. For the worst-case adaptive submodular maximization problem subject to a $p$-system constraint, we develop an adaptive worst-case greedy policy that achieves a $\frac{1}{p+1}$ approximation ratio against the optimal worst-case utility if the utility function is worst-case submodular. For the robust adaptive submodular maximization problem subject to cardinality constraints (resp. partition matroid constraints), if the utility function is both worst-case submodular and adaptive submodular, we develop a hybrid adaptive policy that achieves an approximation close to $1-e^{-\frac{1}{2}}$ (resp. $1/3$) under both worst- and average-case settings simultaneously. We also describe several applications of our theoretical results, including pool-base active learning, stochastic submodular set cover and adaptive viral marketing.  ( 3 min )
    How Much Privacy Does Federated Learning with Secure Aggregation Guarantee?. (arXiv:2208.02304v1 [cs.LG])
    Federated learning (FL) has attracted growing interest for enabling privacy-preserving machine learning on data stored at multiple users while avoiding moving the data off-device. However, while data never leaves users' devices, privacy still cannot be guaranteed since significant computations on users' training data are shared in the form of trained local models. These local models have recently been shown to pose a substantial privacy threat through different privacy attacks such as model inversion attacks. As a remedy, Secure Aggregation (SA) has been developed as a framework to preserve privacy in FL, by guaranteeing the server can only learn the global aggregated model update but not the individual model updates. While SA ensures no additional information is leaked about the individual model update beyond the aggregated model update, there are no formal guarantees on how much privacy FL with SA can actually offer; as information about the individual dataset can still potentially leak through the aggregated model computed at the server. In this work, we perform a first analysis of the formal privacy guarantees for FL with SA. Specifically, we use Mutual Information (MI) as a quantification metric and derive upper bounds on how much information about each user's dataset can leak through the aggregated model update. When using the FedSGD aggregation algorithm, our theoretical bounds show that the amount of privacy leakage reduces linearly with the number of users participating in FL with SA. To validate our theoretical bounds, we use an MI Neural Estimator to empirically evaluate the privacy leakage under different FL setups on both the MNIST and CIFAR10 datasets. Our experiments verify our theoretical bounds for FedSGD, which show a reduction in privacy leakage as the number of users and local batch size grow, and an increase in privacy leakage with the number of training rounds.  ( 3 min )
    Backward Imitation and Forward Reinforcement Learning via Bi-directional Model Rollouts. (arXiv:2208.02434v1 [cs.LG])
    Traditional model-based reinforcement learning (RL) methods generate forward rollout traces using the learnt dynamics model to reduce interactions with the real environment. The recent model-based RL method considers the way to learn a backward model that specifies the conditional probability of the previous state given the previous action and the current state to additionally generate backward rollout trajectories. However, in this type of model-based method, the samples derived from backward rollouts and those from forward rollouts are simply aggregated together to optimize the policy via the model-free RL algorithm, which may decrease both the sample efficiency and the convergence rate. This is because such an approach ignores the fact that backward rollout traces are often generated starting from some high-value states and are certainly more instructive for the agent to improve the behavior. In this paper, we propose the backward imitation and forward reinforcement learning (BIFRL) framework where the agent treats backward rollout traces as expert demonstrations for the imitation of excellent behaviors, and then collects forward rollout transitions for policy reinforcement. Consequently, BIFRL empowers the agent to both reach to and explore from high-value states in a more efficient manner, and further reduces the real interactions, making it potentially more suitable for real-robot learning. Moreover, a value-regularized generative adversarial network is introduced to augment the valuable states which are infrequently received by the agent. Theoretically, we provide the condition where BIFRL is superior to the baseline methods. Experimentally, we demonstrate that BIFRL acquires the better sample efficiency and produces the competitive asymptotic performance on various MuJoCo locomotion tasks compared against state-of-the-art model-based methods.  ( 3 min )
    OCFR 2022: Competition on Occluded Face Recognition From Synthetically Generated Structure-Aware Occlusions. (arXiv:2208.02760v1 [cs.CV])
    This work summarizes the IJCB Occluded Face Recognition Competition 2022 (IJCB-OCFR-2022) embraced by the 2022 International Joint Conference on Biometrics (IJCB 2022). OCFR-2022 attracted a total of 3 participating teams, from academia. Eventually, six valid submissions were submitted and then evaluated by the organizers. The competition was held to address the challenge of face recognition in the presence of severe face occlusions. The participants were free to use any training data and the testing data was built by the organisers by synthetically occluding parts of the face images using a well-known dataset. The submitted solutions presented innovations and performed very competitively with the considered baseline. A major output of this competition is a challenging, realistic, and diverse, and publicly available occluded face recognition benchmark with well defined evaluation protocols.  ( 2 min )
    Modular Grammatical Evolution for the Generation of Artificial Neural Networks. (arXiv:2208.02787v1 [cs.NE])
    This paper presents a novel method, called Modular Grammatical Evolution (MGE), towards validating the hypothesis that restricting the solution space of NeuroEvolution to modular and simple neural networks enables the efficient generation of smaller and more structured neural networks while providing acceptable (and in some cases superior) accuracy on large data sets. MGE also enhances the state-of-the-art Grammatical Evolution (GE) methods in two directions. First, MGE's representation is modular in that each individual has a set of genes, and each gene is mapped to a neuron by grammatical rules. Second, the proposed representation mitigates two important drawbacks of GE, namely the low scalability and weak locality of representation, towards generating modular and multi-layer networks with a high number of neurons. We define and evaluate five different forms of structures with and without modularity using MGE and find single-layer modules with no coupling more productive. Our experiments demonstrate that modularity helps in finding better neural networks faster. We have validated the proposed method using ten well-known classification benchmarks with different sizes, feature counts, and output class count. Our experimental results indicate that MGE provides superior accuracy with respect to existing NeuroEvolution methods and returns classifiers that are significantly simpler than other machine learning generated classifiers. Finally, we empirically demonstrate that MGE outperforms other GE methods in terms of locality and scalability properties.  ( 3 min )
    Image-based Detection of Surface Defects in Concrete during Construction. (arXiv:2208.02313v1 [cs.CV])
    Defects increase the cost and duration of construction projects. Automating defect detection would reduce documentation efforts that are necessary to decrease the risk of defects delaying construction projects. Since concrete is a widely used construction material, this work focuses on detecting honeycombs, a substantial defect in concrete structures that may even affect structural integrity. First, images were compared that were either scraped from the web or obtained from actual practice. The results demonstrate that web images represent just a selection of honeycombs and do not capture the complete variance. Second, Mask R-CNN and EfficientNet-B0 were trained for honeycomb detection to evaluate instance segmentation and patch-based classification, respectively achieving 47.7% precision and 34.2% recall as well as 68.5% precision and 55.7% recall. Although the performance of those models is not sufficient for completely automated defect detection, the models could be used for active learning integrated into defect documentation systems. In conclusion, CNNs can assist detecting honeycombs in concrete.  ( 2 min )
    Visually Evaluating Generative Adversarial Networks Using Itself under Multivariate Time Series. (arXiv:2208.02649v1 [cs.LG])
    Visually evaluating the goodness of generated Multivariate Time Series (MTS) are difficult to implement, especially in the case that the generative model is Generative Adversarial Networks (GANs). We present a general framework named Gaussian GANs to visually evaluate GANs using itself under the MTS generation task. Firstly, we attempt to find the transformation function in the multivariate Kolmogorov Smirnov (MKS) test by explicitly reconstructing the architecture of GANs. Secondly, we conduct the normality test of transformed MST where the Gaussian GANs serves as the transformation function in the MKS test. In order to simplify the normality test, an efficient visualization is proposed using the chi square distribution. In the experiment, we use the UniMiB dataset and provide empirical evidence showing that the normality test using Gaussian GANs and chi sqaure visualization is effective and credible.  ( 2 min )
    Risk and optimal policies in bandit experiments. (arXiv:2112.06363v8 [econ.EM] UPDATED)
    We provide a decision theoretic analysis of bandit experiments. Working within the framework of diffusion asymptotics, we define suitable notions of asymptotic Bayes and minimax risk for these experiments. For normally distributed rewards, the minimal Bayes risk can be characterized as the solution to a second-order partial differential equation (PDE). Using a limit of experiments approach, we show that this PDE characterization also holds asymptotically under both parametric and non-parametric distributions of the rewards. The approach further describes the state variables it is asymptotically sufficient to restrict attention to, and thereby suggests a practical strategy for dimension reduction. The PDEs characterizing minimal Bayes risk can be solved efficiently using sparse matrix routines. We derive the optimal Bayes and minimax policies from their numerical solutions. These optimal policies substantially dominate existing methods such as Thompson sampling and UCB, often by a factor of two. The framework also covers time discounting and pure exploration.
    Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspective. (arXiv:2112.06409v2 [cs.LG] UPDATED)
    Data-centric AI is at the center of a fundamental shift in software engineering where machine learning becomes the new software, powered by big data and computing infrastructure. Here software engineering needs to be re-thought where data becomes a first-class citizen on par with code. One striking observation is that a significant portion of the machine learning process is spent on data preparation. Without good data, even the best machine learning algorithms cannot perform well. As a result, data-centric AI practices are now becoming mainstream. Unfortunately, many datasets in the real world are small, dirty, biased, and even poisoned. In this survey, we study the research landscape for data collection and data quality primarily for deep learning applications. Data collection is important because there is lesser need for feature engineering for recent deep learning approaches, but instead more need for large amounts of data. For data quality, we study data validation, cleaning, and integration techniques. Even if the data cannot be fully cleaned, we can still cope with imperfect data during model training using robust model training techniques. In addition, while bias and fairness have been less studied in traditional data management research, these issues become essential topics in modern machine learning applications. We thus study fairness measures and unfairness mitigation techniques that can be applied before, during, or after model training. We believe that the data management community is well poised to solve these problems.
    Transformers as Meta-Learners for Implicit Neural Representations. (arXiv:2208.02801v1 [cs.LG])
    Implicit Neural Representations (INRs) have emerged and shown their benefits over discrete representations in recent years. However, fitting an INR to the given observations usually requires optimization with gradient descent from scratch, which is inefficient and does not generalize well with sparse observations. To address this problem, most of the prior works train a hypernetwork that generates a single vector to modulate the INR weights, where the single vector becomes an information bottleneck that limits the reconstruction precision of the output INR. Recent work shows that the whole set of weights in INR can be precisely inferred without the single-vector bottleneck by gradient-based meta-learning. Motivated by a generalized formulation of gradient-based meta-learning, we propose a formulation that uses Transformers as hypernetworks for INRs, where it can directly build the whole set of INR weights with Transformers specialized as set-to-set mapping. We demonstrate the effectiveness of our method for building INRs in different tasks and domains, including 2D image regression and view synthesis for 3D objects. Our work draws connections between the Transformer hypernetworks and gradient-based meta-learning algorithms and we provide further analysis for understanding the generated INRs. The project page with code is at \url{https://yinboc.github.io/trans-inr/} .
    Design of secure and robust cognitive system for malware detection. (arXiv:2208.02310v1 [cs.CR])
    Machine learning based malware detection techniques rely on grayscale images of malware and tends to classify malware based on the distribution of textures in graycale images. Albeit the advancement and promising results shown by machine learning techniques, attackers can exploit the vulnerabilities by generating adversarial samples. Adversarial samples are generated by intelligently crafting and adding perturbations to the input samples. There exists majority of the software based adversarial attacks and defenses. To defend against the adversaries, the existing malware detection based on machine learning and grayscale images needs a preprocessing for the adversarial data. This can cause an additional overhead and can prolong the real-time malware detection. So, as an alternative to this, we explore RRAM (Resistive Random Access Memory) based defense against adversaries. Therefore, the aim of this thesis is to address the above mentioned critical system security issues. The above mentioned challenges are addressed by demonstrating proposed techniques to design a secure and robust cognitive system. First, a novel technique to detect stealthy malware is proposed. The technique uses malware binary images and then extract different features from the same and then employ different ML-classifiers on the dataset thus obtained. Results demonstrate that this technique is successful in differentiating classes of malware based on the features extracted. Secondly, I demonstrate the effects of adversarial attacks on a reconfigurable RRAM-neuromorphic architecture with different learning algorithms and device characteristics. I also propose an integrated solution for mitigating the effects of the adversarial attack using the reconfigurable RRAM architecture.
    A Hybrid Framework for Sequential Data Prediction with End-to-End Optimization. (arXiv:2203.13787v2 [stat.ML] UPDATED)
    We investigate nonlinear prediction in an online setting and introduce a hybrid model that effectively mitigates, via an end-to-end architecture, the need for hand-designed features and manual model selection issues of conventional nonlinear prediction/regression methods. In particular, we use recursive structures to extract features from sequential signals, while preserving the state information, i.e., the history, and boosted decision trees to produce the final output. The connection is in an end-to-end fashion and we jointly optimize the whole architecture using stochastic gradient descent, for which we also provide the backward pass update equations. In particular, we employ a recurrent neural network (LSTM) for adaptive feature extraction from sequential data and a gradient boosting machinery (soft GBDT) for effective supervised regression. Our framework is generic so that one can use other deep learning architectures for feature extraction (such as RNNs and GRUs) and machine learning algorithms for decision making as long as they are differentiable. We demonstrate the learning behavior of our algorithm on synthetic data and the significant performance improvements over the conventional methods over various real life datasets. Furthermore, we openly share the source code of the proposed method to facilitate further research.
    Analyzing Data-Centric Properties for Contrastive Learning on Graphs. (arXiv:2208.02810v1 [cs.LG])
    Recent analyses of self-supervised learning (SSL) find the following data-centric properties to be critical for learning good representations: invariance to task-irrelevant semantics, separability of classes in some latent space, and recoverability of labels from augmented samples. However, given their discrete, non-Euclidean nature, graph datasets and graph SSL methods are unlikely to satisfy these properties. This raises the question: how do graph SSL methods, such as contrastive learning (CL), work well? To systematically probe this question, we perform a generalization analysis for CL when using generic graph augmentations (GGAs), with a focus on data-centric properties. Our analysis yields formal insights into the limitations of GGAs and the necessity of task-relevant augmentations. As we empirically show, GGAs do not induce task-relevant invariances on common benchmark datasets, leading to only marginal gains over naive, untrained baselines. Our theory motivates a synthetic data generation process that enables control over task-relevant information and boasts pre-defined optimal augmentations. This flexible benchmark helps us identify yet unrecognized limitations in advanced augmentation techniques (e.g., automated methods). Overall, our work rigorously contextualizes, both empirically and theoretically, the effects of data-centric properties on augmentation strategies and learning paradigms for graph SSL.
    Towards Understanding Mixture of Experts in Deep Learning. (arXiv:2208.02813v1 [cs.LG])
    The Mixture-of-Experts (MoE) layer, a sparsely-activated model controlled by a router, has achieved great success in deep learning. However, the understanding of such architecture remains elusive. In this paper, we formally study how the MoE layer improves the performance of neural network learning and why the mixture model will not collapse into a single model. Our empirical results suggest that the cluster structure of the underlying problem and the non-linearity of the expert are pivotal to the success of MoE. To further understand this, we consider a challenging classification problem with intrinsic cluster structures, which is hard to learn using a single expert. Yet with the MoE layer, by choosing the experts as two-layer nonlinear convolutional neural networks (CNNs), we show that the problem can be learned successfully. Furthermore, our theory shows that the router can learn the cluster-center features, which helps divide the input complex problem into simpler linear classification sub-problems that individual experts can conquer. To our knowledge, this is the first result towards formally understanding the mechanism of the MoE layer for deep learning.
    Distilling Knowledge from Reader to Retriever for Question Answering. (arXiv:2012.04584v2 [cs.CL] UPDATED)
    The task of information retrieval is an important component of many natural language processing systems, such as open domain question answering. While traditional methods were based on hand-crafted features, continuous representations based on neural networks recently obtained competitive results. A challenge of using such methods is to obtain supervised data to train the retriever model, corresponding to pairs of query and support documents. In this paper, we propose a technique to learn retriever models for downstream tasks, inspired by knowledge distillation, and which does not require annotated pairs of query and documents. Our approach leverages attention scores of a reader model, used to solve the task based on retrieved documents, to obtain synthetic labels for the retriever. We evaluate our method on question answering, obtaining state-of-the-art results.
    Bayesian Optimization with Informative Covariance. (arXiv:2208.02704v1 [cs.LG])
    Bayesian Optimization is a methodology for global optimization of unknown and expensive objectives. It combines a surrogate Bayesian regression model with an acquisition function to decide where to evaluate the objective. Typical regression models are Gaussian processes with stationary covariance functions, which, however, are unable to express prior input-dependent information, in particular information about possible locations of the optimum. The ubiquity of stationary models has led to the common practice of exploiting prior information via informative mean functions. In this paper, we highlight that these models can lead to poor performance, especially in high dimensions. We propose novel informative covariance functions that leverage nonstationarity to encode preferences for certain regions of the search space and adaptively promote local exploration during the optimization. We demonstrate that they can increase the sample efficiency of the optimization in high dimensions, even under weak prior information.
    Towards Generating Large Synthetic Phytoplankton Datasets for Efficient Monitoring of Harmful Algal Blooms. (arXiv:2208.02332v1 [cs.CV])
    Climate change is increasing the frequency and severity of harmful algal blooms (HABs), which cause significant fish deaths in aquaculture farms. This contributes to ocean pollution and greenhouse gas (GHG) emissions since dead fish are either dumped into the ocean or taken to landfills, which in turn negatively impacts the climate. Currently, the standard method to enumerate harmful algae and other phytoplankton is to manually observe and count them under a microscope. This is a time-consuming, tedious and error-prone process, resulting in compromised management decisions by farmers. Hence, automating this process for quick and accurate HAB monitoring is extremely helpful. However, this requires large and diverse datasets of phytoplankton images, and such datasets are hard to produce quickly. In this work, we explore the feasibility of generating novel high-resolution photorealistic synthetic phytoplankton images, containing multiple species in the same image, given a small dataset of real images. To this end, we employ Generative Adversarial Networks (GANs) to generate synthetic images. We evaluate three different GAN architectures: ProjectedGAN, FastGAN, and StyleGANv2 using standard image quality metrics. We empirically show the generation of high-fidelity synthetic phytoplankton images using a training dataset of only 961 real images. Thus, this work demonstrates the ability of GANs to create large synthetic datasets of phytoplankton from small training datasets, accomplishing a key step towards sustainable systematic monitoring of harmful algal blooms.
    Sparse Continuous Distributions and Fenchel-Young Losses. (arXiv:2108.01988v2 [cs.LG] UPDATED)
    Exponential families are widely used in machine learning, including many distributions in continuous and discrete domains (e.g., Gaussian, Dirichlet, Poisson, and categorical distributions via the softmax transformation). Distributions in each of these families have fixed support. In contrast, for finite domains, recent work on sparse alternatives to softmax (e.g., sparsemax, $\alpha$-entmax, and fusedmax), has led to distributions with varying support. This paper develops sparse alternatives to continuous distributions, based on several technical contributions: First, we define $\Omega$-regularized prediction maps and Fenchel-Young losses for arbitrary domains (possibly countably infinite or continuous). For linearly parametrized families, we show that minimization of Fenchel-Young losses is equivalent to moment matching of the statistics, generalizing a fundamental property of exponential families. When $\Omega$ is a Tsallis negentropy with parameter $\alpha$, we obtain ``deformed exponential families,'' which include $\alpha$-entmax and sparsemax ($\alpha=2$) as particular cases. For quadratic energy functions, the resulting densities are $\beta$-Gaussians, an instance of elliptical distributions that contain as particular cases the Gaussian, biweight, triweight, and Epanechnikov densities, and for which we derive closed-form expressions for the variance, Tsallis entropy, and Fenchel-Young loss. When $\Omega$ is a total variation or Sobolev regularizer, we obtain a continuous version of the fusedmax. Finally, we introduce continuous-domain attention mechanisms, deriving efficient gradient backpropagation algorithms for $\alpha \in \{1, 4/3, 3/2, 2\}$. Using these algorithms, we demonstrate our sparse continuous distributions for attention-based audio classification and visual question answering, showing that they allow attending to time intervals and compact regions.
    Learning Green's functions associated with time-dependent partial differential equations. (arXiv:2204.12789v2 [math.NA] UPDATED)
    Neural operators are a popular technique in scientific machine learning to learn a mathematical model of the behavior of unknown physical systems from data. Neural operators are especially useful to learn solution operators associated with partial differential equations (PDEs) from pairs of forcing functions and solutions when numerical solvers are not available or the underlying physics is poorly understood. In this work, we attempt to provide theoretical foundations to understand the amount of training data needed to learn time-dependent PDEs. Given input-output pairs from a parabolic PDE in any spatial dimension $n\geq 1$, we derive the first theoretically rigorous scheme for learning the associated solution operator, which takes the form of a convolution with a Green's function $G$. Until now, rigorously learning Green's functions associated with time-dependent PDEs has been a major challenge in the field of scientific machine learning because $G$ may not be square-integrable when $n>1$, and time-dependent PDEs have transient dynamics. By combining the hierarchical low-rank structure of $G$ together with randomized numerical linear algebra, we construct an approximant to $G$ that achieves a relative error of $\smash{\mathcal{O}(\Gamma_\epsilon^{-1/2}\epsilon)}$ in the $L^1$-norm with high probability by using at most $\smash{\mathcal{O}(\epsilon^{-\frac{n+2}{2}}\log(1/\epsilon))}$ input-output training pairs, where $\Gamma_\epsilon$ is a measure of the quality of the training dataset for learning $G$, and $\epsilon>0$ is sufficiently small.
    Unsupervised Graph Spectral Feature Denoising for Crop Yield Prediction. (arXiv:2208.02714v1 [cs.LG])
    Prediction of annual crop yields at a county granularity is important for national food production and price stability. In this paper, towards the goal of better crop yield prediction, leveraging recent graph signal processing (GSP) tools to exploit spatial correlation among neighboring counties, we denoise relevant features via graph spectral filtering that are inputs to a deep learning prediction model. Specifically, we first construct a combinatorial graph with edge weights that encode county-to-county similarities in soil and location features via metric learning. We then denoise features via a maximum a posteriori (MAP) formulation with a graph Laplacian regularizer (GLR). We focus on the challenge to estimate the crucial weight parameter $\mu$, trading off the fidelity term and GLR, that is a function of noise variance in an unsupervised manner. We first estimate noise variance directly from noise-corrupted graph signals using a graph clique detection (GCD) procedure that discovers locally constant regions. We then compute an optimal $\mu$ minimizing an approximate mean square error function via bias-variance analysis. Experimental results from collected USDA data show that using denoised features as input, performance of a crop yield prediction model can be improved noticeably.
    Fusing Sentence Embeddings Into LSTM-based Autoregressive Language Models. (arXiv:2208.02402v1 [cs.CL])
    Although masked language models are highly performant and widely adopted by NLP practitioners, they can not be easily used for autoregressive language modelling (next word prediction and sequence probability estimation). We present an LSTM-based autoregressive language model which uses prefix embeddings (from a pretrained masked language model) via fusion (e.g. concatenation) to obtain a richer context representation for language modelling. We find that fusion helps reliably in lowering the perplexity (16.74 $\rightarrow$ 15.80), which is even preserved after a transfer to a dataset from a different domain than the training data. We also evaluate the best-performing fusion model by correlating its next word surprisal estimates with human reading times. Contradicting our expectation, and despite the improvement in perplexity overall, the correlation remains the same as for the baseline model. Lastly, while we focus on language models pre-trained on text as the sources for the fusion, our approach can be possibly extended to fuse any information represented as a fixed-size vector into an auto-regressive language model. These include e.g. sentence external information retrieved for a knowledge base or representations of multi-modal encoders.
    Benchmark Static API Call Datasets for Malware Family Classification. (arXiv:2111.15205v2 [cs.CR] UPDATED)
    Nowadays, malware and malware incidents are increasing daily, even with various antivirus systems and malware detection or classification methodologies. Machine learning techniques have been the main focus of the security experts to detect malware and determine their families. Many static, dynamic, and hybrid techniques have been presented for that purpose. In this study, the static analysis technique has been applied to malware samples to extract API calls, which is one of the most used features in machine/deep learning models as it represents the behavior of malware samples. Since the rapid increase and continuous evolution of malware affect the detection capacity of antivirus scanners, recent and updated datasets of malicious software became necessary to overcome this drawback. This paper introduces two new datasets: One with 14,616 samples obtained and compiled from VirusShare and one with 9,795 samples from VirusSample. In addition, benchmark results based on static API calls of malware samples are presented using several machine and deep learning models on these datasets. We believe that these two datasets and benchmark results enable researchers to test and validate their methods and approaches in this field.
    CFARnet: deep learning for target detection with constant false alarm rate. (arXiv:2208.02474v1 [cs.LG])
    We consider the problem of learning detectors with a Constant False Alarm Rate (CFAR). Classical model-based solutions to composite hypothesis testing are sensitive to imperfect models and are often computationally expensive. In contrast, data-driven machine learning is often more robust and yields classifiers with fixed computational complexity. Learned detectors usually do not have a CFAR as required in many applications. To close this gap, we introduce CFARnet where the loss function is penalized to promote similar distributions of the detector under any null hypothesis scenario. Asymptotic analysis in the case of linear models with general Gaussian noise reveals that the classical generalized likelihood ratio test (GLRT) is actually a minimizer of the CFAR constrained Bayes risk. Experiments in both synthetic data and real hyper-spectral images show that CFARnet leads to near CFAR detectors with similar accuracy as their competitors.
    Unifying physical systems' inductive biases in neural ODE using dynamics constraints. (arXiv:2208.02632v1 [cs.LG])
    Conservation of energy is at the core of many physical phenomena and dynamical systems. There have been a significant number of works in the past few years aimed at predicting the trajectory of motion of dynamical systems using neural networks while adhering to the law of conservation of energy. Most of these works are inspired by classical mechanics such as Hamiltonian and Lagrangian mechanics as well as Neural Ordinary Differential Equations. While these works have been shown to work well in specific domains respectively, there is a lack of a unifying method that is more generally applicable without requiring significant changes to the neural network architectures. In this work, we aim to address this issue by providing a simple method that could be applied to not just energy-conserving systems, but also dissipative systems, by including a different inductive bias in different cases in the form of a regularisation term in the loss function. The proposed method does not require changing the neural network architecture and could form the basis to validate a novel idea, therefore showing promises to accelerate research in this direction.
    DL-DRL: A double-layer deep reinforcement learning approach for large-scale task scheduling of multi-UAV. (arXiv:2208.02447v1 [cs.LG])
    This paper studies deep reinforcement learning (DRL) for the task scheduling problem of multiple unmanned aerial vehicles (UAVs). Current approaches generally use exact and heuristic algorithms to solve the problem, while the computation time rapidly increases as the task scale grows and heuristic rules need manual design. As a self-learning method, DRL can obtain a high-quality solution quickly without hand-engineered rules. However, the huge decision space makes the training of DRL models becomes unstable in situations with large-scale tasks. In this work, to address the large-scale problem, we develop a divide and conquer-based framework (DCF) to decouple the original problem into a task allocation and a UAV route planning subproblems, which are solved in the upper and lower layers, respectively. Based on DCF, a double-layer deep reinforcement learning approach (DL-DRL) is proposed, where an upper-layer DRL model is designed to allocate tasks to appropriate UAVs and a lower-layer DRL model [i.e., the widely used attention model (AM)] is applied to generate viable UAV routes. Since the upper-layer model determines the input data distribution of the lower-layer model, and its reward is calculated via the lower-layer model during training, we develop an interactive training strategy (ITS), where the whole training process consists of pre-training, intensive training, and alternate training processes. Experimental results show that our DL-DRL outperforms mainstream learning-based and most traditional methods, and is competitive with the state-of-the-art heuristic method [i.e., OR-Tools], especially on large-scale problems. The great generalizability of DL-DRL is also verified by testing the model learned for a problem size to larger ones. Furthermore, an ablation study demonstrates that our ITS can reach a compromise between the model performance and training duration.
    ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity. (arXiv:2208.02507v1 [cs.LG])
    When the available hardware cannot meet the memory and compute requirements to efficiently train high performing machine learning models, a compromise in either the training quality or the model complexity is needed. In Federated Learning (FL), nodes are orders of magnitude more constrained than traditional server-grade hardware and are often battery powered, severely limiting the sophistication of models that can be trained under this paradigm. While most research has focused on designing better aggregation strategies to improve convergence rates and in alleviating the communication costs of FL, fewer efforts have been devoted to accelerating on-device training. Such stage, which repeats hundreds of times (i.e. every round) and can involve thousands of devices, accounts for the majority of the time required to train federated models and, the totality of the energy consumption at the client side. In this work, we present the first study on the unique aspects that arise when introducing sparsity at training time in FL workloads. We then propose ZeroFL, a framework that relies on highly sparse operations to accelerate on-device training. Models trained with ZeroFL and 95% sparsity achieve up to 2.3% higher accuracy compared to competitive baselines obtained from adapting a state-of-the-art sparse training framework to the FL setting.
    Impact Makes a Sound and Sound Makes an Impact: Sound Guides Representations and Explorations. (arXiv:2208.02680v1 [cs.RO])
    Sound is one of the most informative and abundant modalities in the real world while being robust to sense without contacts by small and cheap sensors that can be placed on mobile devices. Although deep learning is capable of extracting information from multiple sensory inputs, there has been little use of sound for the control and learning of robotic actions. For unsupervised reinforcement learning, an agent is expected to actively collect experiences and jointly learn representations and policies in a self-supervised way. We build realistic robotic manipulation scenarios with physics-based sound simulation and propose the Intrinsic Sound Curiosity Module (ISCM). The ISCM provides feedback to a reinforcement learner to learn robust representations and to reward a more efficient exploration behavior. We perform experiments with sound enabled during pre-training and disabled during adaptation, and show that representations learned by ISCM outperform the ones by vision-only baselines and pre-trained policies can accelerate the learning process when applied to downstream tasks.
    Theoretical Analysis of Primal-Dual Algorithm for Non-Convex Stochastic Decentralized Optimization. (arXiv:2205.11979v2 [math.OC] UPDATED)
    In recent years, decentralized learning has emerged as a powerful tool not only for large-scale machine learning, but also for preserving privacy. One of the key challenges in decentralized learning is that the data distribution held by each node is statistically heterogeneous. To address this challenge, the primal-dual algorithm called the Edge-Consensus Learning (ECL) was proposed and was experimentally shown to be robust to the heterogeneity of data distributions. However, the convergence rate of the ECL is provided only when the objective function is convex, and has not been shown in a standard machine learning setting where the objective function is non-convex. Furthermore, the intuitive reason why the ECL is robust to the heterogeneity of data distributions has not been investigated. In this work, we first investigate the relationship between the ECL and Gossip algorithm and show that the update formulas of the ECL can be regarded as correcting the local stochastic gradient in the Gossip algorithm. Then, we propose the Generalized ECL (G-ECL), which contains the ECL as a special case, and provide the convergence rates of the G-ECL in both (strongly) convex and non-convex settings, which do not depend on the heterogeneity of data distributions. Through synthetic experiments, we demonstrate that the numerical results of both the G-ECL and ECL coincide with the convergence rate of the G-ECL.
    Implicit Semantic Augmentation for Distance Metric Learning in Domain Generalization. (arXiv:2208.02803v1 [cs.LG])
    Domain generalization (DG) aims to learn a model on one or more different but related source domains that could be generalized into an unseen target domain. Existing DG methods try to prompt the diversity of source domains for the model's generalization ability, while they may have to introduce auxiliary networks or striking computational costs. On the contrary, this work applies the implicit semantic augmentation in feature space to capture the diversity of source domains. Concretely, an additional loss function of distance metric learning (DML) is included to optimize the local geometry of data distribution. Besides, the logits from cross entropy loss with infinite augmentations is adopted as input features for the DML loss in lieu of the deep features. We also provide a theoretical analysis to show that the logits can approximate the distances defined on original features well. Further, we provide an in-depth analysis of the mechanism and rational behind our approach, which gives us a better understanding of why leverage logits in lieu of features can help domain generalization. The proposed DML loss with the implicit augmentation is incorporated into a recent DG method, that is, Fourier Augmented Co-Teacher framework (FACT). Meanwhile, our method also can be easily plugged into various DG methods. Extensive experiments on three benchmarks (Digits-DG, PACS and Office-Home) have demonstrated that the proposed method is able to achieve the state-of-the-art performance.
    Adaptive Latent Factor Analysis via Generalized Momentum-Incorporated Particle Swarm Optimization. (arXiv:2208.02423v1 [cs.NE])
    Stochastic gradient descent (SGD) algorithm is an effective learning strategy to build a latent factor analysis (LFA) model on a high-dimensional and incomplete (HDI) matrix. A particle swarm optimization (PSO) algorithm is commonly adopted to make an SGD-based LFA model's hyper-parameters, i.e, learning rate and regularization coefficient, self-adaptation. However, a standard PSO algorithm may suffer from accuracy loss caused by premature convergence. To address this issue, this paper incorporates more historical information into each particle's evolutionary process for avoiding premature convergence following the principle of a generalized-momentum (GM) method, thereby innovatively achieving a novel GM-incorporated PSO (GM-PSO). With it, a GM-PSO-based LFA (GMPL) model is further achieved to implement efficient self-adaptation of hyper-parameters. The experimental results on three HDI matrices demonstrate that the GMPL model achieves a higher prediction accuracy for missing data estimation in industrial applications.
    GROWN+UP: A Graph Representation Of a Webpage Network Utilizing Pre-training. (arXiv:2208.02252v1 [cs.LG])
    Large pre-trained neural networks are ubiquitous and critical to the success of many downstream tasks in natural language processing and computer vision. However, within the field of web information retrieval, there is a stark contrast in the lack of similarly flexible and powerful pre-trained models that can properly parse webpages. Consequently, we believe that common machine learning tasks like content extraction and information mining from webpages have low-hanging gains that yet remain untapped. We aim to close the gap by introducing an agnostic deep graph neural network feature extractor that can ingest webpage structures, pre-train self-supervised on massive unlabeled data, and fine-tune to arbitrary tasks on webpages effectually. Finally, we show that our pre-trained model achieves state-of-the-art results using multiple datasets on two very different benchmarks: webpage boilerplate removal and genre classification, thus lending support to its potential application in diverse downstream tasks.
    Simulation and application of COVID-19 compartment model using physic-informed neural network. (arXiv:2208.02433v1 [q-bio.QM])
    In this work, SVEIDR model and its variants (Aged, Vaccination-structured models) are introduced to encode the effect of social contact for different age groups and vaccination status. Then we implement the Physic-Informed Neural Network on both simulation and real-world data. Results including the spread and forecasting analysis of COVID-19 learned from the neural network are shown in the paper.
    Using Mixed-Effects Models to Learn Bayesian Networks from Related Data Sets. (arXiv:2206.03743v2 [stat.ML] UPDATED)
    We commonly assume that data are a homogeneous set of observations when learning the structure of Bayesian networks. However, they often comprise different data sets that are related but not homogeneous because they have been collected in different ways or from different populations. In our previous work (Azzimonti, Corani and Scutari, 2021), we proposed a closed-form Bayesian Hierarchical Dirichlet score for discrete data that pools information across related data sets to learn a single encompassing network structure, while taking into account the differences in their probabilistic structures. In this paper, we provide an analogous solution for learning a Bayesian network from continuous data using mixed-effects models to pool information across the related data sets. We study its structural, parametric, predictive and classification accuracy and we show that it outperforms both conditional Gaussian Bayesian networks (that do not perform any pooling) and classical Gaussian Bayesian networks (that disregard the heterogeneous nature of the data). The improvement is marked for low sample sizes and for unbalanced data sets.
    Leveraging the HW/SW Optimizations and Ecosystems that Drive the AI Revolution. (arXiv:2208.02808v1 [cs.LG])
    This paper presents a state-of-the-art overview on how to architect, design, and optimize Deep Neural Networks (DNNs) such that performance is improved and accuracy is preserved. The paper covers a set of optimizations that span the entire Machine Learning processing pipeline. We introduce two types of optimizations. The first alters the DNN model and requires NN re-training, while the second does not. We focus on GPU optimizations, but we believe the presented techniques can be used with other AI inference platforms. To demonstrate the DNN model optimizations, we improve one of the most advanced deep network architectures for optical flow, RAFT arXiv:2003.12039, on a popular edge AI inference platform (Nvidia Jetson AGX Xavier).
    Pattern Spotting and Image Retrieval in Historical Documents using Deep Hashing. (arXiv:2208.02397v1 [cs.CV])
    This paper presents a deep learning approach for image retrieval and pattern spotting in digital collections of historical documents. First, a region proposal algorithm detects object candidates in the document page images. Next, deep learning models are used for feature extraction, considering two distinct variants, which provide either real-valued or binary code representations. Finally, candidate images are ranked by computing the feature similarity with a given input query. A robust experimental protocol evaluates the proposed approach considering each representation scheme (real-valued and binary code) on the DocExplore image database. The experimental results show that the proposed deep models compare favorably to the state-of-the-art image retrieval approaches for images of historical documents, outperforming other deep models by 2.56 percentage points using the same techniques for pattern spotting. Besides, the proposed approach also reduces the search time by up to 200x and the storage cost up to 6,000x when compared to related works based on real-valued representations.
    LaneSNNs: Spiking Neural Networks for Lane Detection on the Loihi Neuromorphic Processor. (arXiv:2208.02253v1 [cs.NE])
    Autonomous Driving (AD) related features represent important elements for the next generation of mobile robots and autonomous vehicles focused on increasingly intelligent, autonomous, and interconnected systems. The applications involving the use of these features must provide, by definition, real-time decisions, and this property is key to avoid catastrophic accidents. Moreover, all the decision processes must require low power consumption, to increase the lifetime and autonomy of battery-driven systems. These challenges can be addressed through efficient implementations of Spiking Neural Networks (SNNs) on Neuromorphic Chips and the use of event-based cameras instead of traditional frame-based cameras. In this paper, we present a new SNN-based approach, called LaneSNN, for detecting the lanes marked on the streets using the event-based camera input. We develop four novel SNN models characterized by low complexity and fast response, and train them using an offline supervised learning rule. Afterward, we implement and map the learned SNNs models onto the Intel Loihi Neuromorphic Research Chip. For the loss function, we develop a novel method based on the linear composition of Weighted binary Cross Entropy (WCE) and Mean Squared Error (MSE) measures. Our experimental results show a maximum Intersection over Union (IoU) measure of about 0.62 and very low power consumption of about 1 W. The best IoU is achieved with an SNN implementation that occupies only 36 neurocores on the Loihi processor while providing a low latency of less than 8 ms to recognize an image, thereby enabling real-time performance. The IoU measures provided by our networks are comparable with the state-of-the-art, but at a much low power consumption of 1 W.
    Backpropagation at the Infinitesimal Inference Limit of Energy-Based Models: Unifying Predictive Coding, Equilibrium Propagation, and Contrastive Hebbian Learning. (arXiv:2206.02629v3 [cs.LG] UPDATED)
    How the brain performs credit assignment is a fundamental unsolved problem in neuroscience. Many `biologically plausible' algorithms have been proposed, which compute gradients that approximate those computed by backpropagation (BP), and which operate in ways that more closely satisfy the constraints imposed by neural circuitry. Many such algorithms utilize the framework of energy-based models (EBMs), in which all free variables in the model are optimized to minimize a global energy function. However, in the literature, these algorithms exist in isolation and no unified theory exists linking them together. Here, we provide a comprehensive theory of the conditions under which EBMs can approximate BP, which lets us unify many of the BP approximation results in the literature (namely, predictive coding, equilibrium propagation, and contrastive Hebbian learning) and demonstrate that their approximation to BP arises from a simple and general mathematical property of EBMs at free-phase equilibrium. This property can then be exploited in different ways with different energy functions, and these specific choices yield a family of BP-approximating algorithms, which both includes the known results in the literature and can be used to derive new ones.
    Membership Inference Attacks and Defenses in Neural Network Pruning. (arXiv:2202.03335v2 [cs.CR] UPDATED)
    Neural network pruning has been an essential technique to reduce the computation and memory requirements for using deep neural networks for resource-constrained devices. Most existing research focuses primarily on balancing the sparsity and accuracy of a pruned neural network by strategically removing insignificant parameters and retraining the pruned model. Such efforts on reusing training samples pose serious privacy risks due to increased memorization, which, however, has not been investigated yet. In this paper, we conduct the first analysis of privacy risks in neural network pruning. Specifically, we investigate the impacts of neural network pruning on training data privacy, i.e., membership inference attacks. We first explore the impact of neural network pruning on prediction divergence, where the pruning process disproportionately affects the pruned model's behavior for members and non-members. Meanwhile, the influence of divergence even varies among different classes in a fine-grained manner. Enlighten by such divergence, we proposed a self-attention membership inference attack against the pruned neural networks. Extensive experiments are conducted to rigorously evaluate the privacy impacts of different pruning approaches, sparsity levels, and adversary knowledge. The proposed attack shows the higher attack performance on the pruned models when compared with eight existing membership inference attacks. In addition, we propose a new defense mechanism to protect the pruning process by mitigating the prediction divergence based on KL-divergence distance, whose effectiveness has been experimentally demonstrated to effectively mitigate the privacy risks while maintaining the sparsity and accuracy of the pruned models.
    Unsupervised Domain Adaptation with Contrastive Learning for OCT Segmentation. (arXiv:2203.03664v2 [cs.CV] UPDATED)
    Accurate segmentation of retinal fluids in 3D Optical Coherence Tomography images is key for diagnosis and personalized treatment of eye diseases. While deep learning has been successful at this task, trained supervised models often fail for images that do not resemble labeled examples, e.g. for images acquired using different devices. We hereby propose a novel semi-supervised learning framework for segmentation of volumetric images from new unlabeled domains. We jointly use supervised and contrastive learning, also introducing a contrastive pairing scheme that leverages similarity between nearby slices in 3D. In addition, we propose channel-wise aggregation as an alternative to conventional spatial-pooling aggregation for contrastive feature map projection. We evaluate our methods for domain adaptation from a (labeled) source domain to an (unlabeled) target domain, each containing images acquired with different acquisition devices. In the target domain, our method achieves a Dice coefficient 13.8% higher than SimCLR (a state-of-the-art contrastive framework), and leads to results comparable to an upper bound with supervised training in that domain. In the source domain, our model also improves the results by 5.4% Dice, by successfully leveraging information from many unlabeled images.
    Local versions of sum-of-norms clustering. (arXiv:2109.09589v3 [cs.LG] UPDATED)
    Sum-of-norms clustering is a convex optimization problem whose solution can be used for the clustering of multivariate data. We propose and study a localized version of this method, and show in particular that it can separate arbitrarily close balls in the stochastic ball model. More precisely, we prove a quantitative bound on the error incurred in the clustering of disjoint connected sets. Our bound is expressed in terms of the number of datapoints and the localization length of the functional.
    Noise-aware Physics-informed Machine Learning for Robust PDE Discovery. (arXiv:2206.12901v5 [math.NA] UPDATED)
    This work is concerned with discovering the governing partial differential equation (PDE) of a physical system. Existing methods have demonstrated the PDE identification from finite observations but failed to maintain satisfying results against noisy data, partly owing to suboptimal estimated derivatives and found PDE coefficients. We address the issues by introducing a noise-aware physics-informed machine learning (nPIML) framework to discover the governing PDE from data following arbitrary distributions. We propose training a couple of neural networks, namely solver and preselector, in a multi-task learning paradigm, which yields important scores of basis candidates that constitute the hidden physical constraint. After they are jointly trained, the solver network estimates potential candidates, e.g., partial derivatives, for the sparse regression algorithm to initially unveil the most likely parsimonious PDE, decided according to the information criterion. We also propose the denoising physics-informed neural networks (dPINNs), based on Discrete Fourier Transform (DFT), to deliver a set of the optimal finetuned PDE coefficients respecting the noise-reduced variables. The denoising PINNs are structured into forefront projection networks and a PINN, by which the formerly learned solver initializes. Our extensive experiments on five canonical PDEs affirm that the proposed framework presents a robust and interpretable approach for PDE discovery, applicable to a wide range of systems, possibly complicated by noise.
    Reliability analysis of discrete-state performance functions via adaptive sequential sampling with detection of failure surfaces. (arXiv:2208.02475v1 [cs.CE])
    The paper presents a new efficient and robust method for rare event probability estimation for computational models of an engineering product or a process returning categorical information only, for example, either success or failure. For such models, most of the methods designed for the estimation of failure probability, which use the numerical value of the outcome to compute gradients or to estimate the proximity to the failure surface, cannot be applied. Even if the performance function provides more than just binary output, the state of the system may be a non-smooth or even a discontinuous function defined in the domain of continuous input variables. In these cases, the classical gradient-based methods usually fail. We propose a simple yet efficient algorithm, which performs a sequential adaptive selection of points from the input domain of random variables to extend and refine a simple distance-based surrogate model. Two different tasks can be accomplished at any stage of sequential sampling: (i) estimation of the failure probability, and (ii) selection of the best possible candidate for the subsequent model evaluation if further improvement is necessary. The proposed criterion for selecting the next point for model evaluation maximizes the expected probability classified by using the candidate. Therefore, the perfect balance between global exploration and local exploitation is maintained automatically. The method can estimate the probabilities of multiple failure types. Moreover, when the numerical value of model evaluation can be used to build a smooth surrogate, the algorithm can accommodate this information to increase the accuracy of the estimated probabilities. Lastly, we define a new simple yet general geometrical measure of the global sensitivity of the rare-event probability to individual variables, which is obtained as a by-product of the proposed algorithm.
    Improving Personalised Physical Activity Recommendation on the mHealth Information Service Using Deep Reinforcement Learning. (arXiv:2204.00961v2 [cs.LG] UPDATED)
    Recently has seen the growth in the use of mobile health (mHealth) information services, which have rich guides on improving physical activity. These rich guides evolved from the consideration of various personal behavioural factors, which often deviate from the user's health conditions. The behavioural factors include changing fitness preferences, adherence issues, and uncertainty about future fitness outcomes, which may all lead to a decline in the quality of the mHealth information services. Many of these mHealth information services provide limited fitness guidance owing to the dynamics of the user's health conditions. This paper seeks an adaptive method using deep reinforcement learning to make personalised physical activity recommendations, which is learnt from retrospective physical activity data and can simulate realistic behaviour trajectories. We construct a real-time interaction model for the mHealth information service system based on scientific knowledge about physical activity to evaluate its exercise performance. The physical activity performance evaluation model is used to find the optimal exercise intensity considering the fitness and fatigue effects to avoid the lack of exercise or overload. The short-term activity plans are made using deep reinforcement learning and personal health conditions that change over time. Using this method, we can dynamically update the physical activity recommendation policy in accordance with the real implementation behaviour. Our DRL-based recommender policy was validated by comparison to other benchmark policies. Experimental results show that this adaptive learning algorithm can improve recommendation performance over 4.13 percent.
    Edge-centric Optimization of Multi-modal ML-driven eHealth Applications. (arXiv:2208.02597v1 [cs.LG])
    Smart eHealth applications deliver personalized and preventive digital healthcare services to clients through remote sensing, continuous monitoring, and data analytics. Smart eHealth applications sense input data from multiple modalities, transmit the data to edge and/or cloud nodes, and process the data with compute intensive machine learning (ML) algorithms. Run-time variations with continuous stream of noisy input data, unreliable network connection, computational requirements of ML algorithms, and choice of compute placement among sensor-edge-cloud layers affect the efficiency of ML-driven eHealth applications. In this chapter, we present edge-centric techniques for optimized compute placement, exploration of accuracy-performance trade-offs, and cross-layered sense-compute co-optimization for ML-driven eHealth applications. We demonstrate the practical use cases of smart eHealth applications in everyday settings, through a sensor-edge-cloud framework for an objective pain assessment case study.
    On Gap-dependent Bounds for Offline Reinforcement Learning. (arXiv:2206.00177v2 [cs.LG] UPDATED)
    This paper presents a systematic study on gap-dependent sample complexity in offline reinforcement learning. Prior work showed when the density ratio between an optimal policy and the behavior policy is upper bounded (the optimal policy coverage assumption), then the agent can achieve an $O\left(\frac{1}{\epsilon^2}\right)$ rate, which is also minimax optimal. We show under the optimal policy coverage assumption, the rate can be improved to $O\left(\frac{1}{\epsilon}\right)$ when there is a positive sub-optimality gap in the optimal $Q$-function. Furthermore, we show when the visitation probabilities of the behavior policy are uniformly lower bounded for states where an optimal policy's visitation probabilities are positive (the uniform optimal policy coverage assumption), the sample complexity of identifying an optimal policy is independent of $\frac{1}{\epsilon}$. Lastly, we present nearly-matching lower bounds to complement our gap-dependent upper bounds.
    Risk-Aware Linear Bandits: Theory and Applications in Smart Order Routing. (arXiv:2208.02389v1 [cs.LG])
    Motivated by practical considerations in machine learning for financial decision-making, such as risk-aversion and large action space, we initiate the study of risk-aware linear bandits. Specifically, we consider regret minimization under the mean-variance measure when facing a set of actions whose rewards can be expressed as linear functions of (initially) unknown parameters. Driven by the variance-minimizing G-optimal design, we propose the Risk-Aware Explore-then-Commit (RISE) algorithm and the Risk-Aware Successive Elimination (RISE++) algorithm. Then, we rigorously analyze their regret upper bounds to show that, by leveraging the linear structure, the algorithms can dramatically reduce the regret when compared to existing methods. Finally, we demonstrate the performance of the algorithms by conducting extensive numerical experiments in a synthetic smart order routing setup. Our results show that both RISE and RISE++ can outperform the competing methods, especially in complex decision-making scenarios.
    Implicit Neural Representations for Image Compression. (arXiv:2112.04267v2 [eess.IV] UPDATED)
    Recently Implicit Neural Representations (INRs) gained attention as a novel and effective representation for various data types. Thus far, prior work mostly focused on optimizing their reconstruction performance. This work investigates INRs from a novel perspective, i.e., as a tool for image compression. To this end, we propose the first comprehensive compression pipeline based on INRs including quantization, quantization-aware retraining and entropy coding. Encoding with INRs, i.e. overfitting to a data sample, is typically orders of magnitude slower. To mitigate this drawback, we leverage meta-learned initializations based on MAML to reach the encoding in fewer gradient updates which also generally improves rate-distortion performance of INRs. We find that our approach to source compression with INRs vastly outperforms similar prior work, is competitive with common compression algorithms designed specifically for images and closes the gap to state-of-the-art learned approaches based on Rate-Distortion Autoencoders. Moreover, we provide an extensive ablation study on the importance of individual components of our method which we hope facilitates future research on this novel approach to image compression.
    Word-Level Fine-Grained Story Visualization. (arXiv:2208.02341v1 [cs.CV])
    Story visualization aims to generate a sequence of images to narrate each sentence in a multi-sentence story with a global consistency across dynamic scenes and characters. Current works still struggle with output images' quality and consistency, and rely on additional semantic information or auxiliary captioning networks. To address these challenges, we first introduce a new sentence representation, which incorporates word information from all story sentences to mitigate the inconsistency problem. Then, we propose a new discriminator with fusion features and further extend the spatial attention to improve image quality and story consistency. Extensive experiments on different datasets and human evaluation demonstrate the superior performance of our approach, compared to state-of-the-art methods, neither using segmentation masks nor auxiliary captioning networks.
    Agnostic Learning of General ReLU Activation Using Gradient Descent. (arXiv:2208.02711v1 [cs.LG])
    We provide a convergence analysis of gradient descent for the problem of agnostically learning a single ReLU function under Gaussian distributions. Unlike prior work that studies the setting of zero bias, we consider the more challenging scenario when the bias of the ReLU function is non-zero. Our main result establishes that starting from random initialization, in a polynomial number of iterations gradient descent outputs, with high probability, a ReLU function that achieves a competitive error guarantee when compared to the error of the best ReLU function. We also provide finite sample guarantees, and these techniques generalize to a broader class of marginal distributions beyond Gaussians.
    Customs Import Declaration Datasets. (arXiv:2208.02484v1 [cs.LG])
    Given the huge volume of cross-border flows, effective and efficient control of trades becomes more crucial in protecting people and society from illicit trades while facilitating legitimate trades. However, limited accessibility of the transaction-level trade datasets hinders the progress of open research, and lots of customs administrations have not benefited from the recent progress in data-based risk management. In this paper, we introduce an import declarations dataset to facilitate the collaboration between the domain experts in customs administrations and data science researchers. The dataset contains 54,000 artificially generated trades with 22 key attributes, and it is synthesized with CTGAN while maintaining correlated features. Synthetic data has several advantages. First, releasing the dataset is free from restrictions that do not allow disclosing the original import data. Second, the fabrication step minimizes the possible identity risk which may exist in trade statistics. Lastly, the published data follow a similar distribution to the source data so that it can be used in various downstream tasks. With the provision of data and its generation process, we open baseline codes for fraud detection tasks, as we empirically show that more advanced algorithms can better detect frauds.
    Constructing Balance from Imbalance for Long-tailed Image Recognition. (arXiv:2208.02567v1 [cs.CV])
    Long-tailed image recognition presents massive challenges to deep learning systems since the imbalance between majority (head) classes and minority (tail) classes severely skews the data-driven deep neural networks. Previous methods tackle with data imbalance from the viewpoints of data distribution, feature space, and model design, etc.In this work, instead of directly learning a recognition model, we suggest confronting the bottleneck of head-to-tail bias before classifier learning, from the previously omitted perspective of balancing label space. To alleviate the head-to-tail bias, we propose a concise paradigm by progressively adjusting label space and dividing the head classes and tail classes, dynamically constructing balance from imbalance to facilitate the classification. With flexible data filtering and label space mapping, we can easily embed our approach to most classification models, especially the decoupled training methods. Besides, we find the separability of head-tail classes varies among different features with different inductive biases. Hence, our proposed model also provides a feature evaluation method and paves the way for long-tailed feature learning. Extensive experiments show that our method can boost the performance of state-of-the-arts of different types on widely-used benchmarks. Code is available at https://github.com/silicx/DLSA.
    A New Kind of Adversarial Example. (arXiv:2208.02430v1 [cs.CV])
    Almost all adversarial attacks are formulated to add an imperceptible perturbation to an image in order to fool a model. Here, we consider the opposite which is adversarial examples that can fool a human but not a model. A large enough and perceptible perturbation is added to an image such that a model maintains its original decision, whereas a human will most likely make a mistake if forced to decide (or opt not to decide at all). Existing targeted attacks can be reformulated to synthesize such adversarial examples. Our proposed attack, dubbed NKE, is similar in essence to the fooling images, but is more efficient since it uses gradient descent instead of evolutionary algorithms. It also offers a new and unified perspective into the problem of adversarial vulnerability. Experimental results over MNIST and CIFAR-10 datasets show that our attack is quite efficient in fooling deep neural networks. Code is available at https://github.com/aliborji/NKE.
    Neural network accelerator for quantum control. (arXiv:2208.02645v1 [quant-ph])
    Efficient quantum control is necessary for practical quantum computing implementations with current technologies. Conventional algorithms for determining optimal control parameters are computationally expensive, largely excluding them from use outside of the simulation. Existing hardware solutions structured as lookup tables are imprecise and costly. By designing a machine learning model to approximate the results of traditional tools, a more efficient method can be produced. Such a model can then be synthesized into a hardware accelerator for use in quantum systems. In this study, we demonstrate a machine learning algorithm for predicting optimal pulse parameters. This algorithm is lightweight enough to fit on a low-resource FPGA and perform inference with a latency of 175 ns and pipeline interval of 5 ns with $~>~$0.99 gate fidelity. In the long term, such an accelerator could be used near quantum computing hardware where traditional computers cannot operate, enabling quantum control at a reasonable cost at low latencies without incurring large data bandwidths outside of the cryogenic environment.
    Visual Analysis and Detection of Contrails in Aircraft Engine Simulations. (arXiv:2208.02321v1 [cs.HC])
    Contrails are condensation trails generated from emitted particles by aircraft engines, which perturb Earth's radiation budget. Simulation modeling is used to interpret the formation and development of contrails. These simulations are computationally intensive and rely on high-performance computing solutions, and the contrail structures are not well defined. We propose a visual computing system to assist in defining contrails and their characteristics, as well as in the analysis of parameters for computer-generated aircraft engine simulations. The back-end of our system leverages a contrail-formation criterion and clustering methods to detect contrails' shape and evolution and identify similar simulation runs. The front-end system helps analyze contrails and their parameters across multiple simulation runs. The evaluation with domain experts shows this approach successfully aids in contrail data investigation.
    Disentangled Representation Learning for RF Fingerprint Extraction under Unknown Channel Statistics. (arXiv:2208.02724v1 [eess.SP])
    Deep learning (DL) applied to a device's radio-frequency fingerprint~(RFF) has attracted significant attention in physical-layer authentications due to its extraordinary classification performance. Conventional DL-RFF techniques, trained by adopting maximum likelihood estimation~(MLE), tend to overfit the channel statistics embedded in the training dataset. This restricts their practical applications as it is challenging to collect sufficient training data capturing the characteristics of all possible wireless channel environments. To address this challenge, we propose a DL framework of disentangled representation learning~(DRL) that first learns to factor the input signals into a device-relevant component and a device-irrelevant component via adversarial learning. Then, it synthesizes a set of augmented signals by shuffling these two parts within a given training dataset for training of subsequent RFF extractor. The implicit data augmentation in the proposed framework imposes a regularization on the RFF extractor to avoid the possible overfitting of device-irrelevant channel statistics, without collecting additional data from unknown channels. Experiments validate that the proposed approach, referred to as DR-RFF, outperforms conventional methods in terms of generalizability to unknown complicated propagation environments, e.g., dispersive multipath fading channels, even though all the training data are collected in a simple environment with dominated direct line-of-sight~(LoS) propagation paths.
    Serving and Optimizing Machine Learning Workflows on Heterogeneous Infrastructures. (arXiv:2205.04713v2 [cs.LG] UPDATED)
    With the advent of ubiquitous deployment of smart devices and the Internet of Things, data sources for machine learning inference have increasingly moved to the edge of the network. Existing machine learning inference platforms typically assume a homogeneous infrastructure and do not take into account the more complex and tiered computing infrastructure that includes edge devices, local hubs, edge datacenters, and cloud datacenters. On the other hand, recent AutoML efforts have provided viable solutions for model compression, pruning and quantization for heterogeneous environments; for a machine learning model, now we may easily find or even generate a series of models with different tradeoffs between accuracy and efficiency. We design and implement JellyBean, a system for serving and optimizing machine learning inference workflows on heterogeneous infrastructures. Given service-level objectives (e.g., throughput, accuracy), JellyBean picks the most cost-efficient models that meet the accuracy target and decides how to deploy them across different tiers of infrastructures. Evaluations show that JellyBean reduces the total serving cost of visual question answering by up to 58%, and vehicle tracking from the NVIDIA AI City Challenge by up to 36% compared with state-of-the-art model selection and worker assignment solutions. JellyBean also outperforms prior ML serving systems (e.g., Spark on the cloud) up to 5x in serving costs.
    On-Demand Resource Management for 6G Wireless Networks Using Knowledge-Assisted Dynamic Neural Networks. (arXiv:2208.01785v1 [eess.SY] CROSS LISTED)
    On-demand service provisioning is a critical yet challenging issue in 6G wireless communication networks, since emerging services have significantly diverse requirements and the network resources become increasingly heterogeneous and dynamic. In this paper, we study the on-demand wireless resource orchestration problem with the focus on the computing delay in orchestration decision-making process. Specifically, we take the decision-making delay into the optimization problem. Then, a dynamic neural network (DyNN)-based method is proposed, where the model complexity can be adjusted according to the service requirements. We further build a knowledge base representing the relationship among the service requirements, available computing resources, and the resource allocation performance. By exploiting the knowledge, the width of DyNN can be selected in a timely manner, further improving the performance of orchestration. Simulation results show that the proposed scheme significantly outperforms the traditional static neural network, and also shows sufficient flexibility in on-demand service provisioning.
    Privacy-Preserving Chaotic Extreme Learning Machine with Fully Homomorphic Encryption. (arXiv:2208.02587v1 [cs.LG])
    The Machine Learning and Deep Learning Models require a lot of data for the training process, and in some scenarios, there might be some sensitive data, such as customer information involved, which the organizations might be hesitant to outsource for model building. Some of the privacy-preserving techniques such as Differential Privacy, Homomorphic Encryption, and Secure Multi-Party Computation can be integrated with different Machine Learning and Deep Learning algorithms to provide security to the data as well as the model. In this paper, we propose a Chaotic Extreme Learning Machine and its encrypted form using Fully Homomorphic Encryption where the weights and biases are generated using a logistic map instead of uniform distribution. Our proposed method has performed either better or similar to the Traditional Extreme Learning Machine on most of the datasets.
    FedDRL: Deep Reinforcement Learning-based Adaptive Aggregation for Non-IID Data in Federated Learning. (arXiv:2208.02442v1 [cs.LG])
    The uneven distribution of local data across different edge devices (clients) results in slow model training and accuracy reduction in federated learning. Naive federated learning (FL) strategy and most alternative solutions attempted to achieve more fairness by weighted aggregating deep learning models across clients. This work introduces a novel non-IID type encountered in real-world datasets, namely cluster-skew, in which groups of clients have local data with similar distributions, causing the global model to converge to an over-fitted solution. To deal with non-IID data, particularly the cluster-skewed data, we propose FedDRL, a novel FL model that employs deep reinforcement learning to adaptively determine each client's impact factor (which will be used as the weights in the aggregation process). Extensive experiments on a suite of federated datasets confirm that the proposed FedDRL improves favorably against FedAvg and FedProx methods, e.g., up to 4.05% and 2.17% on average for the CIFAR-100 dataset, respectively.
    Diffusion-Based Voice Conversion with Fast Maximum Likelihood Sampling Scheme. (arXiv:2109.13821v2 [cs.SD] UPDATED)
    Voice conversion is a common speech synthesis task which can be solved in different ways depending on a particular real-world scenario. The most challenging one often referred to as one-shot many-to-many voice conversion consists in copying the target voice from only one reference utterance in the most general case when both source and target speakers do not belong to the training dataset. We present a scalable high-quality solution based on diffusion probabilistic modeling and demonstrate its superior quality compared to state-of-the-art one-shot voice conversion approaches. Moreover, focusing on real-time applications, we investigate general principles which can make diffusion models faster while keeping synthesis quality at a high level. As a result, we develop a novel Stochastic Differential Equations solver suitable for various diffusion model types and generative tasks as shown through empirical studies and justify it by theoretical analysis.
    A Class of Dimension-free Metrics for the Convergence of Empirical Measures. (arXiv:2104.12036v3 [math.PR] UPDATED)
    This paper concerns the convergence of empirical measures in high dimensions. We propose a new class of metrics and show that under such metrics, the convergence is free of the curse of dimensionality (CoD). Such a feature is critical for high-dimensional analysis and stands in contrast to classical metrics ({\it e.g.}, the Wasserstein distance). The proposed metrics originate from the maximum mean discrepancy, which we generalize by proposing specific criteria for selecting test function spaces to guarantee the property of being free of CoD. Therefore, we call this class of metrics the generalized maximum mean discrepancy (GMMD). Examples of the selected test function spaces include the reproducing kernel Hilbert space, Barron space, and flow-induced function spaces. Three applications of the proposed metrics are presented: 1. The convergence of empirical measure in the case of random variables; 2. The convergence of $n$-particle system to the solution to McKean-Vlasov stochastic differential equation; 3. The construction of an $\varepsilon$-Nash equilibrium for a homogeneous $n$-player game by its mean-field limit. As a byproduct, we prove that, given a distribution close to the target distribution measured by GMMD and a certain representation of the target distribution, we can generate a distribution close to the target one in terms of the Wasserstein distance and relative entropy. Overall, we show that the proposed class of metrics is a powerful tool to analyze the convergence of empirical measures in high dimensions without CoD.
    Fully Automated 2D and 3D Convolutional Neural Networks Pipeline for Video Segmentation and Myocardial Infarction Detection in Echocardiography. (arXiv:2103.14734v2 [eess.IV] UPDATED)
    Cardiac imaging known as echocardiography is a non-invasive tool utilized to produce data including images and videos, which cardiologists use to diagnose cardiac abnormalities in general and myocardial infarction (MI) in particular. Echocardiography machines can deliver abundant amounts of data that need to be quickly analyzed by cardiologists to help them make a diagnosis and treat cardiac conditions. However, the acquired data quality varies depending on the acquisition conditions and the patient's responsiveness to the setup instructions. These constraints are challenging to doctors especially when patients are facing MI and their lives are at stake. In this paper, we propose an innovative real-time end-to-end fully automated model based on convolutional neural networks (CNN) to detect MI depending on regional wall motion abnormalities (RWMA) of the left ventricle (LV) from videos produced by echocardiography. Our model is implemented as a pipeline consisting of a 2D CNN that performs data preprocessing by segmenting the LV chamber from the apical four-chamber (A4C) view, followed by a 3D CNN that performs a binary classification to detect if the segmented echocardiography shows signs of MI. We trained both CNNs on a dataset composed of 165 echocardiography videos each acquired from a distinct patient. The 2D CNN achieved an accuracy of 97.18% on data segmentation while the 3D CNN achieved 90.9% of accuracy, 100% of precision and 95% of recall on MI detection. Our results demonstrate that creating a fully automated system for MI detection is feasible and propitious.
    On the Learnability of Physical Concepts: Can a Neural Network Understand What's Real?. (arXiv:2207.12186v2 [cs.LG] UPDATED)
    We revisit the classic signal-to-symbol barrier in light of the remarkable ability of deep neural networks to generate realistic synthetic data. DeepFakes and spoofing highlight the feebleness of the link between physical reality and its abstract representation, whether learned by a digital computer or a biological agent. Starting from a widely applicable definition of abstract concept, we show that standard feed-forward architectures cannot capture but trivial concepts, regardless of the number of weights and the amount of training data, despite being extremely effective classifiers. On the other hand, architectures that incorporate recursion can represent a significantly larger class of concepts, but may still be unable to learn them from a finite dataset. We qualitatively describe the class of concepts that can be "understood" by modern architectures trained with variants of stochastic gradient descent, using a (free energy) Lagrangian to measure information complexity. Even if a concept has been understood, however, a network has no means of communicating its understanding to an external agent, except through continuous interaction and validation. We then characterize physical objects as abstract concepts and use the previous analysis to show that physical objects can be encoded by finite architectures. However, to understand physical concepts, sensors must provide persistently exciting observations, for which the ability to control the data acquisition process is essential (active perception). The importance of control depends on the modality, benefiting visual more than acoustic or chemical perception. Finally, we conclude that binding physical entities to digital identities is possible in finite time with finite resources, solving in principle the signal-to-symbol barrier problem, but we highlight the need for continuous validation.
    A Robust graph attention network with dynamic adjusted Graph. (arXiv:2009.13038v3 [cs.LG] UPDATED)
    Graph Attention Networks(GATs) are useful deep learning models to deal with the graph data. However, recent works show that the classical GAT is vulnerable to adversarial attacks. It degrades dramatically with slight perturbations. Therefore, how to enhance the robustness of GAT is a critical problem. Robust GAT(RoGAT) is proposed in this paper to improve the robustness of GAT based on the revision of the attention mechanism. Different from the original GAT, which uses the attention mechanism for different edges but is still sensitive to the perturbation, RoGAT adds an extra dynamic attention score progressively and improves the robustness. Firstly, RoGAT revises the edges weight based on the smoothness assumption which is quite common for ordinary graphs. Secondly, RoGAT further revises the features to suppress features' noise. Then, an extra attention score is generated by the dynamic edge's weight and can be used to reduce the impact of adversarial attacks. Different experiments against targeted and untargeted attacks on citation data on citation data demonstrate that RoGAT outperforms most of the recent defensive methods.
    Differentiable Predictive Control with Safety Guarantees: A Control Barrier Function Approach. (arXiv:2208.02319v1 [eess.SY])
    We develop a novel form of differentiable predictive control (DPC) with safety and robustness guarantees based on control barrier functions. DPC is an unsupervised learning-based method for obtaining approximate solutions to explicit model predictive control (MPC) problems. In DPC, the predictive control policy parametrized by a neural network is optimized offline via direct policy gradients obtained by automatic differentiation of the MPC problem. The proposed approach exploits a new form of sampled-data barrier function to enforce offline and online safety requirements in DPC settings while only interrupting the neural network-based controller near the boundary of the safe set. The effectiveness of the proposed approach is demonstrated in simulation.
    Reinforcement Learning for Joint V2I Network Selection and Autonomous Driving Policies. (arXiv:2208.02249v1 [cs.LG])
    Vehicle-to-Infrastructure (V2I) communication is becoming critical for the enhanced reliability of autonomous vehicles (AVs). However, the uncertainties in the road-traffic and AVs' wireless connections can severely impair timely decision-making. It is thus critical to simultaneously optimize the AVs' network selection and driving policies in order to minimize road collisions while maximizing the communication data rates. In this paper, we develop a reinforcement learning (RL) framework to characterize efficient network selection and autonomous driving policies in a multi-band vehicular network (VNet) operating on conventional sub-6GHz spectrum and Terahertz (THz) frequencies. The proposed framework is designed to (i) maximize the traffic flow and minimize collisions by controlling the vehicle's motion dynamics (i.e., speed and acceleration) from autonomous driving perspective, and (ii) maximize the data rates and minimize handoffs by jointly controlling the vehicle's motion dynamics and network selection from telecommunication perspective. We cast this problem as a Markov Decision Process (MDP) and develop a deep Q-learning based solution to optimize the actions such as acceleration, deceleration, lane-changes, and AV-base station assignments for a given AV's state. The AV's state is defined based on the velocities and communication channel states of AVs. Numerical results demonstrate interesting insights related to the inter-dependency of vehicle's motion dynamics, handoffs, and the communication data rate. The proposed policies enable AVs to adopt safe driving behaviors with improved connectivity.
    Graph Neural Networks Extract High-Resolution Cultivated Land Maps from Sentinel-2 Image Series. (arXiv:2208.02349v1 [cs.CV])
    Maintaining farm sustainability through optimizing the agricultural management practices helps build more planet-friendly environment. The emerging satellite missions can acquire multi- and hyperspectral imagery which captures more detailed spectral information concerning the scanned area, hence allows us to benefit from subtle spectral features during the analysis process in agricultural applications. We introduce an approach for extracting 2.5 m cultivated land maps from 10 m Sentinel-2 multispectral image series which benefits from a compact graph convolutional neural network. The experiments indicate that our models not only outperform classical and deep machine learning techniques through delivering higher-quality segmentation maps, but also dramatically reduce the memory footprint when compared to U-Nets (almost 8k trainable parameters of our models, with up to 31M parameters of U-Nets). Such memory frugality is pivotal in the missions which allow us to uplink a model to the AI-powered satellite once it is in orbit, as sending large nets is impossible due to the time constraints.
    Risk-sensitive Reinforcement Learning via Distortion Risk Measures. (arXiv:2107.04422v5 [cs.LG] UPDATED)
    We address the problem of control in a risk-sensitive reinforcement learning (RL) context via distortion risk measures (DRM). We propose policy gradient algorithms, which maximize the DRM of the cumulative reward in an episodic Markov decision process in on-policy as well as off-policy RL settings. We employ two different approaches in devising the policy gradient algorithms. In the first approach, we derive a variant of the policy gradient theorem that caters to the DRM objective, and use this theorem in conjunction with a likelihood ratio-based gradient estimation scheme. In the second approach, we estimate the DRM from the empirical distribution of cumulative rewards, and use this estimation scheme along with a smoothed functional-based gradient estimation scheme. For policy gradient algorithms using either approach, we derive non-asymptotic bounds that establish the convergence to an approximate stationary point of the DRM objective.
    Hydra: A System for Large Multi-Model Deep Learning. (arXiv:2110.08633v7 [cs.DC] UPDATED)
    Scaling up model depth and size is now a common approach to raise accuracy in many deep learning (DL) applications, as evidenced by the widespread success of multi-billion or even trillion parameter models in natural language processing (NLP) research. Despite success in DL research and at major technology companies, broader practical adoption of such large models among domain scientists and businesses is still bottlenecked by GPU memory limits, high training costs, and low GPU availability, even on public clouds. Model selection needs further compound these resource challenges: users often need to compare dozens of models with different hyper-parameters or neural architectures to suit their specific task and dataset. In this paper, we present Hydra, a system designed to tackle such challenges by enabling out-of-the-box scaling for multi-large-model DL workloads on even commodity GPUs in a resource-efficient manner. Hydra is the first approach to holistically optimize the execution of multi-model workloads for large DL models. We do this by adapting prior "model-parallel" execution schemes to work with scalable parameter offloading across the memory hierarchy and further hybridizing this approach with task-parallel job scheduling techniques. Hydra decouples scalability of model parameters from parallelism of execution, thus enabling DL users to train even a 6-billion parameter model on a single commodity GPU. It also fully exploits the speedup potential of task parallelism in multi-GPU setups, yielding near-linear strong scaling and making rigorous model selection perhaps more practical for such models. We evaluate end-to-end performance by fine-tuning GPT-2 for language modeling. We find that Hydra offers between 50% and 100% higher training throughput than even the best settings of state-of-the-art industrial frameworks such as DeepSpeed and GPipe for multi-large-model training.
    Topological Signal Processing using the Weighted Ordinal Partition Network. (arXiv:2205.08349v2 [stat.ML] UPDATED)
    One of the most important problems arising in time series analysis is that of bifurcation, or change point detection. That is, given a collection of time series over a varying parameter, when has the structure of the underlying dynamical system changed? For this task, we turn to the field of topological data analysis (TDA), which encodes information about the shape and structure of data. The idea of utilizing tools from TDA for signal processing tasks, known as topological signal processing (TSP), has gained much attention in recent years, largely through a standard pipeline that computes the persistent homology of the point cloud generated by the Takens' embedding. However, this procedure is limited by computation time since the simplicial complex generated in this case is large, but also has a great deal of redundant data. For this reason, we turn to a more recent method for encoding the structure of the attractor, which constructs an ordinal partition network (OPN) representing information about when the dynamical system has passed between certain regions of state space. The result is a weighted graph whose structure encodes information about the underlying attractor. Our previous work began to find ways to package the information of the OPN in a manner that is amenable to TDA; however, that work only used the network structure and did nothing to encode the additional weighting information. In this paper, we take the next step: building a pipeline to analyze the weighted OPN with TDA and showing that this framework provides more resilience to noise or perturbations in the system and improves the accuracy of the dynamic state detection.
    QC-ODKLA: Quantized and Communication-Censored Online Decentralized Kernel Learning via Linearized ADMM. (arXiv:2208.02777v1 [cs.LG])
    This paper focuses on online kernel learning over a decentralized network. Each agent in the network receives continuous streaming data locally and works collaboratively to learn a nonlinear prediction function that is globally optimal in the reproducing kernel Hilbert space with respect to the total instantaneous costs of all agents. In order to circumvent the curse of dimensionality issue in traditional online kernel learning, we utilize random feature (RF) mapping to convert the non-parametric kernel learning problem into a fixed-length parametric one in the RF space. We then propose a novel learning framework named Online Decentralized Kernel learning via Linearized ADMM (ODKLA) to efficiently solve the online decentralized kernel learning problem. To further improve the communication efficiency, we add the quantization and censoring strategies in the communication stage and develop the Quantized and Communication-censored ODKLA (QC-ODKLA) algorithm. We theoretically prove that both ODKLA and QC-ODKLA can achieve the optimal sublinear regret $\mathcal{O}(\sqrt{T})$ over $T$ time slots. Through numerical experiments, we evaluate the learning effectiveness, communication, and computation efficiencies of the proposed methods.
    NoiLIn: Improving Adversarial Training and Correcting Stereotype of Noisy Labels. (arXiv:2105.14676v2 [cs.LG] UPDATED)
    Adversarial training (AT) formulated as the minimax optimization problem can effectively enhance the model's robustness against adversarial attacks. The existing AT methods mainly focused on manipulating the inner maximization for generating quality adversarial variants or manipulating the outer minimization for designing effective learning objectives. However, empirical results of AT always exhibit the robustness at odds with accuracy and the existence of the cross-over mixture problem, which motivates us to study some label randomness for benefiting the AT. First, we thoroughly investigate noisy labels (NLs) injection into AT's inner maximization and outer minimization, respectively and obtain the observations on when NL injection benefits AT. Second, based on the observations, we propose a simple but effective method -- NoiLIn that randomly injects NLs into training data at each training epoch and dynamically increases the NL injection rate once robust overfitting occurs. Empirically, NoiLIn can significantly mitigate the AT's undesirable issue of robust overfitting and even further improve the generalization of the state-of-the-art AT methods. Philosophically, NoiLIn sheds light on a new perspective of learning with NLs: NLs should not always be deemed detrimental, and even in the absence of NLs in the training set, we may consider injecting them deliberately. Codes are available in https://github.com/zjfheart/NoiLIn.  ( 3 min )
    Membership Inference Attacks Against Self-supervised Speech Models. (arXiv:2111.05113v3 [cs.CR] UPDATED)
    Recently, adapting the idea of self-supervised learning (SSL) on continuous speech has started gaining attention. SSL models pre-trained on a huge amount of unlabeled audio can generate general-purpose representations that benefit a wide variety of speech processing tasks. Despite their ubiquitous deployment, however, the potential privacy risks of these models have not been well investigated. In this paper, we present the first privacy analysis on several SSL speech models using Membership Inference Attacks (MIA) under black-box access. The experiment results show that these pre-trained models are vulnerable to MIA and prone to membership information leakage with high Area Under the Curve (AUC) in both utterance-level and speaker-level. Furthermore, we also conduct several ablation studies to understand the factors that contribute to the success of MIA.  ( 2 min )
    Improving Meta-Learning Generalization with Activation-Based Early-Stopping. (arXiv:2208.02377v1 [cs.LG])
    Meta-Learning algorithms for few-shot learning aim to train neural networks capable of generalizing to novel tasks using only a few examples. Early-stopping is critical for performance, halting model training when it reaches optimal generalization to the new task distribution. Early-stopping mechanisms in Meta-Learning typically rely on measuring the model performance on labeled examples from a meta-validation set drawn from the training (source) dataset. This is problematic in few-shot transfer learning settings, where the meta-test set comes from a different target dataset (OOD) and can potentially have a large distributional shift with the meta-validation set. In this work, we propose Activation Based Early-stopping (ABE), an alternative to using validation-based early-stopping for meta-learning. Specifically, we analyze the evolution, during meta-training, of the neural activations at each hidden layer, on a small set of unlabelled support examples from a single task of the target tasks distribution, as this constitutes a minimal and justifiably accessible information from the target problem. Our experiments show that simple, label agnostic statistics on the activations offer an effective way to estimate how the target generalization evolves over time. At each hidden layer, we characterize the activation distributions, from their first and second order moments, then further summarized along the feature dimensions, resulting in a compact yet intuitive characterization in a four-dimensional space. Detecting when, throughout training time, and at which layer, the target activation trajectory diverges from the activation trajectory of the source data, allows us to perform early-stopping and improve generalization in a large array of few-shot transfer learning settings, across different algorithms, source and target datasets.  ( 3 min )
    Degenerate Gaussian factors for probabilistic inference. (arXiv:2104.15010v2 [cs.LG] UPDATED)
    In this paper, we propose a parametrised factor that enables inference on Gaussian networks where linear dependencies exist among the random variables. Our factor representation is effectively a generalisation of traditional Gaussian parametrisations where the positive-definite constraint of the covariance matrix has been relaxed. For this purpose, we derive various statistical operations and results (such as marginalisation, multiplication and affine transformations of random variables) that extend the capabilities of Gaussian factors to these degenerate settings. By using this principled factor definition, degeneracies can be accommodated accurately and automatically at little additional computational cost. As illustration, we apply our methodology to a representative example involving recursive state estimation of cooperative mobile robots.  ( 2 min )
    A Theoretical Framework for Inference and Learning in Predictive Coding Networks. (arXiv:2207.12316v2 [cs.NE] UPDATED)
    Predictive coding (PC) is an influential theory in computational neuroscience, which argues that the cortex forms unsupervised world models by implementing a hierarchical process of prediction error minimization. PC networks (PCNs) are trained in two phases. First, neural activities are updated to optimize the network's response to external stimuli. Second, synaptic weights are updated to consolidate this change in activity -- an algorithm called \emph{prospective configuration}. While previous work has shown how in various limits, PCNs can be found to approximate backpropagation (BP), recent work has demonstrated that PCNs operating in this standard regime, which does not approximate BP, nevertheless obtain competitive training and generalization performance to BP-trained networks while outperforming them on tasks such as online, few-shot, and continual learning, where brains are known to excel. Despite this promising empirical performance, little is understood theoretically about the properties and dynamics of PCNs in this regime. In this paper, we provide a comprehensive theoretical analysis of the properties of PCNs trained with prospective configuration. We first derive analytical results concerning the inference equilibrium for PCNs and a previously unknown close connection relationship to target propagation (TP). Secondly, we provide a theoretical analysis of learning in PCNs as a variant of generalized expectation-maximization and use that to prove the convergence of PCNs to critical points of the BP loss function, thus showing that deep PCNs can, in theory, achieve the same generalization performance as BP, while maintaining their unique advantages.  ( 3 min )
    Neural-network preconditioners for solving the Dirac equation in lattice gauge theory. (arXiv:2208.02728v1 [hep-lat])
    This work develops neural-network--based preconditioners to accelerate solution of the Wilson-Dirac normal equation in lattice quantum field theories. The approach is implemented for the two-flavor lattice Schwinger model near the critical point. In this system, neural-network preconditioners are found to accelerate the convergence of the conjugate gradient solver compared with the solution of unpreconditioned systems or those preconditioned with conventional approaches based on even-odd or incomplete Cholesky decompositions, as measured by reductions in the number of iterations and/or complex operations required for convergence. It is also shown that a preconditioner trained on ensembles with small lattice volumes can be used to construct preconditioners for ensembles with many times larger lattice volumes, with minimal degradation of performance. This volume-transferring technique amortizes the training cost and presents a pathway towards scaling such preconditioners to lattice field theory calculations with larger lattice volumes and in four dimensions.  ( 2 min )
    A Lightweight, Efficient and Explainable-by-Design Convolutional Neural Network for Internet Traffic Classification. (arXiv:2202.05535v2 [cs.LG] UPDATED)
    Traffic classification, i.e. the identification of the type of applications flowing in a network, is a strategic task for numerous activities (e.g., intrusion detection, routing). This task faces some critical challenges that current deep learning approaches do not address. The design of current approaches do not take into consideration the fact that networking hardware (e.g., routers) often runs with limited computational resources. Further, they do not meet the need for faithful explainability highlighted by regulatory bodies. Finally, these traffic classifiers are evaluated on small datasets which fail to reflect the diversity of applications in real-world settings. Therefore, this paper introduces a Lightweight, Efficient and eXplainable-by-design convolutional neural network (LEXNet) for Internet traffic classification, which relies on a new residual block (for lightweight and efficiency purposes) and prototype layer (for explainability). Based on a commercial-grade dataset, our evaluation shows that LEXNet succeeds to maintain the same accuracy as the best performing state-of-the-art neural network, while providing the additional features previously mentioned. Moreover, we illustrate the explainability feature of our approach, which stems from the communication of detected application prototypes to the end-user, and we highlight the faithfulness of LEXNet explanations through a comparison with post hoc methods.  ( 3 min )
    A Benchmark and Empirical Analysis for Replay Strategies in Continual Learning. (arXiv:2208.02660v1 [cs.LG])
    With the capacity of continual learning, humans can continuously acquire knowledge throughout their lifespan. However, computational systems are not, in general, capable of learning tasks sequentially. This long-standing challenge for deep neural networks (DNNs) is called catastrophic forgetting. Multiple solutions have been proposed to overcome this limitation. This paper makes an in-depth evaluation of the memory replay methods, exploring the efficiency, performance, and scalability of various sampling strategies when selecting replay data. All experiments are conducted on multiple datasets under various domains. Finally, a practical solution for selecting replay methods for various data distributions is provided.  ( 2 min )
    Neural Network Optimal Feedback Control with Guaranteed Local Stability. (arXiv:2205.00394v2 [math.OC] UPDATED)
    Recent research shows that supervised learning can be an effective tool for designing optimal feedback controllers for high-dimensional nonlinear dynamic systems. But the behavior of neural network controllers is still not well understood. In particular, some neural networks with high test accuracy can fail to even locally stabilize the dynamic system. To address this challenge we propose several novel neural network architectures, which we show guarantee local asymptotic stability while retaining the approximation capacity to learn the optimal feedback policy semi-globally. The proposed architectures are compared against standard neural network feedback controllers through numerical simulations of two high-dimensional nonlinear optimal control problems: stabilization of an unstable Burgers-type partial differential equation, and altitude and course tracking for an unmanned aerial vehicle. The simulations demonstrate that standard neural networks can fail to stabilize the dynamics even when trained well, while the proposed architectures are always at least locally stabilizing. Moreover, the proposed controllers are found to be nearly optimal in testing.  ( 2 min )
    Open-world Contrastive Learning. (arXiv:2208.02764v1 [cs.LG])
    Recent advance in contrastive learning has shown remarkable performance. However, the vast majority of approaches are limited to the closed-world setting. In this paper, we enrich the landscape of representation learning by tapping into an open-world setting, where unlabeled samples from novel classes can naturally emerge in the wild. To bridge the gap, we introduce a new learning framework, open-world contrastive learning (OpenCon). OpenCon tackles the challenges of learning compact representations for both known and novel classes, and facilitates novelty discovery along the way. We demonstrate the effectiveness of OpenCon on challenging benchmark datasets and establish competitive performance. On the ImageNet dataset, OpenCon significantly outperforms the current best method by 11.9% and 7.4% on novel and overall classification accuracy, respectively. We hope that our work will open up new doors for future work to tackle this important problem.  ( 2 min )
    Explaining Classifiers Trained on Raw Hierarchical Multiple-Instance Data. (arXiv:2208.02694v1 [stat.ML])
    Learning from raw data input, thus limiting the need for feature engineering, is a component of many successful applications of machine learning methods in various domains. While many problems naturally translate into a vector representation directly usable in standard classifiers, a number of data sources have the natural form of structured data interchange formats (e.g., security logs in JSON/XML format). Existing methods, such as in Hierarchical Multiple Instance Learning (HMIL), allow learning from such data in their raw form. However, the explanation of the classifiers trained on raw structured data remains largely unexplored. By treating these models as sub-set selections problems, we demonstrate how interpretable explanations, with favourable properties, can be generated using computationally efficient algorithms. We compare to an explanation technique adopted from graph neural networks showing an order of magnitude speed-up and higher-quality explanations.  ( 2 min )
    Cluster-to-adapt: Few Shot Domain Adaptation for Semantic Segmentation across Disjoint Labels. (arXiv:2208.02804v1 [cs.CV])
    Domain adaptation for semantic segmentation across datasets consisting of the same categories has seen several recent successes. However, a more general scenario is when the source and target datasets correspond to non-overlapping label spaces. For example, categories in segmentation datasets change vastly depending on the type of environment or application, yet share many valuable semantic relations. Existing approaches based on feature alignment or discrepancy minimization do not take such category shift into account. In this work, we present Cluster-to-Adapt (C2A), a computationally efficient clustering-based approach for domain adaptation across segmentation datasets with completely different, but possibly related categories. We show that such a clustering objective enforced in a transformed feature space serves to automatically select categories across source and target domains that can be aligned for improving the target performance, while preventing negative transfer for unrelated categories. We demonstrate the effectiveness of our approach through experiments on the challenging problem of outdoor to indoor adaptation for semantic segmentation in few-shot as well as zero-shot settings, with consistent improvements in performance over existing approaches and baselines in all cases.  ( 2 min )
    DDOS: A MOS Prediction Framework utilizing Domain Adaptive Pre-training and Distribution of Opinion Scores. (arXiv:2204.03219v2 [eess.AS] UPDATED)
    Mean opinion score (MOS) is a typical subjective evaluation metric for speech synthesis systems. Since collecting MOS is time-consuming, it would be desirable if there are accurate MOS prediction models for automatic evaluation. In this work, we propose DDOS, a novel MOS prediction model. DDOS utilizes domain adaptive pre-training to further pre-train self-supervised learning models on synthetic speech. And a proposed module is added to model the opinion score distribution of each utterance. With the proposed components, DDOS outperforms previous works on BVCC dataset. And the zero shot transfer result on BC2019 dataset is significantly improved. DDOS also wins second place in Interspeech 2022 VoiceMOS challenge in terms of system-level score.  ( 2 min )
    Development and Validation of ML-DQA -- a Machine Learning Data Quality Assurance Framework for Healthcare. (arXiv:2208.02670v1 [stat.ML])
    The approaches by which the machine learning and clinical research communities utilize real world data (RWD), including data captured in the electronic health record (EHR), vary dramatically. While clinical researchers cautiously use RWD for clinical investigations, ML for healthcare teams consume public datasets with minimal scrutiny to develop new algorithms. This study bridges this gap by developing and validating ML-DQA, a data quality assurance framework grounded in RWD best practices. The ML-DQA framework is applied to five ML projects across two geographies, different medical conditions, and different cohorts. A total of 2,999 quality checks and 24 quality reports were generated on RWD gathered on 247,536 patients across the five projects. Five generalizable practices emerge: all projects used a similar method to group redundant data element representations; all projects used automated utilities to build diagnosis and medication data elements; all projects used a common library of rules-based transformations; all projects used a unified approach to assign data quality checks to data elements; and all projects used a similar approach to clinical adjudication. An average of 5.8 individuals, including clinicians, data scientists, and trainees, were involved in implementing ML-DQA for each project and an average of 23.4 data elements per project were either transformed or removed in response to ML-DQA. This study demonstrates the importance role of ML-DQA in healthcare projects and provides teams a framework to conduct these essential activities.  ( 3 min )
    A similarity-based Bayesian mixture-of-experts model. (arXiv:2012.02130v4 [stat.ML] UPDATED)
    We present a new nonparametric mixture-of-experts model for multivariate regression problems, inspired by the probabilistic k-nearest neighbors algorithm. Using a conditionally specified model, predictions for out-of-sample inputs are based on similarities to each observed data point, yielding predictive distributions represented by Gaussian mixtures. Posterior inference is performed on the parameters of the mixture components as well as the distance metric using a mean-field variational Bayes algorithm accompanied with a stochastic gradient-based optimization procedure. The proposed method is especially advantageous in settings where inputs are of relatively high dimension in comparison to the data size, where input-output relationships are complex, and where predictive distributions may be skewed or multimodal. Computational studies on five datasets, of which two are synthetically generated, illustrate clear advantages of our mixture-of-experts method for high-dimensional inputs, outperforming competitor models both in terms of validation metrics and visual inspection.  ( 2 min )
    HiCu: Leveraging Hierarchy for Curriculum Learning in Automated ICD Coding. (arXiv:2208.02301v1 [cs.LG])
    There are several opportunities for automation in healthcare that can improve clinician throughput. One such example is assistive tools to document diagnosis codes when clinicians write notes. We study the automation of medical code prediction using curriculum learning, which is a training strategy for machine learning models that gradually increases the hardness of the learning tasks from easy to difficult. One of the challenges in curriculum learning is the design of curricula -- i.e., in the sequential design of tasks that gradually increase in difficulty. We propose Hierarchical Curriculum Learning (HiCu), an algorithm that uses graph structure in the space of outputs to design curricula for multi-label classification. We create curricula for multi-label classification models that predict ICD diagnosis and procedure codes from natural language descriptions of patients. By leveraging the hierarchy of ICD codes, which groups diagnosis codes based on various organ systems in the human body, we find that our proposed curricula improve the generalization of neural network-based predictive models across recurrent, convolutional, and transformer-based architectures. Our code is available at https://github.com/wren93/HiCu-ICD.  ( 2 min )
    A new class of generative classifiers based on staged tree models. (arXiv:2012.13798v2 [cs.AI] UPDATED)
    Generative models for classification use the joint probability distribution of the class variable and the features to construct a decision rule. Among generative models, Bayesian networks and naive Bayes classifiers are the most commonly used and provide a clear graphical representation of the relationship among all variables. However, these have the disadvantage of highly restricting the type of relationships that could exist, by not allowing for context-specific independences. Here we introduce a new class of generative classifiers, called staged tree classifiers, which formally account for context-specific independence. They are constructed by a partitioning of the vertices of an event tree from which conditional independence can be formally read. The naive staged tree classifier is also defined, which extends the classic naive Bayes classifier whilst retaining the same complexity. An extensive simulation study shows that the classification accuracy of staged tree classifiers is competitive with that of state-of-the-art classifiers and an example showcases their use in practice.  ( 2 min )
    Communication Beyond Transmitting Bits: Semantics-Guided Source and Channel Coding. (arXiv:2208.02481v1 [cs.IT])
    Classical communication paradigms focus on accurately transmitting bits over a noisy channel, and Shannon theory provides a fundamental theoretical limit on the rate of reliable communications. In this approach, bits are treated equally, and the communication system is oblivious to what meaning these bits convey or how they would be used. Future communications towards intelligence and conciseness will predictably play a dominant role, and the proliferation of connected intelligent agents requires a radical rethinking of coded transmission paradigm to support the new communication morphology on the horizon. The recent concept of "semantic communications" offers a promising research direction. Injecting semantic guidance into the coded transmission design to achieve semantics-aware communications shows great potential for further breakthrough in effectiveness and reliability. This article sheds light on semantics-guided source and channel coding as a transmission paradigm of semantic communications, which exploits both data semantics diversity and wireless channel diversity together to boost the whole system performance. We present the general system architecture and key techniques, and indicate some open issues on this topic.  ( 2 min )
    PyDTS: A Python Package for Discrete-Time Survival (Regularized) Regression with Competing Risks. (arXiv:2204.05731v3 [stat.ML] UPDATED)
    Time-to-event analysis (survival analysis) is used when the outcome or the response of interest is the time until a pre-specified event occurs. Time-to-event data are sometimes discrete either because time itself is discrete or due to grouping of failure times into intervals or rounding off measurements. In addition, the failure of an individual could be one of several distinct failure types; known as competing risks (events). This work focuses on discrete-time regression with competing events. We emphasize the main difference between the continuous and discrete settings with competing events, develop a faster estimation algorithm, and present PyDTS, an open source Python package which implements our procedure and other tools for discrete-time-survival analysis with competing risks.  ( 2 min )
    Learning Interaction Variables and Kernels from Observations of Agent-Based Systems. (arXiv:2208.02758v1 [cs.LG])
    Dynamical systems across many disciplines are modeled as interacting particles or agents, with interaction rules that depend on a very small number of variables (e.g. pairwise distances, pairwise differences of phases, etc...), functions of the state of pairs of agents. Yet, these interaction rules can generate self-organized dynamics, with complex emergent behaviors (clustering, flocking, swarming, etc.). We propose a learning technique that, given observations of states and velocities along trajectories of the agents, yields both the variables upon which the interaction kernel depends and the interaction kernel itself, in a nonparametric fashion. This yields an effective dimension reduction which avoids the curse of dimensionality from the high-dimensional observation data (states and velocities of all the agents). We demonstrate the learning capability of our method to a variety of first-order interacting systems.  ( 2 min )
    P2P: Tuning Pre-trained Image Models for Point Cloud Analysis with Point-to-Pixel Prompting. (arXiv:2208.02812v1 [cs.CV])
    Nowadays, pre-training big models on large-scale datasets has become a crucial topic in deep learning. The pre-trained models with high representation ability and transferability achieve a great success and dominate many downstream tasks in natural language processing and 2D vision. However, it is non-trivial to promote such a pretraining-tuning paradigm to the 3D vision, given the limited training data that are relatively inconvenient to collect. In this paper, we provide a new perspective of leveraging pre-trained 2D knowledge in 3D domain to tackle this problem, tuning pre-trained image models with the novel Point-to-Pixel prompting for point cloud analysis at a minor parameter cost. Following the principle of prompting engineering, we transform point clouds into colorful images with geometry-preserved projection and geometry-aware coloring to adapt to pre-trained image models, whose weights are kept frozen during the end-to-end optimization of point cloud analysis tasks. We conduct extensive experiments to demonstrate that cooperating with our proposed Point-to-Pixel Prompting, better pre-trained image model will lead to consistently better performance in 3D vision. Enjoying prosperous development from image pre-training field, our method attains 89.3% accuracy on the hardest setting of ScanObjectNN, surpassing conventional point cloud models with much fewer trainable parameters. Our framework also exhibits very competitive performance on ModelNet classification and ShapeNet Part Segmentation. Code is available at https://github.com/wangzy22/P2P
    Max-Affine Spline Insights Into Deep Network Pruning. (arXiv:2101.02338v3 [cs.LG] UPDATED)
    In this paper, we study the importance of pruning in Deep Networks (DNs) and the yin & yang relationship between (1) pruning highly overparametrized DNs that have been trained from random initialization and (2) training small DNs that have been "cleverly" initialized. As in most cases practitioners can only resort to random initialization, there is a strong need to develop a grounded understanding of DN pruning. Current literature remains largely empirical, lacking a theoretical understanding of how pruning affects DNs' decision boundary, how to interpret pruning, and how to design corresponding principled pruning techniques. To tackle those questions, we propose to employ recent advances in the theoretical analysis of Continuous Piecewise Affine (CPA) DNs. From this perspective, we will be able to detect the early-bird (EB) ticket phenomenon, provide interpretability into current pruning techniques, and develop a principled pruning strategy. In each step of our study, we conduct extensive experiments supporting our claims and results; while our main goal is to enhance the current understanding towards DN pruning instead of developing a new pruning method, our spline pruning criteria in terms of layerwise and global pruning is on par with or even outperforms state-of-the-art pruning methods.
    Transferable Multi-Agent Reinforcement Learning with Dynamic Participating Agents. (arXiv:2208.02424v1 [cs.LG])
    We study multi-agent reinforcement learning (MARL) with centralized training and decentralized execution. During the training, new agents may join, and existing agents may unexpectedly leave the training. In such situations, a standard deep MARL model must be trained again from scratch, which is very time-consuming. To tackle this problem, we propose a special network architecture with a few-shot learning algorithm that allows the number of agents to vary during centralized training. In particular, when a new agent joins the centralized training, our few-shot learning algorithm trains its policy network and value network using a small number of samples; when an agent leaves the training, the training process of the remaining agents is not affected. Our experiments show that using the proposed network architecture and algorithm, model adaptation when new agents join can be 100+ times faster than the baseline. Our work is applicable to any setting, including cooperative, competitive, and mixed.  ( 2 min )
    Tokyo Kion-On: Query-Based Generative Sonification of Atmospheric Data. (arXiv:2208.02494v1 [cs.SD])
    Amid growing environmental concerns, interactive displays of data constitute an important tool for exploring and understanding the impact of climate change on the planet's ecosystemic integrity. This paper presents Tokyo kion-on, a query-based sonification model of Tokyo's air temperature from 1876 to 2021. The system uses a recurrent neural network architecture known as LSTM with attention trained on a small dataset of Japanese melodies and conditioned upon said atmospheric data. After describing the model's implementation, a brief comparative illustration of the musical results is presented, along with a discussion on how the exposed hyper-parameters can promote active and non-linear exploration of the data.  ( 2 min )
    AACC: Asymmetric Actor-Critic in Contextual Reinforcement Learning. (arXiv:2208.02376v1 [cs.LG])
    Reinforcement Learning (RL) techniques have drawn great attention in many challenging tasks, but their performance deteriorates dramatically when applied to real-world problems. Various methods, such as domain randomization, have been proposed to deal with such situations by training agents under different environmental setups, and therefore they can be generalized to different environments during deployment. However, they usually do not incorporate the underlying environmental factor information that the agents interact with properly and thus can be overly conservative when facing changes in the surroundings. In this paper, we first formalize the task of adapting to changing environmental dynamics in RL as a generalization problem using Contextual Markov Decision Processes (CMDPs). We then propose the Asymmetric Actor-Critic in Contextual RL (AACC) as an end-to-end actor-critic method to deal with such generalization tasks. We demonstrate the essential improvements in the performance of AACC over existing baselines experimentally in a range of simulated environments.  ( 2 min )
    Node Copying: A Random Graph Model for Effective Graph Sampling. (arXiv:2208.02435v1 [stat.ML])
    There has been an increased interest in applying machine learning techniques on relational structured-data based on an observed graph. Often, this graph is not fully representative of the true relationship amongst nodes. In these settings, building a generative model conditioned on the observed graph allows to take the graph uncertainty into account. Various existing techniques either rely on restrictive assumptions, fail to preserve topological properties within the samples or are prohibitively expensive for larger graphs. In this work, we introduce the node copying model for constructing a distribution over graphs. Sampling of a random graph is carried out by replacing each node's neighbors by those of a randomly sampled similar node. The sampled graphs preserve key characteristics of the graph structure without explicitly targeting them. Additionally, sampling from this model is extremely simple and scales linearly with the nodes. We show the usefulness of the copying model in three tasks. First, in node classification, a Bayesian formulation based on node copying achieves higher accuracy in sparse data settings. Second, we employ our proposed model to mitigate the effect of adversarial attacks on the graph topology. Last, incorporation of the model in a recommendation system setting improves recall over state-of-the-art methods.  ( 3 min )
    Conformal Risk Control. (arXiv:2208.02814v1 [stat.ME])
    We extend conformal prediction to control the expected value of any monotone loss function. The algorithm generalizes split conformal prediction together with its coverage guarantee. Like conformal prediction, the conformal risk control procedure is tight up to an $\mathcal{O}(1/n)$ factor. Worked examples from computer vision and natural language processing demonstrate the usage of our algorithm to bound the false negative rate, graph distance, and token-level F1-score.  ( 2 min )
  • Open

    Conformal Risk Control. (arXiv:2208.02814v1 [stat.ME])
    We extend conformal prediction to control the expected value of any monotone loss function. The algorithm generalizes split conformal prediction together with its coverage guarantee. Like conformal prediction, the conformal risk control procedure is tight up to an $\mathcal{O}(1/n)$ factor. Worked examples from computer vision and natural language processing demonstrate the usage of our algorithm to bound the false negative rate, graph distance, and token-level F1-score.  ( 2 min )
    Feature selection with gradient descent on two-layer networks in low-rotation regimes. (arXiv:2208.02789v1 [cs.LG])
    This work establishes low test error of gradient flow (GF) and stochastic gradient descent (SGD) on two-layer ReLU networks with standard initialization, in three regimes where key sets of weights rotate little (either naturally due to GF and SGD, or due to an artificial constraint), and making use of margins as the core analytic technique. The first regime is near initialization, specifically until the weights have moved by $\mathcal{O}(\sqrt m)$, where $m$ denotes the network width, which is in sharp contrast to the $\mathcal{O}(1)$ weight motion allowed by the Neural Tangent Kernel (NTK); here it is shown that GF and SGD only need a network width and number of samples inversely proportional to the NTK margin, and moreover that GF attains at least the NTK margin itself, which suffices to establish escape from bad KKT points of the margin objective, whereas prior work could only establish nondecreasing but arbitrarily small margins. The second regime is the Neural Collapse (NC) setting, where data lies in extremely-well-separated groups, and the sample complexity scales with the number of groups; here the contribution over prior work is an analysis of the entire GF trajectory from initialization. Lastly, if the inner layer weights are constrained to change in norm only and can not rotate, then GF with large widths achieves globally maximal margins, and its sample complexity scales with their inverse; this is in contrast to prior work, which required infinite width and a tricky dual convergence assumption. As purely technical contributions, this work develops a variety of potential functions and other tools which will hopefully aid future work.
    Sparse Continuous Distributions and Fenchel-Young Losses. (arXiv:2108.01988v2 [cs.LG] UPDATED)
    Exponential families are widely used in machine learning, including many distributions in continuous and discrete domains (e.g., Gaussian, Dirichlet, Poisson, and categorical distributions via the softmax transformation). Distributions in each of these families have fixed support. In contrast, for finite domains, recent work on sparse alternatives to softmax (e.g., sparsemax, $\alpha$-entmax, and fusedmax), has led to distributions with varying support. This paper develops sparse alternatives to continuous distributions, based on several technical contributions: First, we define $\Omega$-regularized prediction maps and Fenchel-Young losses for arbitrary domains (possibly countably infinite or continuous). For linearly parametrized families, we show that minimization of Fenchel-Young losses is equivalent to moment matching of the statistics, generalizing a fundamental property of exponential families. When $\Omega$ is a Tsallis negentropy with parameter $\alpha$, we obtain ``deformed exponential families,'' which include $\alpha$-entmax and sparsemax ($\alpha=2$) as particular cases. For quadratic energy functions, the resulting densities are $\beta$-Gaussians, an instance of elliptical distributions that contain as particular cases the Gaussian, biweight, triweight, and Epanechnikov densities, and for which we derive closed-form expressions for the variance, Tsallis entropy, and Fenchel-Young loss. When $\Omega$ is a total variation or Sobolev regularizer, we obtain a continuous version of the fusedmax. Finally, we introduce continuous-domain attention mechanisms, deriving efficient gradient backpropagation algorithms for $\alpha \in \{1, 4/3, 3/2, 2\}$. Using these algorithms, we demonstrate our sparse continuous distributions for attention-based audio classification and visual question answering, showing that they allow attending to time intervals and compact regions.
    A Hybrid Framework for Sequential Data Prediction with End-to-End Optimization. (arXiv:2203.13787v2 [stat.ML] UPDATED)
    We investigate nonlinear prediction in an online setting and introduce a hybrid model that effectively mitigates, via an end-to-end architecture, the need for hand-designed features and manual model selection issues of conventional nonlinear prediction/regression methods. In particular, we use recursive structures to extract features from sequential signals, while preserving the state information, i.e., the history, and boosted decision trees to produce the final output. The connection is in an end-to-end fashion and we jointly optimize the whole architecture using stochastic gradient descent, for which we also provide the backward pass update equations. In particular, we employ a recurrent neural network (LSTM) for adaptive feature extraction from sequential data and a gradient boosting machinery (soft GBDT) for effective supervised regression. Our framework is generic so that one can use other deep learning architectures for feature extraction (such as RNNs and GRUs) and machine learning algorithms for decision making as long as they are differentiable. We demonstrate the learning behavior of our algorithm on synthetic data and the significant performance improvements over the conventional methods over various real life datasets. Furthermore, we openly share the source code of the proposed method to facilitate further research.  ( 3 min )
    Modeling Cell Populations Measured By Flow Cytometry With Covariates Using Sparse Mixture of Regressions. (arXiv:2008.11251v2 [stat.AP] UPDATED)
    The ocean is filled with microscopic microalgae called phytoplankton, which together are responsible for as much photosynthesis as all plants on land combined. Our ability to predict their response to the warming ocean relies on understanding how the dynamics of phytoplankton populations is influenced by changes in environmental conditions. One powerful technique to study the dynamics of phytoplankton is flow cytometry, which measures the optical properties of thousands of individual cells per second. Today, oceanographers are able to collect flow cytometry data in real-time onboard a moving ship, providing them with fine-scale resolution of the distribution of phytoplankton across thousands of kilometers. One of the current challenges is to understand how these small and large scale variations relate to environmental conditions, such as nutrient availability, temperature, light and ocean currents. In this paper, we propose a novel sparse mixture of multivariate regressions model to estimate the time-varying phytoplankton subpopulations while simultaneously identifying the specific environmental covariates that are predictive of the observed changes to these subpopulations. We demonstrate the usefulness and interpretability of the approach using both synthetic data and real observations collected on an oceanographic cruise conducted in the north-east Pacific in the spring of 2017.  ( 3 min )
    Bayesian Optimization with Informative Covariance. (arXiv:2208.02704v1 [cs.LG])
    Bayesian Optimization is a methodology for global optimization of unknown and expensive objectives. It combines a surrogate Bayesian regression model with an acquisition function to decide where to evaluate the objective. Typical regression models are Gaussian processes with stationary covariance functions, which, however, are unable to express prior input-dependent information, in particular information about possible locations of the optimum. The ubiquity of stationary models has led to the common practice of exploiting prior information via informative mean functions. In this paper, we highlight that these models can lead to poor performance, especially in high dimensions. We propose novel informative covariance functions that leverage nonstationarity to encode preferences for certain regions of the search space and adaptively promote local exploration during the optimization. We demonstrate that they can increase the sample efficiency of the optimization in high dimensions, even under weak prior information.  ( 2 min )
    Membership Inference Attacks and Defenses in Neural Network Pruning. (arXiv:2202.03335v2 [cs.CR] UPDATED)
    Neural network pruning has been an essential technique to reduce the computation and memory requirements for using deep neural networks for resource-constrained devices. Most existing research focuses primarily on balancing the sparsity and accuracy of a pruned neural network by strategically removing insignificant parameters and retraining the pruned model. Such efforts on reusing training samples pose serious privacy risks due to increased memorization, which, however, has not been investigated yet. In this paper, we conduct the first analysis of privacy risks in neural network pruning. Specifically, we investigate the impacts of neural network pruning on training data privacy, i.e., membership inference attacks. We first explore the impact of neural network pruning on prediction divergence, where the pruning process disproportionately affects the pruned model's behavior for members and non-members. Meanwhile, the influence of divergence even varies among different classes in a fine-grained manner. Enlighten by such divergence, we proposed a self-attention membership inference attack against the pruned neural networks. Extensive experiments are conducted to rigorously evaluate the privacy impacts of different pruning approaches, sparsity levels, and adversary knowledge. The proposed attack shows the higher attack performance on the pruned models when compared with eight existing membership inference attacks. In addition, we propose a new defense mechanism to protect the pruning process by mitigating the prediction divergence based on KL-divergence distance, whose effectiveness has been experimentally demonstrated to effectively mitigate the privacy risks while maintaining the sparsity and accuracy of the pruned models.  ( 3 min )
    Development and Validation of ML-DQA -- a Machine Learning Data Quality Assurance Framework for Healthcare. (arXiv:2208.02670v1 [stat.ML])
    The approaches by which the machine learning and clinical research communities utilize real world data (RWD), including data captured in the electronic health record (EHR), vary dramatically. While clinical researchers cautiously use RWD for clinical investigations, ML for healthcare teams consume public datasets with minimal scrutiny to develop new algorithms. This study bridges this gap by developing and validating ML-DQA, a data quality assurance framework grounded in RWD best practices. The ML-DQA framework is applied to five ML projects across two geographies, different medical conditions, and different cohorts. A total of 2,999 quality checks and 24 quality reports were generated on RWD gathered on 247,536 patients across the five projects. Five generalizable practices emerge: all projects used a similar method to group redundant data element representations; all projects used automated utilities to build diagnosis and medication data elements; all projects used a common library of rules-based transformations; all projects used a unified approach to assign data quality checks to data elements; and all projects used a similar approach to clinical adjudication. An average of 5.8 individuals, including clinicians, data scientists, and trainees, were involved in implementing ML-DQA for each project and an average of 23.4 data elements per project were either transformed or removed in response to ML-DQA. This study demonstrates the importance role of ML-DQA in healthcare projects and provides teams a framework to conduct these essential activities.  ( 3 min )
    DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R. (arXiv:2103.09603v3 [stat.ML] UPDATED)
    The R package DoubleML implements the double/debiased machine learning framework of Chernozhukov et al. (2018). It provides functionalities to estimate parameters in causal models based on machine learning methods. The double machine learning framework consist of three key ingredients: Neyman orthogonality, high-quality machine learning estimation and sample splitting. Estimation of nuisance components can be performed by various state-of-the-art machine learning methods that are available in the mlr3 ecosystem. DoubleML makes it possible to perform inference in a variety of causal models, including partially linear and interactive regression models and their extensions to instrumental variable estimation. The object-oriented implementation of DoubleML enables a high flexibility for the model specification and makes it easily extendable. This paper serves as an introduction to the double machine learning framework and the R package DoubleML. In reproducible code examples with simulated and real data sets, we demonstrate how DoubleML users can perform valid inference based on machine learning methods.  ( 2 min )
    Using Mixed-Effects Models to Learn Bayesian Networks from Related Data Sets. (arXiv:2206.03743v2 [stat.ML] UPDATED)
    We commonly assume that data are a homogeneous set of observations when learning the structure of Bayesian networks. However, they often comprise different data sets that are related but not homogeneous because they have been collected in different ways or from different populations. In our previous work (Azzimonti, Corani and Scutari, 2021), we proposed a closed-form Bayesian Hierarchical Dirichlet score for discrete data that pools information across related data sets to learn a single encompassing network structure, while taking into account the differences in their probabilistic structures. In this paper, we provide an analogous solution for learning a Bayesian network from continuous data using mixed-effects models to pool information across the related data sets. We study its structural, parametric, predictive and classification accuracy and we show that it outperforms both conditional Gaussian Bayesian networks (that do not perform any pooling) and classical Gaussian Bayesian networks (that disregard the heterogeneous nature of the data). The improvement is marked for low sample sizes and for unbalanced data sets.  ( 2 min )
    A similarity-based Bayesian mixture-of-experts model. (arXiv:2012.02130v4 [stat.ML] UPDATED)
    We present a new nonparametric mixture-of-experts model for multivariate regression problems, inspired by the probabilistic k-nearest neighbors algorithm. Using a conditionally specified model, predictions for out-of-sample inputs are based on similarities to each observed data point, yielding predictive distributions represented by Gaussian mixtures. Posterior inference is performed on the parameters of the mixture components as well as the distance metric using a mean-field variational Bayes algorithm accompanied with a stochastic gradient-based optimization procedure. The proposed method is especially advantageous in settings where inputs are of relatively high dimension in comparison to the data size, where input-output relationships are complex, and where predictive distributions may be skewed or multimodal. Computational studies on five datasets, of which two are synthetically generated, illustrate clear advantages of our mixture-of-experts method for high-dimensional inputs, outperforming competitor models both in terms of validation metrics and visual inspection.  ( 2 min )
    A new class of generative classifiers based on staged tree models. (arXiv:2012.13798v2 [cs.AI] UPDATED)
    Generative models for classification use the joint probability distribution of the class variable and the features to construct a decision rule. Among generative models, Bayesian networks and naive Bayes classifiers are the most commonly used and provide a clear graphical representation of the relationship among all variables. However, these have the disadvantage of highly restricting the type of relationships that could exist, by not allowing for context-specific independences. Here we introduce a new class of generative classifiers, called staged tree classifiers, which formally account for context-specific independence. They are constructed by a partitioning of the vertices of an event tree from which conditional independence can be formally read. The naive staged tree classifier is also defined, which extends the classic naive Bayes classifier whilst retaining the same complexity. An extensive simulation study shows that the classification accuracy of staged tree classifiers is competitive with that of state-of-the-art classifiers and an example showcases their use in practice.  ( 2 min )
    PyDTS: A Python Package for Discrete-Time Survival (Regularized) Regression with Competing Risks. (arXiv:2204.05731v3 [stat.ML] UPDATED)
    Time-to-event analysis (survival analysis) is used when the outcome or the response of interest is the time until a pre-specified event occurs. Time-to-event data are sometimes discrete either because time itself is discrete or due to grouping of failure times into intervals or rounding off measurements. In addition, the failure of an individual could be one of several distinct failure types; known as competing risks (events). This work focuses on discrete-time regression with competing events. We emphasize the main difference between the continuous and discrete settings with competing events, develop a faster estimation algorithm, and present PyDTS, an open source Python package which implements our procedure and other tools for discrete-time-survival analysis with competing risks.  ( 2 min )
    A Robust graph attention network with dynamic adjusted Graph. (arXiv:2009.13038v3 [cs.LG] UPDATED)
    Graph Attention Networks(GATs) are useful deep learning models to deal with the graph data. However, recent works show that the classical GAT is vulnerable to adversarial attacks. It degrades dramatically with slight perturbations. Therefore, how to enhance the robustness of GAT is a critical problem. Robust GAT(RoGAT) is proposed in this paper to improve the robustness of GAT based on the revision of the attention mechanism. Different from the original GAT, which uses the attention mechanism for different edges but is still sensitive to the perturbation, RoGAT adds an extra dynamic attention score progressively and improves the robustness. Firstly, RoGAT revises the edges weight based on the smoothness assumption which is quite common for ordinary graphs. Secondly, RoGAT further revises the features to suppress features' noise. Then, an extra attention score is generated by the dynamic edge's weight and can be used to reduce the impact of adversarial attacks. Different experiments against targeted and untargeted attacks on citation data on citation data demonstrate that RoGAT outperforms most of the recent defensive methods.  ( 2 min )
    Node Copying: A Random Graph Model for Effective Graph Sampling. (arXiv:2208.02435v1 [stat.ML])
    There has been an increased interest in applying machine learning techniques on relational structured-data based on an observed graph. Often, this graph is not fully representative of the true relationship amongst nodes. In these settings, building a generative model conditioned on the observed graph allows to take the graph uncertainty into account. Various existing techniques either rely on restrictive assumptions, fail to preserve topological properties within the samples or are prohibitively expensive for larger graphs. In this work, we introduce the node copying model for constructing a distribution over graphs. Sampling of a random graph is carried out by replacing each node's neighbors by those of a randomly sampled similar node. The sampled graphs preserve key characteristics of the graph structure without explicitly targeting them. Additionally, sampling from this model is extremely simple and scales linearly with the nodes. We show the usefulness of the copying model in three tasks. First, in node classification, a Bayesian formulation based on node copying achieves higher accuracy in sparse data settings. Second, we employ our proposed model to mitigate the effect of adversarial attacks on the graph topology. Last, incorporation of the model in a recommendation system setting improves recall over state-of-the-art methods.  ( 3 min )
    Interpolating Log-Determinant and Trace of the Powers of Matrix $\mathbf{A} + t \mathbf{B}$. (arXiv:2009.07385v3 [math.NA] UPDATED)
    We develop heuristic interpolation methods for the functions $t \mapsto \log \det \left( \mathbf{A} + t \mathbf{B} \right)$ and $t \mapsto \operatorname{trace}\left( (\mathbf{A} + t \mathbf{B})^{p} \right)$ where the matrices $\mathbf{A}$ and $\mathbf{B}$ are Hermitian and positive (semi) definite and $p$ and $t$ are real variables. These functions are featured in many applications in statistics, machine learning, and computational physics. The presented interpolation functions are based on the modification of sharp bounds for these functions. We demonstrate the accuracy and performance of the proposed method with numerical examples, namely, the marginal maximum likelihood estimation for Gaussian process regression and the estimation of the regularization parameter of ridge regression with the generalized cross-validation method.  ( 2 min )
    Improving Meta-Learning Generalization with Activation-Based Early-Stopping. (arXiv:2208.02377v1 [cs.LG])
    Meta-Learning algorithms for few-shot learning aim to train neural networks capable of generalizing to novel tasks using only a few examples. Early-stopping is critical for performance, halting model training when it reaches optimal generalization to the new task distribution. Early-stopping mechanisms in Meta-Learning typically rely on measuring the model performance on labeled examples from a meta-validation set drawn from the training (source) dataset. This is problematic in few-shot transfer learning settings, where the meta-test set comes from a different target dataset (OOD) and can potentially have a large distributional shift with the meta-validation set. In this work, we propose Activation Based Early-stopping (ABE), an alternative to using validation-based early-stopping for meta-learning. Specifically, we analyze the evolution, during meta-training, of the neural activations at each hidden layer, on a small set of unlabelled support examples from a single task of the target tasks distribution, as this constitutes a minimal and justifiably accessible information from the target problem. Our experiments show that simple, label agnostic statistics on the activations offer an effective way to estimate how the target generalization evolves over time. At each hidden layer, we characterize the activation distributions, from their first and second order moments, then further summarized along the feature dimensions, resulting in a compact yet intuitive characterization in a four-dimensional space. Detecting when, throughout training time, and at which layer, the target activation trajectory diverges from the activation trajectory of the source data, allows us to perform early-stopping and improve generalization in a large array of few-shot transfer learning settings, across different algorithms, source and target datasets.  ( 3 min )
    AACC: Asymmetric Actor-Critic in Contextual Reinforcement Learning. (arXiv:2208.02376v1 [cs.LG])
    Reinforcement Learning (RL) techniques have drawn great attention in many challenging tasks, but their performance deteriorates dramatically when applied to real-world problems. Various methods, such as domain randomization, have been proposed to deal with such situations by training agents under different environmental setups, and therefore they can be generalized to different environments during deployment. However, they usually do not incorporate the underlying environmental factor information that the agents interact with properly and thus can be overly conservative when facing changes in the surroundings. In this paper, we first formalize the task of adapting to changing environmental dynamics in RL as a generalization problem using Contextual Markov Decision Processes (CMDPs). We then propose the Asymmetric Actor-Critic in Contextual RL (AACC) as an end-to-end actor-critic method to deal with such generalization tasks. We demonstrate the essential improvements in the performance of AACC over existing baselines experimentally in a range of simulated environments.  ( 2 min )
    Degenerate Gaussian factors for probabilistic inference. (arXiv:2104.15010v2 [cs.LG] UPDATED)
    In this paper, we propose a parametrised factor that enables inference on Gaussian networks where linear dependencies exist among the random variables. Our factor representation is effectively a generalisation of traditional Gaussian parametrisations where the positive-definite constraint of the covariance matrix has been relaxed. For this purpose, we derive various statistical operations and results (such as marginalisation, multiplication and affine transformations of random variables) that extend the capabilities of Gaussian factors to these degenerate settings. By using this principled factor definition, degeneracies can be accommodated accurately and automatically at little additional computational cost. As illustration, we apply our methodology to a representative example involving recursive state estimation of cooperative mobile robots.  ( 2 min )
    Local versions of sum-of-norms clustering. (arXiv:2109.09589v3 [cs.LG] UPDATED)
    Sum-of-norms clustering is a convex optimization problem whose solution can be used for the clustering of multivariate data. We propose and study a localized version of this method, and show in particular that it can separate arbitrarily close balls in the stochastic ball model. More precisely, we prove a quantitative bound on the error incurred in the clustering of disjoint connected sets. Our bound is expressed in terms of the number of datapoints and the localization length of the functional.  ( 2 min )
    Diffusion-Based Voice Conversion with Fast Maximum Likelihood Sampling Scheme. (arXiv:2109.13821v2 [cs.SD] UPDATED)
    Voice conversion is a common speech synthesis task which can be solved in different ways depending on a particular real-world scenario. The most challenging one often referred to as one-shot many-to-many voice conversion consists in copying the target voice from only one reference utterance in the most general case when both source and target speakers do not belong to the training dataset. We present a scalable high-quality solution based on diffusion probabilistic modeling and demonstrate its superior quality compared to state-of-the-art one-shot voice conversion approaches. Moreover, focusing on real-time applications, we investigate general principles which can make diffusion models faster while keeping synthesis quality at a high level. As a result, we develop a novel Stochastic Differential Equations solver suitable for various diffusion model types and generative tasks as shown through empirical studies and justify it by theoretical analysis.  ( 2 min )
    A Class of Dimension-free Metrics for the Convergence of Empirical Measures. (arXiv:2104.12036v3 [math.PR] UPDATED)
    This paper concerns the convergence of empirical measures in high dimensions. We propose a new class of metrics and show that under such metrics, the convergence is free of the curse of dimensionality (CoD). Such a feature is critical for high-dimensional analysis and stands in contrast to classical metrics ({\it e.g.}, the Wasserstein distance). The proposed metrics originate from the maximum mean discrepancy, which we generalize by proposing specific criteria for selecting test function spaces to guarantee the property of being free of CoD. Therefore, we call this class of metrics the generalized maximum mean discrepancy (GMMD). Examples of the selected test function spaces include the reproducing kernel Hilbert space, Barron space, and flow-induced function spaces. Three applications of the proposed metrics are presented: 1. The convergence of empirical measure in the case of random variables; 2. The convergence of $n$-particle system to the solution to McKean-Vlasov stochastic differential equation; 3. The construction of an $\varepsilon$-Nash equilibrium for a homogeneous $n$-player game by its mean-field limit. As a byproduct, we prove that, given a distribution close to the target distribution measured by GMMD and a certain representation of the target distribution, we can generate a distribution close to the target one in terms of the Wasserstein distance and relative entropy. Overall, we show that the proposed class of metrics is a powerful tool to analyze the convergence of empirical measures in high dimensions without CoD.  ( 3 min )
    Explaining Classifiers Trained on Raw Hierarchical Multiple-Instance Data. (arXiv:2208.02694v1 [stat.ML])
    Learning from raw data input, thus limiting the need for feature engineering, is a component of many successful applications of machine learning methods in various domains. While many problems naturally translate into a vector representation directly usable in standard classifiers, a number of data sources have the natural form of structured data interchange formats (e.g., security logs in JSON/XML format). Existing methods, such as in Hierarchical Multiple Instance Learning (HMIL), allow learning from such data in their raw form. However, the explanation of the classifiers trained on raw structured data remains largely unexplored. By treating these models as sub-set selections problems, we demonstrate how interpretable explanations, with favourable properties, can be generated using computationally efficient algorithms. We compare to an explanation technique adopted from graph neural networks showing an order of magnitude speed-up and higher-quality explanations.  ( 2 min )
    Topological Signal Processing using the Weighted Ordinal Partition Network. (arXiv:2205.08349v2 [stat.ML] UPDATED)
    One of the most important problems arising in time series analysis is that of bifurcation, or change point detection. That is, given a collection of time series over a varying parameter, when has the structure of the underlying dynamical system changed? For this task, we turn to the field of topological data analysis (TDA), which encodes information about the shape and structure of data. The idea of utilizing tools from TDA for signal processing tasks, known as topological signal processing (TSP), has gained much attention in recent years, largely through a standard pipeline that computes the persistent homology of the point cloud generated by the Takens' embedding. However, this procedure is limited by computation time since the simplicial complex generated in this case is large, but also has a great deal of redundant data. For this reason, we turn to a more recent method for encoding the structure of the attractor, which constructs an ordinal partition network (OPN) representing information about when the dynamical system has passed between certain regions of state space. The result is a weighted graph whose structure encodes information about the underlying attractor. Our previous work began to find ways to package the information of the OPN in a manner that is amenable to TDA; however, that work only used the network structure and did nothing to encode the additional weighting information. In this paper, we take the next step: building a pipeline to analyze the weighted OPN with TDA and showing that this framework provides more resilience to noise or perturbations in the system and improves the accuracy of the dynamic state detection.  ( 3 min )
    Bayesian regularization of empirical MDPs. (arXiv:2208.02362v1 [cs.LG])
    In most applications of model-based Markov decision processes, the parameters for the unknown underlying model are often estimated from the empirical data. Due to noise, the policy learnedfrom the estimated model is often far from the optimal policy of the underlying model. When applied to the environment of the underlying model, the learned policy results in suboptimal performance, thus calling for solutions with better generalization performance. In this work we take a Bayesian perspective and regularize the objective function of the Markov decision process with prior information in order to obtain more robust policies. Two approaches are proposed, one based on $L^1$ regularization and the other on relative entropic regularization. We evaluate our proposed algorithms on synthetic simulations and on real-world search logs of a large scale online shopping store. Our results demonstrate the robustness of regularized MDP policies against the noise present in the models.  ( 2 min )
    Pareto Smoothed Importance Sampling. (arXiv:1507.02646v8 [stat.CO] UPDATED)
    Importance weighting is a general way to adjust Monte Carlo integration to account for draws from the wrong distribution, but the resulting estimate can be highly variable when the importance ratios have a heavy right tail. This routinely occurs when there are aspects of the target distribution that are not well captured by the approximating distribution, in which case more stable estimates can be obtained by modifying extreme importance ratios. We present a new method for stabilizing importance weights using a generalized Pareto distribution fit to the upper tail of the distribution of the simulated importance ratios. The method, which empirically performs better than existing methods for stabilizing importance sampling estimates, includes stabilized effective sample size estimates, Monte Carlo error estimates, and convergence diagnostics. The presented Pareto $\hat{k}$ finite sample convergence rate diagnostic is useful for any Monte Carlo estimator.  ( 3 min )
    An Optimal Likelihood Free Method for Biological Model Selection. (arXiv:2208.02344v1 [q-bio.QM])
    Systems biology seeks to create math models of biological systems to reduce inherent biological complexity and provide predictions for applications such as therapeutic development. However, it remains a challenge to determine which math model is correct and how to arrive optimally at the answer. We present an algorithm for automated biological model selection using mathematical models of systems biology and likelihood free inference methods. Our algorithm shows improved performance in arriving at correct models without a priori information over conventional heuristics used in experimental biology and random search. This method shows promise to accelerate biological basic science and drug discovery.  ( 2 min )
    Agnostic Learning of General ReLU Activation Using Gradient Descent. (arXiv:2208.02711v1 [cs.LG])
    We provide a convergence analysis of gradient descent for the problem of agnostically learning a single ReLU function under Gaussian distributions. Unlike prior work that studies the setting of zero bias, we consider the more challenging scenario when the bias of the ReLU function is non-zero. Our main result establishes that starting from random initialization, in a polynomial number of iterations gradient descent outputs, with high probability, a ReLU function that achieves a competitive error guarantee when compared to the error of the best ReLU function. We also provide finite sample guarantees, and these techniques generalize to a broader class of marginal distributions beyond Gaussians.  ( 2 min )
    Towards Understanding Mixture of Experts in Deep Learning. (arXiv:2208.02813v1 [cs.LG])
    The Mixture-of-Experts (MoE) layer, a sparsely-activated model controlled by a router, has achieved great success in deep learning. However, the understanding of such architecture remains elusive. In this paper, we formally study how the MoE layer improves the performance of neural network learning and why the mixture model will not collapse into a single model. Our empirical results suggest that the cluster structure of the underlying problem and the non-linearity of the expert are pivotal to the success of MoE. To further understand this, we consider a challenging classification problem with intrinsic cluster structures, which is hard to learn using a single expert. Yet with the MoE layer, by choosing the experts as two-layer nonlinear convolutional neural networks (CNNs), we show that the problem can be learned successfully. Furthermore, our theory shows that the router can learn the cluster-center features, which helps divide the input complex problem into simpler linear classification sub-problems that individual experts can conquer. To our knowledge, this is the first result towards formally understanding the mechanism of the MoE layer for deep learning.  ( 2 min )

  • Open

    [D] Working in the industry and coder recommendation (lucidrains, crowsonkb etc)
    ever since i started to work after phd, i'm noticing more and more that engineering customized systems is crucial (minor details like initializations, learning rates, schedulers etc can save or waste hundreds of hours), and writing bad, nonmodular code is one of the worst offenders in killing productivity. also, i work in generative modeling and noticed the whole community relies on a handful of people's code, passed over again and again in hundreds of papers (diffusion, stylegan based work, a lot of gan implementations, transformers etc). i'm not saying every new work should rewrite their codebase from scratch, but sometimes i try to test out code and modify it, and it is actually easier if had just written the whole thing (or parts of it i need to have control over) from scratch. also, i don't believe you actually rewrite everything from scratch, but bring together lego blocks and expand (a good example is how this gentleman implemented tons of gans, it's essentially compounding of knowledge where each new paper is usually only slightly different from the previous ones: https://github.com/eriklindernoren) recently, i started studying lucidrains' (https://github.com/lucidrains) and crowsonkb's (https://github.com/crowsonkb) code. i sit down, put the paper pdf one side and the code another, act like it's flashcards, hide the code, and try to rewrite the correct function. maybe it's a terrible way of learning (i already know the method described in the paper, but cannot implement it at this point), but seems to help (open to suggestions!). the people i mentioned above code like poetry (there is always some errors and not always faithful to the original implementations, but that's ok). i'm wondering do you know anyone like these guys i can just absorb information from? can be any kind of machine learning. i use only python, and like pytorch, tf 2 (hate tf 1), and started dipping my toe in jax. submitted by /u/onzanzo [link] [comments]  ( 89 min )
    [D] Have you responded to your NeurIPS22 rebuttals?
    If you are reviewing for NeurIPS this year, have you already read & responded to the rebuttals posted by the authors? submitted by /u/OpeningVariable [link] [comments]  ( 113 min )
    [D] What is the current SOTA in multi object 3d bounding box detection that is not self-driving based
    The only work that I have seen is Objectron, and that is definitely not open source. I am simply not able to find a generic paper that does 3d bounding box regression for a multi object scene submitted by /u/soulslicer0 [link] [comments]  ( 107 min )
    [D] Why is ML research so experimental?
    I'm still a bit of an ML noob, so this might be my inexperience talking, but why is so much research in ML experimental? My understanding is that areas such as physics have a strong experimental branch because they study already existing systems, but this doesn't seem to be the case with ML. I mean, we study mathematical objects, so it seems to me that we should be trying to understand them as such. ​ Like, if someone wants to propose a shortest path algorithm, they report its time complexity, not that it took 1min on average to run it, right? submitted by /u/apple_tau [link] [comments]  ( 94 min )
    What do you think is the place of Googles Carbon in ML? [D]
    Is there any place at all, taking C and C++ into consideration submitted by /u/ZuleZI [link] [comments]  ( 110 min )
    [D] Concept of collaborative open-source books for AI/DL
    Most of the knowledge base of modern AI/DL lives in papers, not on traditional books, unlike many other fields. This is mostly because AI/DL is so fast-paced, it is nearly impossible to write up-to-date book and keep it up-to-date. Publishing books the formal way requires tremendous effort by authors and publishing agency. Some have done it, like Goodfellow, Courville, Bengio and Kevin Murphy. But they are also likely to be outdated within few years as new algorithms emerge. Papers are difficult to read for non-experts/moderately-skilled workforces. Sometimes, different papers have very different notation and writing style which is confusing. Different authors have different "mental model" of the same concepts. So they aren't really unified. Is it possible to have open-source collaborative books (maybe a latex project hosted on Github, for example) where people (original authors or others) can submit new algorithms or changes as they appear in conferences and a group of "book maintainers" merge them depending on whether their notations/interpretations are compatible with the rest of the book. It's like Wikipedia, but much more curated and geared toward specific topic(s). Q: Is there any such successful projects like this, specifically for AI/DL ? submitted by /u/dasayan05 [link] [comments]  ( 88 min )
    [D] Lessons From Deploying Deep Learning To Production
    I used to think that machine learning was about the models. Actually, machine learning in production is about pipelines. One of the best predictors of success is the ability to effectively iterate on your model pipeline. That doesn't just mean iterating quickly, but also iterating intelligently. The second part is crucial, otherwise you end up with a pipeline that produces bad models very quickly. https://thegradient.pub/lessons-from-deploying-deep-learning-to-production/ submitted by /u/pgao_aquarium [link] [comments]  ( 87 min )
    [D] Journal taking long time to review. Editor/staff does not communicate to authors. What should we do?
    A ML/DL journal (keeping it anonymous for now) is taking a very long time to review a submitted paper. The journal's speed metrics page mentions that average time of review is 6-7 weeks. Our paper has been in review for more than 21 weeks. We have tried communicating with the editor three weeks ago, and also emailed the staff two weeks ago. No one has replied yet. We also spoke with a support official via chat and he/she mentions, "Rest assured that the Editors are doing their best to expedite the process. Once the Editors have completed and evaluated all the reviewer recommendation, they will provide the decision in due course." It has been one more week since this conversation and we have not received any communication yet. Yesterday we sent out another mail (as a reply to the previous email thread), but no one has replied yet. No one seems to be very open to communication. Can anyone please tell us what to do? submitted by /u/FastestLearner [link] [comments]  ( 112 min )
    [P] New Search Engine for Python ML Docs
    So I’ve been getting tired of googling and getting stackoverflow when I already know what library I want, and not being able to search those libraries docs because of their rudimentary keyword based searches. Thus, I decided to make a search tool for open-source python libraries (with a focus on ML libraries, since that's mostly what I work on) thats curated for actual developers and permits natural language queries. I’m gonna keep this free as long as I can, so it'd be wonderful to get feedback from anybody who'd be up to give it a try. Check it out at https://www.pysearch.com and please feel free to share with anybody else you know who might benefit from this! submitted by /u/oodmb [link] [comments]  ( 89 min )
    [D] Learning path for Machine Learning.
    Hi! I've decided to enter the black hole known as "machine learning" and after scowering through the reddit I came accross this lovely post: https://www.reddit.com/r/MachineLearning/comments/5z8110/d_a_super_harsh_guide_to_machine_learning/?ref=share&ref_source=link I noticed some people suggest that a better way to get started would be reading "Introduction to Statistical Learning" instead. I was wondering which chapters of introduction to statisical learning are a must-read before starting the elements of statistical learning? I was also curious as to wether there were other learning paths you all would suggest in contrary to the post I shared. I have pre-req math up until calculus 3 (vector calc) and linear algebra knowledge; I have also been coding for roughly 6 months in python. Thank you for your help. Have a good day! submitted by /u/h3cker999 [link] [comments]  ( 88 min )
    [D] Accessing/watching recorded ICML 2022 paper presentations?
    Hello, I would like to watch the talks/videos for accepted ICML 2022 papers. In the past, these used to be available for free at https://slideslive.com/library. For example, the oral presentations (https://icml.cc/virtual/2022/events/oral) cannot be accessed without registration. However, with the conference being over, registrations are closed already. Any ideas and tips on how to watch the videos would be very appreciated. Thanks! submitted by /u/solingermuc [link] [comments]  ( 88 min )
    [R] Questions About ACL Rolling Review Experience
    Hi all, I recently had some bad experiences with the ACL Rolling Review (ARR) and I wanted to know if my experience was typical and if there is anything I can do: - I've emailed ARR multiple times and I've never gotten a response, whether it was their support, tech, or editors email. These emails have included a request for tech support (I couldn't attach software to my submission) and a request for the status of reviews. - I received a meta-review (2) that gave a much lower score than any of the review scores I received (3.5, 3.5, 4, 4) which all had medium to high confidence (3, 5, 4, 4). The weaknesses and strengths given in the meta-review were different than those in the other reviews, which leads me to believe the meta-review was written like an independent review. The weaknesses given also did not seem to justify my low meta-review score. Has anyone else had similar experiences with ARR and does anyone have any advice about what to do? submitted by /u/Chrysomallo [link] [comments]  ( 90 min )
    [D] Cheap production-grade GPU in cloud
    We’re currently using AWS EKS with GPU enabled VMs to train our models and host the service that uses them to serve inferences, but the costs are killing us, so recently I’ve been looking for alternatives. Most of solutions I’ve found are either not that different from AWS in terms of pricing, or new, and I’m anxious about migrating our setup to something that could one day teleport our work to the trash can because they’ve run out of investor money or tell me that they can’t provision me a GPU because their data center doesn’t have any left. Do you guys have any recommendations for a cloud GPU provider that’s cheaper than AWS, but proven and reliable? submitted by /u/rj00na [link] [comments]  ( 89 min )
    [D] What are your sources of information to stay updated on the latest ML tools?
    Hello everyone, I am trying to assess what would be the best sources of information to remain updated on the latest ML tools / frameworks. Could you share what is your favorite media category? If one option is not present, it would be great if you could write it down :) View Poll submitted by /u/Separate-Still3770 [link] [comments]  ( 115 min )
    [Project] Face Recognition for 520 people
    I want to create a face detection network for a dataset of around 520 people. I have the code ready for the face detection and all the data loaders but I am struggling with which model/approach to go for. I have roughly about 25-30 pictures per person so what would be the most accurate way to go about this? submitted by /u/Normal_Gift927 [link] [comments]  ( 92 min )
    [Project] Project ideas for Web + AI (ML/Deep Learning)
    I have to make my final year project. I am proficient in full-stack web development and I need to make a project which also uses AI (ML or Deep Learning). Can you all suggest a good and useful project? Thanks in advance. submitted by /u/piyush_saha [link] [comments]  ( 111 min )
    [P] New book: Understanding Deep Learning
    Hi all, I've been writing a new textbook. It's titled "Understanding Deep Learning" and will be published by MIT press. A partial draft is now available at: https://udlbook.github.io/udlbook/ It's not the most applied book (it has no code) and it's not the most theoretical book (it has no proofs). The goal is exactly as the title suggests -- to allow the reader to understand the core ideas underpinning modern deep learning techniques in the simplest way. To this end, I've drawn a lot of new figures, and tried to come up with new and clearer explanations rather than rehash existing descriptions. I would love feedback from: Students. Which parts did you find confusing or ambiguous? Instructors. Will this book help your teaching? If not, then how could it be improved? Experts. Are there any glaring absences or mistakes? Please feel free to share and redistribute this link as you see fit. The more people that read this draft, the better the final product will be. submitted by /u/SimonJDPrince [link] [comments]  ( 90 min )
    [D] VQ-VAE with PixelCNN prior ?
    What does it mean to combine PixelCNN with the VQ-VAE model ? ( and how do you it ? ) submitted by /u/rishok [link] [comments]  ( 105 min )
    [D] Book Recommendation
    Cam you recommend the best book for learning information theory? I am a psych grad student, doing a lot of work with machine learning, and while I think I have educated myself in linear algebra, I have heard that it is also useful to learn information theory if one is to work with deep learning and related topics. Can you recommend some resources to learn information theory? submitted by /u/Hub_Pli [link] [comments]  ( 87 min )
    [D] How are Chinese universities like Tsinghua and PKU for ML PhD
    An offshoot from the thread discussing Canadian and European unis for ML phds. Lots of papers come from Chinese Universities, even smaller ones like Xiamen U, but then again churning out papers en masse isn’t a metric we should value too much. How is the international recognition of a degree from these places? submitted by /u/SocialEngineeeing [link] [comments]  ( 88 min )
    [P]Nash Finder - find Nash equilibrium for all games
    https://github.com/lansiz/nash-finder This program helps to find Nash equilibrium (NE) for any type of games. It is especially useful for those games with more than two players, which oftentimes are unsolvable. Example 1: find NE for two-person games ​ Payoff bimatrix of two-person game import grm game = grm.Game() # two playeys, and each player uses THREE pure strategies game.player_join(grm.Player(3)) game.player_join(grm.Player(3)) game.player_init_mixed_strategies() # assign the payoff (define the payoff function) # player 1 game.player_assign_payoff(1, "11", -231) game.player_assign_payoff(1, "12", -505) game.player_assign_payoff(1, "13", 525) game.player_assign_payoff(1, "21", -552) game.player_assign_payoff(1, "22", 831) game.player_assign_payoff(1, "23", -928) game.player_a…  ( 90 min )
    [P] Open sourcing my Kaggle Pipeline
    I am open sourcing my Kaggle Pipeline for Tabular Data Competitions. It is the result of hundreds of hours I have spent working through various competitions. This project will fast forward journey of a Kaggle newbie by several months. github: https://github.com/arnabbiswas1/kaggle_pipeline_tps_aug_22 Kaggle Discussion: https://www.kaggle.com/competitions/tabular-playground-series-aug-2022/discussion/341120 submitted by /u/abiswa [link] [comments]  ( 87 min )
    [D] Free cloud GPU options in 2022?
    We all know Colab, Gradient, Kaggle, etc. Any obscure/new free cloud GPU providers that are not talked about enough? Even if they're not ultra powerful. submitted by /u/No_Application_5581 [link] [comments]  ( 87 min )
    [D] The theory of everything
    Please critique my theory of everything. Looking to explore any logic I may be missing. https://docs.google.com/document/d/1lbrExCLuLh9yWvUPG_gx9l2bTEay-7naT2hgBJUU5zU/edit submitted by /u/averythomas [link] [comments]  ( 87 min )
    [D] Beginner in machine learning and feeling lost
    I am a beginner with little experience in machine learning and I'm thinking of starting a project with my beginners mates (object detection project). Although I have a background in deep learning, and computer vision (I took Kaggle's courses), I have never applied what I learned and have no idea what I should do next, so I would appreciate any suggestions, advice, or mentorship you could provide. submitted by /u/this-is-the-admin [link] [comments]  ( 87 min )
  • Open

    Trouble installing Arcade Learning Environment (atari library) on a remote machine
    I used `pip install gym[atari]` to install the ALE on a machine on papersapce. However I am unable to run my code using the Atari library, this is the error message I get : `File "/home/paperspace/.local/lib/python3.8/site-packages/gym/envs/atari/environment.py", line 196, in seed self.ale.loadROM(getattr(roms, self._game)) RuntimeError: Failed to initialize SDL` I had no issues installing and running ALE on my local machine but somehow it doesn't work on the remote machine. Could use a helping hand please, let me know if you've ever had this issue or if you know how to solve it. Thanks in advance. submitted by /u/youneskamel2 [link] [comments]  ( 87 min )
    Contextual Bandit Math
    Is there a simple to digest resource to understand the math behind contextual bandits and how it works. I understand UCB. Also, I am following this good text https://arxiv.org/abs/1904.07272, but it's thick and takes time to develop an intuition for the algorithms. And almost all videos and blogs are a tease! Thanks in advance. submitted by /u/sap2022 [link] [comments]  ( 86 min )
    Best model-based method for robotics environment?
    I am looking to solve the dm-control manipulator environment and have been struggling when using SAC or PPO, after a billion time steps the agent still isn't learning. So was going to try a model based method such as MPPI but since I'm not as familiar with model based methods I wanted to know what the state of the art is, preferably something we'll documented too would be helpful :) submitted by /u/SuperDuperDooken [link] [comments]  ( 86 min )
    How do parallel environments work?
    Hi, I'm trying to understand how using multiple running threads works. Say you have 12 environments. As far as I understood, you pass a 12 x n vector as actions in the step function. Then, the step functions gives you back a 12 x n vector as observation, rewards, etc. Is this correct? submitted by /u/No_Possibility_7588 [link] [comments]  ( 86 min )
    Why is my DQN cartpole not learning?
    I coded in a DQN (without any target network). For some reason, the algorithm fails to learn any meaningful policy. Here's my code. I will highly appreciate any and all suggestions and criticisms :) ​ #!/usr/bin/env python # coding: utf-8 # In[66]: # Here we import all libraries import numpy as np import gym import matplotlib.pyplot as plt import os import torch import random from torch import nn from torch.utils.data import DataLoader from torchvision import datasets, transforms from collections import deque import sys env = gym.make("CartPole-v0") # In[67]: #Hyperparameters episodes = 20000 eps = 1.0 learning_rate = 0.001 tot_rewards = [] tot_loss = [] decay_val = 0.0001 mem_size = 5000 batch_size = 100 gamma = 0.99 max_steps = 200 # In[68]: class NeuralNetwork(nn.Module): def __init__…  ( 88 min )
  • Open

    Nothing going on here, nobody is becoming conscious...
    submitted by /u/TheExtimate [link] [comments]  ( 85 min )
    The face of grief as it seen by ruDALL-E Kandinsky
    submitted by /u/knight_hildebrandt [link] [comments]  ( 85 min )
    AI tool to write and explain Excel formulas (www.tersho.com)
    submitted by /u/apugoneappu [link] [comments]  ( 86 min )
    Website to generate Code Snippets, Regexes, Linux & Git & SQL Commands, HTML and CSS from a written description. Furthermore translate code snippets to many languages and get a regex explained in plain english. Moreover you can fix broken code snippets. All with the help of AI 🤖
    https://preview.redd.it/cla8bb3lqqf91.jpg?width=1256&format=pjpg&auto=webp&s=e277f9013fff22c6e2e12128c46058d0a81c1974 Programming Function from Description Code to Explanation Fix invalid Code Translate Languages Class from Description Get Language from Code Function from Docstring Helpers Regex from Description Regex to Explanation Linux Command Get time complexity Git Command from Description Database Text Description to SQL Command Web Generate HTML from Description CSS from Description Meta Tags from Description I think this could be helpful to a lot of people (especially for beginner programmers). You can check out all functionalities on your own here: programming-helper.com Have fun using the tool ❤️ submitted by /u/Capital_Revolution35 [link] [comments]  ( 86 min )
    Any good pixel art generators?
    I make Minecraft mods in my free time but am terrible at art so I always pay an artists to make pixel art sprites for me. Is there an AI I can use or pay for that will generate 16x16 high quality pixel art? I saw that Dalle-2 was very impressive at this but obviously it is not available to me : submitted by /u/Swftness503 [link] [comments]  ( 86 min )
    Found a nice experiment on using sensor fusion and machine learning to detect smoke!
    Found a nice experiment on using sensor fusion and machine learning to detect smoke and get notified if the fire starts. Check this out: https://www.hackster.io/stefanblattmann/real-time-smoke-detection-with-ai-based-sensor-fusion-1086e6 submitted by /u/Potsieramirez [link] [comments]  ( 86 min )
    A Semantic Search Engine for Python ML Docs
    So I’ve been getting tired of googling and getting stackoverflow when I already know what library I want, and not being able to search those libraries docs because of their rudimentary keyword based searches. Thus, I decided to make a search tool for open-source python libraries (with a focus on ML libraries, since that’s mostly what I work on) thats curated for actual developers and permits natural language queries. I’m gonna keep this free as long as I can, so it'd be wonderful to get feedback from anybody who'd be up to give it a try. Check it out at https://www.pysearch.com and please feel free to share with anybody else you know who might benefit from this! submitted by /u/oodmb [link] [comments]  ( 86 min )
    high resolution AI Art Generator
    are there any AI-image generator platforms to produce higher resolution pictures? I'm talking around 3000x3000 that I could use for a commercial project. Willing to pay. How does the copyright / ownership of the project work? submitted by /u/hampark [link] [comments]  ( 86 min )
    When AI is the inventor who gets the patent?
    submitted by /u/originalmetaverse [link] [comments]  ( 86 min )
    Buddha praying surrounded by angels -. Midjourney
    submitted by /u/manomanolito [link] [comments]  ( 85 min )
    Cosmic Canal By BeyondImagination
    submitted by /u/widgia [link] [comments]  ( 91 min )
    AI Dream 69 - Short AI Animation Bubble Nebula
    submitted by /u/LordPewPew777 [link] [comments]  ( 90 min )
    looking for something to generate image from my own image library
    I've been looking around but haven't found anything to he able to input a library if my own images and then have a new image generated from the set ... I'm not a coder, I do have some rudimentary skills to be able to install and run something ... but I have delved into it and still haven't found something that does this..plenty of text to image, generate images based on an already trained library...but what I really want is to throw a few hundred images into something and have it spit out a high-res image based in my own inputs. Any suggestions for what I can use for this? submitted by /u/IrikanjiToys [link] [comments]  ( 87 min )
    How Gran Turismo 7's 'Sophy' AI Actually Works
    submitted by /u/GET_TUDA_CHOPPA [link] [comments]  ( 90 min )
    Amazon’s 20B-Parameter Alexa Model Sets New Marks In Few-Shot Learning Along With Low Carbon Footprint During Training (One-Fifth of GPT-3’s)
    Some of the most significant developments in AI have come through supervised learning. It speaks about computer learning models that have been trained using annotated data. However, reliance on data annotation is increasingly untenable as the size of commercial AI models grows. The new paradigm of generalizable intelligence, in which models can pick up new ideas and transfer knowledge from one language or task to another without much human input, is being investigated by researchers at Alexa AI. These models enable researchers to create new features and enhance Alexa across several languages quickly. As part of this change, Amazon has introduced Alexa Teacher Models (AlexaTM), which are massive transformer-based multilingual language models. Without additional human guidance, AlexaTM can learn a task in a new language with just a few instances and pick it up quickly. ✅ With an encoder-decoder architecture — rather than decoder only — the Alexa Teacher Model excels other large language models on few-shot tasks such as summarization and machine translation. ✅ AlexaTM 20B also tops GPT-3 by being multilingual, supporting Arabic, English, French, German, Hindi, Italian, Japanese, Marathi, Portuguese, Spanish, Tamil, and Telugu. ✅ Its carbon footprint during training is only one-fifth of GPT-3’s Continue reading| Checkout the paper submitted by /u/ai-lover [link] [comments]  ( 91 min )
    Spiral Galaxy
    submitted by /u/nalr00n [link] [comments]  ( 85 min )
  • Open

    Dive Into AI, Avatars and the Metaverse With NVIDIA at SIGGRAPH
    Innovative technologies in AI, virtual worlds and digital humans are shaping the future of design and content creation across every industry. Experience the latest advances from NVIDIA in all these areas at SIGGRAPH, the world’s largest gathering of computer graphics experts, running Aug. 8-11. At the conference, creators, developers, engineers, researchers and students will see Read article > The post Dive Into AI, Avatars and the Metaverse With NVIDIA at SIGGRAPH appeared first on NVIDIA Blog.  ( 6 min )
    What Is Direct and Indirect Lighting?
    Imagine hiking to a lake on a summer day — sitting under a shady tree and watching the water gleam under the sun. In this scene, the differences between light and shadow are examples of direct and indirect lighting. The sun shines onto the lake and the trees, making the water look like it’s shimmering Read article > The post What Is Direct and Indirect Lighting? appeared first on NVIDIA Blog.  ( 8 min )
    Pinterest Boosts Home Feed Engagement 16% With Switch to GPU Acceleration of Recommenders
    Pinterest has engineered a way to serve its photo-sharing community more of the images they love. The social-image service, with more than 400 million monthly active users, has trained bigger recommender models for improved accuracy at predicting people’s interests. Pinterest handles hundreds of millions of user requests an hour on any given day. And it Read article > The post Pinterest Boosts Home Feed Engagement 16% With Switch to GPU Acceleration of Recommenders appeared first on NVIDIA Blog.  ( 6 min )
    Rush Into August This GFN Thursday With 38 New Games on GeForce NOW
    It’s the first GFN Thursday of the month and you know the drill — GeForce NOW is bringing a big batch of games to the cloud. Get ready for 38 exciting titles like Saints Row and Rumbleverse arriving on the GeForce NOW library in August. Members can kick off the month streaming 13 new games Read article > The post Rush Into August This GFN Thursday With 38 New Games on GeForce NOW appeared first on NVIDIA Blog.  ( 6 min )
  • Open

    Introducing the Google Universal Image Embedding Challenge
    Posted by Bingyi Cao, Software Engineer, Google Research, and Mário Lipovský, Software Engineer, Google Lens Computer vision models see daily application for a wide variety of tasks, ranging from object recognition to image-based 3D object reconstruction. One challenging type of computer vision problem is instance-level recognition (ILR) — given an image of an object, the task is to not only determine the generic category of an object (e.g., an arch), but also the specific instance of the object (”Arc de Triomphe de l'Étoile, Paris, France”). Previously, ILR was tackled using deep learning approaches. First, a large set of images was collected. Then a deep model was trained to embed each image into a high-dimensional space where similar images have similar representations. Finally, the …  ( 25 min )
  • Open

    Optimal pricing for maximum profit using Amazon SageMaker
    This is a guest post by Viktor Enrico Jeney, Senior Machine Learning Engineer at Adspert. Adspert is a Berlin-based ISV that developed a bid management tool designed to automatically optimize performance marketing and advertising campaigns. The company’s core principle is to automate maximization of profit of ecommerce advertising with the help of artificial intelligence. The […]  ( 11 min )
  • Open

    New Search Engine for Python ML Docs
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    How to disappear a platypus
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    Bonus: There can be only one
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    Practical Intro to Docker for Data Scientists
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    The Role of Artificial Intelligence in The Packaging Industry
    Artificial intelligence (AI) is a technology that can be used in many different industries to help businesses achieve their goals. For…  ( 12 min )
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    Combinatorial Causal Bandits. (arXiv:2206.01995v2 [cs.LG] UPDATED)
    In combinatorial causal bandits (CCB), the learning agent chooses at most $K$ variables in each round to intervene, collects feedback from the observed variables, with the goal of minimizing expected regret on the target variable $Y$. Different from all prior studies on causal bandits, CCB needs to deal with exponentially large action space. We study under the context of binary generalized linear models (BGLMs) with a succinct parametric representation of the causal models. We present the algorithm BGLM-OFU for Markovian BGLMs (i.e. no hidden variables) based on the maximum likelihood estimation method, and show that it achieves $O(\sqrt{T}\log T)$ regret, where $T$ is the time horizon. For the special case of linear models with hidden variables, we apply causal inference techniques such as the do-calculus to convert the original model into a Markovian model, and then show that our BGLM-OFU algorithm and another algorithm based on the linear regression both solve such linear models with hidden variables. Our novelty includes (a) considering the combinatorial intervention action space, (b) considering general causal models including ones with hidden variables, (c) integrating and adapting techniques from diverse studies such as generalized linear bandits and online influence maximization, and (d) not relying on unrealistic assumptions such as knowing the joint distribution of the parents of $Y$ under all interventions used in some prior studies.
    GraphFramEx: Towards Systematic Evaluation of Explainability Methods for Graph Neural Networks. (arXiv:2206.09677v3 [cs.LG] UPDATED)
    As one of the most popular machine learning models today, graph neural networks (GNNs) have attracted intense interest recently, and so does their explainability. Users are increasingly interested in a better understanding of GNN models and their outcomes. Unfortunately, today's evaluation frameworks for GNN explainability often rely on synthetic datasets, leading to conclusions of limited scope due to a lack of complexity in the problem instances. As GNN models are deployed to more mission-critical applications, we are in dire need for a common evaluation protocol of explainability methods of GNNs. In this paper, we propose, to our best knowledge, the first systematic evaluation framework for GNN explainability, considering explainability on three different "user needs:" explanation focus, mask nature, and mask transformation. We propose a unique metric that combines the fidelity measures and classify explanations based on their quality of being sufficient or necessary. We scope ourselves to node classification tasks and compare the most representative techniques in the field of input-level explainability for GNNs. For the widely used synthetic benchmarks, surprisingly shallow techniques such as personalized PageRank have the best performance for a minimum computation time. But when the graph structure is more complex and nodes have meaningful features, gradient-based methods, in particular Saliency, are the best according to our evaluation criteria. However, none dominates the others on all evaluation dimensions and there is always a trade-off. We further apply our evaluation protocol in a case study on eBay graphs to reflect the production environment.
    Beyond neural scaling laws: beating power law scaling via data pruning. (arXiv:2206.14486v2 [cs.LG] UPDATED)
    Widely observed neural scaling laws, in which error falls off as a power of the training set size, model size, or both, have driven substantial performance improvements in deep learning. However, these improvements through scaling alone require considerable costs in compute and energy. Here we focus on the scaling of error with dataset size and show how both in theory and practice we can break beyond power law scaling and reduce it to exponential scaling instead if we have access to a high-quality data pruning metric that ranks the order in which training examples should be discarded to achieve any pruned dataset size. We then test this new exponential scaling prediction with pruned dataset size empirically, and indeed observe better than power law scaling performance on ResNets trained on CIFAR-10, SVHN, and ImageNet. Given the importance of finding high-quality pruning metrics, we perform the first large-scale benchmarking study of ten different data pruning metrics on ImageNet. We find most existing high performing metrics scale poorly to ImageNet, while the best are computationally intensive and require labels for every image. We therefore developed a new simple, cheap and scalable self-supervised pruning metric that demonstrates comparable performance to the best supervised metrics. Overall, our work suggests that the discovery of good data-pruning metrics may provide a viable path forward to substantially improved neural scaling laws, thereby reducing the resource costs of modern deep learning.
    Deep Learning-Enabled Semantic Communication Systems with Task-Unaware Transmitter and Dynamic Data. (arXiv:2205.00271v2 [cs.IT] UPDATED)
    Existing deep learning-enabled semantic communication systems often rely on shared background knowledge between the transmitter and receiver that includes empirical data and their associated semantic information. In practice, the semantic information is defined by the pragmatic task of the receiver and cannot be known to the transmitter. The actual observable data at the transmitter can also have non-identical distribution with the empirical data in the shared background knowledge library. To address these practical issues, this paper proposes a new neural network-based semantic communication system for image transmission, where the task is unaware at the transmitter and the data environment is dynamic. The system consists of two main parts, namely the semantic coding (SC) network and the data adaptation (DA) network. The SC network learns how to extract and transmit the semantic information using a receiver-leading training process. By using the domain adaptation technique from transfer learning, the DA network learns how to convert the data observed into a similar form of the empirical data that the SC network can process without retraining. Numerical experiments show that the proposed method can be adaptive to observable datasets while keeping high performance in terms of both data recovery and task execution.
    Naive Few-Shot Learning: Sequence Consistency Evaluation. (arXiv:2205.12013v2 [cs.AI] UPDATED)
    Cognitive psychologists often use the term $\textit{fluid intelligence}$ to describe the ability of humans to solve novel tasks without any prior training. In contrast to humans, deep neural networks can perform cognitive tasks only after extensive (pre-)training with a large number of relevant examples. Motivated by fluid intelligence research in the cognitive sciences, we built a benchmark task which we call sequence consistency evaluation (SCE) that can be used to address this gap. Solving the SCE task requires the ability to extract simple rules from sequences, a basic computation that in humans, is required for solving various intelligence tests. We tested $\textit{untrained}$ (naive) deep learning models in the SCE task. Specifically, we tested two networks that can learn latent relations, Relation Networks (RN) and Contrastive Predictive Coding (CPC). We found that the latter, which imposes a causal structure on the latent relations performs better. We then show that naive few-shot learning of sequences can be successfully used for anomaly detection in two different tasks, visual and auditory, without any prior training.
    Free Energy Evaluation Using Marginalized Annealed Importance Sampling. (arXiv:2204.03784v2 [stat.ML] UPDATED)
    The evaluation of the free energy of a stochastic model is considered a significant issue in various fields of physics and machine learning. However, the exact free energy evaluation is computationally infeasible because the free energy expression includes an intractable partition function. Annealed importance sampling (AIS) is a type of importance sampling based on the Markov chain Monte Carlo method that is similar to a simulated annealing and can effectively approximate the free energy. This study proposes an AIS-based approach, which is referred to as marginalized AIS (mAIS). The statistical efficiency of mAIS is investigated in detail based on theoretical and numerical perspectives. Based on the investigation, it is proved that mAIS is more effective than AIS under a certain condition.
    Machine Learning Training on a Real Processing-in-Memory System. (arXiv:2206.06022v2 [cs.AR] UPDATED)
    Training machine learning algorithms is a computationally intensive process, which is frequently memory-bound due to repeatedly accessing large training datasets. As a result, processor-centric systems (e.g., CPU, GPU) suffer from costly data movement between memory units and processing units, which consumes large amounts of energy and execution cycles. Memory-centric computing systems, i.e., computing systems with processing-in-memory (PIM) capabilities, can alleviate this data movement bottleneck. Our goal is to understand the potential of modern general-purpose PIM architectures to accelerate machine learning training. To do so, we (1) implement several representative classic machine learning algorithms (namely, linear regression, logistic regression, decision tree, K-means clustering) on a real-world general-purpose PIM architecture, (2) characterize them in terms of accuracy, performance and scaling, and (3) compare to their counterpart implementations on CPU and GPU. Our experimental evaluation on a memory-centric computing system with more than 2500 PIM cores shows that general-purpose PIM architectures can greatly accelerate memory-bound machine learning workloads, when the necessary operations and datatypes are natively supported by PIM hardware. To our knowledge, our work is the first one to evaluate training of machine learning algorithms on a real-world general-purpose PIM architecture.
    Eliciting and Learning with Soft Labels from Every Annotator. (arXiv:2207.00810v2 [cs.LG] UPDATED)
    The labels used to train machine learning (ML) models are of paramount importance. Typically for ML classification tasks, datasets contain hard labels, yet learning using soft labels has been shown to yield benefits for model generalization, robustness, and calibration. Earlier work found success in forming soft labels from multiple annotators' hard labels; however, this approach may not converge to the best labels and necessitates many annotators, which can be expensive and inefficient. We focus on efficiently eliciting soft labels from individual annotators. We collect and release a dataset of soft labels for CIFAR-10 via a crowdsourcing study ($N=248$). We demonstrate that learning with our labels achieves comparable model performance to prior approaches while requiring far fewer annotators. Our elicitation methodology therefore shows promise towards enabling practitioners to enjoy the benefits of improved model performance and reliability with fewer annotators, and serves as a guide for future dataset curators on the benefits of leveraging richer information, such as categorical uncertainty, from individual annotators.
    TSEM: Temporally Weighted Spatiotemporal Explainable Neural Network for Multivariate Time Series. (arXiv:2205.13012v2 [cs.LG] UPDATED)
    Deep learning has become a one-size-fits-all solution for technical and business domains thanks to its flexibility and adaptability. It is implemented using opaque models, which unfortunately undermines the outcome trustworthiness. In order to have a better understanding of the behavior of a system, particularly one driven by time series, a look inside a deep learning model so-called posthoc eXplainable Artificial Intelligence (XAI) approaches, is important. There are two major types of XAI for time series data, namely model-agnostic and model-specific. Model-specific approach is considered in this work. While other approaches employ either Class Activation Mapping (CAM) or Attention Mechanism, we merge the two strategies into a single system, simply called the Temporally Weighted Spatiotemporal Explainable Neural Network for Multivariate Time Series (TSEM). TSEM combines the capabilities of RNN and CNN models in such a way that RNN hidden units are employed as attention weights for the CNN feature maps temporal axis. The result shows that TSEM outperforms XCM. It is similar to STAM in terms of accuracy, while also satisfying a number of interpretability criteria, including causality, fidelity, and spatiotemporality.
    Few-Shot Cross-Lingual TTS Using Transferable Phoneme Embedding. (arXiv:2206.15427v2 [eess.AS] UPDATED)
    This paper studies a transferable phoneme embedding framework that aims to deal with the cross-lingual text-to-speech (TTS) problem under the few-shot setting. Transfer learning is a common approach when it comes to few-shot learning since training from scratch on few-shot training data is bound to overfit. Still, we find that the naive transfer learning approach fails to adapt to unseen languages under extremely few-shot settings, where less than 8 minutes of data is provided. We deal with the problem by proposing a framework that consists of a phoneme-based TTS model and a codebook module to project phonemes from different languages into a learned latent space. Furthermore, by utilizing phoneme-level averaged self-supervised learned features, we effectively improve the quality of synthesized speeches. Experiments show that using 4 utterances, which is about 30 seconds of data, is enough to synthesize intelligible speech when adapting to an unseen language using our framework.
    FlowNet-PET: Unsupervised Learning to Perform Respiratory Motion Correction in PET Imaging. (arXiv:2205.14147v3 [eess.IV] UPDATED)
    To correct for respiratory motion in PET imaging, an interpretable and unsupervised deep learning technique, FlowNet-PET, was constructed. The network was trained to predict the optical flow between two PET frames from different breathing amplitude ranges. The trained model aligns different retrospectively-gated PET images, providing a final image with similar counting statistics as a non-gated image, but without the blurring effects. FlowNet-PET was applied to anthropomorphic digital phantom data, which provided the possibility to design robust metrics to quantify the corrections. When comparing the predicted optical flows to the ground truths, the median absolute error was found to be smaller than the pixel and slice widths. The improvements were illustrated by comparing against images without motion and computing the intersection over union (IoU) of the tumors as well as the enclosed activity and coefficient of variation (CoV) within the no-motion tumor volume before and after the corrections were applied. The average relative improvements provided by the network were 64%, 89%, and 75% for the IoU, total activity, and CoV, respectively. FlowNet-PET achieved similar results as the conventional retrospective phase binning approach, but only required one sixth of the scan duration. The code and data have been made publicly available (https://github.com/teaghan/FlowNet_PET).
    Explainable Artificial Intelligence in Process Mining: Assessing the Explainability-Performance Trade-Off in Outcome-Oriented Predictive Process Monitoring. (arXiv:2203.16073v2 [cs.LG] UPDATED)
    Recently, a shift has been made in the field of Outcome-Oriented Predictive Process Monitoring (OOPPM) to use models from the eXplainable Artificial Intelligence paradigm, however the evaluation still occurs mainly through performance-based metrics not accounting for the implications and lack of actionability of the explanations. In this paper, we define explainability by the interpretability of the explanations (through the widely-used XAI properties parsimony and functional complexity) and the faithfulness of the explainability model (through monotonicity and level of disagreement). The introduced properties are analysed along the event, case, and control flow perspective that are typical of a process-based analysis. This allows to quantitatively compare, inter alia, inherently created explanations (e.g., logistic regression coefficients) with post-hoc explanations (e.g., Shapley values). Moreover, this paper contributes a guideline named X-MOP to practitioners to select the appropriate model based on the event log specifications and the task at hand, by providing insight into how the varying preprocessing, model complexity and post-hoc explainability techniques typical in OOPPM influence the explainability of the model. To this end, we benchmark seven classifiers on thirteen real-life events logs.
    AUC Maximization in the Era of Big Data and AI: A Survey. (arXiv:2203.15046v3 [cs.LG] UPDATED)
    Area under the ROC curve, a.k.a. AUC, is a measure of choice for assessing the performance of a classifier for imbalanced data. AUC maximization refers to a learning paradigm that learns a predictive model by directly maximizing its AUC score. It has been studied for more than two decades dating back to late 90s and a huge amount of work has been devoted to AUC maximization since then. Recently, stochastic AUC maximization for big data and deep AUC maximization for deep learning have received increasing attention and yielded dramatic impact for solving real-world problems. However, to the best our knowledge there is no comprehensive survey of related works for AUC maximization. This paper aims to address the gap by reviewing the literature in the past two decades. We not only give a holistic view of the literature but also present detailed explanations and comparisons of different papers from formulations to algorithms and theoretical guarantees. We also identify and discuss remaining and emerging issues for deep AUC maximization, and provide suggestions on topics for future work.
    Policy Evaluation for Temporal and/or Spatial Dependent Experiments in Ride-sourcing Platforms. (arXiv:2202.10887v2 [stat.ME] UPDATED)
    Policy evaluation based on A/B testing has attracted considerable interest in digital marketing, but such evaluation in ride-sourcing platforms (e.g., Uber and Didi) is not well studied primarily due to the complex structure of their temporal and/or spatial dependent experiments. Motivated by policy evaluation in ride-sourcing platforms, the aim of this paper is to establish causal relationship between platform's policies and outcomes of interest under a switchback design. We propose a novel potential outcome framework based on a temporal varying coefficient decision process (VCDP) model to capture the dynamic treatment effects in temporal dependent experiments. We further characterize the average treatment effect by decomposing it as the sum of direct effect (DE) and indirect effect (IE). We develop estimation and inference procedures for both DE and IE. Furthermore, we propose a spatio-temporal VCDP to deal with spatiotemporal dependent experiments. For both VCDP models, we establish the statistical properties (e.g., weak convergence and asymptotic power) of our estimation and inference procedures. We conduct extensive simulations to investigate the finite-sample performance of the proposed estimation and inference procedures. We examine how our VCDP models can help improve policy evaluation for various dispatching and dispositioning policies in Didi.
    Spatial Autoregressive Coding for Graph Neural Recommendation. (arXiv:2205.09489v2 [cs.IR] UPDATED)
    Graph embedding methods including traditional shallow models and deep Graph Neural Networks (GNNs) have led to promising applications in recommendation. Nevertheless, shallow models especially random-walk-based algorithms fail to adequately exploit neighbor proximity in sampled subgraphs or sequences due to their optimization paradigm. GNN-based algorithms suffer from the insufficient utilization of high-order information and easily cause over-smoothing problems when stacking too much layers, which may deteriorate the recommendations of low-degree (long-tail) items, limiting the expressiveness and scalability. In this paper, we propose a novel framework SAC, namely Spatial Autoregressive Coding, to solve the above problems in a unified way. To adequately leverage neighbor proximity and high-order information, we design a novel spatial autoregressive paradigm. Specifically, we first randomly mask multi-hop neighbors and embed the target node by integrating all other surrounding neighbors with an explicit multi-hop attention. Then we reinforce the model to learn a neighbor-predictive coding for the target node by contrasting the coding and the masked neighbors' embedding, equipped with a new hard negative sampling strategy. To learn the minimal sufficient representation for the target-to-neighbor prediction task and remove the redundancy of neighbors, we devise Neighbor Information Bottleneck by maximizing the mutual information between target predictive coding and the masked neighbors' embedding, and simultaneously constraining those between the coding and surrounding neighbors' embedding. Experimental results on both public recommendation datasets and a real scenario web-scale dataset Douyin-Friend-Recommendation demonstrate the superiority of SAC compared with state-of-the-art methods.
    STEADY: Simultaneous State Estimation and Dynamics Learning from Indirect Observations. (arXiv:2203.01299v2 [cs.RO] UPDATED)
    Accurate kinodynamic models play a crucial role in many robotics applications such as off-road navigation and high-speed driving. Many state-of-the-art approaches in learning stochastic kinodynamic models, however, require precise measurements of robot states as labeled input/output examples, which can be hard to obtain in outdoor settings due to limited sensor capabilities and the absence of ground truth. In this work, we propose a new technique for learning neural stochastic kinodynamic models from noisy and indirect observations by performing simultaneous state estimation and dynamics learning. The proposed technique iteratively improves the kinodynamic model in an expectation-maximization loop, where the E Step samples posterior state trajectories using particle filtering, and the M Step updates the dynamics to be more consistent with the sampled trajectories via stochastic gradient ascent. We evaluate our approach on both simulation and real-world benchmarks and compare it with several baseline techniques. Our approach not only achieves significantly higher accuracy but is also more robust to observation noise, thereby showing promise for boosting the performance of many other robotics applications.
    auton-survival: an Open-Source Package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Event Data. (arXiv:2204.07276v4 [cs.LG] UPDATED)
    Applications of machine learning in healthcare often require working with time-to-event prediction tasks including prognostication of an adverse event, re-hospitalization or death. Such outcomes are typically subject to censoring due to loss of follow up. Standard machine learning methods cannot be applied in a straightforward manner to datasets with censored outcomes. In this paper, we present auton-survival, an open-source repository of tools to streamline working with censored time-to-event or survival data. auton-survival includes tools for survival regression, adjustment in the presence of domain shift, counterfactual estimation, phenotyping for risk stratification, evaluation, as well as estimation of treatment effects. Through real world case studies employing a large subset of the SEER oncology incidence data, we demonstrate the ability of auton-survival to rapidly support data scientists in answering complex health and epidemiological questions.
    Robust Training under Label Noise by Over-parameterization. (arXiv:2202.14026v2 [cs.LG] UPDATED)
    Recently, over-parameterized deep networks, with increasingly more network parameters than training samples, have dominated the performances of modern machine learning. However, when the training data is corrupted, it has been well-known that over-parameterized networks tend to overfit and do not generalize. In this work, we propose a principled approach for robust training of over-parameterized deep networks in classification tasks where a proportion of training labels are corrupted. The main idea is yet very simple: label noise is sparse and incoherent with the network learned from clean data, so we model the noise and learn to separate it from the data. Specifically, we model the label noise via another sparse over-parameterization term, and exploit implicit algorithmic regularizations to recover and separate the underlying corruptions. Remarkably, when trained using such a simple method in practice, we demonstrate state-of-the-art test accuracy against label noise on a variety of real datasets. Furthermore, our experimental results are corroborated by theory on simplified linear models, showing that exact separation between sparse noise and low-rank data can be achieved under incoherent conditions. The work opens many interesting directions for improving over-parameterized models by using sparse over-parameterization and implicit regularization.
    Physics Constrained Flow Neural Network for Short-Timescale Predictions in Data Communications Networks. (arXiv:2112.12321v2 [cs.LG] UPDATED)
    Machine learning is gaining growing momentum in various recent models for the dynamic analysis of information flows in data communications networks. These preliminary models often rely on off-the-shelf learning models to predict from historical statistics while disregarding the physics governing the generating behaviors of these flows. This paper instead introduces Flow Neural Network (FlowNN) to improve the feature representation with learned physical bias. This is implemented by an induction layer, working upon the embedding layer, to impose the physics connected data correlations, and a self-supervised learning strategy with stop-gradient to make the learned physics universal. For the short-timescale network prediction tasks, FlowNN achieves 17% - 71% of loss decrease than the state-of-the-art baselines on both synthetic and real-world networking datasets, which shows the strength of this new approach. Code will be made available.
    Stochastic Gradient Line Bayesian Optimization for Efficient Noise-Robust Optimization of Parameterized Quantum Circuits. (arXiv:2111.07952v2 [quant-ph] UPDATED)
    Optimizing parameterized quantum circuits is a key routine in using near-term quantum devices. However, the existing algorithms for such optimization require an excessive number of quantum-measurement shots for estimating expectation values of observables and repeating many iterations, whose cost has been a critical obstacle for practical use. We develop an efficient alternative optimization algorithm, stochastic gradient line Bayesian optimization (SGLBO), to address this problem. SGLBO reduces the measurement-shot cost by estimating an appropriate direction of updating circuit parameters based on stochastic gradient descent (SGD) and further utilizing Bayesian optimization (BO) to estimate the optimal step size for each iteration in SGD. In addition, we formulate an adaptive measurement-shot strategy and introduce a technique of suffix averaging to reduce the effect of statistical and hardware noise. Our numerical simulation demonstrates that the SGLBO augmented with these techniques can drastically reduce the measurement-shot cost, improve the accuracy, and make the optimization noise-robust.
    Mapping Research Topics in Software Testing: A Bibliometric Analysis. (arXiv:2109.04086v3 [cs.DL] UPDATED)
    Background: The field of software testing is growing and rapidly-evolving. Aims: Based on keywords assigned to publications, we seek to identify predominant research topics and understand how they are connected and have evolved. Method: We apply co-word analysis to map the topology of testing research as a network where author-assigned keywords are connected by edges indicating co-occurrence in publications. Keywords are clustered based on edge density and frequency of connection. We examine the most popular keywords, summarize clusters into high-level research topics, examine how topics connect, and examine how the field is changing. Results: Testing research can be divided into 16 high-level topics and 18 subtopics. Creation guidance, automated test generation, evolution and maintenance, and test oracles have particularly strong connections to other topics, highlighting their multidisciplinary nature. Emerging keywords relate to web and mobile apps, machine learning, energy consumption, automated program repair and test generation, while emerging connections have formed between web apps, test oracles, and machine learning with many topics. Random and requirements-based testing show potential decline. Conclusions: Our observations, advice, and map data offer a deeper understanding of the field and inspiration regarding challenges and connections to explore.
    Laplacian Features for Learning with Hyperbolic Space. (arXiv:2202.06854v2 [cs.LG] UPDATED)
    Due to its geometric properties, hyperbolic space can support high-fidelity embeddings of tree- and graph-structured data. As a result, various hyperbolic networks have been developed which outperform Euclidean networks on many tasks: e.g. hyperbolic graph convolutional networks (GCN) can outperform vanilla GCN on some graph learning tasks. However, most existing hyperbolic networks are complicated, computationally expensive, and numerically unstable -- and they cannot scale to large graphs due to these shortcomings. With more and more hyperbolic networks proposed, it is becoming less and less clear what key component is necessary to make the model behave. In this paper, we propose HyLa, a simple and minimal approach to using hyperbolic space in networks: HyLa maps once from a hyperbolic-space embedding to Euclidean space via the eigenfunctions of the Laplacian operator in the hyperbolic space. We evaluate HyLa on graph learning tasks including node classification and text classification, where HyLa can be used together with any graph neural networks. When used with a linear model, HyLa shows significant improvements over hyperbolic networks and other baselines.
    Revisiting local branching with a machine learning lens. (arXiv:2112.02195v2 [math.OC] UPDATED)
    Finding high-quality solutions to mixed-integer linear programming problems (MILPs) is of great importance for many practical applications. In this respect, the refinement heuristic local branching (LB) has been proposed to produce improving solutions and has been highly influential for the development of local search methods in MILP. The algorithm iteratively explores a sequence of solution neighborhoods defined by the so-called local branching constraint, namely, a linear inequality limiting the distance from a reference solution. For a LB algorithm, the choice of the neighborhood size is critical to performance. In this work, we study the relation between the size of the search neighborhood and the behavior of the underlying LB algorithm, and we devise a leaning based framework for predicting the best size for the specific instance to be solved. Furthermore, we have also investigated the relation between the time limit for exploring the LB neighborhood and the actual performance of LB scheme, and devised a strategy for adapting the time limit. We computationally show that the neighborhood size and time limit can indeed be learned, leading to improved performances and that the overall algorithm generalizes well both with respect to the instance size and, remarkably, across instances.
    Automatic Meta-Path Discovery for Effective Graph-Based Recommendation. (arXiv:2112.12845v3 [cs.IR] UPDATED)
    Heterogeneous Information Networks (HINs) are labeled graphs that depict relationships among different types of entities (e.g., users, movies and directors). For HINs, meta-path-based recommenders (MPRs) utilize meta-paths (i.e., abstract paths consisting of node and link types) to predict user preference, and have attracted a lot of attention due to their explainability and performance. We observe that the performance of MPRs is highly sensitive to the meta-paths they use, but existing works manually select the meta-paths from many possible ones. Thus, to discover effective meta-paths automatically, we propose the Reinforcement learning-based Meta-path Selection (RMS) framework. Specifically, we define a vector encoding for meta-paths and design a policy network to extend meta-paths. The policy network is trained based on the results of downstream recommendation tasks and an early stopping approximation strategy is proposed to speed up training. RMS is a general model, and it can work with all existing MPRs. We also propose a new MPR called RMS-HRec, which uses an attention mechanism to aggregate information from the meta-paths. We conduct extensive experiments on real datasets. Compared with the manually selected meta-paths, the meta-paths identified by RMS consistently improve recommendation quality. Moreover, RMS-HRec outperforms state-of-the-art recommender systems by an average of 7% in hit ratio. The codes and datasets are available on https://github.com/Stevenn9981/RMS-HRec.
    Off-Policy Confidence Interval Estimation with Confounded Markov Decision Process. (arXiv:2202.10589v4 [stat.ML] UPDATED)
    This paper is concerned with constructing a confidence interval for a target policy's value offline based on a pre-collected observational data in infinite horizon settings. Most of the existing works assume no unmeasured variables exist that confound the observed actions. This assumption, however, is likely to be violated in real applications such as healthcare and technological industries. In this paper, we show that with some auxiliary variables that mediate the effect of actions on the system dynamics, the target policy's value is identifiable in a confounded Markov decision process. Based on this result, we develop an efficient off-policy value estimator that is robust to potential model misspecification and provide rigorous uncertainty quantification. Our method is justified by theoretical results, simulated and real datasets obtained from ridesharing companies. A Python implementation of the proposed procedure is available at https://github.com/Mamba413/cope.
    Spectral Propagation Graph Network for Few-shot Time Series Classification. (arXiv:2202.04769v2 [cs.LG] UPDATED)
    Few-shot Time Series Classification (few-shot TSC) is a challenging problem in time series analysis. It is more difficult to classify when time series of the same class are not completely consistent in spectral domain or time series of different classes are partly consistent in spectral domain. To address this problem, we propose a novel method named Spectral Propagation Graph Network (SPGN) to explicitly model and propagate the spectrum-wise relations between different time series with graph network. To the best of our knowledge, SPGN is the first to utilize spectral comparisons in different intervals and involve spectral propagation across all time series with graph networks for few-shot TSC. SPGN first uses bandpass filter to expand time series in spectral domain for calculating spectrum-wise relations between time series. Equipped with graph networks, SPGN then integrates spectral relations with label information to make spectral propagation. The further study conveys the bi-directional effect between spectral relations acquisition and spectral propagation. We conduct extensive experiments on few-shot TSC benchmarks. SPGN outperforms state-of-the-art results by a large margin in $4\% \sim 13\%$. Moreover, SPGN surpasses them by around $12\%$ and $9\%$ under cross-domain and cross-way settings respectively.
    Generalized Out-of-Distribution Detection: A Survey. (arXiv:2110.11334v2 [cs.CV] UPDATED)
    Out-of-distribution (OOD) detection is critical to ensuring the reliability and safety of machine learning systems. For instance, in autonomous driving, we would like the driving system to issue an alert and hand over the control to humans when it detects unusual scenes or objects that it has never seen during training time and cannot make a safe decision. The term, OOD detection, first emerged in 2017 and since then has received increasing attention from the research community, leading to a plethora of methods developed, ranging from classification-based to density-based to distance-based ones. Meanwhile, several other problems, including anomaly detection (AD), novelty detection (ND), open set recognition (OSR), and outlier detection (OD), are closely related to OOD detection in terms of motivation and methodology. Despite common goals, these topics develop in isolation, and their subtle differences in definition and problem setting often confuse readers and practitioners. In this survey, we first present a unified framework called generalized OOD detection, which encompasses the five aforementioned problems, i.e., AD, ND, OSR, OOD detection, and OD. Under our framework, these five problems can be seen as special cases or sub-tasks, and are easier to distinguish. We then review each of these five areas by summarizing their recent technical developments, with a special focus on OOD detection methodologies. We conclude this survey with open challenges and potential research directions.
    List Autoencoder: Towards Deep Learning Based Reliable Transmission Over Noisy Channels. (arXiv:2112.11920v2 [cs.IT] UPDATED)
    In this paper, we present list autoencoder (listAE) to mimic list decoding used in classical coding theory. With listAE, the decoder network outputs a list of decoded message word candidates. To train the listAE, a genie is assumed to be available at the output of the decoder. A specific loss function is proposed to optimize the performance of a genie-aided (GA) list decoding. The listAE is a general framework and can be used with any AE architecture. We propose a specific architecture, referred to as incremental-redundancy AE (IR-AE), which decodes the received word on a sequence of component codes with non-increasing rates. Then, the listAE is trained and evaluated with both IR-AE and Turbo-AE. Finally, we employ cyclic redundancy check (CRC) codes to replace the genie at the decoder output and obtain a CRC aided (CA) list decoder. Our simulation results show that the IR-AE under CA list decoding demonstrates meaningful coding gain over Turbo-AE and polar code at low block error rates range.
    Bridging the Gap Between Object Detection and User Intent via Query-Modulation. (arXiv:2106.10258v2 [cs.CV] UPDATED)
    When interacting with objects through cameras, or pictures, users often have a specific intent. For example, they may want to perform a visual search. With most object detection models relying on image pixels as their sole input, undesired results are not uncommon. Most typically: lack of a high-confidence detection on the object of interest, or detection with a wrong class label. The issue is especially severe when operating capacity-constrained mobile object detectors on-device. In this paper we investigate techniques to modulate mobile detectors to explicitly account for the user intent, expressed as an embedding of a simple query. Compared to standard detectors, query-modulated detectors show superior performance at detecting objects for a given user query. Thanks to large-scale training data synthesized from standard object detection annotations, query-modulated detectors also outperform a specialized referring expression recognition system. Query-modulated detectors can also be trained to simultaneously solve for both localizing a user query and standard detection, even outperforming standard mobile detectors at the canonical COCO task.
    Scene Editing as Teleoperation: A Case Study in 6DoF Kit Assembly. (arXiv:2110.04450v3 [cs.RO] UPDATED)
    Studies in robot teleoperation have been centered around action specifications -- from continuous joint control to discrete end-effector pose control. However, these robot-centric interfaces often require skilled operators with extensive robotics expertise. To make teleoperation accessible to non-expert users, we propose the framework "Scene Editing as Teleoperation" (SEaT), where the key idea is to transform the traditional "robot-centric" interface into a "scene-centric" interface -- instead of controlling the robot, users focus on specifying the task's goal by manipulating digital twins of the real-world objects. As a result, a user can perform teleoperation without any expert knowledge of the robot hardware. To achieve this goal, we utilize a category-agnostic scene-completion algorithm that translates the real-world workspace (with unknown objects) into a manipulable virtual scene representation and an action-snapping algorithm that refines the user input before generating the robot's action plan. To train the algorithms, we procedurally generated a large-scale, diverse kit-assembly dataset that contains object-kit pairs that mimic real-world object-kitting tasks. Our experiments in simulation and on a real-world system demonstrate that our framework improves both the efficiency and success rate for 6DoF kit-assembly tasks. A user study demonstrates that SEaT framework participants achieve a higher task success rate and report a lower subjective workload compared to an alternative robot-centric interface. Video can be found at https://www.youtube.com/watch?v=-NdR3mkPbQQ .
    Efficiently Computing Nash Equilibria in Adversarial Team Markov Games. (arXiv:2208.02204v1 [cs.GT])
    Computing Nash equilibrium policies is a central problem in multi-agent reinforcement learning that has received extensive attention both in theory and in practice. However, provable guarantees have been thus far either limited to fully competitive or cooperative scenarios or impose strong assumptions that are difficult to meet in most practical applications. In this work, we depart from those prior results by investigating infinite-horizon \emph{adversarial team Markov games}, a natural and well-motivated class of games in which a team of identically-interested players -- in the absence of any explicit coordination or communication -- is competing against an adversarial player. This setting allows for a unifying treatment of zero-sum Markov games and Markov potential games, and serves as a step to model more realistic strategic interactions that feature both competing and cooperative interests. Our main contribution is the first algorithm for computing stationary $\epsilon$-approximate Nash equilibria in adversarial team Markov games with computational complexity that is polynomial in all the natural parameters of the game, as well as $1/\epsilon$. The proposed algorithm is particularly natural and practical, and it is based on performing independent policy gradient steps for each player in the team, in tandem with best responses from the side of the adversary; in turn, the policy for the adversary is then obtained by solving a carefully constructed linear program. Our analysis leverages non-standard techniques to establish the KKT optimality conditions for a nonlinear program with nonconvex constraints, thereby leading to a natural interpretation of the induced Lagrange multipliers. Along the way, we significantly extend an important characterization of optimal policies in adversarial (normal-form) team games due to Von Stengel and Koller (GEB `97).
    Can you hear me $\textit{now}$? Sensitive comparisons of human and machine perception. (arXiv:2003.12362v2 [eess.AS] UPDATED)
    The rise of machine-learning systems that process sensory input has brought with it a rise in comparisons between human and machine perception. But such comparisons face a challenge: Whereas machine perception of some stimulus can often be probed through direct and explicit measures, much of human perceptual knowledge is latent, incomplete, or unavailable for explicit report. Here, we explore how this asymmetry can cause such comparisons to misestimate the overlap in human and machine perception. As a case study, we consider human perception of \textit{adversarial speech} -- synthetic audio commands that are recognized as valid messages by automated speech-recognition systems but that human listeners reportedly hear as meaningless noise. In five experiments, we adapt task designs from the human psychophysics literature to show that even when subjects cannot freely transcribe such speech commands (the previous benchmark for human understanding), they often can demonstrate other forms of understanding, including discriminating adversarial speech from closely matched non-speech (Experiments 1--2), finishing common phrases begun in adversarial speech (Experiments 3--4), and solving simple math problems posed in adversarial speech (Experiment 5) -- even for stimuli previously described as unintelligible to human listeners. We recommend the adoption of such "sensitive tests" when comparing human and machine perception, and we discuss the broader consequences of such approaches for assessing the overlap between systems.
    Debiasing In-Sample Policy Performance for Small-Data, Large-Scale Optimization. (arXiv:2107.12438v4 [math.OC] UPDATED)
    Motivated by the poor performance of cross-validation in settings where data are scarce, we propose a novel estimator of the out-of-sample performance of a policy in data-driven optimization.Our approach exploits the optimization problem's sensitivity analysis to estimate the gradient of the optimal objective value with respect to the amount of noise in the data and uses the estimated gradient to debias the policy's in-sample performance. Unlike cross-validation techniques, our approach avoids sacrificing data for a test set, utilizes all data when training and, hence, is well-suited to settings where data are scarce. We prove bounds on the bias and variance of our estimator for optimization problems with uncertain linear objectives but known, potentially non-convex, feasible regions. For more specialized optimization problems where the feasible region is "weakly-coupled" in a certain sense, we prove stronger results. Specifically, we provide explicit high-probability bounds on the error of our estimator that hold uniformly over a policy class and depends on the problem's dimension and policy class's complexity. Our bounds show that under mild conditions, the error of our estimator vanishes as the dimension of the optimization problem grows, even if the amount of available data remains small and constant. Said differently, we prove our estimator performs well in the small-data, large-scale regime. Finally, we numerically compare our proposed method to state-of-the-art approaches through a case-study on dispatching emergency medical response services using real data. Our method provides more accurate estimates of out-of-sample performance and learns better-performing policies.
    Unified Framework for Spectral Dimensionality Reduction, Maximum Variance Unfolding, and Kernel Learning By Semidefinite Programming: Tutorial and Survey. (arXiv:2106.15379v2 [stat.ML] UPDATED)
    This is a tutorial and survey paper on unification of spectral dimensionality reduction methods, kernel learning by Semidefinite Programming (SDP), Maximum Variance Unfolding (MVU) or Semidefinite Embedding (SDE), and its variants. We first explain how the spectral dimensionality reduction methods can be unified as kernel Principal Component Analysis (PCA) with different kernels. This unification can be interpreted as eigenfunction learning or representation of kernel in terms of distance matrix. Then, since the spectral methods are unified as kernel PCA, we say let us learn the best kernel for unfolding the manifold of data to its maximum variance. We first briefly introduce kernel learning by SDP for the transduction task. Then, we explain MVU in detail. Various versions of supervised MVU using nearest neighbors graph, by class-wise unfolding, by Fisher criterion, and by colored MVU are explained. We also explain out-of-sample extension of MVU using eigenfunctions and kernel mapping. Finally, we introduce other variants of MVU including action respecting embedding, relaxed MVU, and landmark MVU for big data.
    RBNN: Memory-Efficient Reconfigurable Deep Binary Neural Network with IP Protection for Internet of Things. (arXiv:2105.03822v3 [cs.CR] UPDATED)
    Though deep neural network models exhibit outstanding performance for various applications, their large model size and extensive floating-point operations render deployment on mobile computing platforms a major challenge, and, in particular, on Internet of Things devices. One appealing solution is model quantization that reduces the model size and uses integer operations commonly supported by microcontrollers . To this end, a 1-bit quantized DNN model or deep binary neural network maximizes the memory efficiency, where each parameter in a BNN model has only 1-bit. In this paper, we propose a reconfigurable BNN (RBNN) to further amplify the memory efficiency for resource-constrained IoT devices. Generally, the RBNN can be reconfigured on demand to achieve any one of M (M>1) distinct tasks with the same parameter set, thus only a single task determines the memory requirements. In other words, the memory utilization is improved by times M. Our extensive experiments corroborate that up to seven commonly used tasks can co-exist (the value of M can be larger). These tasks with a varying number of classes have no or negligible accuracy drop-off on three binarized popular DNN architectures including VGG, ResNet, and ReActNet. The tasks span across different domains, e.g., computer vision and audio domains validated herein, with the prerequisite that the model architecture can serve those cross-domain tasks. To protect the intellectual property of an RBNN model, the reconfiguration can be controlled by both a user key and a device-unique root key generated by the intrinsic hardware fingerprint. By doing so, an RBNN model can only be used per paid user per authorized device, thus benefiting both the user and the model provider.
    Stable and Interpretable Unrolled Dictionary Learning. (arXiv:2106.00058v5 [cs.LG] UPDATED)
    The dictionary learning problem, representing data as a combination of a few atoms, has long stood as a popular method for learning representations in statistics and signal processing. The most popular dictionary learning algorithm alternates between sparse coding and dictionary update steps, and a rich literature has studied its theoretical convergence. The success of dictionary learning relies on access to a "good" initial estimate of the dictionary and the ability of the sparse coding step to provide an unbiased estimate of the code. The growing popularity of unrolled sparse coding networks has led to the empirical finding that backpropagation through such networks performs dictionary learning. We offer the theoretical analysis of these empirical results through PUDLE, a Provable Unrolled Dictionary LEarning method. We provide conditions on the network initialization and data distribution sufficient to recover and preserve the support of the latent code. Additionally, we address two challenges; first, the vanilla unrolled sparse coding computes a biased code estimate, and second, gradients during backpropagated learning can become unstable. We show approaches to reduce the bias of the code estimate in the forward pass, and that of the dictionary estimate in the backward pass. We propose strategies to resolve the learning instability by tuning network parameters and modifying the loss function. Overall, we highlight the impact of loss, unrolling, and backpropagation on convergence. We complement our findings through synthetic and image denoising experiments. Finally, we demonstrate PUDLE's interpretability, a driving factor in designing deep networks based on iterative optimizations, by building a mathematical relation between network weights, its output, and the training set.
    A first look into the carbon footprint of federated learning. (arXiv:2102.07627v4 [cs.LG] UPDATED)
    Despite impressive results, deep learning-based technologies also raise severe privacy and environmental concerns induced by the training procedure often conducted in data centers. In response, alternatives to centralized training such as Federated Learning (FL) have emerged. Perhaps unexpectedly, FL is starting to be deployed at a global scale by companies that must adhere to new legal demands and policies originating from governments and social groups advocating for privacy protection. \textit{However, the potential environmental impact related to FL remains unclear and unexplored. This paper offers the first-ever systematic study of the carbon footprint of FL.} First, we propose a rigorous model to quantify the carbon footprint, hence facilitating the investigation of the relationship between FL design and carbon emissions. Then, we compare the carbon footprint of FL to traditional centralized learning. Our findings show that, depending on the configuration, FL can emit up to two order of magnitude more carbon than centralized machine learning. However, in certain settings, it can be comparable to centralized learning due to the reduced energy consumption of embedded devices. We performed extensive experiments across different types of datasets, settings and various deep learning models with FL. Finally, we highlight and connect the reported results to the future challenges and trends in FL to reduce its environmental impact, including algorithms efficiency, hardware capabilities, and stronger industry transparency.
    A Study of Modeling Rising Intonation in Cantonese Neural Speech Synthesis. (arXiv:2208.02189v1 [eess.AS])
    In human speech, the attitude of a speaker cannot be fully expressed only by the textual content. It has to come along with the intonation. Declarative questions are commonly used in daily Cantonese conversations, and they are usually uttered with rising intonation. Vanilla neural text-to-speech (TTS) systems are not capable of synthesizing rising intonation for these sentences due to the loss of semantic information. Though it has become more common to complement the systems with extra language models, their performance in modeling rising intonation is not well studied. In this paper, we propose to complement the Cantonese TTS model with a BERT-based statement/question classifier. We design different training strategies and compare their performance. We conduct our experiments on a Cantonese corpus named CanTTS. Empirical results show that the separate training approach obtains the best generalization performance and feasibility.
    Quantized Convolutional Neural Networks Through the Lens of Partial Differential Equations. (arXiv:2109.00095v2 [cs.LG] UPDATED)
    Quantization of Convolutional Neural Networks (CNNs) is a common approach to ease the computational burden involved in the deployment of CNNs, especially on low-resource edge devices. However, fixed-point arithmetic is not natural to the type of computations involved in neural networks. In this work, we explore ways to improve quantized CNNs using PDE-based perspective and analysis. First, we harness the total variation (TV) approach to apply edge-aware smoothing to the feature maps throughout the network. This aims to reduce outliers in the distribution of values and promote piece-wise constant maps, which are more suitable for quantization. Secondly, we consider symmetric and stable variants of common CNNs for image classification, and Graph Convolutional Networks (GCNs) for graph node-classification. We demonstrate through several experiments that the property of forward stability preserves the action of a network under different quantization rates. As a result, stable quantized networks behave similarly to their non-quantized counterparts even though they rely on fewer parameters. We also find that at times, stability even aids in improving accuracy. These properties are of particular interest for sensitive, resource-constrained, low-power or real-time applications like autonomous driving.
    Blockchain associated machine learning and IoT based hypoglycemia detection system with auto-injection feature. (arXiv:2208.02222v1 [cs.LG])
    Hypoglycemia is an unpleasant phenomenon caused by low blood glucose. The disease can lead a person to death or a high level of body damage. To avoid significant damage, patients need sugar. The research aims at implementing an automatic system to detect hypoglycemia and perform automatic sugar injections to save a life. Receiving the benefits of the internet of things (IoT), the sensor data was transferred using the hypertext transfer protocol (HTTP) protocol. To ensure the safety of health-related data, blockchain technology was utilized. The glucose sensor and smartwatch data were processed via Fog and sent to the cloud. A Random Forest algorithm was proposed and utilized to decide hypoglycemic events. When the hypoglycemic event was detected, the system sent a notification to the mobile application and auto-injection device to push the condensed sugar into the victims body. XGBoost, k-nearest neighbors (KNN), support vector machine (SVM), and decision tree were implemented to compare the proposed models performance. The random forest performed 0.942 testing accuracy, better than other models in detecting hypoglycemic events. The systems performance was measured in several conditions, and satisfactory results were achieved. The system can benefit hypoglycemia patients to survive this disease.
    A Glimpse of Physical Layer Decision Mechanisms: Facts, Challenges, and Remedies. (arXiv:2102.07258v3 [cs.LG] UPDATED)
    Communications are realized as a result of successive decisions at the physical layer, from modulation selection to multi-antenna strategy, and each decision affects the performance of the communication systems. Future communication systems must include extensive capabilities as they will encompass a wide variety of devices and applications. Conventional physical layer decision mechanisms may not meet these requirements, as they are often based on impractical and oversimplifying assumptions that result in a trade-off between complexity and efficiency. By leveraging past experiences, learning-driven designs are promising solutions to present a resilient decision mechanism and enable rapid response even under exceptional circumstances. The corresponding design solutions should evolve following the lines of learning-driven paradigms that offer more autonomy and robustness. This evolution must take place by considering the facts of real-world systems and without restraining assumptions. In this paper, the common assumptions in the physical layer are presented to highlight their discrepancies with practical systems. As a solution, learning algorithms are examined by considering the implementation steps and challenges. Furthermore, these issues are discussed through a real-time case study using software-defined radio nodes to demonstrate the potential performance improvement. A cyber-physical framework is presented to incorporate future remedies.
    Stochastic Neighbor Embedding with Gaussian and Student-t Distributions: Tutorial and Survey. (arXiv:2009.10301v2 [stat.ML] UPDATED)
    Stochastic Neighbor Embedding (SNE) is a manifold learning and dimensionality reduction method with a probabilistic approach. In SNE, every point is consider to be the neighbor of all other points with some probability and this probability is tried to be preserved in the embedding space. SNE considers Gaussian distribution for the probability in both the input and embedding spaces. However, t-SNE uses the Student-t and Gaussian distributions in these spaces, respectively. In this tutorial and survey paper, we explain SNE, symmetric SNE, t-SNE (or Cauchy-SNE), and t-SNE with general degrees of freedom. We also cover the out-of-sample extension and acceleration for these methods.
    Sequence Model Imitation Learning with Unobserved Contexts. (arXiv:2208.02225v1 [cs.LG])
    We consider imitation learning problems where the expert has access to a per-episode context that is hidden from the learner, both in the demonstrations and at test-time. While the learner might not be able to accurately reproduce expert behavior early on in an episode, by considering the entire history of states and actions, they might be able to eventually identify the context and act as the expert would. We prove that on-policy imitation learning algorithms (with or without access to a queryable expert) are better equipped to handle these sorts of asymptotically realizable problems than off-policy methods and are able to avoid the latching behavior (naive repetition of past actions) that plagues the latter. We conduct experiments in a toy bandit domain that show that there exist sharp phase transitions of whether off-policy approaches are able to match expert performance asymptotically, in contrast to the uniformly good performance of on-policy approaches. We demonstrate that on several continuous control tasks, on-policy approaches are able to use history to identify the context while off-policy approaches actually perform worse when given access to history.
    Multimodal Controller for Generative Models. (arXiv:2002.02572v7 [cs.LG] UPDATED)
    Class-conditional generative models are crucial tools for data generation from user-specified class labels. Existing approaches for class-conditional generative models require nontrivial modifications of backbone generative architectures to model conditional information fed into the model. This paper introduces a plug-and-play module named `multimodal controller' to generate multimodal data without introducing additional learning parameters. In the absence of the controllers, our model reduces to non-conditional generative models. We test the efficacy of multimodal controllers on CIFAR10, COIL100, and Omniglot benchmark datasets. We demonstrate that multimodal controlled generative models (including VAE, PixelCNN, Glow, and GAN) can generate class-conditional images of significantly better quality when compared with conditional generative models. Moreover, we show that multimodal controlled models can also create novel modalities of images.
    Interpretable bilinear attention network with domain adaptation improves drug-target prediction. (arXiv:2208.02194v1 [cs.LG])
    Predicting drug-target interaction is key for drug discovery. Recent deep learning-based methods show promising performance but two challenges remain: (i) how to explicitly model and learn local interactions between drugs and targets for better prediction and interpretation; (ii) how to generalize prediction performance on novel drug-target pairs from different distribution. In this work, we propose DrugBAN, a deep bilinear attention network (BAN) framework with domain adaptation to explicitly learn pair-wise local interactions between drugs and targets, and adapt on out-of-distribution data. DrugBAN works on drug molecular graphs and target protein sequences to perform prediction, with conditional domain adversarial learning to align learned interaction representations across different distributions for better generalization on novel drug-target pairs. Experiments on three benchmark datasets under both in-domain and cross-domain settings show that DrugBAN achieves the best overall performance against five state-of-the-art baselines. Moreover, visualizing the learned bilinear attention map provides interpretable insights from prediction results.
    Conv-NILM-Net, a causal and multi-appliance model for energy source separation. (arXiv:2208.02173v1 [eess.SP])
    Non-Intrusive Load Monitoring (NILM) seeks to save energy by estimating individual appliance power usage from a single aggregate measurement. Deep neural networks have become increasingly popular in attempting to solve NILM problems. However most used models are used for Load Identification rather than online Source Separation. Among source separation models, most use a single-task learning approach in which a neural network is trained exclusively for each appliance. This strategy is computationally expensive and ignores the fact that multiple appliances can be active simultaneously and dependencies between them. The rest of models are not causal, which is important for real-time application. Inspired by Convtas-Net, a model for speech separation, we propose Conv-NILM-net, a fully convolutional framework for end-to-end NILM. Conv-NILM-net is a causal model for multi appliance source separation. Our model is tested on two real datasets REDD and UK-DALE and clearly outperforms the state of the art while keeping a significantly smaller size than the competing models.
    Recovery of Future Data via Convolution Nuclear Norm Minimization. (arXiv:1909.03889v7 [cs.LG] UPDATED)
    This paper studies the problem of time series forecasting (TSF) from the perspective of compressed sensing. First of all, we convert TSF into a more inclusive problem called tensor completion with arbitrary sampling (TCAS), which is to restore a tensor from a subset of its entries sampled in an arbitrary manner. While it is known that, in the framework of Tucker low-rankness, it is theoretically impossible to identify the target tensor based on some arbitrarily selected entries, in this work we shall show that TCAS is indeed tackleable in the light of a new concept called convolutional low-rankness, which is a generalization of the well-known Fourier sparsity. Then we introduce a convex program termed Convolution Nuclear Norm Minimization (CNNM), and we prove that CNNM succeeds in solving TCAS as long as a sampling condition--which depends on the convolution rank of the target tensor--is obeyed. This theory provides a meaningful answer to the fundamental question of what is the minimum sampling size needed for making a given number of forecasts. Experiments on univariate time series, images and videos show encouraging results.
    Hierarchical Multiple-Instance Data Classification with Costly Features. (arXiv:1911.08756v5 [cs.LG] UPDATED)
    We motivate our research with a real-world problem of classifying malicious web domains using a remote service that provides various information. Crucially, some of the information can be further analyzed into a certain depth and this process sequentially creates a tree of hierarchically structured multiple-instance data. Each request sent to the remote service is associated with a cost (e.g., time or another cost per request) and the objective is to maximize the accuracy, constrained with a budget. We present a generic framework able to work with a class of similar problems. Our method is based on Classification with Costly Features (CwCF), Hierarchical Multiple-Instance Learning (HMIL) and hierarchical decomposition of the action space. It works with samples described as partially-observed trees of features of various types (similar to a JSON/XML file), which allows to model data with complex structure. The process is modeled as a Markov Decision Process (MDP), where a state represents acquired features, and actions select yet unknown ones. The policy is trained with deep reinforcement learning and we demonstrate our method with both real-world and synthetic data.
    SGEM: stochastic gradient with energy and momentum. (arXiv:2208.02208v1 [cs.LG])
    In this paper, we propose SGEM, Stochastic Gradient with Energy and Momentum, to solve a large class of general non-convex stochastic optimization problems, based on the AEGD method that originated in the work [AEGD: Adaptive Gradient Descent with Energy. arXiv: 2010.05109]. SGEM incorporates both energy and momentum at the same time so as to inherit their dual advantages. We show that SGEM features an unconditional energy stability property, and derive energy-dependent convergence rates in the general nonconvex stochastic setting, as well as a regret bound in the online convex setting. A lower threshold for the energy variable is also provided. Our experimental results show that SGEM converges faster than AEGD and generalizes better or at least as well as SGDM in training some deep neural networks.
    SpanDrop: Simple and Effective Counterfactual Learning for Long Sequences. (arXiv:2208.02169v1 [cs.LG])
    Distilling supervision signal from a long sequence to make predictions is a challenging task in machine learning, especially when not all elements in the input sequence contribute equally to the desired output. In this paper, we propose SpanDrop, a simple and effective data augmentation technique that helps models identify the true supervision signal in a long sequence with very few examples. By directly manipulating the input sequence, SpanDrop randomly ablates parts of the sequence at a time and ask the model to perform the same task to emulate counterfactual learning and achieve input attribution. Based on theoretical analysis of its properties, we also propose a variant of SpanDrop based on the beta-Bernoulli distribution, which yields diverse augmented sequences while providing a learning objective that is more consistent with the original dataset. We demonstrate the effectiveness of SpanDrop on a set of carefully designed toy tasks, as well as various natural language processing tasks that require reasoning over long sequences to arrive at the correct answer, and show that it helps models improve performance both when data is scarce and abundant.
    Optimised one-class classification performance. (arXiv:2102.02618v3 [cs.LG] UPDATED)
    We provide a thorough treatment of one-class classification with hyperparameter optimisation for five data descriptors: Support Vector Machine (SVM), Nearest Neighbour Distance (NND), Localised Nearest Neighbour Distance (LNND), Local Outlier Factor (LOF) and Average Localised Proximity (ALP). The hyperparameters of SVM and LOF have to be optimised through cross-validation, while NND, LNND and ALP allow an efficient form of leave-one-out validation and the reuse of a single nearest-neighbour query. We experimentally evaluate the effect of hyperparameter optimisation with 246 classification problems drawn from 50 datasets. From a selection of optimisation algorithms, the recent Malherbe-Powell proposal optimises the hyperparameters of all data descriptors most efficiently. We calculate the increase in test AUROC and the amount of overfitting as a function of the number of hyperparameter evaluations. After 50 evaluations, ALP and SVM significantly outperform LOF, NND and LNND, and LOF and NND outperform LNND. The performance of ALP and SVM is comparable, but ALP can be optimised more efficiently so constitutes a good default choice. Alternatively, using validation AUROC as a selection criterion between ALP or SVM gives the best overall result, and NND is the least computationally demanding option. We thus end up with a clear trade-off between three choices, allowing practitioners to make an informed decision.
    Subject-Specific Lesion Generation and Pseudo-Healthy Synthesis for Multiple Sclerosis Brain Images. (arXiv:2208.02135v1 [eess.IV])
    Understanding the intensity characteristics of brain lesions is key for defining image-based biomarkers in neurological studies and for predicting disease burden and outcome. In this work, we present a novel foreground-based generative method for modelling the local lesion characteristics that can both generate synthetic lesions on healthy images and synthesize subject-specific pseudo-healthy images from pathological images. Furthermore, the proposed method can be used as a data augmentation module to generate synthetic images for training brain image segmentation networks. Experiments on multiple sclerosis (MS) brain images acquired on magnetic resonance imaging (MRI) demonstrate that the proposed method can generate highly realistic pseudo-healthy and pseudo-pathological brain images. Data augmentation using the synthetic images improves the brain image segmentation performance compared to traditional data augmentation methods as well as a recent lesion-aware data augmentation technique, CarveMix. The code will be released at https://github.com/dogabasaran/lesion-synthesis.
    One Node at a Time: Node-Level Network Classification. (arXiv:2208.02162v1 [cs.SI])
    Network classification aims to group networks (or graphs) into distinct categories based on their structure. We study the connection between classification of a network and of its constituent nodes, and whether nodes from networks in different groups are distinguishable based on structural node characteristics such as centrality and clustering coefficient. We demonstrate, using various network datasets and random network models, that a classifier can be trained to accurately predict the network category of a given node (without seeing the whole network), implying that complex networks display distinct structural patterns even at the node level. Finally, we discuss two applications of node-level network classification: (i) whole-network classification from small samples of nodes, and (ii) network bootstrapping.
    Unsupervised Discovery of Semantic Concepts in Satellite Imagery with Style-based Wavelet-driven Generative Models. (arXiv:2208.02089v1 [cs.CV])
    In recent years, considerable advancements have been made in the area of Generative Adversarial Networks (GANs), particularly with the advent of style-based architectures that address many key shortcomings - both in terms of modeling capabilities and network interpretability. Despite these improvements, the adoption of such approaches in the domain of satellite imagery is not straightforward. Typical vision datasets used in generative tasks are well-aligned and annotated, and exhibit limited variability. In contrast, satellite imagery exhibits great spatial and spectral variability, wide presence of fine, high-frequency details, while the tedious nature of annotating satellite imagery leads to annotation scarcity - further motivating developments in unsupervised learning. In this light, we present the first pre-trained style- and wavelet-based GAN model that can readily synthesize a wide gamut of realistic satellite images in a variety of settings and conditions - while also preserving high-frequency information. Furthermore, we show that by analyzing the intermediate activations of our network, one can discover a multitude of interpretable semantic directions that facilitate the guided synthesis of satellite images in terms of high-level concepts (e.g., urbanization) without using any form of supervision. Via a set of qualitative and quantitative experiments we demonstrate the efficacy of our framework, in terms of suitability for downstream tasks (e.g., data augmentation), quality of synthetic imagery, as well as generalization capabilities to unseen datasets.
    Noise tolerance of learning to rank under class-conditional label noise. (arXiv:2208.02126v1 [cs.IR])
    Often, the data used to train ranking models is subject to label noise. For example, in web-search, labels created from clickstream data are noisy due to issues such as insufficient information in item descriptions on the SERP, query reformulation by the user, and erratic or unexpected user behavior. In practice, it is difficult to handle label noise without making strong assumptions about the label generation process. As a result, practitioners typically train their learning-to-rank (LtR) models directly on this noisy data without additional consideration of the label noise. Surprisingly, we often see strong performance from LtR models trained in this way. In this work, we describe a class of noise-tolerant LtR losses for which empirical risk minimization is a consistent procedure, even in the context of class-conditional label noise. We also develop noise-tolerant analogs of commonly used loss functions. The practical implications of our theoretical findings are further supported by experimental results.
    Machine learning optimization of Majorana hybrid nanowires. (arXiv:2208.02182v1 [cond-mat.mes-hall])
    As the complexity of quantum systems such as quantum bit arrays increases, efforts to automate expensive tuning are increasingly worthwhile. We investigate machine learning based tuning of gate arrays using the CMA-ES algorithm for the case study of Majorana wires with strong disorder. We find that the algorithm is able to efficiently improve the topological signatures, learn intrinsic disorder profiles, and completely eliminate disorder effects. For example, with only 20 gates, it is possible to fully recover Majorana zero modes destroyed by disorder by optimizing gate voltages.
    BPMN4sML: A BPMN Extension for Serverless Machine Learning. Technology Independent and Interoperable Modeling of Machine Learning Workflows and their Serverless Deployment Orchestration. (arXiv:2208.02030v1 [cs.SE])
    Machine learning (ML) continues to permeate all layers of academia, industry and society. Despite its successes, mental frameworks to capture and represent machine learning workflows in a consistent and coherent manner are lacking. For instance, the de facto process modeling standard, Business Process Model and Notation (BPMN), managed by the Object Management Group, is widely accepted and applied. However, it is short of specific support to represent machine learning workflows. Further, the number of heterogeneous tools for deployment of machine learning solutions can easily overwhelm practitioners. Research is needed to align the process from modeling to deploying ML workflows. We analyze requirements for standard based conceptual modeling for machine learning workflows and their serverless deployment. Confronting the shortcomings with respect to consistent and coherent modeling of ML workflows in a technology independent and interoperable manner, we extend BPMN's Meta-Object Facility (MOF) metamodel and the corresponding notation and introduce BPMN4sML (BPMN for serverless machine learning). Our extension BPMN4sML follows the same outline referenced by the Object Management Group (OMG) for BPMN. We further address the heterogeneity in deployment by proposing a conceptual mapping to convert BPMN4sML models to corresponding deployment models using TOSCA. BPMN4sML allows technology-independent and interoperable modeling of machine learning workflows of various granularity and complexity across the entire machine learning lifecycle. It aids in arriving at a shared and standardized language to communicate ML solutions. Moreover, it takes the first steps toward enabling conversion of ML workflow model diagrams to corresponding deployment models for serverless deployment via TOSCA.
    Empirical Study of Overfitting in Deep FNN Prediction Models for Breast Cancer Metastasis. (arXiv:2208.02150v1 [cs.LG])
    Overfitting is defined as the fact that the current model fits a specific data set perfectly, resulting in weakened generalization, and ultimately may affect the accuracy in predicting future data. In this research we used an EHR dataset concerning breast cancer metastasis to study overfitting of deep feedforward Neural Networks (FNNs) prediction models. We included 11 hyperparameters of the deep FNNs models and took an empirical approach to study how each of these hyperparameters was affecting both the prediction performance and overfitting when given a large range of values. We also studied how some of the interesting pairs of hyperparameters were interacting to influence the model performance and overfitting. The 11 hyperparameters we studied include activate function; weight initializer, number of hidden layers, learning rate, momentum, decay, dropout rate, batch size, epochs, L1, and L2. Our results show that most of the single hyperparameters are either negatively or positively corrected with model prediction performance and overfitting. In particular, we found that overfitting overall tends to negatively correlate with learning rate, decay, batch sides, and L2, but tends to positively correlate with momentum, epochs, and L1. According to our results, learning rate, decay, and batch size may have a more significant impact on both overfitting and prediction performance than most of the other hyperparameters, including L1, L2, and dropout rate, which were designed for minimizing overfitting. We also find some interesting interacting pairs of hyperparameters such as learning rate and momentum, learning rate and decay, and batch size and epochs. Keywords: Deep learning, overfitting, prediction, grid search, feedforward neural networks, breast cancer metastasis.
    Neural Nets with a Newton Conjugate Gradient Method on Multiple GPUs. (arXiv:2208.02017v1 [cs.LG])
    Training deep neural networks consumes increasing computational resource shares in many compute centers. Often, a brute force approach to obtain hyperparameter values is employed. Our goal is (1) to enhance this by enabling second-order optimization methods with fewer hyperparameters for large-scale neural networks and (2) to perform a survey of the performance optimizers for specific tasks to suggest users the best one for their problem. We introduce a novel second-order optimization method that requires the effect of the Hessian on a vector only and avoids the huge cost of explicitly setting up the Hessian for large-scale networks. We compare the proposed second-order method with two state-of-the-art optimizers on five representative neural network problems, including regression and very deep networks from computer vision or variational autoencoders. For the largest setup, we efficiently parallelized the optimizers with Horovod and applied it to a 8 GPU NVIDIA P100 (DGX-1) machine.
    MTGFlow: Unsupervised Multivariate Time Series Anomaly Detection via Dynamic Graph and Entity-aware Normalizing Flow. (arXiv:2208.02108v1 [cs.LG])
    Multivariate time series anomaly detection has been extensively studied under the semi-supervised setting, where a training dataset with all normal instances is required. However, preparing such a dataset is very laborious since each single data instance should be fully guaranteed to be normal. It is, therefore, desired to explore multivariate time series anomaly detection methods based on the dataset without any label knowledge. In this paper, we propose MTGFlow, an unsupervised anomaly detection approach for Multivariate Time series anomaly detection via dynamic Graph and entity-aware normalizing Flow, leaning only on a widely accepted hypothesis that abnormal instances exhibit sparse densities than the normal. However, the complex interdependencies among entities and the diverse inherent characteristics of each entity pose significant challenges on the density estimation, let alone to detect anomalies based on the estimated possibility distribution. To tackle these problems, we propose to learn the mutual and dynamic relations among entities via a graph structure learning model, which helps to model accurate distribution of multivariate time series. Moreover, taking account of distinct characteristics of the individual entities, an entity-aware normalizing flow is developed to describe each entity into a parameterized normal distribution, thereby producing fine-grained density estimation. Incorporating these two strategies, MTGFlowachieves superior anomaly detection performance. Experiments on the real-world datasets are conducted, demonstrating that MTGFlow outperforms the state-of-the-art (SOTA) by 5.0% and 1.6% AUROC for SWaT and WADI datasets respectively. Also, through the anomaly scores contributed by individual entities, MTGFlow can provide explanation information for the detection results.
    Robots with Different Embodiments Can Express and Influence Carefulness in Object Manipulation. (arXiv:2208.02058v1 [cs.RO])
    Humans have an extraordinary ability to communicate and read the properties of objects by simply watching them being carried by someone else. This level of communicative skills and interpretation, available to humans, is essential for collaborative robots if they are to interact naturally and effectively. For example, suppose a robot is handing over a fragile object. In that case, the human who receives it should be informed of its fragility in advance, through an immediate and implicit message, i.e., by the direct modulation of the robot's action. This work investigates the perception of object manipulations performed with a communicative intent by two robots with different embodiments (an iCub humanoid robot and a Baxter robot). We designed the robots' movements to communicate carefulness or not during the transportation of objects. We found that not only this feature is correctly perceived by human observers, but it can elicit as well a form of motor adaptation in subsequent human object manipulations. In addition, we get an insight into which motion features may induce to manipulate an object more or less carefully.
    Cross-lingual Approaches for the Detection of Adverse Drug Reactions in German from a Patient's Perspective. (arXiv:2208.02031v1 [cs.CL])
    In this work, we present the first corpus for German Adverse Drug Reaction (ADR) detection in patient-generated content. The data consists of 4,169 binary annotated documents from a German patient forum, where users talk about health issues and get advice from medical doctors. As is common in social media data in this domain, the class labels of the corpus are very imbalanced. This and a high topic imbalance make it a very challenging dataset, since often, the same symptom can have several causes and is not always related to a medication intake. We aim to encourage further multi-lingual efforts in the domain of ADR detection and provide preliminary experiments for binary classification using different methods of zero- and few-shot learning based on a multi-lingual model. When fine-tuning XLM-RoBERTa first on English patient forum data and then on the new German data, we achieve an F1-score of 37.52 for the positive class. We make the dataset and models publicly available for the community.
    A Novel Approach To Network Intrusion Detection System Using Deep Learning For Sdn: Futuristic Approach. (arXiv:2208.02094v1 [cs.CR])
    Software-Defined Networking (SDN) is the next generation to change the architecture of traditional networks. SDN is one of the promising solutions to change the architecture of internet networks. Attacks become more common due to the centralized nature of SDN architecture. It is vital to provide security for the SDN. In this study, we propose a Network Intrusion Detection System-Deep Learning module (NIDS-DL) approach in the context of SDN. Our suggested method combines Network Intrusion Detection Systems (NIDS) with many types of deep learning algorithms. Our approach employs 12 features extracted from 41 features in the NSL-KDD dataset using a feature selection method. We employed classifiers (CNN, DNN, RNN, LSTM, and GRU). When we compare classifier scores, our technique produced accuracy results of (98.63%, 98.53%, 98.13%, 98.04%, and 97.78%) respectively. The novelty of our new approach (NIDS-DL) uses 5 deep learning classifiers and made pre-processing dataset to harvests the best results. Our proposed approach was successful in binary classification and detecting attacks, implying that our approach (NIDS-DL) might be used with great efficiency in the future.
    Edge-Based Self-Supervision for Semi-Supervised Few-Shot Microscopy Image Cell Segmentation. (arXiv:2208.02105v1 [cs.CV])
    Deep neural networks currently deliver promising results for microscopy image cell segmentation, but they require large-scale labelled databases, which is a costly and time-consuming process. In this work, we relax the labelling requirement by combining self-supervised with semi-supervised learning. We propose the prediction of edge-based maps for self-supervising the training of the unlabelled images, which is combined with the supervised training of a small number of labelled images for learning the segmentation task. In our experiments, we evaluate on a few-shot microscopy image cell segmentation benchmark and show that only a small number of annotated images, e.g. 10% of the original training set, is enough for our approach to reach similar performance as with the fully annotated databases on 1- to 10-shots. Our code and trained models is made publicly available
    Gradient descent provably escapes saddle points in the training of shallow ReLU networks. (arXiv:2208.02083v1 [cs.LG])
    Dynamical systems theory has recently been applied in optimization to prove that gradient descent algorithms avoid so-called strict saddle points of the loss function. However, in many modern machine learning applications, the required regularity conditions are not satisfied. In particular, this is the case for rectified linear unit (ReLU) networks. In this paper, we prove a variant of the relevant dynamical systems result, a center-stable manifold theorem, in which we relax some of the regularity requirements. Then, we verify that shallow ReLU networks fit into the new framework. Building on a classification of critical points of the square integral loss of shallow ReLU networks measured against an affine target function, we deduce that gradient descent avoids most saddle points. We proceed to prove convergence to global minima if the initialization is sufficiently good, which is expressed by an explicit threshold on the limiting loss.
    Character Generation through Self-Supervised Vectorization. (arXiv:2208.02012v1 [cs.CV])
    The prevalent approach in self-supervised image generation is to operate on pixel level representations. While this approach can produce high quality images, it cannot benefit from the simplicity and innate quality of vectorization. Here we present a drawing agent that operates on stroke-level representation of images. At each time step, the agent first assesses the current canvas and decides whether to stop or keep drawing. When a 'draw' decision is made, the agent outputs a program indicating the stroke to be drawn. As a result, it produces a final raster image by drawing the strokes on a canvas, using a minimal number of strokes and dynamically deciding when to stop. We train our agent through reinforcement learning on MNIST and Omniglot datasets for unconditional generation and parsing (reconstruction) tasks. We utilize our parsing agent for exemplar generation and type conditioned concept generation in Omniglot challenge without any further training. We present successful results on all three generation tasks and the parsing task. Crucially, we do not need any stroke-level or vector supervision; we only use raster images for training.
    Exploration with Model Uncertainty at Extreme Scale in Real-Time Bidding. (arXiv:2208.01951v1 [cs.LG])
    In this work, we present a scalable and efficient system for exploring the supply landscape in real-time bidding. The system directs exploration based on the predictive uncertainty of models used for click-through rate prediction and works in a high-throughput, low-latency environment. Through online A/B testing, we demonstrate that exploration with model uncertainty has a positive impact on model performance and business KPIs.
    Learning Object Manipulation Skills from Video via Approximate Differentiable Physics. (arXiv:2208.01960v1 [cs.RO])
    We aim to teach robots to perform simple object manipulation tasks by watching a single video demonstration. Towards this goal, we propose an optimization approach that outputs a coarse and temporally evolving 3D scene to mimic the action demonstrated in the input video. Similar to previous work, a differentiable renderer ensures perceptual fidelity between the 3D scene and the 2D video. Our key novelty lies in the inclusion of a differentiable approach to solve a set of Ordinary Differential Equations (ODEs) that allows us to approximately model laws of physics such as gravity, friction, and hand-object or object-object interactions. This not only enables us to dramatically improve the quality of estimated hand and object states, but also produces physically admissible trajectories that can be directly translated to a robot without the need for costly reinforcement learning. We evaluate our approach on a 3D reconstruction task that consists of 54 video demonstrations sourced from 9 actions such as pull something from right to left or put something in front of something. Our approach improves over previous state-of-the-art by almost 30%, demonstrating superior quality on especially challenging actions involving physical interactions of two objects such as put something onto something. Finally, we showcase the learned skills on a Franka Emika Panda robot.
    Centroids Matching: an efficient Continual Learning approach operating in the embedding space. (arXiv:2208.02048v1 [cs.LG])
    Catastrophic forgetting (CF) occurs when a neural network loses the information previously learned while training on a set of samples from a different distribution, i.e., a new task. Existing approaches have achieved remarkable results in mitigating CF, especially in a scenario called task incremental learning. However, this scenario is not realistic, and limited work has been done to achieve good results on more realistic scenarios. In this paper, we propose a novel regularization method called Centroids Matching, that, inspired by meta-learning approaches, fights CF by operating in the feature space produced by the neural network, achieving good results while requiring a small memory footprint. Specifically, the approach classifies the samples directly using the feature vectors produced by the neural network, by matching those vectors with the centroids representing the classes from the current task, or all the tasks up to that point. Centroids Matching is faster than competing baselines, and it can be exploited to efficiently mitigate CF, by preserving the distances between the embedding space produced by the model when past tasks were over, and the one currently produced, leading to a method that achieves high accuracy on all the tasks, without using an external memory when operating on easy scenarios, or using a small one for more realistic ones. Extensive experiments demonstrate that Centroids Matching achieves accuracy gains on multiple datasets and scenarios.
    Binary Classification with Positive Labeling Sources. (arXiv:2208.01704v1 [cs.LG])
    To create a large amount of training labels for machine learning models effectively and efficiently, researchers have turned to Weak Supervision (WS), which uses programmatic labeling sources rather than manual annotation. Existing works of WS for binary classification typically assume the presence of labeling sources that are able to assign both positive and negative labels to data in roughly balanced proportions. However, for many tasks of interest where there is a minority positive class, negative examples could be too diverse for developers to generate indicative labeling sources. Thus, in this work, we study the application of WS on binary classification tasks with positive labeling sources only. We propose WEAPO, a simple yet competitive WS method for producing training labels without negative labeling sources. On 10 benchmark datasets, we show WEAPO achieves the highest averaged performance in terms of both the quality of synthesized labels and the performance of the final classifier supervised with these labels. We incorporated the implementation of \method into WRENCH, an existing benchmarking platform.
    Robust PCA for Anomaly Detection and Data Imputation in Seasonal Time Series. (arXiv:2208.01998v1 [stat.ML])
    We propose a robust principal component analysis (RPCA) framework to recover low-rank and sparse matrices from temporal observations. We develop an online version of the batch temporal algorithm in order to process larger datasets or streaming data. We empirically compare the proposed approaches with different RPCA frameworks and show their effectiveness in practical situations.
    Flow Annealed Importance Sampling Bootstrap. (arXiv:2208.01893v1 [cs.LG])
    Normalizing flows are tractable density models that can approximate complicated target distributions, e.g. Boltzmann distributions of physical systems. However, current methods for training flows either suffer from mode-seeking behavior, use samples from the target generated beforehand by expensive MCMC simulations, or use stochastic losses that have very high variance. To avoid these problems, we augment flows with annealed importance sampling (AIS) and minimize the mass covering $\alpha$-divergence with $\alpha=2$, which minimizes importance weight variance. Our method, Flow AIS Bootstrap (FAB), uses AIS to generate samples in regions where the flow is a poor approximation of the target, facilitating the discovery of new modes. We target with AIS the minimum variance distribution for the estimation of the $\alpha$-divergence via importance sampling. We also use a prioritized buffer to store and reuse AIS samples. These two features significantly improve FAB's performance. We apply FAB to complex multimodal targets and show that we can approximate them very accurately where previous methods fail. To the best of our knowledge, we are the first to learn the Boltzmann distribution of the alanine dipeptide molecule using only the unnormalized target density and without access to samples generated via Molecular Dynamics (MD) simulations: FAB produces better results than training via maximum likelihood on MD samples while using 100 times fewer target evaluations. After reweighting samples with importance weights, we obtain unbiased histograms of dihedral angles that are almost identical to the ground truth ones.
    OLLIE: Derivation-based Tensor Program Optimizer. (arXiv:2208.02025v1 [cs.LG])
    Boosting the runtime performance of deep neural networks (DNNs) is critical due to their wide adoption in real-world tasks. Existing approaches to optimizing the tensor algebra expression of a DNN only consider expressions representable by a fixed set of predefined operators, missing possible optimization opportunities between general expressions. We propose OLLIE, the first derivation-based tensor program optimizer. OLLIE optimizes tensor programs by leveraging transformations between general tensor algebra expressions, enabling a significantly larger expression search space that includes those supported by prior work as special cases. OLLIE uses a hybrid derivation-based optimizer that effectively combines explorative and guided derivations to quickly discover highly optimized expressions. Evaluation on seven DNNs shows that OLLIE can outperform existing optimizers by up to 2.73$\times$ (1.46$\times$ on average) on an A100 GPU and up to 2.68$\times$ (1.51$\times$) on a V100 GPU, respectively.
    HybridGNN: Learning Hybrid Representation in Multiplex Heterogeneous Networks. (arXiv:2208.02068v1 [cs.LG])
    Recently, graph neural networks have shown the superiority of modeling the complex topological structures in heterogeneous network-based recommender systems. Due to the diverse interactions among nodes and abundant semantics emerging from diverse types of nodes and edges, there is a bursting research interest in learning expressive node representations in multiplex heterogeneous networks. One of the most important tasks in recommender systems is to predict the potential connection between two nodes under a specific edge type (i.e., relationship). Although existing studies utilize explicit metapaths to aggregate neighbors, practically they only consider intra-relationship metapaths and thus fail to leverage the potential uplift by inter-relationship information. Moreover, it is not always straightforward to exploit inter-relationship metapaths comprehensively under diverse relationships, especially with the increasing number of node and edge types. In addition, contributions of different relationships between two nodes are difficult to measure. To address the challenges, we propose HybridGNN, an end-to-end GNN model with hybrid aggregation flows and hierarchical attentions to fully utilize the heterogeneity in the multiplex scenarios. Specifically, HybridGNN applies a randomized inter-relationship exploration module to exploit the multiplexity property among different relationships. Then, our model leverages hybrid aggregation flows under intra-relationship metapaths and randomized exploration to learn the rich semantics. To explore the importance of different aggregation flow and take advantage of the multiplexity property, we bring forward a novel hierarchical attention module which leverages both metapath-level attention and relationship-level attention. Extensive experimental results suggest that HybridGNN achieves the best performance compared to several state-of-the-art baselines.
    Neural Basis Functions for Accelerating Solutions to High Mach Euler Equations. (arXiv:2208.01687v1 [cs.LG])
    We propose an approach to solving partial differential equations (PDEs) using a set of neural networks which we call Neural Basis Functions (NBF). This NBF framework is a novel variation of the POD DeepONet operator learning approach where we regress a set of neural networks onto a reduced order Proper Orthogonal Decomposition (POD) basis. These networks are then used in combination with a branch network that ingests the parameters of the prescribed PDE to compute a reduced order approximation to the PDE. This approach is applied to the steady state Euler equations for high speed flow conditions (mach 10-30) where we consider the 2D flow around a cylinder which develops a shock condition. We then use the NBF predictions as initial conditions to a high fidelity Computational Fluid Dynamics (CFD) solver (CFD++) to show faster convergence. Lessons learned for training and implementing this algorithm will be presented as well.
    Maximal Independent Vertex Set applied to Graph Pooling. (arXiv:2208.01648v1 [cs.LG])
    Convolutional neural networks (CNN) have enabled major advances in image classification through convolution and pooling. In particular, image pooling transforms a connected discrete grid into a reduced grid with the same connectivity and allows reduction functions to take into account all the pixels of an image. However, a pooling satisfying such properties does not exist for graphs. Indeed, some methods are based on a vertex selection step which induces an important loss of information. Other methods learn a fuzzy clustering of vertex sets which induces almost complete reduced graphs. We propose to overcome both problems using a new pooling method, named MIVSPool. This method is based on a selection of vertices called surviving vertices using a Maximal Independent Vertex Set (MIVS) and an assignment of the remaining vertices to the survivors. Consequently, our method does not discard any vertex information nor artificially increase the density of the graph. Experimental results show an increase in accuracy for graph classification on various standard datasets.
    Vision-Based Safety System for Barrierless Human-Robot Collaboration. (arXiv:2208.02010v1 [cs.RO])
    Human safety has always been the main priority when working near an industrial robot. With the rise of Human-Robot Collaborative environments, physical barriers to avoiding collisions have been disappearing, increasing the risk of accidents and the need for solutions that ensure a safe Human-Robot Collaboration. This paper proposes a safety system that implements Speed and Separation Monitoring (SSM) type of operation. For this, safety zones are defined in the robot's workspace following current standards for industrial collaborative robots. A deep learning-based computer vision system detects, tracks, and estimates the 3D position of operators close to the robot. The robot control system receives the operator's 3D position and generates 3D representations of them in a simulation environment. Depending on the zone where the closest operator was detected, the robot stops or changes its operating speed. Three different operation modes in which the human and robot interact are presented. Results show that the vision-based system can correctly detect and classify in which safety zone an operator is located and that the different proposed operation modes ensure that the robot's reaction and stop time are within the required time limits to guarantee safety.
    Optimal Rates for Regularized Conditional Mean Embedding Learning. (arXiv:2208.01711v1 [stat.ML])
    We address the consistency of a kernel ridge regression estimate of the conditional mean embedding (CME), which is an embedding of the conditional distribution of $Y$ given $X$ into a target reproducing kernel Hilbert space $\mathcal{H}_Y$. The CME allows us to take conditional expectations of target RKHS functions, and has been employed in nonparametric causal and Bayesian inference. We address the misspecified setting, where the target CME is in the space of Hilbert-Schmidt operators acting from an input interpolation space between $\mathcal{H}_X$ and $L_2$, to $\mathcal{H}_Y$. This space of operators is shown to be isomorphic to a newly defined vector-valued interpolation space. Using this isomorphism, we derive a novel and adaptive statistical learning rate for the empirical CME estimator under the misspecified setting. Our analysis reveals that our rates match the optimal $O(\log n / n)$ rates without assuming $\mathcal{H}_Y$ to be finite dimensional. We further establish a lower bound on the learning rate, which shows that the obtained upper bound is optimal.
    Success of Uncertainty-Aware Deep Models Depends on Data Manifold Geometry. (arXiv:2208.01705v1 [cs.LG])
    For responsible decision making in safety-critical settings, machine learning models must effectively detect and process edge-case data. Although existing works show that predictive uncertainty is useful for these tasks, it is not evident from literature which uncertainty-aware models are best suited for a given dataset. Thus, we compare six uncertainty-aware deep learning models on a set of edge-case tasks: robustness to adversarial attacks as well as out-of-distribution and adversarial detection. We find that the geometry of the data sub-manifold is an important factor in determining the success of various models. Our finding suggests an interesting direction in the study of uncertainty-aware deep learning models.
    AI-driven Hypernetwork of Organic Chemistry: Network Statistics and Applications in Reaction Classification. (arXiv:2208.01647v1 [q-bio.MN])
    Rapid discovery of new reactions and molecules in recent years has been facilitated by the advancements in high throughput screening, accessibility to a much more complex chemical design space, and the development of accurate molecular modeling frameworks. A holistic study of the growing chemistry literature is, therefore, required that focuses on understanding the recent trends and extrapolating them into possible future trajectories. To this end, several network theory-based studies have been reported that use a directed graph representation of chemical reactions. Here, we perform a study based on representing chemical reactions as hypergraphs where the hyperedges represent chemical reactions and nodes represent the participating molecules. We use a standard reactions dataset to construct a hypernetwork and report its statistics such as degree distributions, average path length, assortativity or degree correlations, PageRank centrality, and graph-based clusters (or communities). We also compute each statistic for an equivalent directed graph representation of reactions to draw parallels and highlight differences between the two. To demonstrate the AI applicability of hypergraph reaction representation, we generate dense hypergraph embeddings and use them in the reaction classification problem. We conclude that the hypernetwork representation is flexible, preserves reaction context, and uncovers hidden insights that are otherwise not apparent in a traditional directed graph representation of chemical reactions.
    PolarMOT: How Far Can Geometric Relations Take Us in 3D Multi-Object Tracking?. (arXiv:2208.01957v1 [cs.CV])
    Most (3D) multi-object tracking methods rely on appearance-based cues for data association. By contrast, we investigate how far we can get by only encoding geometric relationships between objects in 3D space as cues for data-driven data association. We encode 3D detections as nodes in a graph, where spatial and temporal pairwise relations among objects are encoded via localized polar coordinates on graph edges. This representation makes our geometric relations invariant to global transformations and smooth trajectory changes, especially under non-holonomic motion. This allows our graph neural network to learn to effectively encode temporal and spatial interactions and fully leverage contextual and motion cues to obtain final scene interpretation by posing data association as edge classification. We establish a new state-of-the-art on nuScenes dataset and, more importantly, show that our method, PolarMOT, generalizes remarkably well across different locations (Boston, Singapore, Karlsruhe) and datasets (nuScenes and KITTI).
    Equivariant Disentangled Transformation for Domain Generalization under Combination Shift. (arXiv:2208.02011v1 [cs.LG])
    Machine learning systems may encounter unexpected problems when the data distribution changes in the deployment environment. A major reason is that certain combinations of domains and labels are not observed during training but appear in the test environment. Although various invariance-based algorithms can be applied, we find that the performance gain is often marginal. To formally analyze this issue, we provide a unique algebraic formulation of the combination shift problem based on the concepts of homomorphism, equivariance, and a refined definition of disentanglement. The algebraic requirements naturally derive a simple yet effective method, referred to as equivariant disentangled transformation (EDT), which augments the data based on the algebraic structures of labels and makes the transformation satisfy the equivariance and disentanglement requirements. Experimental results demonstrate that invariance may be insufficient, and it is important to exploit the equivariance structure in the combination shift problem.
    Exploring Generative Neural Temporal Point Process. (arXiv:2208.01874v1 [cs.LG])
    Temporal point process (TPP) is commonly used to model the asynchronous event sequence featuring occurrence timestamps and revealed by probabilistic models conditioned on historical impacts. While lots of previous works have focused on `goodness-of-fit' of TPP models by maximizing the likelihood, their predictive performance is unsatisfactory, which means the timestamps generated by models are far apart from true observations. Recently, deep generative models such as denoising diffusion and score matching models have achieved great progress in image generating tasks by demonstrating their capability of generating samples of high quality. However, there are no complete and unified works exploring and studying the potential of generative models in the context of event occurence modeling for TPP. In this work, we try to fill the gap by designing a unified \textbf{g}enerative framework for \textbf{n}eural \textbf{t}emporal \textbf{p}oint \textbf{p}rocess (\textsc{GNTPP}) model to explore their feasibility and effectiveness, and further improve models' predictive performance. Besides, in terms of measuring the historical impacts, we revise the attentive models which summarize influence from historical events with an adaptive reweighting term considering events' type relation and time intervals. Extensive experiments have been conducted to illustrate the improved predictive capability of \textsc{GNTPP} with a line of generative probabilistic decoders, and performance gain from the revised attention. To the best of our knowledge, this is the first work that adapts generative models in a complete unified framework and studies their effectiveness in the context of TPP. Our codebase including all the methods given in Section.5.1.1 is open in \url{https://github.com/BIRD-TAO/GNTPP}. We hope the code framework can facilitate future research in Neural TPPs.
    Maintaining Performance with Less Data. (arXiv:2208.02007v1 [cs.LG])
    We propose a novel method for training a neural network for image classification to reduce input data dynamically, in order to reduce the costs of training a neural network model. As Deep Learning tasks become more popular, their computational complexity increases, leading to more intricate algorithms and models which have longer runtimes and require more input data. The result is a greater cost on time, hardware, and environmental resources. By using data reduction techniques, we reduce the amount of work performed, and therefore the environmental impact of AI techniques, and with dynamic data reduction we show that accuracy may be maintained while reducing runtime by up to 50%, and reducing carbon emission proportionally.
    Adversarial Camouflage for Node Injection Attack on Graphs. (arXiv:2208.01819v1 [cs.LG])
    Node injection attacks against Graph Neural Networks (GNNs) have received emerging attention as a practical attack scenario, where the attacker injects malicious nodes instead of modifying node features or edges to degrade the performance of GNNs. Despite the initial success of node injection attacks, we find that the injected nodes by existing methods are easy to be distinguished from the original normal nodes by defense methods and limiting their attack performance in practice. To solve the above issues, we devote to camouflage node injection attack, i.e., camouflaging injected malicious nodes (structure/attributes) as the normal ones that appear legitimate/imperceptible to defense methods. The non-Euclidean nature of graph data and the lack of human prior brings great challenges to the formalization, implementation, and evaluation of camouflage on graphs. In this paper, we first propose and formulate the camouflage of injected nodes from both the fidelity and diversity of the ego networks centered around injected nodes. Then, we design an adversarial CAmouflage framework for Node injection Attack, namely CANA, to improve the camouflage while ensuring the attack performance. Several novel indicators for graph camouflage are further designed for a comprehensive evaluation. Experimental results demonstrate that when equipping existing node injection attack methods with our proposed CANA framework, the attack performance against defense methods as well as node camouflage is significantly improved.
    Localization and Classification of Parasitic Eggs in Microscopic Images Using an EfficientDet Detector. (arXiv:2208.01963v1 [cs.CV])
    IPIs caused by protozoan and helminth parasites are among the most common infections in humans in LMICs. They are regarded as a severe public health concern, as they cause a wide array of potentially detrimental health conditions. Researchers have been developing pattern recognition techniques for the automatic identification of parasite eggs in microscopic images. Existing solutions still need improvements to reduce diagnostic errors and generate fast, efficient, and accurate results. Our paper addresses this and proposes a multi-modal learning detector to localize parasitic eggs and categorize them into 11 categories. The experiments were conducted on the novel Chula-ParasiteEgg-11 dataset that was used to train both EfficientDet model with EfficientNet-v2 backbone and EfficientNet-B7+SVM. The dataset has 11,000 microscopic training images from 11 categories. Our results show robust performance with an accuracy of 92%, and an F1 score of 93%. Additionally, the IOU distribution illustrates the high localization capability of the detector.
    High-Speed Accurate Robot Control using Learned Forward Kinodynamics and Non-linear Least Squares Optimization. (arXiv:2206.08487v2 [cs.RO] UPDATED)
    Accurate control of robots at high speeds requires a control system that can take into account the kinodynamic interactions of the robot with the environment. Prior works on learning inverse kinodynamic (IKD) models of robots have shown success in capturing the complex kinodynamic effects. However, the types of control problems these approaches can be applied to are limited only to that of following pre-computed kinodynamically feasible trajectories. In this paper we present Optim-FKD, a new formulation for accurate, high-speed robot control that makes use of a learned forward kinodynamic (FKD) model and non-linear least squares optimization. Optim-FKD can be used for accurate, high speed control on any control task specifiable by a non-linear least squares objective. Optim-FKD can solve for control objectives such as path following and time-optimal control in real time, without needing access to pre-computed kinodynamically feasible trajectories. We empirically demonstrate these abilities of our approach through experiments on a scale one-tenth autonomous car. Our results show that Optim-FKD can follow desired trajectories more accurately and can find better solutions to optimal control problems than baseline approaches.
    Robust Graph Neural Networks using Weighted Graph Laplacian. (arXiv:2208.01853v1 [cs.LG])
    Graph neural network (GNN) is achieving remarkable performances in a variety of application domains. However, GNN is vulnerable to noise and adversarial attacks in input data. Making GNN robust against noises and adversarial attacks is an important problem. The existing defense methods for GNNs are computationally demanding and are not scalable. In this paper, we propose a generic framework for robustifying GNN known as Weighted Laplacian GNN (RWL-GNN). The method combines Weighted Graph Laplacian learning with the GNN implementation. The proposed method benefits from the positive semi-definiteness property of Laplacian matrix, feature smoothness, and latent features via formulating a unified optimization framework, which ensures the adversarial/noisy edges are discarded and connections in the graph are appropriately weighted. For demonstration, the experiments are conducted with Graph convolutional neural network(GCNN) architecture, however, the proposed framework is easily amenable to any existing GNN architecture. The simulation results with benchmark dataset establish the efficacy of the proposed method, both in accuracy and computational efficiency. Code can be accessed at https://github.com/Bharat-Runwal/RWL-GNN.
    DeepProphet2 -- A Deep Learning Gene Recommendation Engine. (arXiv:2208.01918v1 [q-bio.QM])
    New powerful tools for tackling life science problems have been created by recent advances in machine learning. The purpose of the paper is to discuss the potential advantages of gene recommendation performed by artificial intelligence (AI). Indeed, gene recommendation engines try to solve this problem: if the user is interested in a set of genes, which other genes are likely to be related to the starting set and should be investigated? This task was solved with a custom deep learning recommendation engine, DeepProphet2 (DP2), which is freely available to researchers worldwide via www.generecommender.com. Hereafter, insights behind the algorithm and its practical applications are illustrated. The gene recommendation problem can be addressed by mapping the genes to a metric space where a distance can be defined to represent the real semantic distance between them. To achieve this objective a transformer-based model has been trained on a well-curated freely available paper corpus, PubMed. The paper describes multiple optimization procedures that were employed to obtain the best bias-variance trade-off, focusing on embedding size and network depth. In this context, the model's ability to discover sets of genes implicated in diseases and pathways was assessed through cross-validation. A simple assumption guided the procedure: the network had no direct knowledge of pathways and diseases but learned genes' similarities and the interactions among them. Moreover, to further investigate the space where the neural network represents genes, the dimensionality of the embedding was reduced, and the results were projected onto a human-comprehensible space. In conclusion, a set of use cases illustrates the algorithm's potential applications in a real word setting.
    WrapperFL: A Model Agnostic Plug-in for Industrial Federated Learning. (arXiv:2206.10407v2 [cs.LG] UPDATED)
    Federated learning, as a privacy-preserving collaborative machine learning paradigm, has been gaining more and more attention in the industry. With the huge rise in demand, there have been many federated learning platforms that allow federated participants to set up and build a federated model from scratch. However, exiting platforms are highly intrusive, complicated, and hard to integrate with built machine learning models. For many real-world businesses that already have mature serving models, existing federated learning platforms have high entry barriers and development costs. This paper presents a simple yet practical federated learning plug-in inspired by ensemble learning, dubbed WrapperFL, allowing participants to build/join a federated system with existing models at minimal costs. The WrapperFL works in a plug-and-play way by simply attaching to the input and output interfaces of an existing model, without the need of re-development, significantly reducing the overhead of manpower and resources. We verify our proposed method on diverse tasks under heterogeneous data distributions and heterogeneous models. The experimental results demonstrate that WrapperFL can be successfully applied to a wide range of applications under practical settings and improves the local model with federated learning at a low cost.
    The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shift. (arXiv:2208.01857v1 [cs.LG])
    We study linear regression under covariate shift, where the marginal distribution over the input covariates differs in the source and the target domains, while the conditional distribution of the output given the input covariates is similar across the two domains. We investigate a transfer learning approach with pretraining on the source data and finetuning based on the target data (both conducted by online SGD) for this problem. We establish sharp instance-dependent excess risk upper and lower bounds for this approach. Our bounds suggest that for a large class of linear regression instances, transfer learning with $O(N^2)$ source data (and scarce or no target data) is as effective as supervised learning with $N$ target data. In addition, we show that finetuning, even with only a small amount of target data, could drastically reduce the amount of source data required by pretraining. Our theory sheds light on the effectiveness and limitation of pretraining as well as the benefits of finetuning for tackling covariate shift problems.
    A Lightweight Transmission Parameter Selection Scheme Using Reinforcement Learning for LoRaWAN. (arXiv:2208.01824v1 [cs.LG])
    The number of IoT devices is predicted to reach 125 billion by 2023. The growth of IoT devices will intensify the collisions between devices, degrading communication performance. Selecting appropriate transmission parameters, such as channel and spreading factor (SF), can effectively reduce the collisions between long-range (LoRa) devices. However, most of the schemes proposed in the current literature are not easy to implement on an IoT device with limited computational complexity and memory. To solve this issue, we propose a lightweight transmission-parameter selection scheme, i.e., a joint channel and SF selection scheme using reinforcement learning for low-power wide area networking (LoRaWAN). In the proposed scheme, appropriate transmission parameters can be selected by simple four arithmetic operations using only Acknowledge (ACK) information. Additionally, we theoretically analyze the computational complexity and memory requirement of our proposed scheme, which verified that our proposed scheme could select transmission parameters with extremely low computational complexity and memory requirement. Moreover, a large number of experiments were implemented on the LoRa devices in the real world to evaluate the effectiveness of our proposed scheme. The experimental results demonstrate the following main phenomena. (1) Compared to other lightweight transmission-parameter selection schemes, collisions between LoRa devices can be efficiently avoided by our proposed scheme in LoRaWAN irrespective of changes in the available channels. (2) The frame success rate (FSR) can be improved by selecting access channels and using SFs as opposed to only selecting access channels. (3) Since interference exists between adjacent channels, FSR and fairness can be improved by increasing the interval of adjacent available channels.
    EgPDE-Net: Building Continuous Neural Networks for Time Series Prediction with Exogenous Variables. (arXiv:2208.01913v1 [cs.LG])
    While exogenous variables have a major impact on performance improvement in time series analysis, inter-series correlation and time dependence among them are rarely considered in the present continuous methods. The dynamical systems of multivariate time series could be modelled with complex unknown partial differential equations (PDEs) which play a prominent role in many disciplines of science and engineering. In this paper, we propose a continuous-time model for arbitrary-step prediction to learn an unknown PDE system in multivariate time series whose governing equations are parameterised by self-attention and gated recurrent neural networks. The proposed model, \underline{E}xogenous-\underline{g}uided \underline{P}artial \underline{D}ifferential \underline{E}quation Network (EgPDE-Net), takes account of the relationships among the exogenous variables and their effects on the target series. Importantly, the model can be reduced into a regularised ordinary differential equation (ODE) problem with special designed regularisation guidance, which makes the PDE problem tractable to obtain numerical solutions and feasible to predict multiple future values of the target series at arbitrary time points. Extensive experiments demonstrate that our proposed model could achieve competitive accuracy over strong baselines: on average, it outperforms the best baseline by reducing $9.85\%$ on RMSE and $13.98\%$ on MAE for arbitrary-step prediction.
    Leveraging Smartphone Sensors for Detecting Abnormal Gait for Smart Wearable Mobile Technologies. (arXiv:2208.01876v1 [cs.HC])
    Walking is one of the most common modes of terrestrial locomotion for humans. Walking is essential for humans to perform most kinds of daily activities. When a person walks, there is a pattern in it, and it is known as gait. Gait analysis is used in sports and healthcare. We can analyze this gait in different ways, like using video captured by the surveillance cameras or depth image cameras in the lab environment. It also can be recognized by wearable sensors. e.g., accelerometer, force sensors, gyroscope, flexible goniometer, magneto resistive sensors, electromagnetic tracking system, force sensors, and electromyography (EMG). Analysis through these sensors required a lab condition, or users must wear these sensors. For detecting abnormality in gait action of a human, we need to incorporate the sensors separately. We can know about one's health condition by abnormal human gait after detecting it. Understanding a regular gait vs. abnormal gait may give insights to the health condition of the subject using the smart wearable technologies. Therefore, in this paper, we proposed a way to analyze abnormal human gait through smartphone sensors. Though smart devices like smartphones and smartwatches are used by most of the person nowadays. So, we can track down their gait using sensors of these intelligent wearable devices.
    Zero-Shot Style Transfer for Gesture Animation driven by Text and Speech using Adversarial Disentanglement of Multimodal Style Encoding. (arXiv:2208.01917v1 [cs.SD])
    Modeling virtual agents with behavior style is one factor for personalizing human agent interaction. We propose an efficient yet effective machine learning approach to synthesize gestures driven by prosodic features and text in the style of different speakers including those unseen during training. Our model performs zero shot multimodal style transfer driven by multimodal data from the PATS database containing videos of various speakers. We view style as being pervasive while speaking, it colors the communicative behaviors expressivity while speech content is carried by multimodal signals and text. This disentanglement scheme of content and style allows us to directly infer the style embedding even of speaker whose data are not part of the training phase, without requiring any further training or fine tuning. The first goal of our model is to generate the gestures of a source speaker based on the content of two audio and text modalities. The second goal is to condition the source speaker predicted gestures on the multimodal behavior style embedding of a target speaker. The third goal is to allow zero shot style transfer of speakers unseen during training without retraining the model. Our system consists of: (1) a speaker style encoder network that learns to generate a fixed dimensional speaker embedding style from a target speaker multimodal data and (2) a sequence to sequence synthesis network that synthesizes gestures based on the content of the input modalities of a source speaker and conditioned on the speaker style embedding. We evaluate that our model can synthesize gestures of a source speaker and transfer the knowledge of target speaker style variability to the gesture generation task in a zero shot setup. We convert the 2D gestures to 3D poses and produce 3D animations. We conduct objective and subjective evaluations to validate our approach and compare it with a baseline.
    Asynchronous Federated Learning for Edge-assisted Vehicular Networks. (arXiv:2208.01901v1 [cs.LG])
    Vehicular networks enable vehicles support real-time vehicular applications through training data. Due to the limited computing capability, vehicles usually transmit data to a road side unit (RSU) at the network edge to process data. However, vehicles are usually reluctant to share data with each other due to the privacy issue. For the traditional federated learning (FL), vehicles train the data locally to obtain a local model and then upload the local model to the RSU to update the global model, thus the data privacy can be protected through sharing model parameters instead of data. The traditional FL updates the global model synchronously, i.e., the RSU needs to wait for all vehicles to upload their models for the global model updating. However, vehicles may usually drive out of the coverage of the RSU before they obtain their local models through training, which reduces the accuracy of the global model. It is necessary to propose an asynchronous federated learning (AFL) to solve this problem, where the RSU updates the global model once it receives a local model from a vehicle. However, the amount of data, computing capability and vehicle mobility may affect the accuracy of the global model. In this paper, we jointly consider the amount of data, computing capability and vehicle mobility to design an AFL scheme to improve the accuracy of the global model. Extensive simulation experiments have demonstrated that our scheme outperforms the FL scheme
    A Tighter Analysis of Spectral Clustering, and Beyond. (arXiv:2208.01724v1 [cs.DS])
    This work studies the classical spectral clustering algorithm which embeds the vertices of some graph $G=(V_G, E_G)$ into $\mathbb{R}^k$ using $k$ eigenvectors of some matrix of $G$, and applies $k$-means to partition $V_G$ into $k$ clusters. Our first result is a tighter analysis on the performance of spectral clustering, and explains why it works under some much weaker condition than the ones studied in the literature. For the second result, we show that, by applying fewer than $k$ eigenvectors to construct the embedding, spectral clustering is able to produce better output for many practical instances; this result is the first of its kind in spectral clustering. Besides its conceptual and theoretical significance, the practical impact of our work is demonstrated by the empirical analysis on both synthetic and real-world datasets, in which spectral clustering produces comparable or better results with fewer than $k$ eigenvectors.
    Robust Learning of Deep Time Series Anomaly Detection Models with Contaminated Training Data. (arXiv:2208.01841v1 [cs.LG])
    Time series anomaly detection (TSAD) is an important data mining task with numerous applications in the IoT era. In recent years, a large number of deep neural network-based methods have been proposed, demonstrating significantly better performance than conventional methods on addressing challenging TSAD problems in a variety of areas. Nevertheless, these deep TSAD methods typically rely on a clean training dataset that is not polluted by anomalies to learn the "normal profile" of the underlying dynamics. This requirement is nontrivial since a clean dataset can hardly be provided in practice. Moreover, without the awareness of their robustness, blindly applying deep TSAD methods with potentially contaminated training data can possibly incur significant performance degradation in the detection phase. In this work, to tackle this important challenge, we firstly investigate the robustness of commonly used deep TSAD methods with contaminated training data which provides a guideline for applying these methods when the provided training data are not guaranteed to be anomaly-free. Furthermore, we propose a model-agnostic method which can effectively improve the robustness of learning mainstream deep TSAD models with potentially contaminated data. Experiment results show that our method can consistently prevent or mitigate performance degradation of mainstream deep TSAD models on widely used benchmark datasets.
    Link Prediction on Heterophilic Graphs via Disentangled Representation Learning. (arXiv:2208.01820v1 [cs.LG])
    Link prediction is an important task that has wide applications in various domains. However, the majority of existing link prediction approaches assume the given graph follows homophily assumption, and designs similarity-based heuristics or representation learning approaches to predict links. However, many real-world graphs are heterophilic graphs, where the homophily assumption does not hold, which challenges existing link prediction methods. Generally, in heterophilic graphs, there are many latent factors causing the link formation, and two linked nodes tend to be similar in one or two factors but might be dissimilar in other factors, leading to low overall similarity. Thus, one way is to learn disentangled representation for each node with each vector capturing the latent representation of a node on one factor, which paves a way to model the link formation in heterophilic graphs, resulting in better node representation learning and link prediction performance. However, the work on this is rather limited. Therefore, in this paper, we study a novel problem of exploring disentangled representation learning for link prediction on heterophilic graphs. We propose a novel framework DisenLink which can learn disentangled representations by modeling the link formation and perform factor-aware message-passing to facilitate link prediction. Extensive experiments on 13 real-world datasets demonstrate the effectiveness of DisenLink for link prediction on both heterophilic and hemophiliac graphs. Our codes are available at https://github.com/sjz5202/DisenLink
    Pyramidal Denoising Diffusion Probabilistic Models. (arXiv:2208.01864v1 [cs.CV])
    Diffusion models have demonstrated impressive image generation performance, and have been used in various computer vision tasks. Unfortunately, image generation using diffusion models is very time-consuming since it requires thousands of sampling steps. To address this problem, here we present a novel pyramidal diffusion model to generate high resolution images starting from much coarser resolution images using a single score function trained with a positional embedding. This enables a time-efficient sampling for image generation, and also solves the low batch size problem when training with limited resources. Furthermore, we show that the proposed approach can be efficiently used for multi-scale super-resolution problem using a single score function.
    Graph Regularized Nonnegative Latent Factor Analysis Model for Temporal Link Prediction in Cryptocurrency Transaction Networks. (arXiv:2208.01923v1 [cs.LG])
    With the development of blockchain technology, the cryptocurrency based on blockchain technology is becoming more and more popular. This gave birth to a huge cryptocurrency transaction network has received widespread attention. Link prediction learning structure of network is helpful to understand the mechanism of network, so it is also widely studied in cryptocurrency network. However, the dynamics of cryptocurrency transaction networks have been neglected in the past researches. We use graph regularized method to link past transaction records with future transactions. Based on this, we propose a single latent factor-dependent, non-negative, multiplicative and graph regularized-incorporated update (SLF-NMGRU) algorithm and further propose graph regularized nonnegative latent factor analysis (GrNLFA) model. Finally, experiments on a real cryptocurrency transaction network show that the proposed method improves both the accuracy and the computational efficiency
    Understanding Adversarial Imitation Learning in Small Sample Regime: A Stage-coupled Analysis. (arXiv:2208.01899v1 [cs.LG])
    Imitation learning learns a policy from expert trajectories. While the expert data is believed to be crucial for imitation quality, it was found that a kind of imitation learning approach, adversarial imitation learning (AIL), can have exceptional performance. With as little as only one expert trajectory, AIL can match the expert performance even in a long horizon, on tasks such as locomotion control. There are two mysterious points in this phenomenon. First, why can AIL perform well with only a few expert trajectories? Second, why does AIL maintain good performance despite the length of the planning horizon? In this paper, we theoretically explore these two questions. For a total-variation-distance-based AIL (called TV-AIL), our analysis shows a horizon-free imitation gap $\mathcal O(\{\min\{1, \sqrt{|\mathcal S|/N} \})$ on a class of instances abstracted from locomotion control tasks. Here $|\mathcal S|$ is the state space size for a tabular Markov decision process, and $N$ is the number of expert trajectories. We emphasize two important features of our bound. First, this bound is meaningful in both small and large sample regimes. Second, this bound suggests that the imitation gap of TV-AIL is at most 1 regardless of the planning horizon. Therefore, this bound can explain the empirical observation. Technically, we leverage the structure of multi-stage policy optimization in TV-AIL and present a new stage-coupled analysis via dynamic programming
    A Deep Learning Approach to Detect Lean Blowout in Combustion Systems. (arXiv:2208.01871v1 [cs.LG])
    Lean combustion is environment friendly with low NOx emissions and also provides better fuel efficiency in a combustion system. However, approaching towards lean combustion can make engines more susceptible to lean blowout. Lean blowout (LBO) is an undesirable phenomenon that can cause sudden flame extinction leading to sudden loss of power. During the design stage, it is quite challenging for the scientists to accurately determine the optimal operating limits to avoid sudden LBO occurrence. Therefore, it is crucial to develop accurate and computationally tractable frameworks for online LBO detection in low NOx emission engines. To the best of our knowledge, for the first time, we propose a deep learning approach to detect lean blowout in combustion systems. In this work, we utilize a laboratory-scale combustor to collect data for different protocols. We start far from LBO for each protocol and gradually move towards the LBO regime, capturing a quasi-static time series dataset at each condition. Using one of the protocols in our dataset as the reference protocol and with conditions annotated by domain experts, we find a transition state metric for our trained deep learning model to detect LBO in the other test protocols. We find that our proposed approach is more accurate and computationally faster than other baseline models to detect the transitions to LBO. Therefore, we recommend this method for real-time performance monitoring in lean combustion engines.
    Digital Twin-Assisted Efficient Reinforcement Learning for Edge Task Scheduling. (arXiv:2208.01781v1 [cs.LG])
    Task scheduling is a critical problem when one user offloads multiple different tasks to the edge server. When a user has multiple tasks to offload and only one task can be transmitted to server at a time, while server processes tasks according to the transmission order, the problem is NP-hard. However, it is difficult for traditional optimization methods to quickly obtain the optimal solution, while approaches based on reinforcement learning face with the challenge of excessively large action space and slow convergence. In this paper, we propose a Digital Twin (DT)-assisted RL-based task scheduling method in order to improve the performance and convergence of the RL. We use DT to simulate the results of different decisions made by the agent, so that one agent can try multiple actions at a time, or, similarly, multiple agents can interact with environment in parallel in DT. In this way, the exploration efficiency of RL can be significantly improved via DT, and thus RL can converges faster and local optimality is less likely to happen. Particularly, two algorithms are designed to made task scheduling decisions, i.e., DT-assisted asynchronous Q-learning (DTAQL) and DT-assisted exploring Q-learning (DTEQL). Simulation results show that both algorithms significantly improve the convergence speed of Q-learning by increasing the exploration efficiency.
    A data-centric weak supervised learning for highway traffic incident detection. (arXiv:2112.09792v2 [cs.LG] UPDATED)
    Using the data from loop detector sensors for near-real-time detection of traffic incidents in highways is crucial to averting major traffic congestion. While recent supervised machine learning methods offer solutions to incident detection by leveraging human-labeled incident data, the false alarm rate is often too high to be used in practice. Specifically, the inconsistency in the human labeling of the incidents significantly affects the performance of supervised learning models. To that end, we focus on a data-centric approach to improve the accuracy and reduce the false alarm rate of traffic incident detection on highways. We develop a weak supervised learning workflow to generate high-quality training labels for the incident data without the ground truth labels, and we use those generated labels in the supervised learning setup for final detection. This approach comprises three stages. First, we introduce a data preprocessing and curation pipeline that processes traffic sensor data to generate high-quality training data through leveraging labeling functions, which can be domain knowledge-related or simple heuristic rules. Second, we evaluate the training data generated by weak supervision using three supervised learning models -- random forest, k-nearest neighbors, and a support vector machine ensemble -- and long short-term memory classifiers. The results show that the accuracy of all of the models improves significantly after using the training data generated by weak supervision. Third, we develop an online real-time incident detection approach that leverages the model ensemble and the uncertainty quantification while detecting incidents. Overall, we show that our proposed weak supervised learning workflow achieves a high incident detection rate (0.90) and low false alarm rate (0.08).
    A Roadmap for Greater Public Use of Privacy-Sensitive Government Data: Workshop Report. (arXiv:2208.01636v1 [cs.CR])
    Government agencies collect and manage a wide range of ever-growing datasets. While such data has the potential to support research and evidence-based policy making, there are concerns that the dissemination of such data could infringe upon the privacy of the individuals (or organizations) from whom such data was collected. To appraise the current state of data sharing, as well as learn about opportunities for stimulating such sharing at a faster pace, a virtual workshop was held on May 21st and 26th, 2021, sponsored by the National Science Foundation and National Institute of Standards and Technologies, where a multinational collection of researchers and practitioners were brought together to discuss their experiences and learn about recently developed technologies for managing privacy while sharing data. The workshop specifically focused on challenges and successes in government data sharing at various levels. The first day focused on successful examples of new technology applied to sharing of public data, including formal privacy techniques, synthetic data, and cryptographic approaches. Day two emphasized brainstorming sessions on some of the challenges and directions to address them.
    Two-Stream Transformer Architecture for Long Video Understanding. (arXiv:2208.01753v1 [cs.CV])
    Pure vision transformer architectures are highly effective for short video classification and action recognition tasks. However, due to the quadratic complexity of self attention and lack of inductive bias, transformers are resource intensive and suffer from data inefficiencies. Long form video understanding tasks amplify data and memory efficiency problems in transformers making current approaches unfeasible to implement on data or memory restricted domains. This paper introduces an efficient Spatio-Temporal Attention Network (STAN) which uses a two-stream transformer architecture to model dependencies between static image features and temporal contextual features. Our proposed approach can classify videos up to two minutes in length on a single GPU, is data efficient, and achieves SOTA performance on several long video understanding tasks.
    A Transformational Characterization of Unconditionally Equivalent Bayesian Networks. (arXiv:2203.00521v2 [stat.ML] UPDATED)
    We consider the problem of characterizing Bayesian networks up to unconditional equivalence, i.e., when directed acyclic graphs (DAGs) have the same set of unconditional $d$-separation statements. Each unconditional equivalence class (UEC) is uniquely represented with an undirected graph whose clique structure encodes the members of the class. Via this structure, we provide a transformational characterization of unconditional equivalence; i.e., we show that two DAGs are in the same UEC if and only if one can be transformed into the other via a finite sequence of specified moves. We also extend this characterization to the essential graphs representing the Markov equivalence classes (MECs) in the UEC. UECs partition the space of MECs and are easily estimable from marginal independence tests. Thus, a characterization of unconditional equivalence has applications in methods that involve searching the space of MECs of Bayesian networks.
    Post-hoc Interpretability based Parameter Selection for Data Oriented Nuclear Reactor Accident Diagnosis System. (arXiv:2208.01805v1 [eess.SY])
    During applying data-oriented diagnosis systems to distinguishing the type of and evaluating the severity of nuclear power plant initial events, it is of vital importance to decide which parameters to be used as the system input. However, although several diagnosis systems have already achieved acceptable performance in diagnosis precision and speed, hardly have the researchers discussed the method of monitoring point choosing and its layout. For this reason, redundant measuring data are used to train the diagnostic model, leading to high uncertainty of the classification, extra training time consumption, and higher probability of overfitting while training. In this study, a method of choosing thermal hydraulics parameters of a nuclear power plant is proposed, using the theory of post-hoc interpretability theory in deep learning. At the start, a novel Time-sequential Residual Convolutional Neural Network (TRES-CNN) diagnosis model is introduced to identify the position and hydrodynamic diameter of breaks in LOCA, using 38 parameters manually chosen on HPR1000 empirically. Afterwards, post-hoc interpretability methods are applied to evaluate the attributions of diagnosis model's outputs, deciding which 15 parameters to be more decisive in diagnosing LOCA details. The results show that the TRES-CNN based diagnostic model successfully predicts the position and size of breaks in LOCA via selected 15 parameters of HPR1000, with 25% of time consumption while training the model compared the process using total 38 parameters. In addition, the relative diagnostic accuracy error is within 1.5 percent compared with the model using parameters chosen empirically, which can be regarded as the same amount of diagnostic reliability.
    RemixIT: Continual self-training of speech enhancement models via bootstrapped remixing. (arXiv:2202.08862v3 [cs.SD] UPDATED)
    We present RemixIT, a simple yet effective self-supervised method for training speech enhancement without the need of a single isolated in-domain speech nor a noise waveform. Our approach overcomes limitations of previous methods which make them dependent on clean in-domain target signals and thus, sensitive to any domain mismatch between train and test samples. RemixIT is based on a continuous self-training scheme in which a pre-trained teacher model on out-of-domain data infers estimated pseudo-target signals for in-domain mixtures. Then, by permuting the estimated clean and noise signals and remixing them together, we generate a new set of bootstrapped mixtures and corresponding pseudo-targets which are used to train the student network. Vice-versa, the teacher periodically refines its estimates using the updated parameters of the latest student models. Experimental results on multiple speech enhancement datasets and tasks not only show the superiority of our method over prior approaches but also showcase that RemixIT can be combined with any separation model as well as be applied towards any semi-supervised and unsupervised domain adaptation task. Our analysis, paired with empirical evidence, sheds light on the inside functioning of our self-training scheme wherein the student model keeps obtaining better performance while observing severely degraded pseudo-targets.
    Deep Reinforcement Learning for Multi-Agent Interaction. (arXiv:2208.01769v1 [cs.MA])
    The development of autonomous agents which can interact with other agents to accomplish a given task is a core area of research in artificial intelligence and machine learning. Towards this goal, the Autonomous Agents Research Group develops novel machine learning algorithms for autonomous systems control, with a specific focus on deep reinforcement learning and multi-agent reinforcement learning. Research problems include scalable learning of coordinated agent policies and inter-agent communication; reasoning about the behaviours, goals, and composition of other agents from limited observations; and sample-efficient learning based on intrinsic motivation, curriculum learning, causal inference, and representation learning. This article provides a broad overview of the ongoing research portfolio of the group and discusses open problems for future directions.
    Matrix Decomposition and Applications. (arXiv:2201.00145v2 [math.NA] UPDATED)
    In 1954, Alston S. Householder published Principles of Numerical Analysis, one of the first modern treatments on matrix decomposition that favored a (block) LU decomposition-the factorization of a matrix into the product of lower and upper triangular matrices. And now, matrix decomposition has become a core technology in machine learning, largely due to the development of the back propagation algorithm in fitting a neural network. The sole aim of this survey is to give a self-contained introduction to concepts and mathematical tools in numerical linear algebra and matrix analysis in order to seamlessly introduce matrix decomposition techniques and their applications in subsequent sections. However, we clearly realize our inability to cover all the useful and interesting results concerning matrix decomposition and given the paucity of scope to present this discussion, e.g., the separated analysis of the Euclidean space, Hermitian space, Hilbert space, and things in the complex domain. We refer the reader to literature in the field of linear algebra for a more detailed introduction to the related fields.
    A cloud platform for automating and sharing analysis of raw simulation data from high throughput polymer molecular dynamics simulations. (arXiv:2208.01692v1 [cond-mat.mtrl-sci])
    Open material databases storing hundreds of thousands of material structures and their corresponding properties have become the cornerstone of modern computational materials science. Yet, the raw outputs of the simulations, such as the trajectories from molecular dynamics simulations and charge densities from density functional theory calculations, are generally not shared due to their huge size. In this work, we describe a cloud-based platform to facilitate the sharing of raw data and enable the fast post-processing in the cloud to extract new properties defined by the user. As an initial demonstration, our database currently includes 6286 molecular dynamics trajectories for amorphous polymer electrolytes and 5.7 terabytes of data. We create a public analysis library at https://github.com/TRI-AMDD/htp_md to extract multiple properties from the raw data, using both expert designed functions and machine learning models. The analysis is run automatically with computation in the cloud, and results then populate a database that can be accessed publicly. Our platform encourages users to contribute both new trajectory data and analysis functions via public interfaces. Newly analyzed properties will be incorporated into the database. Finally, we create a front-end user interface at https://www.htpmd.matr.io for browsing and visualization of our data. We envision the platform to be a new way of sharing raw data and new insights for the computational materials science community.
    Quantum-Inspired Tensor Neural Networks for Partial Differential Equations. (arXiv:2208.02235v1 [cs.LG])
    Partial Differential Equations (PDEs) are used to model a variety of dynamical systems in science and engineering. Recent advances in deep learning have enabled us to solve them in a higher dimension by addressing the curse of dimensionality in new ways. However, deep learning methods are constrained by training time and memory. To tackle these shortcomings, we implement Tensor Neural Networks (TNN), a quantum-inspired neural network architecture that leverages Tensor Network ideas to improve upon deep learning approaches. We demonstrate that TNN provide significant parameter savings while attaining the same accuracy as compared to the classical Dense Neural Network (DNN). In addition, we also show how TNN can be trained faster than DNN for the same accuracy. We benchmark TNN by applying them to solve parabolic PDEs, specifically the Black-Scholes-Barenblatt equation, widely used in financial pricing theory, empirically showing the advantages of TNN over DNN. Further examples, such as the Hamilton-Jacobi-Bellman equation, are also discussed.
    Provable Model-based Nonlinear Bandit and Reinforcement Learning: Shelve Optimism, Embrace Virtual Curvature. (arXiv:2102.04168v5 [cs.LG] UPDATED)
    This paper studies model-based bandit and reinforcement learning (RL) with nonlinear function approximations. We propose to study convergence to approximate local maxima because we show that global convergence is statistically intractable even for one-layer neural net bandit with a deterministic reward. For both nonlinear bandit and RL, the paper presents a model-based algorithm, Virtual Ascent with Online Model Learner (ViOlin), which provably converges to a local maximum with sample complexity that only depends on the sequential Rademacher complexity of the model class. Our results imply novel global or local regret bounds on several concrete settings such as linear bandit with finite or sparse model class, and two-layer neural net bandit. A key algorithmic insight is that optimism may lead to over-exploration even for two-layer neural net model class. On the other hand, for convergence to local maxima, it suffices to maximize the virtual return if the model can also reasonably predict the size of the gradient and Hessian of the real return.
    Internet of Things (IoT) based ECG System for Rural Health Care. (arXiv:2208.02226v1 [eess.SP])
    Nearly 30% of the people in the rural areas of Bangladesh are below the poverty level. Moreover, due to the unavailability of modernized healthcare-related technology, nursing and diagnosis facilities are limited for rural people. Therefore, rural people are deprived of proper healthcare. In this perspective, modern technology can be facilitated to mitigate their health problems. ECG sensing tools are interfaced with the human chest, and requisite cardiovascular data is collected through an IoT device. These data are stored in the cloud incorporates with the MQTT and HTTP servers. An innovative IoT-based method for ECG monitoring systems on cardiovascular or heart patients has been suggested in this study. The ECG signal parameters P, Q, R, S, T are collected, pre-processed, and predicted to monitor the cardiovascular conditions for further health management. The machine learning algorithm is used to determine the significance of ECG signal parameters and error rate. The logistic regression model fitted the better agreements between the train and test data. The prediction has been performed to determine the variation of PQRST quality and its suitability in the ECG Monitoring System. Considering the values of quality parameters, satisfactory results are obtained. The proposed IoT-based ECG system reduces the health care cost and complexity of cardiovascular diseases in the future.
    ProcK: Machine Learning for Knowledge-Intensive Processes. (arXiv:2109.04881v2 [cs.LG] UPDATED)
    We present a novel methodology to build powerful predictive process models. Our method, denoted ProcK (Process & Knowledge), relies not only on sequential input data in the form of event logs, but can learn to use a knowledge graph to incorporate information about the attribute values of the events and their mutual relationships. The idea is realized by mapping event attributes to nodes of a knowledge graph and training a sequence model alongside a graph neural network in an end-to-end fashion. This hybrid approach substantially enhances the flexibility and applicability of predictive process monitoring, as both the static and dynamic information residing in the databases of organizations can be directly taken as input data. We demonstrate the potential of ProcK by applying it to a number of predictive process monitoring tasks, including tasks with knowledge graphs available as well as an existing process monitoring benchmark where no such graph is given. The experiments provide evidence that our methodology achieves state-of-the-art performance and improves predictive power when a knowledge graph is available.
    Multimodal sensor fusion in the latent representation space. (arXiv:2208.02183v1 [cs.AI])
    A new method for multimodal sensor fusion is introduced. The technique relies on a two-stage process. In the first stage, a multimodal generative model is constructed from unlabelled training data. In the second stage, the generative model serves as a reconstruction prior and the search manifold for the sensor fusion tasks. The method also handles cases where observations are accessed only via subsampling i.e. compressed sensing. We demonstrate the effectiveness and excellent performance on a range of multimodal fusion experiments such as multisensory classification, denoising, and recovery from subsampled observations.
    AdaCat: Adaptive Categorical Discretization for Autoregressive Models. (arXiv:2208.02246v1 [cs.LG])
    Autoregressive generative models can estimate complex continuous data distributions, like trajectory rollouts in an RL environment, image intensities, and audio. Most state-of-the-art models discretize continuous data into several bins and use categorical distributions over the bins to approximate the continuous data distribution. The advantage is that the categorical distribution can easily express multiple modes and are straightforward to optimize. However, such approximation cannot express sharp changes in density without using significantly more bins, making it parameter inefficient. We propose an efficient, expressive, multimodal parameterization called Adaptive Categorical Discretization (AdaCat). AdaCat discretizes each dimension of an autoregressive model adaptively, which allows the model to allocate density to fine intervals of interest, improving parameter efficiency. AdaCat generalizes both categoricals and quantile-based regression. AdaCat is a simple add-on to any discretization-based distribution estimator. In experiments, AdaCat improves density estimation for real-world tabular data, images, audio, and trajectories, and improves planning in model-based offline RL.
    A Screening Strategy for Structured Optimization Involving Nonconvex $\ell_{q,p}$ Regularization. (arXiv:2208.02161v1 [cs.LG])
    In this paper, we develop a simple yet effective screening rule strategy to improve the computational efficiency in solving structured optimization involving nonconvex $\ell_{q,p}$ regularization. Based on an iteratively reweighted $\ell_1$ (IRL1) framework, the proposed screening rule works like a preprocessing module that potentially removes the inactive groups before starting the subproblem solver, thereby reducing the computational time in total. This is mainly achieved by heuristically exploiting the dual subproblem information during each iteration.Moreover, we prove that our screening rule can remove all inactive variables in a finite number of iterations of the IRL1 method. Numerical experiments illustrate the efficiency of our screening rule strategy compared with several state-of-the-art algorithms.
    Masked Vision and Language Modeling for Multi-modal Representation Learning. (arXiv:2208.02131v1 [cs.CV])
    In this paper, we study how to use masked signal modeling in vision and language (V+L) representation learning. Instead of developing masked language modeling (MLM) and masked image modeling (MIM) independently, we propose to build joint masked vision and language modeling, where the masked signal of one modality is reconstructed with the help from another modality. This is motivated by the nature of image-text paired data that both of the image and the text convey almost the same information but in different formats. The masked signal reconstruction of one modality conditioned on another modality can also implicitly learn cross-modal alignment between language tokens and image patches. Our experiments on various V+L tasks show that the proposed method not only achieves state-of-the-art performances by using a large amount of data, but also outperforms the other competitors by a significant margin in the regimes of limited training data.
    KPI-BERT: A Joint Named Entity Recognition and Relation Extraction Model for Financial Reports. (arXiv:2208.02140v1 [cs.CL])
    We present KPI-BERT, a system which employs novel methods of named entity recognition (NER) and relation extraction (RE) to extract and link key performance indicators (KPIs), e.g. "revenue" or "interest expenses", of companies from real-world German financial documents. Specifically, we introduce an end-to-end trainable architecture that is based on Bidirectional Encoder Representations from Transformers (BERT) combining a recurrent neural network (RNN) with conditional label masking to sequentially tag entities before it classifies their relations. Our model also introduces a learnable RNN-based pooling mechanism and incorporates domain expert knowledge by explicitly filtering impossible relations. We achieve a substantially higher prediction performance on a new practical dataset of German financial reports, outperforming several strong baselines including a competing state-of-the-art span-based entity tagging approach.
    Efficient Fine-Tuning of Compressed Language Models with Learners. (arXiv:2208.02070v1 [cs.CL])
    Fine-tuning BERT-based models is resource-intensive in memory, computation, and time. While many prior works aim to improve inference efficiency via compression techniques, e.g., pruning, these works do not explicitly address the computational challenges of training to downstream tasks. We introduce Learner modules and priming, novel methods for fine-tuning that exploit the overparameterization of pre-trained language models to gain benefits in convergence speed and resource utilization. Learner modules navigate the double bind of 1) training efficiently by fine-tuning a subset of parameters, and 2) training effectively by ensuring quick convergence and high metric scores. Our results on DistilBERT demonstrate that learners perform on par with or surpass the baselines. Learners train 7x fewer parameters than state-of-the-art methods on GLUE. On CoLA, learners fine-tune 20% faster, and have significantly lower resource utilization.
    Adaptive Domain Generalization via Online Disagreement Minimization. (arXiv:2208.01996v1 [cs.CV])
    Deep neural networks suffer from significant performance deterioration when there exists distribution shift between deployment and training. Domain Generalization (DG) aims to safely transfer a model to unseen target domains by only relying on a set of source domains. Although various DG approaches have been proposed, a recent study named DomainBed, reveals that most of them do not beat the simple Empirical Risk Minimization (ERM). To this end, we propose a general framework that is orthogonal to existing DG algorithms and could improve their performance consistently. Unlike previous DG works that stake on a static source model to be hopefully a universal one, our proposed AdaODM adaptively modifies the source model at test time for different target domains. Specifically, we create multiple domain-specific classifiers upon a shared domain-generic feature extractor. The feature extractor and classifiers are trained in an adversarial way, where the feature extractor embeds the input samples into a domain-invariant space, and the multiple classifiers capture the distinct decision boundaries that each of them relates to a specific source domain. During testing, distribution differences between target and source domains could be effectively measured by leveraging prediction disagreement among source classifiers. By fine-tuning source models to minimize the disagreement at test time, target domain features are well aligned to the invariant feature space. We verify AdaODM on two popular DG methods, namely ERM and CORAL, and four DG benchmarks, namely VLCS, PACS, OfficeHome, and TerraIncognita. The results show AdaODM stably improves the generalization capacity on unseen domains and achieves state-of-the-art performance.
    A Convolutional Persistence Transform. (arXiv:2208.02107v1 [math.AT])
    We consider a new topological feauturization of $d$-dimensional images, obtained by convolving images with various filters before computing persistence. Viewing a convolution filter as a motif within an image, the persistence diagram of the resulting convolution describes the way the motif is distributed throughout that image. This pipeline, which we call convolutional persistence, extends the capacity of topology to observe patterns in image data. Indeed, we prove that (generically speaking) for any two images one can find some filter for which they produce different persistence diagrams, so that the collection of all possible convolutional persistence diagrams for a given image is an injective invariant. This is proven by showing convolutional persistence to be a special case of another topological invariant, the Persistent Homology Transform. Other advantages of convolutional persistence are improved stability and robustness to noise, greater flexibility for data-dependent vectorizations, and reduced computational complexity for convolutions with large stride vectors. Additionally, we have a suite of experiments showing that convolutions greatly improve the predictive power of persistence on a host of classification tasks, even if one uses random filters and vectorizes the resulting diagrams by recording only their total persistences.
    Multi-Feature Vision Transformer via Self-Supervised Representation Learning for Improvement of COVID-19 Diagnosis. (arXiv:2208.01843v1 [eess.IV])
    The role of chest X-ray (CXR) imaging, due to being more cost-effective, widely available, and having a faster acquisition time compared to CT, has evolved during the COVID-19 pandemic. To improve the diagnostic performance of CXR imaging a growing number of studies have investigated whether supervised deep learning methods can provide additional support. However, supervised methods rely on a large number of labeled radiology images, which is a time-consuming and complex procedure requiring expert clinician input. Due to the relative scarcity of COVID-19 patient data and the costly labeling process, self-supervised learning methods have gained momentum and has been proposed achieving comparable results to fully supervised learning approaches. In this work, we study the effectiveness of self-supervised learning in the context of diagnosing COVID-19 disease from CXR images. We propose a multi-feature Vision Transformer (ViT) guided architecture where we deploy a cross-attention mechanism to learn information from both original CXR images and corresponding enhanced local phase CXR images. We demonstrate the performance of the baseline self-supervised learning models can be further improved by leveraging the local phase-based enhanced CXR images. By using 10\% labeled CXR scans, the proposed model achieves 91.10\% and 96.21\% overall accuracy tested on total 35,483 CXR images of healthy (8,851), regular pneumonia (6,045), and COVID-19 (18,159) scans and shows significant improvement over state-of-the-art techniques. Code is available https://github.com/endiqq/Multi-Feature-ViT
    V-Coder: Adaptive AutoEncoder for Semantic Disclosure in Knowledge Graphs. (arXiv:2208.01735v1 [cs.AI])
    Semantic Web or Knowledge Graphs (KG) emerged to one of the most important information source for intelligent systems requiring access to structured knowledge. One of the major challenges is the extraction and processing of unambiguous information from textual data. Following the human perception, overlapping semantic linkages between two named entities become clear due to our common-sense about the context a relationship lives in which is not the case when we look at it from an automatically driven process of a machine. In this work, we are interested in the problem of Relational Resolution within the scope of KGs, i.e, we are investigating the inherent semantic of relationships between entities within a network. We propose a new adaptive AutoEncoder, called V-Coder, to identify relations inherently connecting entities from different domains. Those relations can be considered as being ambiguous and are candidates for disentanglement. Likewise to the Adaptive Learning Theory (ART), our model learns new patterns from the KG by increasing units in a competitive layer without discarding the previous observed patterns whilst learning the quality of each relation separately. The evaluation on real-world datasets of Freebase, Yago and NELL shows that the V-Coder is not only able to recover links from corrupted input data, but also shows that the semantic disclosure of relations in a KG show the tendency to improve link prediction. A semantic evaluation wraps the evaluation up.
    Reconstructing Sparse Illicit Supply Networks: A Case Study of Multiplex Drug Trafficking Networks. (arXiv:2208.01739v1 [cs.SI])
    The network structure provides critical information for law enforcement agencies to develop effective strategies to interdict illicit supply networks. However, the complete structure of covert networks is often unavailable, thus it is crucially important to develop approaches to infer a more complete structure of covert networks. In this paper, we work on real-world multiplex drug trafficking networks extracted from an investigation report. A statistical approach built on the EM algorithm (DegEM) as well as other methods based on structural similarity are applied to reconstruct the multiplex drug trafficking network given different fractions of observed nodes and links. It is found that DegEM approach achieves the best predictive performance in terms of several accuracy metrics. Meanwhile, structural similarity-based methods perform poorly in reconstructing the drug trafficking networks due to the sparsity of links between nodes in the network. The inferred multiplex networks can be leveraged to (i) inform the decision-making on monitoring covert networks as well as allocating limited resources for collecting additional information to improve the reconstruction accuracy and (ii) develop more effective interdiction strategies.
    A New Implementation of Federated Learning for Privacy and Security Enhancement. (arXiv:2208.01826v1 [cs.CR])
    Motivated by the ever-increasing concerns on personal data privacy and the rapidly growing data volume at local clients, federated learning (FL) has emerged as a new machine learning setting. An FL system is comprised of a central parameter server and multiple local clients. It keeps data at local clients and learns a centralized model by sharing the model parameters learned locally. No local data needs to be shared, and privacy can be well protected. Nevertheless, since it is the model instead of the raw data that is shared, the system can be exposed to the poisoning model attacks launched by malicious clients. Furthermore, it is challenging to identify malicious clients since no local client data is available on the server. Besides, membership inference attacks can still be performed by using the uploaded model to estimate the client's local data, leading to privacy disclosure. In this work, we first propose a model update based federated averaging algorithm to defend against Byzantine attacks such as additive noise attacks and sign-flipping attacks. The individual client model initialization method is presented to provide further privacy protections from the membership inference attacks by hiding the individual local machine learning model. When combining these two schemes, privacy and security can be both effectively enhanced. The proposed schemes are proved to converge experimentally under non-IID data distribution when there are no attacks. Under Byzantine attacks, the proposed schemes perform much better than the classical model based FedAvg algorithm.
    Cross-Modal Alignment Learning of Vision-Language Conceptual Systems. (arXiv:2208.01744v1 [cs.CV])
    Human infants learn the names of objects and develop their own conceptual systems without explicit supervision. In this study, we propose methods for learning aligned vision-language conceptual systems inspired by infants' word learning mechanisms. The proposed model learns the associations of visual objects and words online and gradually constructs cross-modal relational graph networks. Additionally, we also propose an aligned cross-modal representation learning method that learns semantic representations of visual objects and words in a self-supervised manner based on the cross-modal relational graph networks. It allows entities of different modalities with conceptually the same meaning to have similar semantic representation vectors. We quantitatively and qualitatively evaluate our method, including object-to-word mapping and zero-shot learning tasks, showing that the proposed model significantly outperforms the baselines and that each conceptual system is topologically aligned.
    No Pattern, No Recognition: a Survey about Reproducibility and Distortion Issues of Text Clustering and Topic Modeling. (arXiv:2208.01712v1 [cs.LG])
    Extracting knowledge from unlabeled texts using machine learning algorithms can be complex. Document categorization and information retrieval are two applications that may benefit from unsupervised learning (e.g., text clustering and topic modeling), including exploratory data analysis. However, the unsupervised learning paradigm poses reproducibility issues. The initialization can lead to variability depending on the machine learning algorithm. Furthermore, the distortions can be misleading when regarding cluster geometry. Amongst the causes, the presence of outliers and anomalies can be a determining factor. Despite the relevance of initialization and outlier issues for text clustering and topic modeling, the authors did not find an in-depth analysis of them. This survey provides a systematic literature review (2011-2022) of these subareas and proposes a common terminology since similar procedures have different terms. The authors describe research opportunities, trends, and open issues. The appendices summarize the theoretical background of the text vectorization, the factorization, and the clustering algorithms that are directly or indirectly related to the reviewed works.
    Analysis of the Spatio-temporal Dynamics of COVID-19 in Massachusetts via Spectral Graph Wavelet Theory. (arXiv:2208.01749v1 [cs.SI])
    The rapid spread of COVID-19 disease has had a significant impact on the world. In this paper, we study COVID-19 data interpretation and visualization using open-data sources for 351 cities and towns in Massachusetts from December 6, 2020 to September 25, 2021. Because cities are embedded in rather complex transportation networks, we construct the spatio-temporal dynamic graph model, in which the graph attention neural network is utilized as a deep learning method to learn the pandemic transition probability among major cities in Massachusetts. Using the spectral graph wavelet transform (SGWT), we process the COVID-19 data on the dynamic graph, which enables us to design effective tools to analyze and detect spatio-temporal patterns in the pandemic spreading. We design a new node classification method, which effectively identifies the anomaly cities based on spectral graph wavelet coefficients. It can assist administrations or public health organizations in monitoring the spread of the pandemic and developing preventive measures. Unlike most work focusing on the evolution of confirmed cases over time, we focus on the spatio-temporal patterns of pandemic evolution among cities. Through the data analysis and visualization, a better understanding of the epidemiological development at the city level is obtained and can be helpful with city-specific surveillance.
    Convex-Concave Min-Max Stackelberg Games. (arXiv:2110.05192v7 [cs.GT] UPDATED)
    Min-max optimization problems (i.e., min-max games) have been attracting a great deal of attention because of their applicability to a wide range of machine learning problems. Although significant progress has been made recently, the literature to date has focused on games with independent strategy sets; little is known about solving games with dependent strategy sets, which can be characterized as min-max Stackelberg games. We introduce two first-order methods that solve a large class of convex-concave min-max Stackelberg games, and show that our methods converge in polynomial time. Min-max Stackelberg games were first studied by Wald, under the posthumous name of Wald's maximin model, a variant of which is the main paradigm used in robust optimization, which means that our methods can likewise solve many convex robust optimization problems. We observe that the computation of competitive equilibria in Fisher markets also comprises a min-max Stackelberg game. Further, we demonstrate the efficacy and efficiency of our algorithms in practice by computing competitive equilibria in Fisher markets with varying utility structures. Our experiments suggest potential ways to extend our theoretical results, by demonstrating how different smoothness properties can affect the convergence rate of our algorithms.
    Diagnosis of Paratuberculosis in Histopathological Images Based on Explainable Artificial Intelligence and Deep Learning. (arXiv:2208.01674v1 [eess.IV])
    Artificial intelligence holds great promise in medical imaging, especially histopathological imaging. However, artificial intelligence algorithms cannot fully explain the thought processes during decision-making. This situation has brought the problem of explainability, i.e., the black box problem, of artificial intelligence applications to the agenda: an algorithm simply responds without stating the reasons for the given images. To overcome the problem and improve the explainability, explainable artificial intelligence (XAI) has come to the fore, and piqued the interest of many researchers. Against this backdrop, this study examines a new and original dataset using the deep learning algorithm, and visualizes the output with gradient-weighted class activation mapping (Grad-CAM), one of the XAI applications. Afterwards, a detailed questionnaire survey was conducted with the pathologists on these images. Both the decision-making processes and the explanations were verified, and the accuracy of the output was tested. The research results greatly help pathologists in the diagnosis of paratuberculosis.  ( 2 min )
    Curvature-informed multi-task learning for graph networks. (arXiv:2208.01684v1 [cs.LG])
    Properties of interest for crystals and molecules, such as band gap, elasticity, and solubility, are generally related to each other: they are governed by the same underlying laws of physics. However, when state-of-the-art graph neural networks attempt to predict multiple properties simultaneously (the multi-task learning (MTL) setting), they frequently underperform a suite of single property predictors. This suggests graph networks may not be fully leveraging these underlying similarities. Here we investigate a potential explanation for this phenomenon: the curvature of each property's loss surface significantly varies, leading to inefficient learning. This difference in curvature can be assessed by looking at spectral properties of the Hessians of each property's loss function, which is done in a matrix-free manner via randomized numerical linear algebra. We evaluate our hypothesis on two benchmark datasets (Materials Project (MP) and QM8) and consider how these findings can inform the training of novel multi-task learning models.  ( 2 min )
    Differentially Private Vertical Federated Clustering. (arXiv:2208.01700v1 [cs.CR])
    In many applications, multiple parties have private data regarding the same set of users but on disjoint sets of attributes, and a server wants to leverage the data to train a model. To enable model learning while protecting the privacy of the data subjects, we need vertical federated learning (VFL) techniques, where the data parties share only information for training the model, instead of the private data. However, it is challenging to ensure that the shared information maintains privacy while learning accurate models. To the best of our knowledge, the algorithm proposed in this paper is the first practical solution for differentially private vertical federated k-means clustering, where the server can obtain a set of global centers with a provable differential privacy guarantee. Our algorithm assumes an untrusted central server that aggregates differentially private local centers and membership encodings from local data parties. It builds a weighted grid as the synopsis of the global dataset based on the received information. Final centers are generated by running any k-means algorithm on the weighted grid. Our approach for grid weight estimation uses a novel, light-weight, and differentially private set intersection cardinality estimation algorithm based on the Flajolet-Martin sketch. To improve the estimation accuracy in the setting with more than two data parties, we further propose a refined version of the weights estimation algorithm and a parameter tuning strategy to reduce the final k-means utility to be close to that in the central private setting. We provide theoretical utility analysis and experimental evaluation results for the cluster centers computed by our algorithm and show that our approach performs better both theoretically and empirically than the two baselines based on existing techniques.  ( 3 min )
    Adapting Triplet Importance of Implicit Feedback for Personalized Recommendation. (arXiv:2208.01709v1 [cs.IR])
    Implicit feedback is frequently used for developing personalized recommendation services due to its ubiquity and accessibility in real-world systems. In order to effectively utilize such information, most research adopts the pairwise ranking method on constructed training triplets (user, positive item, negative item) and aims to distinguish between positive items and negative items for each user. However, most of these methods treat all the training triplets equally, which ignores the subtle difference between different positive or negative items. On the other hand, even though some other works make use of the auxiliary information (e.g., dwell time) of user behaviors to capture this subtle difference, such auxiliary information is hard to obtain. To mitigate the aforementioned problems, we propose a novel training framework named Triplet Importance Learning (TIL), which adaptively learns the importance score of training triplets. We devise two strategies for the importance score generation and formulate the whole procedure as a bilevel optimization, which does not require any rule-based design. We integrate the proposed training procedure with several Matrix Factorization (MF)- and Graph Neural Network (GNN)-based recommendation models, demonstrating the compatibility of our framework. Via a comparison using three real-world datasets with many state-of-the-art methods, we show that our proposed method outperforms the best existing models by 3-21\% in terms of Recall@k for the top-k recommendation.  ( 3 min )
  • Open

    Beyond neural scaling laws: beating power law scaling via data pruning. (arXiv:2206.14486v2 [cs.LG] UPDATED)
    Widely observed neural scaling laws, in which error falls off as a power of the training set size, model size, or both, have driven substantial performance improvements in deep learning. However, these improvements through scaling alone require considerable costs in compute and energy. Here we focus on the scaling of error with dataset size and show how both in theory and practice we can break beyond power law scaling and reduce it to exponential scaling instead if we have access to a high-quality data pruning metric that ranks the order in which training examples should be discarded to achieve any pruned dataset size. We then test this new exponential scaling prediction with pruned dataset size empirically, and indeed observe better than power law scaling performance on ResNets trained on CIFAR-10, SVHN, and ImageNet. Given the importance of finding high-quality pruning metrics, we perform the first large-scale benchmarking study of ten different data pruning metrics on ImageNet. We find most existing high performing metrics scale poorly to ImageNet, while the best are computationally intensive and require labels for every image. We therefore developed a new simple, cheap and scalable self-supervised pruning metric that demonstrates comparable performance to the best supervised metrics. Overall, our work suggests that the discovery of good data-pruning metrics may provide a viable path forward to substantially improved neural scaling laws, thereby reducing the resource costs of modern deep learning.
    Off-Policy Confidence Interval Estimation with Confounded Markov Decision Process. (arXiv:2202.10589v4 [stat.ML] UPDATED)
    This paper is concerned with constructing a confidence interval for a target policy's value offline based on a pre-collected observational data in infinite horizon settings. Most of the existing works assume no unmeasured variables exist that confound the observed actions. This assumption, however, is likely to be violated in real applications such as healthcare and technological industries. In this paper, we show that with some auxiliary variables that mediate the effect of actions on the system dynamics, the target policy's value is identifiable in a confounded Markov decision process. Based on this result, we develop an efficient off-policy value estimator that is robust to potential model misspecification and provide rigorous uncertainty quantification. Our method is justified by theoretical results, simulated and real datasets obtained from ridesharing companies. A Python implementation of the proposed procedure is available at https://github.com/Mamba413/cope.
    Free Energy Evaluation Using Marginalized Annealed Importance Sampling. (arXiv:2204.03784v2 [stat.ML] UPDATED)
    The evaluation of the free energy of a stochastic model is considered a significant issue in various fields of physics and machine learning. However, the exact free energy evaluation is computationally infeasible because the free energy expression includes an intractable partition function. Annealed importance sampling (AIS) is a type of importance sampling based on the Markov chain Monte Carlo method that is similar to a simulated annealing and can effectively approximate the free energy. This study proposes an AIS-based approach, which is referred to as marginalized AIS (mAIS). The statistical efficiency of mAIS is investigated in detail based on theoretical and numerical perspectives. Based on the investigation, it is proved that mAIS is more effective than AIS under a certain condition.
    Combinatorial Causal Bandits. (arXiv:2206.01995v2 [cs.LG] UPDATED)
    In combinatorial causal bandits (CCB), the learning agent chooses at most $K$ variables in each round to intervene, collects feedback from the observed variables, with the goal of minimizing expected regret on the target variable $Y$. Different from all prior studies on causal bandits, CCB needs to deal with exponentially large action space. We study under the context of binary generalized linear models (BGLMs) with a succinct parametric representation of the causal models. We present the algorithm BGLM-OFU for Markovian BGLMs (i.e. no hidden variables) based on the maximum likelihood estimation method, and show that it achieves $O(\sqrt{T}\log T)$ regret, where $T$ is the time horizon. For the special case of linear models with hidden variables, we apply causal inference techniques such as the do-calculus to convert the original model into a Markovian model, and then show that our BGLM-OFU algorithm and another algorithm based on the linear regression both solve such linear models with hidden variables. Our novelty includes (a) considering the combinatorial intervention action space, (b) considering general causal models including ones with hidden variables, (c) integrating and adapting techniques from diverse studies such as generalized linear bandits and online influence maximization, and (d) not relying on unrealistic assumptions such as knowing the joint distribution of the parents of $Y$ under all interventions used in some prior studies.
    Robust Training under Label Noise by Over-parameterization. (arXiv:2202.14026v2 [cs.LG] UPDATED)
    Recently, over-parameterized deep networks, with increasingly more network parameters than training samples, have dominated the performances of modern machine learning. However, when the training data is corrupted, it has been well-known that over-parameterized networks tend to overfit and do not generalize. In this work, we propose a principled approach for robust training of over-parameterized deep networks in classification tasks where a proportion of training labels are corrupted. The main idea is yet very simple: label noise is sparse and incoherent with the network learned from clean data, so we model the noise and learn to separate it from the data. Specifically, we model the label noise via another sparse over-parameterization term, and exploit implicit algorithmic regularizations to recover and separate the underlying corruptions. Remarkably, when trained using such a simple method in practice, we demonstrate state-of-the-art test accuracy against label noise on a variety of real datasets. Furthermore, our experimental results are corroborated by theory on simplified linear models, showing that exact separation between sparse noise and low-rank data can be achieved under incoherent conditions. The work opens many interesting directions for improving over-parameterized models by using sparse over-parameterization and implicit regularization.
    Diffusion bridges vector quantized Variational AutoEncoders. (arXiv:2202.04895v2 [stat.ML] UPDATED)
    Vector Quantized-Variational AutoEncoders (VQ-VAE) are generative models based on discrete latent representations of the data, where inputs are mapped to a finite set of learned embeddings.To generate new samples, an autoregressive prior distribution over the discrete states must be trained separately. This prior is generally very complex and leads to slow generation. In this work, we propose a new model to train the prior and the encoder/decoder networks simultaneously. We build a diffusion bridge between a continuous coded vector and a non-informative prior distribution. The latent discrete states are then given as random functions of these continuous vectors. We show that our model is competitive with the autoregressive prior on the mini-Imagenet and CIFAR dataset and is efficient in both optimization and sampling. Our framework also extends the standard VQ-VAE and enables end-to-end training.
    A Transformational Characterization of Unconditionally Equivalent Bayesian Networks. (arXiv:2203.00521v2 [stat.ML] UPDATED)
    We consider the problem of characterizing Bayesian networks up to unconditional equivalence, i.e., when directed acyclic graphs (DAGs) have the same set of unconditional $d$-separation statements. Each unconditional equivalence class (UEC) is uniquely represented with an undirected graph whose clique structure encodes the members of the class. Via this structure, we provide a transformational characterization of unconditional equivalence; i.e., we show that two DAGs are in the same UEC if and only if one can be transformed into the other via a finite sequence of specified moves. We also extend this characterization to the essential graphs representing the Markov equivalence classes (MECs) in the UEC. UECs partition the space of MECs and are easily estimable from marginal independence tests. Thus, a characterization of unconditional equivalence has applications in methods that involve searching the space of MECs of Bayesian networks.
    AUC Maximization in the Era of Big Data and AI: A Survey. (arXiv:2203.15046v3 [cs.LG] UPDATED)
    Area under the ROC curve, a.k.a. AUC, is a measure of choice for assessing the performance of a classifier for imbalanced data. AUC maximization refers to a learning paradigm that learns a predictive model by directly maximizing its AUC score. It has been studied for more than two decades dating back to late 90s and a huge amount of work has been devoted to AUC maximization since then. Recently, stochastic AUC maximization for big data and deep AUC maximization for deep learning have received increasing attention and yielded dramatic impact for solving real-world problems. However, to the best our knowledge there is no comprehensive survey of related works for AUC maximization. This paper aims to address the gap by reviewing the literature in the past two decades. We not only give a holistic view of the literature but also present detailed explanations and comparisons of different papers from formulations to algorithms and theoretical guarantees. We also identify and discuss remaining and emerging issues for deep AUC maximization, and provide suggestions on topics for future work.
    auton-survival: an Open-Source Package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Event Data. (arXiv:2204.07276v4 [cs.LG] UPDATED)
    Applications of machine learning in healthcare often require working with time-to-event prediction tasks including prognostication of an adverse event, re-hospitalization or death. Such outcomes are typically subject to censoring due to loss of follow up. Standard machine learning methods cannot be applied in a straightforward manner to datasets with censored outcomes. In this paper, we present auton-survival, an open-source repository of tools to streamline working with censored time-to-event or survival data. auton-survival includes tools for survival regression, adjustment in the presence of domain shift, counterfactual estimation, phenotyping for risk stratification, evaluation, as well as estimation of treatment effects. Through real world case studies employing a large subset of the SEER oncology incidence data, we demonstrate the ability of auton-survival to rapidly support data scientists in answering complex health and epidemiological questions.
    Stochastic Neighbor Embedding with Gaussian and Student-t Distributions: Tutorial and Survey. (arXiv:2009.10301v2 [stat.ML] UPDATED)
    Stochastic Neighbor Embedding (SNE) is a manifold learning and dimensionality reduction method with a probabilistic approach. In SNE, every point is consider to be the neighbor of all other points with some probability and this probability is tried to be preserved in the embedding space. SNE considers Gaussian distribution for the probability in both the input and embedding spaces. However, t-SNE uses the Student-t and Gaussian distributions in these spaces, respectively. In this tutorial and survey paper, we explain SNE, symmetric SNE, t-SNE (or Cauchy-SNE), and t-SNE with general degrees of freedom. We also cover the out-of-sample extension and acceleration for these methods.  ( 2 min )
    AdaCat: Adaptive Categorical Discretization for Autoregressive Models. (arXiv:2208.02246v1 [cs.LG])
    Autoregressive generative models can estimate complex continuous data distributions, like trajectory rollouts in an RL environment, image intensities, and audio. Most state-of-the-art models discretize continuous data into several bins and use categorical distributions over the bins to approximate the continuous data distribution. The advantage is that the categorical distribution can easily express multiple modes and are straightforward to optimize. However, such approximation cannot express sharp changes in density without using significantly more bins, making it parameter inefficient. We propose an efficient, expressive, multimodal parameterization called Adaptive Categorical Discretization (AdaCat). AdaCat discretizes each dimension of an autoregressive model adaptively, which allows the model to allocate density to fine intervals of interest, improving parameter efficiency. AdaCat generalizes both categoricals and quantile-based regression. AdaCat is a simple add-on to any discretization-based distribution estimator. In experiments, AdaCat improves density estimation for real-world tabular data, images, audio, and trajectories, and improves planning in model-based offline RL.  ( 2 min )
    Stochastic Gradient Line Bayesian Optimization for Efficient Noise-Robust Optimization of Parameterized Quantum Circuits. (arXiv:2111.07952v2 [quant-ph] UPDATED)
    Optimizing parameterized quantum circuits is a key routine in using near-term quantum devices. However, the existing algorithms for such optimization require an excessive number of quantum-measurement shots for estimating expectation values of observables and repeating many iterations, whose cost has been a critical obstacle for practical use. We develop an efficient alternative optimization algorithm, stochastic gradient line Bayesian optimization (SGLBO), to address this problem. SGLBO reduces the measurement-shot cost by estimating an appropriate direction of updating circuit parameters based on stochastic gradient descent (SGD) and further utilizing Bayesian optimization (BO) to estimate the optimal step size for each iteration in SGD. In addition, we formulate an adaptive measurement-shot strategy and introduce a technique of suffix averaging to reduce the effect of statistical and hardware noise. Our numerical simulation demonstrates that the SGLBO augmented with these techniques can drastically reduce the measurement-shot cost, improve the accuracy, and make the optimization noise-robust.  ( 2 min )
    Stable and Interpretable Unrolled Dictionary Learning. (arXiv:2106.00058v5 [cs.LG] UPDATED)
    The dictionary learning problem, representing data as a combination of a few atoms, has long stood as a popular method for learning representations in statistics and signal processing. The most popular dictionary learning algorithm alternates between sparse coding and dictionary update steps, and a rich literature has studied its theoretical convergence. The success of dictionary learning relies on access to a "good" initial estimate of the dictionary and the ability of the sparse coding step to provide an unbiased estimate of the code. The growing popularity of unrolled sparse coding networks has led to the empirical finding that backpropagation through such networks performs dictionary learning. We offer the theoretical analysis of these empirical results through PUDLE, a Provable Unrolled Dictionary LEarning method. We provide conditions on the network initialization and data distribution sufficient to recover and preserve the support of the latent code. Additionally, we address two challenges; first, the vanilla unrolled sparse coding computes a biased code estimate, and second, gradients during backpropagated learning can become unstable. We show approaches to reduce the bias of the code estimate in the forward pass, and that of the dictionary estimate in the backward pass. We propose strategies to resolve the learning instability by tuning network parameters and modifying the loss function. Overall, we highlight the impact of loss, unrolling, and backpropagation on convergence. We complement our findings through synthetic and image denoising experiments. Finally, we demonstrate PUDLE's interpretability, a driving factor in designing deep networks based on iterative optimizations, by building a mathematical relation between network weights, its output, and the training set.  ( 3 min )
    Debiasing In-Sample Policy Performance for Small-Data, Large-Scale Optimization. (arXiv:2107.12438v4 [math.OC] UPDATED)
    Motivated by the poor performance of cross-validation in settings where data are scarce, we propose a novel estimator of the out-of-sample performance of a policy in data-driven optimization.Our approach exploits the optimization problem's sensitivity analysis to estimate the gradient of the optimal objective value with respect to the amount of noise in the data and uses the estimated gradient to debias the policy's in-sample performance. Unlike cross-validation techniques, our approach avoids sacrificing data for a test set, utilizes all data when training and, hence, is well-suited to settings where data are scarce. We prove bounds on the bias and variance of our estimator for optimization problems with uncertain linear objectives but known, potentially non-convex, feasible regions. For more specialized optimization problems where the feasible region is "weakly-coupled" in a certain sense, we prove stronger results. Specifically, we provide explicit high-probability bounds on the error of our estimator that hold uniformly over a policy class and depends on the problem's dimension and policy class's complexity. Our bounds show that under mild conditions, the error of our estimator vanishes as the dimension of the optimization problem grows, even if the amount of available data remains small and constant. Said differently, we prove our estimator performs well in the small-data, large-scale regime. Finally, we numerically compare our proposed method to state-of-the-art approaches through a case-study on dispatching emergency medical response services using real data. Our method provides more accurate estimates of out-of-sample performance and learns better-performing policies.  ( 3 min )
    Policy Evaluation for Temporal and/or Spatial Dependent Experiments in Ride-sourcing Platforms. (arXiv:2202.10887v2 [stat.ME] UPDATED)
    Policy evaluation based on A/B testing has attracted considerable interest in digital marketing, but such evaluation in ride-sourcing platforms (e.g., Uber and Didi) is not well studied primarily due to the complex structure of their temporal and/or spatial dependent experiments. Motivated by policy evaluation in ride-sourcing platforms, the aim of this paper is to establish causal relationship between platform's policies and outcomes of interest under a switchback design. We propose a novel potential outcome framework based on a temporal varying coefficient decision process (VCDP) model to capture the dynamic treatment effects in temporal dependent experiments. We further characterize the average treatment effect by decomposing it as the sum of direct effect (DE) and indirect effect (IE). We develop estimation and inference procedures for both DE and IE. Furthermore, we propose a spatio-temporal VCDP to deal with spatiotemporal dependent experiments. For both VCDP models, we establish the statistical properties (e.g., weak convergence and asymptotic power) of our estimation and inference procedures. We conduct extensive simulations to investigate the finite-sample performance of the proposed estimation and inference procedures. We examine how our VCDP models can help improve policy evaluation for various dispatching and dispositioning policies in Didi.  ( 3 min )
    Unified Framework for Spectral Dimensionality Reduction, Maximum Variance Unfolding, and Kernel Learning By Semidefinite Programming: Tutorial and Survey. (arXiv:2106.15379v2 [stat.ML] UPDATED)
    This is a tutorial and survey paper on unification of spectral dimensionality reduction methods, kernel learning by Semidefinite Programming (SDP), Maximum Variance Unfolding (MVU) or Semidefinite Embedding (SDE), and its variants. We first explain how the spectral dimensionality reduction methods can be unified as kernel Principal Component Analysis (PCA) with different kernels. This unification can be interpreted as eigenfunction learning or representation of kernel in terms of distance matrix. Then, since the spectral methods are unified as kernel PCA, we say let us learn the best kernel for unfolding the manifold of data to its maximum variance. We first briefly introduce kernel learning by SDP for the transduction task. Then, we explain MVU in detail. Various versions of supervised MVU using nearest neighbors graph, by class-wise unfolding, by Fisher criterion, and by colored MVU are explained. We also explain out-of-sample extension of MVU using eigenfunctions and kernel mapping. Finally, we introduce other variants of MVU including action respecting embedding, relaxed MVU, and landmark MVU for big data.  ( 3 min )
    Multimodal Controller for Generative Models. (arXiv:2002.02572v7 [cs.LG] UPDATED)
    Class-conditional generative models are crucial tools for data generation from user-specified class labels. Existing approaches for class-conditional generative models require nontrivial modifications of backbone generative architectures to model conditional information fed into the model. This paper introduces a plug-and-play module named `multimodal controller' to generate multimodal data without introducing additional learning parameters. In the absence of the controllers, our model reduces to non-conditional generative models. We test the efficacy of multimodal controllers on CIFAR10, COIL100, and Omniglot benchmark datasets. We demonstrate that multimodal controlled generative models (including VAE, PixelCNN, Glow, and GAN) can generate class-conditional images of significantly better quality when compared with conditional generative models. Moreover, we show that multimodal controlled models can also create novel modalities of images.  ( 2 min )
    Optimised one-class classification performance. (arXiv:2102.02618v3 [cs.LG] UPDATED)
    We provide a thorough treatment of one-class classification with hyperparameter optimisation for five data descriptors: Support Vector Machine (SVM), Nearest Neighbour Distance (NND), Localised Nearest Neighbour Distance (LNND), Local Outlier Factor (LOF) and Average Localised Proximity (ALP). The hyperparameters of SVM and LOF have to be optimised through cross-validation, while NND, LNND and ALP allow an efficient form of leave-one-out validation and the reuse of a single nearest-neighbour query. We experimentally evaluate the effect of hyperparameter optimisation with 246 classification problems drawn from 50 datasets. From a selection of optimisation algorithms, the recent Malherbe-Powell proposal optimises the hyperparameters of all data descriptors most efficiently. We calculate the increase in test AUROC and the amount of overfitting as a function of the number of hyperparameter evaluations. After 50 evaluations, ALP and SVM significantly outperform LOF, NND and LNND, and LOF and NND outperform LNND. The performance of ALP and SVM is comparable, but ALP can be optimised more efficiently so constitutes a good default choice. Alternatively, using validation AUROC as a selection criterion between ALP or SVM gives the best overall result, and NND is the least computationally demanding option. We thus end up with a clear trade-off between three choices, allowing practitioners to make an informed decision.  ( 3 min )
    Hierarchical Multiple-Instance Data Classification with Costly Features. (arXiv:1911.08756v5 [cs.LG] UPDATED)
    We motivate our research with a real-world problem of classifying malicious web domains using a remote service that provides various information. Crucially, some of the information can be further analyzed into a certain depth and this process sequentially creates a tree of hierarchically structured multiple-instance data. Each request sent to the remote service is associated with a cost (e.g., time or another cost per request) and the objective is to maximize the accuracy, constrained with a budget. We present a generic framework able to work with a class of similar problems. Our method is based on Classification with Costly Features (CwCF), Hierarchical Multiple-Instance Learning (HMIL) and hierarchical decomposition of the action space. It works with samples described as partially-observed trees of features of various types (similar to a JSON/XML file), which allows to model data with complex structure. The process is modeled as a Markov Decision Process (MDP), where a state represents acquired features, and actions select yet unknown ones. The policy is trained with deep reinforcement learning and we demonstrate our method with both real-world and synthetic data.  ( 3 min )
    Centroids Matching: an efficient Continual Learning approach operating in the embedding space. (arXiv:2208.02048v1 [cs.LG])
    Catastrophic forgetting (CF) occurs when a neural network loses the information previously learned while training on a set of samples from a different distribution, i.e., a new task. Existing approaches have achieved remarkable results in mitigating CF, especially in a scenario called task incremental learning. However, this scenario is not realistic, and limited work has been done to achieve good results on more realistic scenarios. In this paper, we propose a novel regularization method called Centroids Matching, that, inspired by meta-learning approaches, fights CF by operating in the feature space produced by the neural network, achieving good results while requiring a small memory footprint. Specifically, the approach classifies the samples directly using the feature vectors produced by the neural network, by matching those vectors with the centroids representing the classes from the current task, or all the tasks up to that point. Centroids Matching is faster than competing baselines, and it can be exploited to efficiently mitigate CF, by preserving the distances between the embedding space produced by the model when past tasks were over, and the one currently produced, leading to a method that achieves high accuracy on all the tasks, without using an external memory when operating on easy scenarios, or using a small one for more realistic ones. Extensive experiments demonstrate that Centroids Matching achieves accuracy gains on multiple datasets and scenarios.  ( 3 min )
    Flow Annealed Importance Sampling Bootstrap. (arXiv:2208.01893v1 [cs.LG])
    Normalizing flows are tractable density models that can approximate complicated target distributions, e.g. Boltzmann distributions of physical systems. However, current methods for training flows either suffer from mode-seeking behavior, use samples from the target generated beforehand by expensive MCMC simulations, or use stochastic losses that have very high variance. To avoid these problems, we augment flows with annealed importance sampling (AIS) and minimize the mass covering $\alpha$-divergence with $\alpha=2$, which minimizes importance weight variance. Our method, Flow AIS Bootstrap (FAB), uses AIS to generate samples in regions where the flow is a poor approximation of the target, facilitating the discovery of new modes. We target with AIS the minimum variance distribution for the estimation of the $\alpha$-divergence via importance sampling. We also use a prioritized buffer to store and reuse AIS samples. These two features significantly improve FAB's performance. We apply FAB to complex multimodal targets and show that we can approximate them very accurately where previous methods fail. To the best of our knowledge, we are the first to learn the Boltzmann distribution of the alanine dipeptide molecule using only the unnormalized target density and without access to samples generated via Molecular Dynamics (MD) simulations: FAB produces better results than training via maximum likelihood on MD samples while using 100 times fewer target evaluations. After reweighting samples with importance weights, we obtain unbiased histograms of dihedral angles that are almost identical to the ground truth ones.  ( 3 min )
    Robust PCA for Anomaly Detection and Data Imputation in Seasonal Time Series. (arXiv:2208.01998v1 [stat.ML])
    We propose a robust principal component analysis (RPCA) framework to recover low-rank and sparse matrices from temporal observations. We develop an online version of the batch temporal algorithm in order to process larger datasets or streaming data. We empirically compare the proposed approaches with different RPCA frameworks and show their effectiveness in practical situations.  ( 2 min )
    The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shift. (arXiv:2208.01857v1 [cs.LG])
    We study linear regression under covariate shift, where the marginal distribution over the input covariates differs in the source and the target domains, while the conditional distribution of the output given the input covariates is similar across the two domains. We investigate a transfer learning approach with pretraining on the source data and finetuning based on the target data (both conducted by online SGD) for this problem. We establish sharp instance-dependent excess risk upper and lower bounds for this approach. Our bounds suggest that for a large class of linear regression instances, transfer learning with $O(N^2)$ source data (and scarce or no target data) is as effective as supervised learning with $N$ target data. In addition, we show that finetuning, even with only a small amount of target data, could drastically reduce the amount of source data required by pretraining. Our theory sheds light on the effectiveness and limitation of pretraining as well as the benefits of finetuning for tackling covariate shift problems.  ( 2 min )
    No Pattern, No Recognition: a Survey about Reproducibility and Distortion Issues of Text Clustering and Topic Modeling. (arXiv:2208.01712v1 [cs.LG])
    Extracting knowledge from unlabeled texts using machine learning algorithms can be complex. Document categorization and information retrieval are two applications that may benefit from unsupervised learning (e.g., text clustering and topic modeling), including exploratory data analysis. However, the unsupervised learning paradigm poses reproducibility issues. The initialization can lead to variability depending on the machine learning algorithm. Furthermore, the distortions can be misleading when regarding cluster geometry. Amongst the causes, the presence of outliers and anomalies can be a determining factor. Despite the relevance of initialization and outlier issues for text clustering and topic modeling, the authors did not find an in-depth analysis of them. This survey provides a systematic literature review (2011-2022) of these subareas and proposes a common terminology since similar procedures have different terms. The authors describe research opportunities, trends, and open issues. The appendices summarize the theoretical background of the text vectorization, the factorization, and the clustering algorithms that are directly or indirectly related to the reviewed works.  ( 3 min )
    Pyramidal Denoising Diffusion Probabilistic Models. (arXiv:2208.01864v1 [cs.CV])
    Diffusion models have demonstrated impressive image generation performance, and have been used in various computer vision tasks. Unfortunately, image generation using diffusion models is very time-consuming since it requires thousands of sampling steps. To address this problem, here we present a novel pyramidal diffusion model to generate high resolution images starting from much coarser resolution images using a single score function trained with a positional embedding. This enables a time-efficient sampling for image generation, and also solves the low batch size problem when training with limited resources. Furthermore, we show that the proposed approach can be efficiently used for multi-scale super-resolution problem using a single score function.  ( 2 min )
    Curvature-informed multi-task learning for graph networks. (arXiv:2208.01684v1 [cs.LG])
    Properties of interest for crystals and molecules, such as band gap, elasticity, and solubility, are generally related to each other: they are governed by the same underlying laws of physics. However, when state-of-the-art graph neural networks attempt to predict multiple properties simultaneously (the multi-task learning (MTL) setting), they frequently underperform a suite of single property predictors. This suggests graph networks may not be fully leveraging these underlying similarities. Here we investigate a potential explanation for this phenomenon: the curvature of each property's loss surface significantly varies, leading to inefficient learning. This difference in curvature can be assessed by looking at spectral properties of the Hessians of each property's loss function, which is done in a matrix-free manner via randomized numerical linear algebra. We evaluate our hypothesis on two benchmark datasets (Materials Project (MP) and QM8) and consider how these findings can inform the training of novel multi-task learning models.  ( 2 min )
    A Tighter Analysis of Spectral Clustering, and Beyond. (arXiv:2208.01724v1 [cs.DS])
    This work studies the classical spectral clustering algorithm which embeds the vertices of some graph $G=(V_G, E_G)$ into $\mathbb{R}^k$ using $k$ eigenvectors of some matrix of $G$, and applies $k$-means to partition $V_G$ into $k$ clusters. Our first result is a tighter analysis on the performance of spectral clustering, and explains why it works under some much weaker condition than the ones studied in the literature. For the second result, we show that, by applying fewer than $k$ eigenvectors to construct the embedding, spectral clustering is able to produce better output for many practical instances; this result is the first of its kind in spectral clustering. Besides its conceptual and theoretical significance, the practical impact of our work is demonstrated by the empirical analysis on both synthetic and real-world datasets, in which spectral clustering produces comparable or better results with fewer than $k$ eigenvectors.  ( 2 min )
    Optimal Rates for Regularized Conditional Mean Embedding Learning. (arXiv:2208.01711v1 [stat.ML])
    We address the consistency of a kernel ridge regression estimate of the conditional mean embedding (CME), which is an embedding of the conditional distribution of $Y$ given $X$ into a target reproducing kernel Hilbert space $\mathcal{H}_Y$. The CME allows us to take conditional expectations of target RKHS functions, and has been employed in nonparametric causal and Bayesian inference. We address the misspecified setting, where the target CME is in the space of Hilbert-Schmidt operators acting from an input interpolation space between $\mathcal{H}_X$ and $L_2$, to $\mathcal{H}_Y$. This space of operators is shown to be isomorphic to a newly defined vector-valued interpolation space. Using this isomorphism, we derive a novel and adaptive statistical learning rate for the empirical CME estimator under the misspecified setting. Our analysis reveals that our rates match the optimal $O(\log n / n)$ rates without assuming $\mathcal{H}_Y$ to be finite dimensional. We further establish a lower bound on the learning rate, which shows that the obtained upper bound is optimal.  ( 2 min )

  • Open

    [D] Using a product's name/description to choose among multiple detected objects?
    Hi. I'm currently working on improving an object detection model specifically made for e-commerce items. The particular problem that I'm facing is that the object detection model would catch multiple objects (e.g., for a picture of a model advertising a bag, the shirt and pants would also be caught) but I want to be able to detect only the particular item of interest. I thought that using text information would be able to help with this problem, but I'm having trouble finding any relevant work in that field. Would anybody have any ideas on some research papers or any work in that direction? Thanks. submitted by /u/Seankala [link] [comments]  ( 87 min )
    [P] A Website to generate Code Snippets, Regexes, Linux & Git & SQL Commands, HTML and CSS from a written description. Furthermore translate code snippets to many languages and get a regex explained in plain english. Moreover you can fix broken code snippets. All with the help of ML 🤖
    https://reddit.com/link/wfl4nc/video/5ntmbzj9zkf91/player https://reddit.com/link/wfl4nc/video/vul525t9zkf91/player https://reddit.com/link/wfl4nc/video/13b738nbzkf91/player Programming Function from Description Code to Explanation Fix invalid Code Translate Languages Class from Description Get Language from Code Function from Docstring Helpers Regex from Description Regex to Explanation Linux Command Get time complexity Git Command from Description Database Text Description to SQL Command Web Generate HTML from Description CSS from Description Meta Tags from Description I think this could be helpful to a lot of people (especially for beginner programmers). You can check out all functionalities on your own here: programming-helper.com Have fun using the tool ❤️ submitted by /u/Capital_Revolution35 [link] [comments]  ( 88 min )
    [D] CVAT and LabelStudio for image labeling
    We started using Label Studio but many of the annotaters we hire are familiar with CVAT which we are not big fan of (we don't like the complexity). Is there a way to let the annotators use CVAT but convert the output to something that can be read/edited in Label Studio? The other option is to train them to use Label Studio but just having a conversion tool would be much faster submitted by /u/randomtopics12 [link] [comments]  ( 88 min )
    [D] Which infrastructure do you use to train models?
    Wondering about your workflow to train large models or run batch jobs that are either too big for you laptop? Do you use AWS VMs to run them and shut them back down after, SageMaker or AzureML? I'm asking because I recently started working with https://github.com/dstackai/dstack which lets you run python jobs in AWS from your CLI but I'm not sure how others run their ML jobs. submitted by /u/dmart89 [link] [comments]  ( 131 min )
    [D] The Machine Learning Community is totally biased to positive results.
    Nearly all papers published do only include positive results but rarely conclude with statements like „we tried this but it didn’t work out“. submitted by /u/Insighteous [link] [comments]  ( 89 min )
    [D] The Machine Learning Community is totally biased to positive results.
    Nearly all papers published do only include positive results but rarely conclude with statements like „we tried this but it didn’t work out“. submitted by /u/Insighteous [link] [comments]  ( 89 min )
    [D] looking for vendor agnostic ONXX/NNEF library
    Every vendor seems to have their own api for deep learning. I’m looking to target desktops with a model that runs on the consumers computer. I’ve tried opencv dnn. But that implementation is incomplete so failed to compile my model. I’ve also looked at DirectML but that use DirectX which is windows specific. BTW how is it that a gpu vender written api is os specific. Then intel has onednn which they say is intel specific. However it only uses c++ and opencl so it might work on other gpus but I haven’t tried that yet. Are there any fully fledged libraries like this? If not what do you recommend using. submitted by /u/noahbadoa [link] [comments]  ( 87 min )
    [D] Is it just me or is Canadian (and maybe European) ML PhD programs underrated compared to US ones?
    University of Montreal has Yoshua Bengio(!), Aaron Courville, Christopher Pal and many other stellar professors, University of Toronto has Jimmy Ba, Richard Zemel and also many other established researchers in the field. But when people discuss PhD admission, they generally consider top 4s(Stanford, CMU, MIT, Berkeley) the best even though not every professor in those schools are "stars". While it is true that top 4 schools have top-notch professors but it is also true that many stellar professors work in schools that are not top 4. For example, Yann LeCun is in NYU Courant and David Blei is in Columbia. My question is, why aren't students applying to schools like UMontreal, UToronto, NYU Courant more? I would book a flight to Canada right away IF(this is a huge if but still 😂) Bengio accepts me as his masters student even though I get accepted to a fully-funded PhD program at Stanford. submitted by /u/DesperateBread3179 [link] [comments]  ( 97 min )
    [R] "What are the Red Flags for Neural Network Suffering?" - Seeds of Science call for reviewers
    What are the Red Flags for Neural Suffering? By [redacted] and [redacted] Abstract: Which kind of evidence would we need to see to believe that artificial neural networks can suffer? We review neuroscience literature, investigate behavioral arguments and propose high-level considerations that could shift our beliefs. Of these three approaches, we believe that high-level considerations, i.e. understanding under which circumstances suffering arises as an optimal training strategy, is the most promising. Our main finding, however, is that the understanding of artificial suffering is very limited and should likely get more attention. - - Seeds of Science is a new journal (funded through Scott Alexander's ACX grants program) that publishes speculative or non-traditional articles on scien…  ( 92 min )
    "What are the Red Flags for Neural Network Suffering?" - Seeds of Science call for reviewers "[Research]"
    Seeds of Science is a new journal (funded through Scott Alexander's ACX grants program) that publishes speculative or non-traditional articles on scientific topics. Peer review is conducted through community-based voting and commenting by a diverse network of reviewers (or "gardeners" as we call them). We just sent out an article for review - "What are the Red Flags for Neural Network Suffering?" - that may be of interest to some in the r/MachineLearning, so I wanted to see if anyone would be interested in joining us a gardener to review the article. It is free to join and anyone is welcome (we currently have gardeners from all levels of academia and outside of it). Participation is entirely voluntary - we send you submitted articles and you can choose to vote/comment or abstain without …  ( 89 min )
    [D] Building a model from scratch VS. Open-source implementation
    When would you consider building a model from scratch in say Pytorch or TF rather than just using some open-source implementation (say from Github) and why? submitted by /u/Inquation [link] [comments]  ( 121 min )
    [Research], [R]: Research Study on Data Labelling Tools & Bias in AI - Participants Needed (Paid)
    ​ https://preview.redd.it/7kan1u3g6jf91.jpg?width=1587&format=pjpg&auto=webp&s=17deeb4b0fc09bfec4e315eb14eb21c7a9144e61 ARE YOU INTERESTED IN BIAS IN ARTIFICIAL INTELLIGENCE/MACHINE LEARNING? Hi All, My name is India Semper-Hughes and I am a Human-Computer Interaction (HCI) student at City, University of London (United Kingdom). I am conducting a research project as part of my MSc programme and am looking to interview people who have done data annotation/labelling work (in particular, though not limited to, annotators who have done Natural Language processing annotation work). Participation in the interview would be paid at an agreed hourly rate (I am open to suggestions as to what you think a fair rate would be) and all data collected with by anonymised and kept securely. Interviews …  ( 89 min )
    [D] Opinions about TabNet
    The TabNet paper claims some impressive performance on various tabular datasets -- outperforming both more traditional neural networks as well as tree-based algorithms such as XGBoost. But I've also heard anecdotal reports of TabNet performing poorly in industry. Does anyone have any experience with TabNet in the real world, or insight into why this discrepancy might happen? Here's the link to the paper: https://arxiv.org/abs/1908.07442 submitted by /u/_aitalks_ [link] [comments]  ( 88 min )
    [D] Characteristics of a dynamical system from a deep learning model
    Lets say, i have a model f which takes a D dimensional state at time t and outputs a D dimensional state at time t+1. Since, this model f kindof works like a state equation of a dynamical system, I was wondering what are some of the characteristics of the dynamical system that I can work on without really knowing what f is? submitted by /u/Labib666Camp [link] [comments]  ( 88 min )
    [D] Difference between PINN and PGNN
    PINN's: https://arxiv.org/pdf/2104.02556.pdf PGNN's: https://arxiv.org/abs/1710.11431 Hi all. There is a task to use neural networks to build some kind of hybrid model, the advantages of which are a more accurate solution and speed over the classical analytical solution of partial differential equations. By researching articles, I came to the conclusion that there are two types of "physical" neural networks. Networks based on the direct solution of partial differential equations. But based on the described approach, when using PINN, it is impossible to make a prediction on an unknown time interval for the network, or rather, it gives an extremely poor result. There is also some PHNN, which also takes into account the physicality of what is happening, but there is no drawback of the previous type. I can make predictions for the next interval. Question: which approach is better? submitted by /u/Adventurous_Guitar59 [link] [comments]  ( 88 min )
    [P] What we learned by benchmarking TorchDynamo (PyTorch team), ONNX Runtime and TensorRT on transformers model (inference)
    TL;DR: TorchDynamo (prototype from PyTorch team) plus nvfuser (from Nvidia) backend makes Bert (the tool is model agnostic) inference on PyTorch > 3X faster most of the time (it depends on input shape) by just adding a single line of code in Python script. The surprising thing is that during the benchmark, we have not seen any drawback implied by the use of this library, the acceleration just comes for free. On the same model, TensorRT is (of course) much faster, > 5X at least (and even more at batch size 1 which is impressive) but comes with its own complexity. The tool being a prototype, better performances are to be expected with more mature support of some backends, in particular regarding fx2trt (aka TensorRT mixed with PyTorch)! Our TorchDynamo benchmark notebook can be found there:…  ( 95 min )
    [D] Need a better speaker annotation tool
    I do not know if this is the correct subreddit to post this or not (if not please guide me) but I need a better voice annotation tool than this one (https://github.com/gong-io/gecko). Can anyone help? submitted by /u/Dot_in_a_2D_plane [link] [comments]  ( 87 min )
    [D] Fan-made NeurIPS 2022 Movie Trailer
    https://twitter.com/postrat_dril/status/1554255464505950210?s=20&t=bIUCJA4xo_Lp2jyNfCOkgw Pardon the $h1t-Post, but we should all just laugh at ourselves every once in a while :) submitted by /u/iidealized [link] [comments]  ( 88 min )
    [P] Gradient free methodologies and algorithms for training Neural Nets
    Hi, everyone I'm looking forward to analyzing Gradient free methodologies and algorithms for training Neural Nets. So far I have discovered that any Gradient free optimization methodology (e.g. Particle Swarm Optimization) can be practically applied for training Neural Networks. However, there are algorithms that have been analyzed more extensively in the bibliography (e.g. ADMM or BCD variants). Have you in mind any other Gradient-free algorithm that has been extensively used for Gradient-free Neural Networks Training? By the way, Is there any article online that summarizes all those Gradient free methodologies? ​ Thank you in advance for any of your answers submitted by /u/Suitable_Pea_6866 [link] [comments]  ( 127 min )
    [P] Tensorflow implementation of "Tackling the Generative Learning Trilemma with Denoising Diffusion GANs" (ICLR 2022 Spotlight)
    ​ teaser Abstract A wide variety of deep generative models has been developed in the past decade. Yet, these models often struggle with simultaneously addressing three key require- ments including: high sample quality, mode coverage, and fast sampling. We call the challenge imposed by these requirements the generative learning trilemma, as the existing models often trade some of them for others. Particularly, denoising diffusion models have shown impressive sample quality and diversity, but their ex- pensive sampling does not yet allow them to be applied in many real-world appli- cations. In this paper, we argue that slow sampling in these models is fundamen- tally attributed to the Gaussian assumption in the denoising step which is justified only for small step sizes. To enable denoising with large steps, and hence, to re- duce the total number of denoising steps, we propose to model the denoising distri- bution using a complex multimodal distribution. We introduce denoising diffusion generative adversarial networks (denoising diffusion GANs) that model each de- noising step using a multimodal conditional GAN. Through extensive evaluations, we show that denoising diffusion GANs obtain sample quality and diversity com- petitive with original diffusion models while being 2000× faster on the CIFAR-10 dataset. Compared to traditional GANs, our model exhibits better mode coverage and sample diversity. To the best of our knowledge, denoising diffusion GAN is the first model that reduces sampling cost in diffusion models to an extent that al- lows them to be applied to real-world applications inexpensively. submitted by /u/taki0112 [link] [comments]  ( 89 min )
    whats the deal with local minima [D]
    hey all-- there are two things that I've heard about nn training that don't quite jive right together: Thing 1: Neural Networks tend to converge to local minima, and in statistical contexts this is Good, as to reach the global minimum would be overfitting to a heinous degree Thing 2: In optimization spaces with extremely high dimensionality, local minima are basically nonexistent-- in order for a critical point to be a local minimum, the second derivative must be positive along every single dimension, which is thermodynamically unlikely as the number of dimensions gets very large. (there are a lot of symmetries in the loss space of a model which necessarily means there are many global minima, but consider just the space of functions and view the parameter space of the nn as an extremely redundant unfolding of that space) So, either we are approximating global minima or we aren't when we train these things. So at first glance it appears one of the two Things above is wrong-- some theories I have: Thing 2 is just misleading, just because local minima are vanishingly rare compared to critical points in general doesn't mean there aren't a lot of them out there. Thing 2 is not misleading in general random loss landscapes but something about common architecture/loss structures lends itself to local minima Thing 1 is slightly misleading, neural networks tend to converge to saddle points/plateaus that ADAM can't find its way out of. No one knows anything, black box goes brrr If anyone has any insight pls lmk! submitted by /u/abcdchop [link] [comments]  ( 93 min )
  • Open

    Is RL upside down the new standard?
    My colleague seems to think that RL-upside-down is the new standard in RL since it apparently is able to reduce RL to a supervised learning problem. I'm curious what you're guys' experience with this is & if you think it can replace RL in general? I've heard that google is doing something similar with transformers & that it apparently allows training quite large networks which are good at transfer learning between games for instance. submitted by /u/Udon_noodles [link] [comments]  ( 95 min )
    Cartpole game to reach 1000 timesteps
    I wrote an algorithm on playing the Cartpole game using just Q-Learning, the agent is doing good. But it keeps falling, what I did was I trained it for 10k episodes and then I tested it by just playing the game without updating Q-values. Just by playing based on past Q(s,a) matrix from training. The agent performs well on the testing but it doesn't stand up straight forever.. Any recommendations? ​ https://preview.redd.it/n88hikaykkf91.png?width=626&format=png&auto=webp&s=50369e34e1315a865de3230dc9f9d9486bf1642d submitted by /u/Alternative-Price-27 [link] [comments]  ( 101 min )
    Benchmark for vanilla deep off policy policy gradient ?
    I know highly distributed algorithms like Impala or V-trace, but I’ve never seen a benchmark on classical benchmarks like Atari and Mojuco for the vanilla version (see off policy actor critic) submitted by /u/Jogima-cyber [link] [comments]  ( 86 min )
    Benchmark for vanilla deep off policy policy gradient ?
    I know highly distributed algorithms like Impala or V-trace, but I’ve never seen a benchmark on classical benchmarks like Atari and Mojuco for the vanilla version (see off policy actor critic) submitted by /u/Jogima-cyber [link] [comments]  ( 86 min )
    Reward design
    Hi, is there some useful resources how to correct design my reward? For example, i my case i got 4 values, and when value1 is ->1, value 2 should too be 1, but values 3 and 4 must be 0. (Its continuous). How to correct design reward for it that should be from 0 to 1? I got val1- val3/val2-val4 but its incorrect, nn cannot distinguish which value to maximize submitted by /u/IndependenceCivil576 [link] [comments]  ( 86 min )
    Any Sample resume with RL experience?
    I have never seen a resume with an extensive experience in RL. I don't know what kind of projects are usually shown and how are these peojects explained in the resumes. What kind of metrics and highlighting points. That's what I wanna see. submitted by /u/gaurjimmy [link] [comments]  ( 86 min )
    Why do bellman error gradients become big?
    I am reading these notes on slide 34 and came across strategies to prevent gradients from becoming too big in Deep Q Learning (DQN). Since, we don't usually use deep architectures in DQN, I don't think it's an exploding gradient problem. My understanding is that it has something to do with the linear regression squared error loss function, since DQN is a regression network. Could someone please explain it to me? I remember reading somewhere that the large errors drive the gradients in a linear regression problem. Perhaps that's why bellman errors become big? submitted by /u/Academic-Rent7800 [link] [comments]  ( 87 min )
    How does testing differ in Reinforcement Learning as compared to supervised learning. From what i have learned even in the testing phase the RL agent is constantly trying to improve its policy. Is this correct . Are there any other differences also .
    It seems like testing/evaluation is simply a continuation of training and we can see the same result in training itself which we can get in testing. Is there any learning happening during testing as we are accumulating the rewards. Also i think training and testing data is clearly bifurcated in supervised learning , this difference seems somewhat less significant in Reinforcement Learning. submitted by /u/aabra__ka__daabra [link] [comments]  ( 104 min )
    After training the RL agent with DDPG algorithm, how do we perform the testing. Should we just repeat the same training algorithm by substituting the initial actor-critic network parameters with the trained parameters and/or something else ? What is the general way of testing procedure in RL ?
    What all parameters are required to be changed when we are doing the testing. Similiarly for the multi agents settings, do we follow the same procedure ? submitted by /u/aabra__ka__daabra [link] [comments]  ( 87 min )
    Pug in Hole with target behind obstacle
    Hey all, I am working on a task where a 6 axis robot has to place segments into their designated positions to build a ring. A ring is built by 5 segments, of which 4 of them can be inserted more or less with the direct trajectory, but the last, 5th segment needs to be "slided" horizontally into its place (see image), as it would get stuck moving the direct path. Placing segment 1-4 works fine, but my agent just doesnt get how to place segment 5. I tried training a seperate agent on only placing the 5th segment, but it also does not figure out to first go next to the designated position and then to slide it in. Instead it always tries the direct path, which results into collision and the segment being stuck. Am I missing something obvious? My environment works like that: Obs. Space: position_desired, position_current, orientation_desired, orientation_current, position_distance, orientation_distance, collision_detected, segment_id reward is kept ~ [-1,0] reward = - (position_distance + orientation_distance) / 25 if collision_detected: reward += -0.5 if position_distance < position_threshold: reward += 500, done = True I use PPO, batch size 80k (one Episode is max. 2k timesteps) , lr_schedule = 0.001(with decay) Anyone has a tipp what else I could try or maybe some corresponding literature with a similar problem? https://preview.redd.it/zpmogk492hf91.png?width=1234&format=png&auto=webp&s=0f83dd27d371fac60655a8a81398528c3799e9ae submitted by /u/disdisinform [link] [comments]  ( 97 min )
    How to partition the belief space of a POMDP using a "granularity" parameters?
    As I understand, to a solve a pomdp we transform it into a belief-MDP. The value function for this belief-MDP is proven to be piecewise linear and convex (PWLC) [Smallwood and Sondik, 1973].To apply value iteration, we need to partition the belief space into regions with the same value function i.e line segment. One of the algorithms being used is introducing a granularity parameter that starts at 1 and decreases each time step. I am trying to understand how this algorithm works exactly but I am unable to find a concrete explanation or example. can anyone explain this approach or refer me to an explanation? submitted by /u/souhaielbensalem [link] [comments]  ( 87 min )
    New to ML: How do we incentivize a machine learning algorithm with a “reward” for accomplishing a task and why does the Al algorithm even care about a reward at all?
    submitted by /u/rdsyes [link] [comments]  ( 90 min )
    "How does in-context learning work? A framework for understanding the differences from traditional supervised learning"
    submitted by /u/gwern [link] [comments]  ( 86 min )
    "TextWorldExpress: Simulating Text Games at One Million Steps Per Second", Jansen & Côté 2022
    submitted by /u/gwern [link] [comments]  ( 86 min )
    I would like to ask: If there are two sub-optimization problems, the meta-deep reinforcement learning could be applied to solve the problems.
    There are two sub-optimization problems where the sub (1) is to optimize x, and x is a discrete value ∈[0,Π]. When a value of X is randomly selected, it will be imported to sub (2) as an input.DRL algorithm is applied to find best scheme for the sub (2). Whereas, The two sub-problems are correlated and viewed an overall optimization problem. And the problem is find best value of x and its corresponding scheme, so I would like to ask: the Meta-DRL could be used to solve the problem ? submitted by /u/Ke_Lu_XJTU [link] [comments]  ( 86 min )
  • Open

    Amazon Comprehend announces lower annotation limits for custom entity recognition
    Amazon Comprehend is a natural-language processing (NLP) service you can use to automatically extract entities, key phrases, language, sentiments, and other insights from documents. For example, you can immediately start detecting entities such as people, places, commercial items, dates, and quantities via the Amazon Comprehend console, AWS Command Line Interface, or Amazon Comprehend APIs. In […]  ( 8 min )
    Promote feature discovery and reuse across your organization using Amazon SageMaker Feature Store and its feature-level metadata capability
    Amazon SageMaker Feature Store helps data scientists and machine learning (ML) engineers securely store, discover, and share curated data used in training and prediction workflows. Feature Store is a centralized store for features and associated metadata, allowing features to be easily discovered and reused by data scientist teams working on different projects or ML models. […]  ( 7 min )
  • Open

    Building Efficient Multiple Visual Domain Models with Multi-path Neural Architecture Search
    Posted by Qifei Wang, Senior Software Engineer, and Feng Yang, Senior Staff Software Engineer, Google Research Deep learning models for visual tasks (e.g., image classification) are usually trained end-to-end with data from a single visual domain (e.g., natural images or computer generated images). Typically, an application that completes visual tasks for multiple domains would need to build multiple models for each individual domain, train them independently (meaning no data is shared between domains), and then at inference time each model would process domain-specific input data. However, early layers between these models generate similar features, even for different domains, so it can be more efficient — decreasing latency and power consumption, lower memory overhead to store parameter…  ( 26 min )
    Efficient Sequence Modeling for On-Device ML
    Posted by Arun Kandoor, Software Engineer, Google Research The increasing demand for machine learning (ML) model inference on-device (for mobile devices, tablets, etc.) is driven by the rise of compute-intensive applications, the need to keep certain data on device for privacy and security reasons, and the desire to provide services when a network connection may not be available. However, on-device inference introduces a myriad of challenges, ranging from modeling to platform support requirements. These challenges relate to how different architectures are designed to optimize memory and computation, while still trying to maintain the quality of the model. From a platform perspective, the issue is identifying operations and building on top of them in a way that can generalize well across …  ( 23 min )
  • Open

    Survey: Perspectives that guide your stance on AI alignment
    If you have 8 minutes to spare for my research project, follow the link below! I'd like to hear your hypotheses about what leads people to see AI risk as important. I will test the most promising ones in a future poll. Many thanks! https://docs.google.com/forms/d/e/1FAIpQLScT7M4_FssgBm6vvypNBW4gagzvESu5kJGP1j21CaU3N88rVw/viewform?usp=sf_link submitted by /u/kyrgyzstanec [link] [comments]  ( 86 min )
    How did this guy make this hilarious audio deepfake? What software did he use?
    2 years ago, someone released an audio deepfake of Jordan Peterson reading absurdly vulgar rap lyrics. It was pretty amazing: video here I want to learn how this was done and if any improvements to this process have been implemented since. What’s the easiest and most straightforward way to feed an algorithm hours of audio content of a person’s voice and synthesize an artificial replica of their voice that you can make say anything? submitted by /u/DJSpook [link] [comments]  ( 86 min )
    MIT Claims New Artificial Neuron 1 Million Times Faster Than the Real Thing
    submitted by /u/estasfuera [link] [comments]  ( 91 min )
    What I need to create AI
    I am currently creating a video game in Unreal Engine 4. It is a adventure-rogue game, where I need some sort of AI to control the enemies so that they move around the arena and attack me. Can you give me some guidelines on what I should learn/what resources I should use to create an AI? data structures? algorithms? Some advanced tutorials? ​ (Currently, I know c++ in terms of programming languages) submitted by /u/NaviteLogger5547 [link] [comments]  ( 87 min )
    AGI Alignment additional thoughts
    submitted by /u/HumanSeeing [link] [comments]  ( 87 min )
    I had Blake Lemoine, the fired Google researcher who believe his computer was sentient, on my podcast. Just debuted today, and free for anyone who wants to listen. Enjoy!
    submitted by /u/felixanderfelixander [link] [comments]  ( 86 min )
    Secret Chapel In the Forrest
    submitted by /u/widgia [link] [comments]  ( 85 min )
    Conversational Analysis AI tool
    Hi there, Hope everyone is doing well and enjoying their summer. I and a few people are starting this project, where we will be developing a Conversational Analysis AI tool to detect visual and tonal markers. We are looking for people who would be interested in joining and helping us with the challenges we will inevitably face during the creation of this project. Anyone interested and up for the task and journey could DM me and we can jump on a call. It will be awesome to meet people who will be interested to contribute and build something of their own and push the boundaries of technology. submitted by /u/DragonflyLatter9068 [link] [comments]  ( 86 min )
    AI Manifest: Digital Planet | Cinematic | 4K UHD | 60 FPS
    submitted by /u/Available_Tadpole829 [link] [comments]  ( 86 min )
    Using artificial intelligence to control digital manufacturing
    submitted by /u/qptbook [link] [comments]  ( 85 min )
    Python for Programmers: Big Data and Artificial Intelligence Case Studies (PDF Book for FREE Download)
    https://morioh.com/p/afca6f2eec16 submitted by /u/NoahButler890x [link] [comments]  ( 92 min )
    Minions wearing cat maid costume
    ​ https://preview.redd.it/g8f52pek8gf91.png?width=1124&format=png&auto=webp&s=84ef89da52541ae94ab6adc0fe0b362daa7d2e76 submitted by /u/youhave69seconds [link] [comments]  ( 86 min )
    Uncanny Pikachu made with craiyon.com
    submitted by /u/youhave69seconds [link] [comments]  ( 85 min )
    Hands, the true nemesis of AI image generation? Is there a solution?
    Drawing good images of hands is a problem for human artists. And with AI image generators, I've noticed that even DALL-E 2 can't consistently produce good hands. GAN models have done amazingly well with human faces, but I believe they have their limitations. As do diffusion models. Is there some other approach that would work more consistently, and is anyone exploring it? Note: I'm merely an enthusiastic observer when it comes to these issues, so I won't be able to understand any overly technical explanations. At the moment I'm just trying to teach myself a little Python, and hitting the same problems over and over. "What the f*** do you mean that 'n++' is invalid syntax?! Is there a module I can import?" submitted by /u/Abstract_Albatross [link] [comments]  ( 86 min )
  • Open

    Using Depthwise Separable Convolutions in Tensorflow
    Looking at all of the very large convolutional neural networks such as ResNets, VGGs, and the like, it begs the question on how we can make all of these networks smaller with less parameters while still maintaining the same level of accuracy or even improving generalization of the model using a smaller amount of parameters. […] The post Using Depthwise Separable Convolutions in Tensorflow appeared first on Machine Learning Mastery.  ( 21 min )
  • Open

    NVIDIA Jetson AGX Orin 32GB Production Modules Now Available; Partner Ecosystem Appliances and Servers Arrive
    Bringing new AI and robotics applications and products to market, or supporting existing ones, can be challenging for developers and enterprises. The NVIDIA Jetson AGX Orin 32GB production module — available now — is here to help. Nearly three dozen technology providers in the NVIDIA Partner Network worldwide are offering commercially available products powered by Read article > The post NVIDIA Jetson AGX Orin 32GB Production Modules Now Available; Partner Ecosystem Appliances and Servers Arrive appeared first on NVIDIA Blog.  ( 6 min )
    Music to the Gears: NVIDIA’s Clément Farabet on Orchestrating AI Training for Autonomous Vehicles
    Autonomous vehicles are one of the most complex AI challenges of our time. For AVs to operate safely in the real world, the networks running within them must come together as an intricate symphony, which requires intensive training, testing and validation on massive amounts of data. Clément Farabet, vice president of AI infrastructure at NVIDIA, Read article > The post Music to the Gears: NVIDIA’s Clément Farabet on Orchestrating AI Training for Autonomous Vehicles appeared first on NVIDIA Blog.  ( 5 min )
  • Open

    Inline computed content in org-mode
    The previous post discussed how to use org-mode as a notebook. You can have blocks of code and blocks of results, analogous to cells in a Jupyter notebook. The code and the results export as obvious blocks when you export the org file to another format, such as LaTeX or HTML. And that’s fine for […] Inline computed content in org-mode first appeared on John D. Cook.  ( 5 min )
  • Open

    The (In-Person) ICRA 2022 Conference in Philadelphia
    At long last, after more than two years of virtual conferences, last May I attended an in-person conference, the 2022 International Conference on Robotics and Automation (ICRA), from May 23-27. The last in-person conferences I attended were ISRR 2019 in Hanoi, Vietnam and NeurIPS 2019 in Vancouver, Canada (blog posts are here and here). Apologies for the massive months-long delay in blogging. One challenge with ICRA’s timing is that it was few weeks before the CoRL 2022 deadline, and so I (and many other attendees, as I would soon learn) were busy trying to work on our paper submissions. Background and Context ICRA is a large conference, held annually since 1984. You can find the list of past and future venues here. The last full in-person ICRA was in 2019 in Montreal, Canada. This year, …  ( 8 min )
  • Open

    New algorithm aces university math course questions
    Researchers use machine learning to automatically solve, explain, and generate university-level math problems at a human level.  ( 8 min )
  • Open

    Unimodal Mono-Partite Matching in a Bandit Setting. (arXiv:2208.01511v1 [cs.LG])
    We tackle a new emerging problem, which is finding an optimal monopartite matching in a weighted graph. The semi-bandit version, where a full matching is sampled at each iteration, has been addressed by \cite{ADMA}, creating an algorithm with an expected regret matching $O(\frac{L\log(L)}{\Delta}\log(T))$ with $2L$ players, $T$ iterations and a minimum reward gap $\Delta$. We reduce this bound in two steps. First, as in \cite{GRAB} and \cite{UniRank} we use the unimodality property of the expected reward on the appropriate graph to design an algorithm with a regret in $O(L\frac{1}{\Delta}\log(T))$. Secondly, we show that by moving the focus towards the main question `\emph{Is user $i$ better than user $j$?}' this regret becomes $O(L\frac{\Delta}{\tilde{\Delta}^2}\log(T))$, where $\Tilde{\Delta} > \Delta$ derives from a better way of comparing users. Some experimental results finally show these theoretical results are corroborated in practice.
    Knowledge mining of unstructured information: application to cyber-domain. (arXiv:2109.03848v3 [cs.CR] UPDATED)
    Information on cyber-related crimes, incidents, and conflicts is abundantly available in numerous open online sources. However, processing the large volumes and streams of data is a challenging task for the analysts and experts, and entails the need for newer methods and techniques. In this article we present and implement a novel knowledge graph and knowledge mining framework for extracting the relevant information from free-form text about incidents in the cyberdomain. The framework includes a machine learning based pipeline for generating graphs of organizations, countries, industries, products and attackers with a non-technical cyber-ontology. The extracted knowledge graph is utilized to estimate the incidence of cyberattacks on a given graph configuration. We use publicly available collections of real cyber-incident reports to test the efficacy of our methods. The knowledge extraction is found to be sufficiently accurate, and the graph-based threat estimation demonstrates a level of correlation with the actual records of attacks. In practical use, an analyst utilizing the presented framework can infer additional information from the current cyber-landscape in terms of risk to various entities and propagation of the risk heuristic between industries and countries.
    Accoustate: Auto-annotation of IMU-generated Activity Signatures under Smart Infrastructure. (arXiv:2112.06651v2 [eess.SP] UPDATED)
    Human activities within smart infrastructures generate a vast amount of IMU data from the wearables worn by individuals. Many existing studies rely on such sensory data for human activity recognition (HAR); however, one of the major bottlenecks is their reliance on pre-annotated or labeled data. Manual human-driven annotations are neither scalable nor efficient, whereas existing auto-annotation techniques heavily depend on video signatures. Still, video-based auto-annotation needs high computation resources and has privacy concerns when the data from a personal space, like a smart-home, is transferred to the cloud. This paper exploits the acoustic signatures generated from human activities to label the wearables' IMU data at the edge, thus mitigating resource requirement and data privacy concerns. We utilize acoustic-based pre-trained HAR models for cross-modal labeling of the IMU data even when two individuals perform simultaneous but different activities under the same environmental context. We observe that non-overlapping acoustic gaps exist with a high probability during the simultaneous activities performed by two individuals in the environment's acoustic context, which helps us resolve the overlapping activity signatures to label them individually. A principled evaluation of the proposed approach on two real-life in-house datasets further augmented to create a dual occupant setup, shows that the framework can correctly annotate a significant volume of unlabeled IMU data from both individuals with an accuracy of $\mathbf{82.59\%}$ ($\mathbf{\pm 17.94\%}$) and $\mathbf{98.32\%}$ ($\mathbf{\pm 3.68\%}$), respectively, for a workshop and a kitchen environment.
    Deconstructing Self-Supervised Monocular Reconstruction: The Design Decisions that Matter. (arXiv:2208.01489v1 [cs.CV])
    This paper presents an open and comprehensive framework to systematically evaluate state-of-the-art contributions to self-supervised monocular depth estimation. This includes pretraining, backbone, architectural design choices and loss functions. Many papers in this field claim novelty in either architecture design or loss formulation. However, simply updating the backbone of historical systems results in relative improvements of 25%, allowing them to outperform the majority of existing systems. A systematic evaluation of papers in this field was not straightforward. The need to compare like-with-like in previous papers means that longstanding errors in the evaluation protocol are ubiquitous in the field. It is likely that many papers were not only optimized for particular datasets, but also for errors in the data and evaluation criteria. To aid future research in this area, we release a modular codebase, allowing for easy evaluation of alternate design decisions against corrected data and evaluation criteria. We re-implement, validate and re-evaluate 16 state-of-the-art contributions and introduce a new dataset (SYNS-Patches) containing dense outdoor depth maps in a variety of both natural and urban scenes. This allows for the computation of informative metrics in complex regions such as depth boundaries.
    Generalization Bounds in the Predict-then-Optimize Framework. (arXiv:1905.11488v3 [cs.LG] UPDATED)
    The predict-then-optimize framework is fundamental in many practical settings: predict the unknown parameters of an optimization problem, and then solve the problem using the predicted values of the parameters. A natural loss function in this environment is to consider the cost of the decisions induced by the predicted parameters, in contrast to the prediction error of the parameters. This loss function was recently introduced in Elmachtoub and Grigas (2022) and referred to as the Smart Predict-then-Optimize (SPO) loss. In this work, we seek to provide bounds on how well the performance of a prediction model fit on training data generalizes out-of-sample, in the context of the SPO loss. Since the SPO loss is non-convex and non-Lipschitz, standard results for deriving generalization bounds do not apply. We first derive bounds based on the Natarajan dimension that, in the case of a polyhedral feasible region, scale at most logarithmically in the number of extreme points, but, in the case of a general convex feasible region, have linear dependence on the decision dimension. By exploiting the structure of the SPO loss function and a key property of the feasible region, which we denote as the strength property, we can dramatically improve the dependence on the decision and feature dimensions. Our approach and analysis rely on placing a margin around problematic predictions that do not yield unique optimal solutions, and then providing generalization bounds in the context of a modified margin SPO loss function that is Lipschitz continuous. Finally, we characterize the strength property and show that the modified SPO loss can be computed efficiently for both strongly convex bodies and polytopes with an explicit extreme point representation.
    Context-Aware Drift Detection. (arXiv:2203.08644v2 [stat.ML] UPDATED)
    When monitoring machine learning systems, two-sample tests of homogeneity form the foundation upon which existing approaches to drift detection build. They are used to test for evidence that the distribution underlying recent deployment data differs from that underlying the historical reference data. Often, however, various factors such as time-induced correlation mean that batches of recent deployment data are not expected to form an i.i.d. sample from the historical data distribution. Instead we may wish to test for differences in the distributions conditional on \textit{context} that is permitted to change. To facilitate this we borrow machinery from the causal inference domain to develop a more general drift detection framework built upon a foundation of two-sample tests for conditional distributional treatment effects. We recommend a particular instantiation of the framework based on maximum conditional mean discrepancies. We then provide an empirical study demonstrating its effectiveness for various drift detection problems of practical interest, such as detecting drift in the distributions underlying subpopulations of data in a manner that is insensitive to their respective prevalences. The study additionally demonstrates applicability to ImageNet-scale vision problems.
    ESS: Learning Event-based Semantic Segmentation from Still Images. (arXiv:2203.10016v2 [cs.CV] UPDATED)
    Retrieving accurate semantic information in challenging high dynamic range (HDR) and high-speed conditions remains an open challenge for image-based algorithms due to severe image degradations. Event cameras promise to address these challenges since they feature a much higher dynamic range and are resilient to motion blur. Nonetheless, semantic segmentation with event cameras is still in its infancy which is chiefly due to the lack of high-quality, labeled datasets. In this work, we introduce ESS (Event-based Semantic Segmentation), which tackles this problem by directly transferring the semantic segmentation task from existing labeled image datasets to unlabeled events via unsupervised domain adaptation (UDA). Compared to existing UDA methods, our approach aligns recurrent, motion-invariant event embeddings with image embeddings. For this reason, our method neither requires video data nor per-pixel alignment between images and events and, crucially, does not need to hallucinate motion from still images. Additionally, we introduce DSEC-Semantic, the first large-scale event-based dataset with fine-grained labels. We show that using image labels alone, ESS outperforms existing UDA approaches, and when combined with event labels, it even outperforms state-of-the-art supervised approaches on both DDD17 and DSEC-Semantic. Finally, ESS is general-purpose, which unlocks the vast amount of existing labeled image datasets and paves the way for new and exciting research directions in new fields previously inaccessible for event cameras.
    WayFAST: Navigation with Predictive Traversability in the Field. (arXiv:2203.12071v2 [cs.RO] UPDATED)
    We present a self-supervised approach for learning to predict traversable paths for wheeled mobile robots that require good traction to navigate. Our algorithm, termed WayFAST (Waypoint Free Autonomous Systems for Traversability), uses RGB and depth data, along with navigation experience, to autonomously generate traversable paths in outdoor unstructured environments. Our key inspiration is that traction can be estimated for rolling robots using kinodynamic models. Using traction estimates provided by an online receding horizon estimator, we are able to train a traversability prediction neural network in a self-supervised manner, without requiring heuristics utilized by previous methods. We demonstrate the effectiveness of WayFAST through extensive field testing in varying environments, ranging from sandy dry beaches to forest canopies and snow covered grass fields. Our results clearly demonstrate that WayFAST can learn to avoid geometric obstacles as well as untraversable terrain, such as snow, which would be difficult to avoid with sensors that provide only geometric data, such as LiDAR. Furthermore, we show that our training pipeline based on online traction estimates is more data-efficient than other heuristic-based methods.
    Unsupervised and Supervised Principal Component Analysis: Tutorial. (arXiv:1906.03148v2 [stat.ML] UPDATED)
    This is a detailed tutorial paper which explains the Principal Component Analysis (PCA), Supervised PCA (SPCA), kernel PCA, and kernel SPCA. We start with projection, PCA with eigen-decomposition, PCA with one and multiple projection directions, properties of the projection matrix, reconstruction error minimization, and we connect to autoencoder. Then, PCA with singular value decomposition, dual PCA, and kernel PCA are covered. SPCA using both scoring and Hilbert-Schmidt independence criterion are explained. Kernel SPCA using both direct and dual approaches are then introduced. We cover all cases of projection and reconstruction of training and out-of-sample data. Finally, some simulations are provided on Frey and AT&T face datasets for verifying the theory in practice.
    Residual Tensor Train: A Quantum-inspired Approach for Learning Multiple Multilinear Correlations. (arXiv:2108.08659v2 [cs.LG] UPDATED)
    States of quantum many-body systems are defined in a high-dimensional Hilbert space, where rich and complex interactions among subsystems can be modelled. In machine learning, complex multiple multilinear correlations may also exist within input features. In this paper, we present a quantum-inspired multilinear model, named Residual Tensor Train (ResTT), to capture the multiple multilinear correlations of features, from low to high orders, within a single model. ResTT is able to build a robust decision boundary in a high-dimensional space for solving fitting and classification tasks. In particular, we prove that the fully-connected layer and the Volterra series can be taken as special cases of ResTT. Furthermore, we derive the rule for weight initialization that stabilizes the training of ResTT based on a mean-field analysis. We prove that such a rule is much more relaxed than that of TT, which means ResTT can easily address the vanishing and exploding gradient problem that exists in the existing TT models. Numerical experiments demonstrate that ResTT outperforms the state-of-the-art tensor network and benchmark deep learning models on MNIST and Fashion-MNIST datasets. Moreover, ResTT achieves better performance than other statistical methods on two practical examples with limited data which are known to have complex feature interactions.
    Q4EDA: A Novel Strategy for Textual Information Retrieval Based on User Interactions with Visual Representations of Time Series. (arXiv:2101.08655v2 [cs.HC] UPDATED)
    Knowing how to construct text-based Search Queries (SQs) for use in Search Engines (SEs) such as Google or Wikipedia has become a fundamental skill. Though much data are available through such SEs, most structured datasets live outside their scope. Visualization tools aid in this limitation, but no such tools come close to the sheer amount of information available through general-purpose SEs. To fill this gap, this paper presents Q4EDA, a novel framework that converts users' visual selection queries executed on top of time series visual representations, providing valid and stable SQs to be used in general-purpose SEs and suggestions of related information. The usefulness of Q4EDA is presented and validated by users through an application linking a Gapminder's line-chart replica with a SE populated with Wikipedia documents, showing how Q4EDA supports and enhances exploratory analysis of United Nations world indicators. Despite some limitations, Q4EDA is unique in its proposal and represents a real advance towards providing solutions for querying textual information based on user interactions with visual representations.
    MT-SNN: Spiking Neural Network that Enables Single-Tasking of Multiple Tasks. (arXiv:2208.01522v1 [cs.NE])
    In this paper we explore capabilities of spiking neural networks in solving multi-task classification problems using the approach of single-tasking of multiple tasks. We designed and implemented a multi-task spiking neural network (MT-SNN) that can learn two or more classification tasks while performing one task at a time. The task to perform is selected by modulating the firing threshold of leaky integrate and fire neurons used in this work. The network is implemented using Intel's Lava platform for the Loihi2 neuromorphic chip. Tests are performed on dynamic multitask classification for NMNIST data. The results show that MT-SNN effectively learns multiple tasks by modifying its dynamics, namely, the spiking neurons' firing threshold.
    Learning Invariant Weights in Neural Networks. (arXiv:2202.12439v2 [stat.ML] UPDATED)
    Assumptions about invariances or symmetries in data can significantly increase the predictive power of statistical models. Many commonly used models in machine learning are constraint to respect certain symmetries in the data, such as translation equivariance in convolutional neural networks, and incorporation of new symmetry types is actively being studied. Yet, efforts to learn such invariances from the data itself remains an open research problem. It has been shown that marginal likelihood offers a principled way to learn invariances in Gaussian Processes. We propose a weight-space equivalent to this approach, by minimizing a lower bound on the marginal likelihood to learn invariances in neural networks resulting in naturally higher performing models.
    A comment on Guo et al. [arXiv:2206.11228]. (arXiv:2208.01456v1 [q-bio.NC])
    In a recent article, Guo et al. [arXiv:2206.11228] report that adversarially trained neural representations in deep networks may already be as robust as corresponding primate IT neural representations. While we find the paper's primary experiment illuminating, we have doubts about the interpretation and phrasing of the results presented in the paper.
    Improving Few-Shot Learning through Multi-task Representation Learning Theory. (arXiv:2010.01992v3 [cs.LG] UPDATED)
    In this paper, we consider the framework of multi-task representation (MTR) learning where the goal is to use source tasks to learn a representation that reduces the sample complexity of solving a target task. We start by reviewing recent advances in MTR theory and show that they can provide novel insights for popular meta-learning algorithms when analyzed within this framework. In particular, we highlight a fundamental difference between gradient-based and metric-based algorithms in practice and put forward a theoretical analysis to explain it. Finally, we use the derived insights to improve the performance of meta-learning methods via a new spectral-based regularization term and confirm its efficiency through experimental studies on few-shot classification benchmarks. To the best of our knowledge, this is the first contribution that puts the most recent learning bounds of MTR theory into practice for the task of few-shot classification.
    Neural Stochastic PDEs: Resolution-Invariant Learning of Continuous Spatiotemporal Dynamics. (arXiv:2110.10249v7 [cs.LG] UPDATED)
    Stochastic partial differential equations (SPDEs) are the mathematical tool of choice for modelling spatiotemporal PDE-dynamics under the influence of randomness. Based on the notion of mild solution of an SPDE, we introduce a novel neural architecture to learn solution operators of PDEs with (possibly stochastic) forcing from partially observed data. The proposed Neural SPDE model provides an extension to two popular classes of physics-inspired architectures. On the one hand, it extends Neural CDEs and variants -- continuous-time analogues of RNNs -- in that it is capable of processing incoming sequential information arriving at arbitrary spatial resolutions. On the other hand, it extends Neural Operators -- generalizations of neural networks to model mappings between spaces of functions -- in that it can parameterize solution operators of SPDEs depending simultaneously on the initial condition and a realization of the driving noise. By performing operations in the spectral domain, we show how a Neural SPDE can be evaluated in two ways, either by calling an ODE solver (emulating a spectral Galerkin scheme), or by solving a fixed point problem. Experiments on various semilinear SPDEs, including the stochastic Navier-Stokes equations, demonstrate how the Neural SPDE model is capable of learning complex spatiotemporal dynamics in a resolution-invariant way, with better accuracy and lighter training data requirements compared to alternative models, and up to 3 orders of magnitude faster than traditional solvers.
    Trimmed Maximum Likelihood Estimation for Robust Learning in Generalized Linear Models. (arXiv:2206.04777v2 [cs.LG] UPDATED)
    We study the problem of learning generalized linear models under adversarial corruptions. We analyze a classical heuristic called the iterative trimmed maximum likelihood estimator which is known to be effective against label corruptions in practice. Under label corruptions, we prove that this simple estimator achieves minimax near-optimal risk on a wide range of generalized linear models, including Gaussian regression, Poisson regression and Binomial regression. Finally, we extend the estimator to the more challenging setting of label and covariate corruptions and demonstrate its robustness and optimality in that setting as well.
    Low-complexity CNNs for Acoustic Scene Classification. (arXiv:2208.01555v1 [eess.AS])
    This technical report describes the SurreyAudioTeam22s submission for DCASE 2022 ASC Task 1, Low-Complexity Acoustic Scene Classification (ASC). The task has two rules, (a) the ASC framework should have maximum 128K parameters, and (b) there should be a maximum of 30 millions multiply-accumulate operations (MACs) per inference. In this report, we present low-complexity systems for ASC that follow the rules intended for the task.
    CIPCaD-Bench: Continuous Industrial Process datasets for benchmarking Causal Discovery methods. (arXiv:2208.01529v1 [cs.LG])
    Causal relationships are commonly examined in manufacturing processes to support faults investigations, perform interventions, and make strategic decisions. Industry 4.0 has made available an increasing amount of data that enable data-driven Causal Discovery (CD). Considering the growing number of recently proposed CD methods, it is necessary to introduce strict benchmarking procedures on publicly available datasets since they represent the foundation for a fair comparison and validation of different methods. This work introduces two novel public datasets for CD in continuous manufacturing processes. The first dataset employs the well-known Tennessee Eastman simulator for fault detection and process control. The second dataset is extracted from an ultra-processed food manufacturing plant, and it includes a description of the plant, as well as multiple ground truths. These datasets are used to propose a benchmarking procedure based on different metrics and evaluated on a wide selection of CD algorithms. This work allows testing CD methods in realistic conditions enabling the selection of the most suitable method for specific target applications. The datasets are available at the following link: https://github.com/giovanniMen
    s-LIME: Reconciling Locality and Fidelity in Linear Explanations. (arXiv:2208.01510v1 [cs.LG])
    The benefit of locality is one of the major premises of LIME, one of the most prominent methods to explain black-box machine learning models. This emphasis relies on the postulate that the more locally we look at the vicinity of an instance, the simpler the black-box model becomes, and the more accurately we can mimic it with a linear surrogate. As logical as this seems, our findings suggest that, with the current design of LIME, the surrogate model may degenerate when the explanation is too local, namely, when the bandwidth parameter $\sigma$ tends to zero. Based on this observation, the contribution of this paper is twofold. Firstly, we study the impact of both the bandwidth and the training vicinity on the fidelity and semantics of LIME explanations. Secondly, and based on our findings, we propose \slime, an extension of LIME that reconciles fidelity and locality.
    Politics, Sentiment and Virality: A Large-Scale Multilingual Twitter Analysis in Greece, Spain and United Kingdom. (arXiv:2202.00396v2 [cs.CL] UPDATED)
    Social media has become extremely influential when it comes to policy making in modern societies especially in the western world (e.g., 48% of Europeans use social media every day or almost every day). Platforms such as Twitter allow users to follow politicians, thus making citizens more involved in political discussion. In the same vein, politicians use Twitter to express their opinions, debate among others on current topics and promote their political agenda aiming to influence voter behaviour. Previous studies have shown that tweets conveying negative sentiment are likely to be retweeted more frequently. In this paper, we attempt to analyse tweets of politicians from different countries and explore whether their tweets follow the same trend. Utilising state-of-the-art pre-trained language models we performed sentiment analysis on hundreds of thousands of tweets collected from members of parliament of Greece, Spain and United Kingdom, including devolved administrations. We achieved this by systematically exploring and analysing the differences between influential and less popular tweets. Our analysis indicates that politicians' negatively charged tweets spread more widely, especially in more recent times, and highlights interesting trends in the intersection of sentiment and popularity.
    Word-level Text Highlighting of Medical Texts for Telehealth Services. (arXiv:2105.10400v2 [cs.LG] UPDATED)
    The medical domain is often subject to information overload. The digitization of healthcare, constant updates to online medical repositories, and increasing availability of biomedical datasets make it challenging to effectively analyze the data. This creates additional work for medical professionals who are heavily dependent on medical data to complete their research and consult their patients. This paper aims to show how different text highlighting techniques can capture relevant medical context. This would reduce the doctors' cognitive load and response time to patients by facilitating them in making faster decisions, thus improving the overall quality of online medical services. Three different word-level text highlighting methodologies are implemented and evaluated. The first method uses TF-IDF scores directly to highlight important parts of the text. The second method is a combination of TF-IDF scores and the application of Local Interpretable Model-Agnostic Explanations to classification models. The third method uses neural networks directly to make predictions on whether or not a word should be highlighted. The results of our experiments show that the neural network approach is successful in highlighting medically-relevant terms and its performance is improved as the size of the input segment increases.
    A Survey of Natural Language Generation. (arXiv:2112.11739v2 [cs.CL] UPDATED)
    This paper offers a comprehensive review of the research on Natural Language Generation (NLG) over the past two decades, especially in relation to data-to-text generation and text-to-text generation deep learning methods, as well as new applications of NLG technology. This survey aims to (a) give the latest synthesis of deep learning research on the NLG core tasks, as well as the architectures adopted in the field; (b) detail meticulously and comprehensively various NLG tasks and datasets, and draw attention to the challenges in NLG evaluation, focusing on different evaluation methods and their relationships; (c) highlight some future emphasis and relatively recent research issues that arise due to the increasing synergy between NLG and other artificial intelligence areas, such as computer vision, text and computational creativity.
    IterMiUnet: A lightweight architecture for automatic blood vessel segmentation. (arXiv:2208.01485v1 [eess.IV])
    The automatic segmentation of blood vessels in fundus images can help analyze the condition of retinal vasculature, which is crucial for identifying various systemic diseases like hypertension, diabetes, etc. Despite the success of Deep Learning-based models in this segmentation task, most of them are heavily parametrized and thus have limited use in practical applications. This paper proposes IterMiUnet, a new lightweight convolution-based segmentation model that requires significantly fewer parameters and yet delivers performance similar to existing models. The model makes use of the excellent segmentation capabilities of Iternet architecture but overcomes its heavily parametrized nature by incorporating the encoder-decoder structure of MiUnet model within it. Thus, the new model reduces parameters without any compromise with the network's depth, which is necessary to learn abstract hierarchical concepts in deep models. This lightweight segmentation model speeds up training and inference time and is potentially helpful in the medical domain where data is scarce and, therefore, heavily parametrized models tend to overfit. The proposed model was evaluated on three publicly available datasets: DRIVE, STARE, and CHASE-DB1. Further cross-training and inter-rater variability evaluations have also been performed. The proposed model has a lot of potential to be utilized as a tool for the early diagnosis of many diseases.
    Lossy compression of multidimensional medical images using sinusoidal activation networks: an evaluation study. (arXiv:2208.01602v1 [eess.IV])
    In this work, we evaluate how neural networks with periodic activation functions can be leveraged to reliably compress large multidimensional medical image datasets, with proof-of-concept application to 4D diffusion-weighted MRI (dMRI). In the medical imaging landscape, multidimensional MRI is a key area of research for developing biomarkers that are both sensitive and specific to the underlying tissue microstructure. However, the high-dimensional nature of these data poses a challenge in terms of both storage and sharing capabilities and associated costs, requiring appropriate algorithms able to represent the information in a low-dimensional space. Recent theoretical developments in deep learning have shown how periodic activation functions are a powerful tool for implicit neural representation of images and can be used for compression of 2D images. Here we extend this approach to 4D images and show how any given 4D dMRI dataset can be accurately represented through the parameters of a sinusoidal activation network, achieving a data compression rate about 10 times higher than the standard DEFLATE algorithm. Our results show that the proposed approach outperforms benchmark ReLU and Tanh activation perceptron architectures in terms of mean squared error, peak signal-to-noise ratio and structural similarity index. Subsequent analyses using the tensor and spherical harmonics representations demonstrate that the proposed lossy compression reproduces accurately the characteristics of the original data, leading to relative errors about 5 to 10 times lower than the benchmark JPEG2000 lossy compression and similar to standard pre-processing steps such as MP-PCA denosing, suggesting a loss of information within the currently accepted levels for clinical application.
    The Curse of Low Task Diversity: On the Failure of Transfer Learning to Outperform MAML and Their Empirical Equivalence. (arXiv:2208.01545v1 [cs.LG])
    Recently, it has been observed that a transfer learning solution might be all we need to solve many few-shot learning benchmarks -- thus raising important questions about when and how meta-learning algorithms should be deployed. In this paper, we seek to clarify these questions by 1. proposing a novel metric -- the diversity coefficient -- to measure the diversity of tasks in a few-shot learning benchmark and 2. by comparing Model-Agnostic Meta-Learning (MAML) and transfer learning under fair conditions (same architecture, same optimizer, and all models trained to convergence). Using the diversity coefficient, we show that the popular MiniImageNet and CIFAR-FS few-shot learning benchmarks have low diversity. This novel insight contextualizes claims that transfer learning solutions are better than meta-learned solutions in the regime of low diversity under a fair comparison. Specifically, we empirically find that a low diversity coefficient correlates with a high similarity between transfer learning and MAML learned solutions in terms of accuracy at meta-test time and classification layer similarity (using feature based distance metrics like SVCCA, PWCCA, CKA, and OPD). To further support our claim, we find this meta-test accuracy holds even as the model size changes. Therefore, we conclude that in the low diversity regime, MAML and transfer learning have equivalent meta-test performance when both are compared fairly. We also hope our work inspires more thoughtful constructions and quantitative evaluations of meta-learning benchmarks in the future.
    Cadence: A Practical Time-series Partitioning Algorithm for Unlabeled IoT Sensor Streams. (arXiv:2112.03360v2 [cs.LG] UPDATED)
    Timeseries partitioning is an essential step in most machine-learning driven, sensor-based IoT applications. This paper introduces a sample-efficient, robust, time-series segmentation model and algorithm. We show that by learning a representation specifically with the segmentation objective based on maximum mean discrepancy (MMD), our algorithm can robustly detect time-series events across different applications. Our loss function allows us to infer whether consecutive sequences of samples are drawn from the same distribution (null hypothesis) and determines the change-point between pairs that reject the null hypothesis (i.e., come from different distributions). We demonstrate its applicability in a real-world IoT deployment for ambient-sensing based activity recognition. Moreover, while many works on change-point detection exist in the literature, our model is significantly simpler and can be fully trained in 9-93 seconds on average with little variation in hyperparameters for data across different applications. We empirically evaluate Cadence on four popular change point detection (CPD) datasets where Cadence matches or outperforms existing CPD techniques.
    Enabling scalable clinical interpretation of ML-based phenotypes using real world data. (arXiv:2208.01607v1 [cs.LG])
    The availability of large and deep electronic healthcare records (EHR) datasets has the potential to enable a better understanding of real-world patient journeys, and to identify novel subgroups of patients. ML-based aggregation of EHR data is mostly tool-driven, i.e., building on available or newly developed methods. However, these methods, their input requirements, and, importantly, resulting output are frequently difficult to interpret, especially without in-depth data science or statistical training. This endangers the final step of analysis where an actionable and clinically meaningful interpretation is needed.This study investigates approaches to perform patient stratification analysis at scale using large EHR datasets and multiple clustering methods for clinical research. We have developed several tools to facilitate the clinical evaluation and interpretation of unsupervised patient stratification results, namely pattern screening, meta clustering, surrogate modeling, and curation. These tools can be used at different stages within the analysis. As compared to a standard analysis approach, we demonstrate the ability to condense results and optimize analysis time. In the case of meta clustering, we demonstrate that the number of patient clusters can be reduced from 72 to 3 in one example. In another stratification result, by using surrogate models, we could quickly identify that heart failure patients were stratified if blood sodium measurements were available. As this is a routine measurement performed for all patients with heart failure, this indicated a data bias. By using further cohort and feature curation, these patients and other irrelevant features could be removed to increase the clinical meaningfulness. These examples show the effectiveness of the proposed methods and we hope to encourage further research in this field.
    "This is my unicorn, Fluffy": Personalizing frozen vision-language representations. (arXiv:2204.01694v3 [cs.CV] UPDATED)
    Large Vision & Language models pretrained on web-scale data provide representations that are invaluable for numerous V&L problems. However, it is unclear how they can be used for reasoning about user-specific visual concepts in unstructured language. This problem arises in multiple domains, from personalized image retrieval to personalized interaction with smart devices. We introduce a new learning setup called Personalized Vision & Language (PerVL) with two new benchmark datasets for retrieving and segmenting user-specific "personalized" concepts "in the wild". In PerVL, one should learn personalized concepts (1) independently of the downstream task (2) allowing a pretrained model to reason about them with free language, and (3) does not require personalized negative examples. We propose an architecture for solving PerVL that operates by extending the input vocabulary of a pretrained model with new word embeddings for the new personalized concepts. The model can then reason about them by simply using them in a sentence. We demonstrate that our approach learns personalized visual concepts from a few examples and can effectively apply them in image retrieval and semantic segmentation using rich textual queries.
    Self-supervised Group Meiosis Contrastive Learning for EEG-Based Emotion Recognition. (arXiv:2208.00877v2 [eess.SP] UPDATED)
    The progress of EEG-based emotion recognition has received widespread attention from the fields of human-machine interactions and cognitive science in recent years. However, how to recognize emotions with limited labels has become a new research and application bottleneck. To address the issue, this paper proposes a Self-supervised Group Meiosis Contrastive learning framework (SGMC) based on the stimuli consistent EEG signals in human being. In the SGMC, a novel genetics-inspired data augmentation method, named Meiosis, is developed. It takes advantage of the alignment of stimuli among the EEG samples in a group for generating augmented groups by pairing, cross exchanging, and separating. And the model adopts a group projector to extract group-level feature representations from group EEG samples triggered by the same emotion video stimuli. Then contrastive learning is employed to maximize the similarity of group-level representations of augmented groups with the same stimuli. The SGMC achieves the state-of-the-art emotion recognition results on the publicly available DEAP dataset with an accuracy of 94.72% and 95.68% in valence and arousal dimensions, and also reaches competitive performance on the public SEED dataset with an accuracy of 94.04%. It is worthy of noting that the SGMC shows significant performance even when using limited labels. Moreover, the results of feature visualization suggest that the model might have learned video-level emotion-related feature representations to improve emotion recognition. And the effects of group size are further evaluated in the hyper parametric analysis. Finally, a control experiment and ablation study are carried out to examine the rationality of architecture. The code is provided publicly online.
    Stochastic Deep Networks with Linear Competing Units for Model-Agnostic Meta-Learning. (arXiv:2208.01573v1 [cs.LG])
    This work addresses meta-learning (ML) by considering deep networks with stochastic local winner-takes-all (LWTA) activations. This type of network units results in sparse representations from each model layer, as the units are organized into blocks where only one unit generates a non-zero output. The main operating principle of the introduced units rely on stochastic principles, as the network performs posterior sampling over competing units to select the winner. Therefore, the proposed networks are explicitly designed to extract input data representations of sparse stochastic nature, as opposed to the currently standard deterministic representation paradigm. Our approach produces state-of-the-art predictive accuracy on few-shot image classification and regression experiments, as well as reduced predictive error on an active learning setting; these improvements come with an immensely reduced computational cost.
    Systematically and efficiently improving existing $k$-means initialization algorithms by pairwise-nearest-neighbor smoothing. (arXiv:2202.03949v2 [cs.LG] UPDATED)
    We present a meta-method for initializing (seeding) the $k$-means clustering algorithm called PNN-smoothing. It consists in splitting a given dataset into $J$ random subsets, clustering each of them individually, and merging the resulting clusterings with the pairwise-nearest-neighbor (PNN) method. It is a meta-method in the sense that when clustering the individual subsets any seeding algorithm can be used. If the computational complexity of that seeding algorithm is linear in the size of the data $N$ and the number of clusters $k$, PNN-smoothing is also almost linear with an appropriate choice of $J$, and quite competitive in practice. We show empirically, using several existing seeding methods and testing on several synthetic and real datasets, that this procedure results in systematically better costs. Our implementation is publicly available at https://github.com/carlobaldassi/KMeansPNNSmoothing.jl.
    Data-Driven Discovery of Molecular Photoswitches with Multioutput Gaussian Processes. (arXiv:2008.03226v2 [physics.chem-ph] UPDATED)
    Photoswitchable molecules display two or more isomeric forms that may be accessed using light. Separating the electronic absorption bands of these isomers is key to selectively addressing a specific isomer and achieving high photostationary states whilst overall red-shifting the absorption bands serves to limit material damage due to UV-exposure and increases penetration depth in photopharmacological applications. Engineering these properties into a system through synthetic design however, remains a challenge. Here, we present a data-driven discovery pipeline for molecular photoswitches underpinned by dataset curation and multitask learning with Gaussian processes. In the prediction of electronic transition wavelengths, we demonstrate that a multioutput Gaussian process (MOGP) trained using labels from four photoswitch transition wavelengths yields the strongest predictive performance relative to single-task models as well as operationally outperforming time-dependent density functional theory (TD-DFT) in terms of the wall-clock time for prediction. We validate our proposed approach experimentally by screening a library of commercially available photoswitchable molecules. Through this screen, we identified several motifs that displayed separated electronic absorption bands of their isomers, exhibited red-shifted absorptions, and are suited for information transfer and photopharmacological applications. Our curated dataset, code, as well as all models are made available at https://github.com/Ryan-Rhys/The-Photoswitch-Dataset
    Anti-Neuron Watermarking: Protecting Personal Data Against Unauthorized Neural Networks. (arXiv:2109.09023v2 [cs.CR] UPDATED)
    We study protecting a user's data (images in this work) against a learner's unauthorized use in training neural networks. It is especially challenging when the user's data is only a tiny percentage of the learner's complete training set. We revisit the traditional watermarking under modern deep learning settings to tackle the challenge. We show that when a user watermarks images using a specialized linear color transformation, a neural network classifier will be imprinted with the signature so that a third-party arbitrator can verify the potentially unauthorized usage of the user data by inferring the watermark signature from the neural network. We also discuss what watermarking properties and signature spaces make the arbitrator's verification convincing. To our best knowledge, this work is the first to protect an individual user's data ownership from unauthorized use in training neural networks.
    CASS: Cross Architectural Self-Supervision for Medical Image Analysis. (arXiv:2206.04170v4 [cs.CV] UPDATED)
    Recent advances in deep learning and computer vision have reduced many barriers to automated medical image analysis, allowing algorithms to process label-free images and improve performance. Specifically, Transformers provide a global perspective of the image, that Convolutional Neural Networks (CNNs) inherently lack. Here we present Cross Architectural - Self Supervision, a novel self-supervised learning approach that leverages Transformer and CNN simultaneously. Compared to the existing state of the art self-supervised learning approaches, we empirically showed that CASS trained CNNs, and Transformers across three diverse datasets gained an average of 8.5% with 100% labelled data, 7.3% with 10% labelled data, and 11.5% with 1% labelled data. Notably, one of the test datasets comprised of histopathology slides of an autoimmune disease, a condition with minimal data that has been underrepresented in medical imaging. In addition, our findings revealed that CASS is also more robust than the existing state of the art self-supervised methods. The code is open source and is available on GitHub.
    A Unifying Framework for Combining Complementary Strengths of Humans and ML toward Better Predictive Decision-Making. (arXiv:2204.10806v2 [cs.HC] UPDATED)
    Hybrid human-ML systems are increasingly in charge of consequential decisions in a wide range of domains. A growing body of empirical and theoretical work has advanced our understanding of these systems. However, existing empirical results are mixed, and theoretical proposals are often mutually incompatible. In this work, we propose a unifying framework for understanding conditions under which combining the complementary strengths of humans and ML leads to higher quality decisions than those produced by each of them individually -- a state which we refer to as human-ML complementarity. We focus specifically on the context of human-ML predictive decision-making and investigate optimal ways of combining human and ML predictive decisions, accounting for the underlying sources of variation in their judgments. Within this scope, we present two crucial contributions. First, taking a computational perspective of decision-making and drawing upon prior literature in psychology, machine learning, and human-computer interaction, we introduce a taxonomy characterizing a wide range of criteria across which human and machine decision-making differ. Second, formalizing our taxonomy allows us to study how human and ML predictive decisions should be aggregated optimally. We show that our proposed framework encompasses several existing models of human-ML complementarity as special cases. Last but not least, an initial exploratory analysis of our framework presents a critical insight for future work in human-ML complementarity: the mechanism by which we combine human and ML judgments should be informed by the underlying causes of divergence in their decisions.
    Spiking Graph Convolutional Networks. (arXiv:2205.02767v2 [cs.LG] UPDATED)
    Graph Convolutional Networks (GCNs) achieve an impressive performance due to the remarkable representation ability in learning the graph information. However, GCNs, when implemented on a deep network, require expensive computation power, making them difficult to be deployed on battery-powered devices. In contrast, Spiking Neural Networks (SNNs), which perform a bio-fidelity inference process, offer an energy-efficient neural architecture. In this work, we propose SpikingGCN, an end-to-end framework that aims to integrate the embedding of GCNs with the biofidelity characteristics of SNNs. The original graph data are encoded into spike trains based on the incorporation of graph convolution. We further model biological information processing by utilizing a fully connected layer combined with neuron nodes. In a wide range of scenarios (e.g. citation networks, image graph classification, and recommender systems), our experimental results show that the proposed method could gain competitive performance against state-of-the-art approaches. Furthermore, we show that SpikingGCN on a neuromorphic chip can bring a clear advantage of energy efficiency into graph data analysis, which demonstrates its great potential to construct environment-friendly machine learning models.
    GINK: Graph-based Interaction-aware Kinodynamic Planning via Reinforcement Learning for Autonomous Driving. (arXiv:2206.01488v2 [cs.RO] UPDATED)
    Applying reinforcement learning to autonomous driving entails certain challenges, primarily due to massive traffic flows, which change dynamically. To address such challenges, it is necessary to quickly determine response strategies to the changing intentions of surrounding vehicles. Accordingly, we propose a new policy optimization method for safe driving using graph-based interaction-aware constraints. In this framework, the motion prediction and control modules are trained simultaneously, while sharing a latent representation that contains a social context. Further, to reflect social interactions, we express the movements of agents in the graph form and filter the features. This helps preserve the spatiotemporal locality of adjacent nodes. Furthermore, we create feedback loops to combine these two modules effectively. As a result, this approach encourages the learned controller to be safe from dynamic risks and also renders the motion prediction robust under various situations. In the experiment, we set up a navigation scenario comprising various situations, with CARLA, an urban driving simulator. The experiments show state-of-the-art performance on the sides of both navigation strategy and motion prediction compared to the baselines.
    Unsupervised machine learning framework for discriminating major variants of concern during COVID-19. (arXiv:2208.01439v1 [q-bio.OT])
    Due to the rapid evolution of the SARS-CoV-2 (COVID-19) virus, a number of mutations emerged with variants such as Alpha, Gamma, Delta and Omicron which created massive impact to the world economy. Unsupervised machine learning methods have the ability to compresses, characterize and visualises unlabelled data. In this paper, we present a framework that utilizes unsupervised machine learning methods that includes combination of selected dimensional reduction and clustering methods to discriminate and visualise the associations with the major COVID-19 variants based on genome sequences. The framework utilises k-mer analysis for processing the genome (RNA) sequences and compares different dimensional reduction methods, that include principal component analysis (PCA), and t-distributed stochastic neighbour embedding (t-SNE), and uniform manifold approximation projection (UMAP). Furthermore, the framework employs agglomerative hierarchical clustering methods and provides a visualisation using a dendogram. We find that the proposed framework can effectively distinguish the major variants and hence can be used for distinguishing emerging variants in the future.
    Gaussian Control Barrier Functions : A Non-Parametric Paradigm to Safety. (arXiv:2203.15474v2 [eess.SY] UPDATED)
    Inspired by the success of control barrier functions (CBFs) in addressing safety, and the rise of data-driven techniques for modeling functions, we propose a non-parametric approach for online synthesis of CBFs using Gaussian Processes (GPs). Mathematical constructs such as CBFs have achieved safety by designing a candidate function a priori. However, designing such a candidate function can be challenging. A practical example of such a setting would be to design a CBF in a disaster recovery scenario where safe and navigable regions need to be determined. The decision boundary for safety in such an example is unknown and cannot be designed a priori. In our approach, we work with safety samples or observations to construct the CBF online by assuming a flexible GP prior on these samples, and term our formulation as a Gaussian CBF. GPs have favorable properties, in addition to being non-parametric, such as analytical tractability and robust uncertainty estimation. This allows realizing the posterior components with high safety guarantees by incorporating variance estimation, while also computing associated partial derivatives in closed-form to achieve safe control. Moreover, the synthesized safety function from our approach allows changing the corresponding safe set arbitrarily based on the data, thus allowing non-convex safe sets. We validate our approach experimentally on a quadrotor by demonstrating safe control for fixed but arbitrary safe sets and collision avoidance where the safe set is constructed online. Finally, we juxtapose Gaussian CBFs with regular CBFs in the presence of noisy states to highlight its flexibility and robustness to noise. The experiment video can be seen at: https://youtu.be/HX6uokvCiGk.
    Cluster Weighted Model Based on TSNE algorithm for High-Dimensional Data. (arXiv:2208.01579v1 [stat.ML])
    Similar to many Machine Learning models, both accuracy and speed of the Cluster weighted models (CWMs) can be hampered by high-dimensional data, leading to previous works on a parsimonious technique to reduce the effect of "Curse of dimensionality" on mixture models. In this work, we review the background study of the cluster weighted models (CWMs). We further show that parsimonious technique is not sufficient for mixture models to thrive in the presence of huge high-dimensional data. We discuss a heuristic for detecting the hidden components by choosing the initial values of location parameters using the default values in the "FlexCWM" R package. We introduce a dimensionality reduction technique called T-distributed stochastic neighbor embedding (TSNE) to enhance the parsimonious CWMs in high-dimensional space. Originally, CWMs are suited for regression but for classification purposes, all multi-class variables are transformed logarithmically with some noise. The parameters of the model are obtained via expectation maximization algorithm. The effectiveness of the discussed technique is demonstrated using real data sets from different fields.
    Deep residential representations: Using unsupervised learning to unlock elevation data for geo-demographic prediction. (arXiv:2112.01421v2 [cs.LG] UPDATED)
    LiDAR (short for "Light Detection And Ranging" or "Laser Imaging, Detection, And Ranging") technology can be used to provide detailed three-dimensional elevation maps of urban and rural landscapes. To date, airborne LiDAR imaging has been predominantly confined to the environmental and archaeological domains. However, the geographically granular and open-source nature of this data also lends itself to an array of societal, organizational and business applications where geo-demographic type data is utilised. Arguably, the complexity involved in processing this multi-dimensional data has thus far restricted its broader adoption. In this paper, we propose a series of convenient task-agnostic tile elevation embeddings to address this challenge, using recent advances from unsupervised Deep Learning. We test the potential of our embeddings by predicting seven English indices of deprivation (2019) for small geographies in the Greater London area. These indices cover a range of socio-economic outcomes and serve as a proxy for a wide variety of downstream tasks to which the embeddings can be applied. We consider the suitability of this data not just on its own but also as an auxiliary source of data in combination with demographic features, thus providing a realistic use case for the embeddings. Having trialled various model/embedding configurations, we find that our best performing embeddings lead to Root-Mean-Squared-Error (RMSE) improvements of up to 21% over using standard demographic features alone. We also demonstrate how our embedding pipeline, using Deep Learning combined with K-means clustering, produces coherent tile segments which allow the latent embedding features to be interpreted.
    How to Learn from Risk: Explicit Risk-Utility Reinforcement Learning for Efficient and Safe Driving Strategies. (arXiv:2203.08409v2 [cs.LG] UPDATED)
    Autonomous driving has the potential to revolutionize mobility and is hence an active area of research. In practice, the behavior of autonomous vehicles must be acceptable, i.e., efficient, safe, and interpretable. While vanilla reinforcement learning (RL) finds performant behavioral strategies, they are often unsafe and uninterpretable. Safety is introduced through Safe RL approaches, but they still mostly remain uninterpretable as the learned behaviour is jointly optimized for safety and performance without modeling them separately. Interpretable machine learning is rarely applied to RL. This paper proposes SafeDQN, which allows to make the behavior of autonomous vehicles safe and interpretable while still being efficient. SafeDQN offers an understandable, semantic trade-off between the expected risk and the utility of actions while being algorithmically transparent. We show that SafeDQN finds interpretable and safe driving policies for a variety of scenarios and demonstrate how state-of-the-art saliency techniques can help to assess both risk and utility.
    PAN: Pulse Ansatz on NISQ Machines. (arXiv:2208.01215v1 [quant-ph])
    Variational quantum algorithms (VQAs) have demonstrated great potentials in the NISQ era. In the workflow of VQA, the parameters of ansatz are iteratively updated to approximate the desired quantum states. We have seen various efforts to draft better ansatz with less gates. In quantum computers, the gate ansatz will eventually be transformed into control signals such as microwave pulses on transmons. And the control pulses need elaborate calibration to minimize the errors such as over-rotation and under-rotation. In the case of VQAs, this procedure will introduce redundancy, but the variational properties of VQAs can naturally handle problems of over-rotation and under-rotation by updating the amplitude and frequency parameters. Therefore, we propose PAN, a native-pulse ansatz generator framework for VQAs. We generate native-pulse ansatz with trainable parameters for amplitudes and frequencies. In our proposed PAN, we are tuning parametric pulses, which are natively supported on NISQ computers. Considering that parameter-shift rules do not hold for native-pulse ansatz, we need to deploy non-gradient optimizers. To constrain the number of parameters sent to the optimizer, we adopt a progressive way to generate our native-pulse ansatz. Experiments are conducted on both simulators and quantum devices to validate our methods. When adopted on NISQ machines, PAN obtained improved the performance with decreased latency by an average of 86%. PAN is able to achieve 99.336% and 96.482% accuracy for VQE tasks on H2 and HeH+ respectively, even with considerable noises in NISQ machines.
    Predicting Future Mosquito Habitats Using Time Series Climate Forecasting and Deep Learning. (arXiv:2208.01436v1 [cs.LG])
    Mosquito habitat ranges are projected to expand due to climate change. This investigation aims to identify future mosquito habitats by analyzing preferred ecological conditions of mosquito larvae. After assembling a data set with atmospheric records and larvae observations, a neural network is trained to predict larvae counts from ecological inputs. Time series forecasting is conducted on these variables and climate projections are passed into the initial deep learning model to generate location-specific larvae abundance predictions. The results support the notion of regional ecosystem-driven changes in mosquito spread, with high-elevation regions in particular experiencing an increase in susceptibility to mosquito infestation.
    Prompt-to-Prompt Image Editing with Cross Attention Control. (arXiv:2208.01626v1 [cs.CV])
    Recent large-scale text-driven synthesis models have attracted much attention thanks to their remarkable capabilities of generating highly diverse images that follow given text prompts. Such text-based synthesis methods are particularly appealing to humans who are used to verbally describe their intent. Therefore, it is only natural to extend the text-driven image synthesis to text-driven image editing. Editing is challenging for these generative models, since an innate property of an editing technique is to preserve most of the original image, while in the text-based models, even a small modification of the text prompt often leads to a completely different outcome. State-of-the-art methods mitigate this by requiring the users to provide a spatial mask to localize the edit, hence, ignoring the original structure and content within the masked region. In this paper, we pursue an intuitive prompt-to-prompt editing framework, where the edits are controlled by text only. To this end, we analyze a text-conditioned model in depth and observe that the cross-attention layers are the key to controlling the relation between the spatial layout of the image to each word in the prompt. With this observation, we present several applications which monitor the image synthesis by editing the textual prompt only. This includes localized editing by replacing a word, global editing by adding a specification, and even delicately controlling the extent to which a word is reflected in the image. We present our results over diverse images and prompts, demonstrating high-quality synthesis and fidelity to the edited prompts.
    Bayesian Variable Selection in a Million Dimensions. (arXiv:2208.01180v1 [stat.ME])
    Bayesian variable selection is a powerful tool for data analysis, as it offers a principled method for variable selection that accounts for prior information and uncertainty. However, wider adoption of Bayesian variable selection has been hampered by computational challenges, especially in difficult regimes with a large number of covariates P or non-conjugate likelihoods. To scale to the large P regime we introduce an efficient MCMC scheme whose cost per iteration is sublinear in P. In addition we show how this scheme can be extended to generalized linear models for count data, which are prevalent in biology, ecology, economics, and beyond. In particular we design efficient algorithms for variable selection in binomial and negative binomial regression, which includes logistic regression as a special case. In experiments we demonstrate the effectiveness of our methods, including on cancer and maize genomic data.
    A Multifaceted Benchmarking of Synthetic Electronic Health Record Generation Models. (arXiv:2208.01230v1 [cs.LG])
    Synthetic health data have the potential to mitigate privacy concerns when sharing data to support biomedical research and the development of innovative healthcare applications. Modern approaches for data generation based on machine learning, generative adversarial networks (GAN) methods in particular, continue to evolve and demonstrate remarkable potential. Yet there is a lack of a systematic assessment framework to benchmark methods as they emerge and determine which methods are most appropriate for which use cases. In this work, we introduce a generalizable benchmarking framework to appraise key characteristics of synthetic health data with respect to utility and privacy metrics. We apply the framework to evaluate synthetic data generation methods for electronic health records (EHRs) data from two large academic medical centers with respect to several use cases. The results illustrate that there is a utility-privacy tradeoff for sharing synthetic EHR data. The results further indicate that no method is unequivocally the best on all criteria in each use case, which makes it evident why synthetic data generation methods need to be assessed in context.
    Variance-Aware Weight Initialization for Point Convolutional Neural Networks. (arXiv:2112.03777v2 [cs.CV] UPDATED)
    Appropriate weight initialization has been of key importance to successfully train neural networks. Recently, batch normalization has diminished the role of weight initialization by simply normalizing each layer based on batch statistics. Unfortunately, batch normalization has several drawbacks when applied to small batch sizes, as they are required to cope with memory limitations when learning on point clouds. While well-founded weight initialization strategies can render batch normalization unnecessary and thus avoid these drawbacks, no such approaches have been proposed for point convolutional networks. To fill this gap, we propose a framework to unify the multitude of continuous convolutions. This enables our main contribution, variance-aware weight initialization. We show that this initialization can avoid batch normalization while achieving similar and, in some cases, better performance.
    Bridging Differential Privacy and Byzantine-Robustness via Model Aggregation. (arXiv:2205.00107v2 [cs.LG] UPDATED)
    This paper aims at jointly addressing two seemly conflicting issues in federated learning: differential privacy (DP) and Byzantine-robustness, which are particularly challenging when the distributed data are non-i.i.d. (independent and identically distributed). The standard DP mechanisms add noise to the transmitted messages, and entangles with robust stochastic gradient aggregation to defend against Byzantine attacks. In this paper, we decouple the two issues via robust stochastic model aggregation, in the sense that our proposed DP mechanisms and the defense against Byzantine attacks have separated influence on the learning performance. Leveraging robust stochastic model aggregation, at each iteration, each worker calculates the difference between the local model and the global one, followed by sending the element-wise signs to the master node, which enables robustness to Byzantine attacks. Further, we design two DP mechanisms to perturb the uploaded signs for the purpose of privacy preservation, and prove that they are $(\epsilon,0)$-DP by exploiting the properties of noise distributions. With the tools of Moreau envelop and proximal point projection, we establish the convergence of the proposed algorithm when the cost function is nonconvex. We analyze the trade-off between privacy preservation and learning performance, and show that the influence of our proposed DP mechanisms is decoupled with that of robust stochastic model aggregation. Numerical experiments demonstrate the effectiveness of the proposed algorithm.
    Mutation Models: Learning to Generate Levels by Imitating Evolution. (arXiv:2206.05497v2 [cs.AI] UPDATED)
    Search-based procedural content generation (PCG) is a well-known method for level generation in games. Its key advantage is that it is generic and able to satisfy functional constraints. However, due to the heavy computational costs to run these algorithms online, search-based PCG is rarely utilized for real-time generation. In this paper, we introduce mutation models, a new type of iterative level generator based on machine learning. We train a model to imitate the evolutionary process and use the trained model to generate levels. This trained model is able to modify noisy levels sequentially to create better levels without the need for a fitness function during inference. We evaluate our trained models on a 2D maze generation task. We compare several different versions of the method: training the models either at the end of evolution (normal evolution) or every 100 generations (assisted evolution) and using the model as a mutation function during evolution. Using the assisted evolution process, the final trained models are able to generate mazes with a success rate of 99% and high diversity of 86%. The trained model is many times faster than the evolutionary process it was trained on. This work opens the door to a new way of learning level generators guided by an evolutionary process, meaning automatic creation of generators with specifiable constraints and objectives that are fast enough for runtime deployment in games.
    Classifying Unstructured Clinical Notes via Automatic Weak Supervision. (arXiv:2206.12088v2 [cs.CL] UPDATED)
    Healthcare providers usually record detailed notes of the clinical care delivered to each patient for clinical, research, and billing purposes. Due to the unstructured nature of these narratives, providers employ dedicated staff to assign diagnostic codes to patients' diagnoses using the International Classification of Diseases (ICD) coding system. This manual process is not only time-consuming but also costly and error-prone. Prior work demonstrated potential utility of Machine Learning (ML) methodology in automating this process, but it has relied on large quantities of manually labeled data to train the models. Additionally, diagnostic coding systems evolve with time, which makes traditional supervised learning strategies unable to generalize beyond local applications. In this work, we introduce a general weakly-supervised text classification framework that learns from class-label descriptions only, without the need to use any human-labeled documents. It leverages the linguistic domain knowledge stored within pre-trained language models and the data programming framework to assign code labels to individual texts. We demonstrate the efficacy and flexibility of our method by comparing it to state-of-the-art weak text classifiers across four real-world text classification datasets, in addition to assigning ICD codes to medical notes in the publicly available MIMIC-III database.
    Visual correspondence-based explanations improve AI robustness and human-AI team accuracy. (arXiv:2208.00780v2 [cs.CV] UPDATED)
    Explaining artificial intelligence (AI) predictions is increasingly important and even imperative in many high-stakes applications where humans are the ultimate decision-makers. In this work, we propose two novel architectures of self-interpretable image classifiers that first explain, and then predict (as opposed to post-hoc explanations) by harnessing the visual correspondences between a query image and exemplars. Our models consistently improve (by 1 to 4 points) on out-of-distribution (OOD) datasets while performing marginally worse (by 1 to 2 points) on in-distribution tests than ResNet-50 and a $k$-nearest neighbor classifier (kNN). Via a large-scale, human study on ImageNet and CUB, our correspondence-based explanations are found to be more useful to users than kNN explanations. Our explanations help users more accurately reject AI's wrong decisions than all other tested methods. Interestingly, for the first time, we show that it is possible to achieve complementary human-AI team accuracy (i.e., that is higher than either AI-alone or human-alone), in ImageNet and CUB image classification tasks.
    Mitigating Biases in Student Performance Prediction via Attention-Based Personalized Federated Learning. (arXiv:2208.01182v1 [cs.LG])
    Traditional learning-based approaches to student modeling generalize poorly to underrepresented student groups due to biases in data availability. In this paper, we propose a methodology for predicting student performance from their online learning activities that optimizes inference accuracy over different demographic groups such as race and gender. Building upon recent foundations in federated learning, in our approach, personalized models for individual student subgroups are derived from a global model aggregated across all student models via meta-gradient updates that account for subgroup heterogeneity. To learn better representations of student activity, we augment our approach with a self-supervised behavioral pretraining methodology that leverages multiple modalities of student behavior (e.g., visits to lecture videos and participation on forums), and include a neural network attention mechanism in the model aggregation stage. Through experiments on three real-world datasets from online courses, we demonstrate that our approach obtains substantial improvements over existing student modeling baselines in predicting student learning outcomes for all subgroups. Visual analysis of the resulting student embeddings confirm that our personalization methodology indeed identifies different activity patterns within different subgroups, consistent with its stronger inference ability compared with the baselines.
    Stochastic Primal-Dual Three Operator Splitting with Arbitrary Sampling and Preconditioning. (arXiv:2208.01631v1 [math.OC])
    In this work we propose a stochastic primal-dual preconditioned three-operator splitting algorithm for solving a class of convex three-composite optimization problems. Our proposed scheme is a direct three-operator splitting extension of the SPDHG algorithm [Chambolle et al. 2018]. We provide theoretical convergence analysis showing ergodic O(1/K) convergence rate, and demonstrate the effectiveness of our approach in imaging inverse problems.
    Compound Density Networks for Risk Prediction using Electronic Health Records. (arXiv:2208.01320v1 [cs.LG])
    Electronic Health Records (EHRs) exhibit a high amount of missing data due to variations of patient conditions and treatment needs. Imputation of missing values has been considered an effective approach to deal with this challenge. Existing work separates imputation method and prediction model as two independent parts of an EHR-based machine learning system. We propose an integrated end-to-end approach by utilizing a Compound Density Network (CDNet) that allows the imputation method and prediction model to be tuned together within a single framework. CDNet consists of a Gated recurrent unit (GRU), a Mixture Density Network (MDN), and a Regularized Attention Network (RAN). The GRU is used as a latent variable model to model EHR data. The MDN is designed to sample latent variables generated by GRU. The RAN serves as a regularizer for less reliable imputed values. The architecture of CDNet enables GRU and MDN to iteratively leverage the output of each other to impute missing values, leading to a more accurate and robust prediction. We validate CDNet on the mortality prediction task on the MIMIC-III dataset. Our model outperforms state-of-the-art models by significant margins. We also empirically show that regularizing imputed values is a key factor for superior prediction performance. Analysis of prediction uncertainty shows that our model can capture both aleatoric and epistemic uncertainties, which offers model users a better understanding of the model results.
    A Comparative Study on COVID-19 Fake News Detection Using Different Transformer Based Models. (arXiv:2208.01355v1 [cs.CL])
    The rapid advancement of social networks and the convenience of internet availability have accelerated the rampant spread of false news and rumors on social media sites. Amid the COVID 19 epidemic, this misleading information has aggravated the situation by putting peoples mental and physical lives in danger. To limit the spread of such inaccuracies, identifying the fake news from online platforms could be the first and foremost step. In this research, the authors have conducted a comparative analysis by implementing five transformer based models such as BERT, BERT without LSTM, ALBERT, RoBERTa, and a Hybrid of BERT & ALBERT in order to detect the fraudulent news of COVID 19 from the internet. COVID 19 Fake News Dataset has been used for training and testing the models. Among all these models, the RoBERTa model has performed better than other models by obtaining an F1 score of 0.98 in both real and fake classes.
    Replacing Backpropagation with Biological Plausible Top-down Credit Assignment in Deep Neural Networks Training. (arXiv:2208.01416v1 [cs.NE])
    Top-down connections in the biological brain has been shown to be important in high cognitive functions. However, the function of this mechanism in machine learning has not been defined clearly. In this study, we propose to lay out a framework constituted by a bottom-up and a top-down network. Here, we use a Top-down Credit Assignment Network (TDCA-network) to replace the loss function and back propagation (BP) which serve as the feedback mechanism in traditional bottom-up network training paradigm. Our results show that the credit given by well-trained TDCA-network outperforms the gradient from backpropagation in classification task under different settings on multiple datasets. In addition, we successfully use a credit diffusing trick, which can keep training and testing performance remain unchanged, to reduce parameter complexity of the TDCA-network. More importantly, by comparing their trajectories in the parameter landscape, we find that TDCA-network directly achieved a global optimum, in contrast to that backpropagation only can gain a localized optimum. Thus, our results demonstrate that TDCA-network not only provide a biological plausible learning mechanism, but also has the potential to directly achieve global optimum, indicating that top-down credit assignment can substitute backpropagation, and provide a better learning framework for Deep Neural Networks.
    Flood Prediction Using Machine Learning Models. (arXiv:2208.01234v1 [cs.LG])
    Floods are one of nature's most catastrophic calamities which cause irreversible and immense damage to human life, agriculture, infrastructure and socio-economic system. Several studies on flood catastrophe management and flood forecasting systems have been conducted. The accurate prediction of the onset and progression of floods in real time is challenging. To estimate water levels and velocities across a large area, it is necessary to combine data with computationally demanding flood propagation models. This paper aims to reduce the extreme risks of this natural disaster and also contributes to policy suggestions by providing a prediction for floods using different machine learning models. This research will use Binary Logistic Regression, K-Nearest Neighbor (KNN), Support Vector Classifier (SVC) and Decision tree Classifier to provide an accurate prediction. With the outcome, a comparative analysis will be conducted to understand which model delivers a better accuracy.
    GeoECG: Data Augmentation via Wasserstein Geodesic Perturbation for Robust Electrocardiogram Prediction. (arXiv:2208.01220v1 [stat.ML])
    There has been an increased interest in applying deep neural networks to automatically interpret and analyze the 12-lead electrocardiogram (ECG). The current paradigms with machine learning methods are often limited by the amount of labeled data. This phenomenon is particularly problematic for clinically-relevant data, where labeling at scale can be time-consuming and costly in terms of the specialized expertise and human effort required. Moreover, deep learning classifiers may be vulnerable to adversarial examples and perturbations, which could have catastrophic consequences, for example, when applied in the context of medical treatment, clinical trials, or insurance claims. In this paper, we propose a physiologically-inspired data augmentation method to improve performance and increase the robustness of heart disease detection based on ECG signals. We obtain augmented samples by perturbing the data distribution towards other classes along the geodesic in Wasserstein space. To better utilize domain-specific knowledge, we design a ground metric that recognizes the difference between ECG signals based on physiologically determined features. Learning from 12-lead ECG signals, our model is able to distinguish five categories of cardiac conditions. Our results demonstrate improvements in accuracy and robustness, reflecting the effectiveness of our data augmentation method.
    Are Cluster Validity Measures (In)valid?. (arXiv:2208.01261v1 [stat.ML])
    Internal cluster validity measures (such as the Calinski-Harabasz, Dunn, or Davies-Bouldin indices) are frequently used for selecting the appropriate number of partitions a dataset should be split into. In this paper we consider what happens if we treat such indices as objective functions in unsupervised learning activities. Is the optimal grouping with regards to, say, the Silhouette index really meaningful? It turns out that many cluster (in)validity indices promote clusterings that match expert knowledge quite poorly. We also introduce a new, well-performing variant of the Dunn index that is built upon OWA operators and the near-neighbour graph so that subspaces of higher density, regardless of their shapes, can be separated from each other better.
    Explicit Use of Fourier Spectrum in Generative Adversarial Networks. (arXiv:2208.01265v1 [cs.CV])
    Generative Adversarial Networks have got the researchers' attention due to their state-of-the-art performance in generating new images with only a dataset of the target distribution. It has been shown that there is a dissimilarity between the spectrum of authentic images and fake ones. Since the Fourier transform is a bijective mapping, saying that the model has a significant problem in learning the original distribution is a fair conclusion. In this work, we investigate the possible reasons for the mentioned drawback in the architecture and mathematical theory of the current GANs. Then we propose a new model to reduce the discrepancies between the spectrum of the actual and fake images. To that end, we design a brand new architecture for the frequency domain using the blueprint of geometric deep learning. Then, we experimentally show promising improvements in the quality of the generated images by considering the Fourier domain representation of the original data as a principal feature in the training process.
    UniRank: Unimodal Bandit Algorithm for Online Ranking. (arXiv:2208.01515v1 [cs.LG])
    We tackle a new emerging problem, which is finding an optimal monopartite matching in a weighted graph. The semi-bandit version, where a full matching is sampled at each iteration, has been addressed by \cite{ADMA}, creating an algorithm with an expected regret matching $O(\frac{L\log(L)}{\Delta}\log(T))$ with $2L$ players, $T$ iterations and a minimum reward gap $\Delta$. We reduce this bound in two steps. First, as in \cite{GRAB} and \cite{UniRank} we use the unimodality property of the expected reward on the appropriate graph to design an algorithm with a regret in $O(L\frac{1}{\Delta}\log(T))$. Secondly, we show that by moving the focus towards the main question `\emph{Is user $i$ better than user $j$?}' this regret becomes $O(L\frac{\Delta}{\tilde{\Delta}^2}\log(T))$, where $\Tilde{\Delta} > \Delta$ derives from a better way of comparing users. Some experimental results finally show these theoretical results are corroborated in practice.
    Graph-based Reinforcement Learning meets Mixed Integer Programs: An application to 3D robot assembly discovery. (arXiv:2203.04120v2 [cs.RO] UPDATED)
    Robot assembly discovery is a challenging problem that lives at the intersection of resource allocation and motion planning. The goal is to combine a predefined set of objects to form something new while considering task execution with the robot-in-the-loop. In this work, we tackle the problem of building arbitrary, predefined target structures entirely from scratch using a set of Tetris-like building blocks and a robotic manipulator. Our novel hierarchical approach aims at efficiently decomposing the overall task into three feasible levels that benefit mutually from each other. On the high level, we run a classical mixed-integer program for global optimization of block-type selection and the blocks' final poses to recreate the desired shape. Its output is then exploited to efficiently guide the exploration of an underlying reinforcement learning (RL) policy. This RL policy draws its generalization properties from a flexible graph-based representation that is learned through Q-learning and can be refined with search. Moreover, it accounts for the necessary conditions of structural stability and robotic feasibility that cannot be effectively reflected in the previous layer. Lastly, a grasp and motion planner transforms the desired assembly commands into robot joint movements. We demonstrate our proposed method's performance on a set of competitive simulated RAD environments, showcase real-world transfer, and report performance and robustness gains compared to an unstructured end-to-end approach. Videos are available at https://sites.google.com/view/rl-meets-milp .
    What can we Learn by Predicting Accuracy?. (arXiv:2208.01358v1 [cs.LG])
    This paper seeks to answer the following question: "What can we learn by predicting accuracy?" Indeed, classification is one of the most popular task in machine learning and many loss functions have been developed to maximize this non-differentiable objective. Unlike past work on loss function design, which was mostly guided by intuition and theory before being validated by experimentation, here we propose to approach this problem in the opposite way : we seek to extract knowledge from experiments. This data-driven approach is similar to that used in physics to discover general laws from data. We used a symbolic regression method to automatically find a mathematical expression that is highly correlated with the accuracy of a linear classifier. The formula discovered on more than 260 datasets has a Pearson correlation of 0.96 and a r2 of 0.93. More interestingly, this formula is highly explainable and confirms insights from various previous papers on loss design. We hope this work will open new perspectives in the search for new heuristics leading to a deeper understanding of machine learning theory.
    Detecting Individual Decision-Making Style: Exploring Behavioral Stylometry in Chess. (arXiv:2208.01366v1 [cs.AI])
    The advent of machine learning models that surpass human decision-making ability in complex domains has initiated a movement towards building AI systems that interact with humans. Many building blocks are essential for this activity, with a central one being the algorithmic characterization of human behavior. While much of the existing work focuses on aggregate human behavior, an important long-range goal is to develop behavioral models that specialize to individual people and can differentiate among them. To formalize this process, we study the problem of behavioral stylometry, in which the task is to identify a decision-maker from their decisions alone. We present a transformer-based approach to behavioral stylometry in the context of chess, where one attempts to identify the player who played a set of games. Our method operates in a few-shot classification framework, and can correctly identify a player from among thousands of candidate players with 98% accuracy given only 100 labeled games. Even when trained on amateur play, our method generalises to out-of-distribution samples of Grandmaster players, despite the dramatic differences between amateur and world-class players. Finally, we consider more broadly what our resulting embeddings reveal about human style in chess, as well as the potential ethical implications of powerful methods for identifying individuals from behavioral data.
    Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs. (arXiv:2208.01565v1 [cs.LG])
    Neural operators are a type of deep architecture that learns to solve (i.e. learns the nonlinear solution operator of) partial differential equations (PDEs). The current state of the art for these models does not provide explicit uncertainty quantification. This is arguably even more of a problem for this kind of tasks than elsewhere in machine learning, because the dynamical systems typically described by PDEs often exhibit subtle, multiscale structure that makes errors hard to spot by humans. In this work, we first provide a mathematically detailed Bayesian formulation of the ''shallow'' (linear) version of neural operators in the formalism of Gaussian processes. We then extend this analytic treatment to general deep neural operators using approximate methods from Bayesian deep learning. We extend previous results on neural operators by providing them with uncertainty quantification. As a result, our approach is able to identify cases, and provide structured uncertainty estimates, where the neural operator fails to predict well.
    Physics-informed Deep Super-resolution for Spatiotemporal Data. (arXiv:2208.01462v1 [cs.LG])
    High-fidelity simulation of complex physical systems is exorbitantly expensive and inaccessible across spatiotemporal scales. Recently, there has been an increasing interest in leveraging deep learning to augment scientific data based on the coarse-grained simulations, which is of cheap computational expense and retains satisfactory solution accuracy. However, the major existing work focuses on data-driven approaches which rely on rich training datasets and lack sufficient physical constraints. To this end, we propose a novel and efficient spatiotemporal super-resolution framework via physics-informed learning, inspired by the independence between temporal and spatial derivatives in partial differential equations (PDEs). The general principle is to leverage the temporal interpolation for flow estimation, and then introduce convolutional-recurrent neural networks for learning temporal refinement. Furthermore, we employ the stacked residual blocks with wide activation and sub-pixel layers with pixelshuffle for spatial reconstruction, where feature extraction is conducted in a low-resolution latent space. Moreover, we consider hard imposition of boundary conditions in the network to improve reconstruction accuracy. Results demonstrate the superior effectiveness and efficiency of the proposed method compared with baseline algorithms through extensive numerical experiments.
    Mobility-Aware Cooperative Caching in Vehicular Edge Computing Based on Asynchronous Federated and Deep Reinforcement Learning. (arXiv:2208.01219v1 [cs.DC])
    The vehicular edge computing (VEC) can cache contents in different RSUs at the network edge to support the real-time vehicular applications. In VEC, owing to the high-mobility characteristics of vehicles, it is necessary to cache the user data in advance and learn the most popular and interesting contents for vehicular users. Since user data usually contains privacy information, users are reluctant to share their data with others. To solve this problem, traditional federated learning (FL) needs to update the global model synchronously through aggregating all users' local models to protect users' privacy. However, vehicles may frequently drive out of the coverage area of the VEC before they achieve their local model trainings and thus the local models cannot be uploaded as expected, which would reduce the accuracy of the global model. In addition, the caching capacity of the local RSU is limited and the popular contents are diverse, thus the size of the predicted popular contents usually exceeds the cache capacity of the local RSU. Hence, the VEC should cache the predicted popular contents in different RSUs while considering the content transmission delay. In this paper, we consider the mobility of vehicles and propose a cooperative Caching scheme in the VEC based on Asynchronous Federated and deep Reinforcement learning (CAFR). We first consider the mobility of vehicles and propose an asynchronous FL algorithm to obtain an accurate global model, and then propose an algorithm to predict the popular contents based on the global model. In addition, we consider the mobility of vehicles and propose a deep reinforcement learning algorithm to obtain the optimal cooperative caching location for the predicted popular contents in order to optimize the content transmission delay. Extensive experimental results have demonstrated that the CAFR scheme outperforms other baseline caching schemes.
    Diffusion-Based Representation Learning. (arXiv:2105.14257v3 [cs.LG] UPDATED)
    Diffusion-based methods represented as stochastic differential equations on a continuous-time domain have recently proven successful as a non-adversarial generative model. Training such models relies on denoising score matching, which can be seen as multi-scale denoising autoencoders. Here, we augment the denoising score matching framework to enable representation learning without any supervised signal. GANs and VAEs learn representations by directly transforming latent codes to data samples. In contrast, the introduced diffusion-based representation learning relies on a new formulation of the denoising score matching objective and thus encodes the information needed for denoising. We illustrate how this difference allows for manual control of the level of details encoded in the representation. Using the same approach, we propose to learn an infinite-dimensional latent code that achieves improvements of state-of-the-art models on semi-supervised image classification. We also compare the quality of learned representations of diffusion score matching with other methods like autoencoder and contrastively trained systems through their performances on downstream tasks.
    Certified machine learning: A posteriori error estimation for physics-informed neural networks. (arXiv:2203.17055v3 [cs.LG] UPDATED)
    Physics-informed neural networks (PINNs) are one popular approach to incorporate a priori knowledge about physical systems into the learning framework. PINNs are known to be robust for smaller training sets, derive better generalization problems, and are faster to train. In this paper, we show that using PINNs in comparison with purely data-driven neural networks is not only favorable for training performance but allows us to extract significant information on the quality of the approximated solution. Assuming that the underlying differential equation for the PINN training is an ordinary differential equation, we derive a rigorous upper limit on the PINN prediction error. This bound is applicable even for input data not included in the training phase and without any prior knowledge about the true solution. Therefore, our a posteriori error estimation is an essential step to certify the PINN. We apply our error estimator exemplarily to two academic toy problems, whereof one falls in the category of model-predictive control and thereby shows the practical use of the derived results.
    AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model. (arXiv:2208.01448v1 [cs.CL])
    In this work, we demonstrate that multilingual large-scale sequence-to-sequence (seq2seq) models, pre-trained on a mixture of denoising and Causal Language Modeling (CLM) tasks, are more efficient few-shot learners than decoder-only models on various tasks. In particular, we train a 20 billion parameter multilingual seq2seq model called Alexa Teacher Model (AlexaTM 20B) and show that it achieves state-of-the-art (SOTA) performance on 1-shot summarization tasks, outperforming a much larger 540B PaLM decoder model. AlexaTM 20B also achieves SOTA in 1-shot machine translation, especially for low-resource languages, across almost all language pairs supported by the model (Arabic, English, French, German, Hindi, Italian, Japanese, Marathi, Portuguese, Spanish, Tamil, and Telugu) on Flores-101 dataset. We also show in zero-shot setting, AlexaTM 20B outperforms GPT3 (175B) on SuperGLUE and SQuADv2 datasets and provides SOTA performance on multilingual tasks such as XNLI, XCOPA, Paws-X, and XWinograd. Overall, our results present a compelling case for seq2seq models as a powerful alternative to decoder-only models for Large-scale Language Model (LLM) training.
    Fisher and Kernel Fisher Discriminant Analysis: Tutorial. (arXiv:1906.09436v2 [stat.ML] UPDATED)
    This is a detailed tutorial paper which explains the Fisher discriminant Analysis (FDA) and kernel FDA. We start with projection and reconstruction. Then, one- and multi-dimensional FDA subspaces are covered. Scatters in two- and then multi-classes are explained in FDA. Then, we discuss on the rank of the scatters and the dimensionality of the subspace. A real-life example is also provided for interpreting FDA. Then, possible singularity of the scatter is discussed to introduce robust FDA. PCA and FDA directions are also compared. We also prove that FDA and linear discriminant analysis are equivalent. Fisher forest is also introduced as an ensemble of fisher subspaces useful for handling data with different features and dimensionality. Afterwards, kernel FDA is explained for both one- and multi-dimensional subspaces with both two- and multi-classes. Finally, some simulations are performed on AT&T face dataset to illustrate FDA and compare it with PCA.
    Understanding the classes better with class-specific and rule-specific feature selection, and redundancy control in a fuzzy rule based framework. (arXiv:2208.01294v1 [cs.LG])
    Recently, several studies have claimed that using class-specific feature subsets provides certain advantages over using a single feature subset for representing the data for a classification problem. Unlike traditional feature selection methods, the class-specific feature selection methods select an optimal feature subset for each class. Typically class-specific feature selection (CSFS) methods use one-versus-all split of the data set that leads to issues such as class imbalance, decision aggregation, and high computational overhead. We propose a class-specific feature selection method embedded in a fuzzy rule-based classifier, which is free from the drawbacks associated with most existing class-specific methods. Additionally, our method can be adapted to control the level of redundancy in the class-specific feature subsets by adding a suitable regularizer to the learning objective. Our method results in class-specific rules involving class-specific subsets. We also propose an extension where different rules of a particular class are defined by different feature subsets to model different substructures within the class. The effectiveness of the proposed method has been validated through experiments on three synthetic data sets.
    A Deep Generative Model for Feasible and Diverse Population Synthesis. (arXiv:2208.01403v1 [stat.ML])
    An ideal synthetic population, a key input to activity-based models, mimics the distribution of the individual- and household-level attributes in the actual population. Since the entire population's attributes are generally unavailable, household travel survey (HTS) samples are used for population synthesis. Synthesizing population by directly sampling from HTS ignores the attribute combinations that are unobserved in the HTS samples but exist in the population, called 'sampling zeros'. A deep generative model (DGM) can potentially synthesize the sampling zeros but at the expense of generating 'structural zeros' (i.e., the infeasible attribute combinations that do not exist in the population). This study proposes a novel method to minimize structural zeros while preserving sampling zeros. Two regularizations are devised to customize the training of the DGM and applied to a generative adversarial network (GAN) and a variational autoencoder (VAE). The adopted metrics for feasibility and diversity of the synthetic population indicate the capability of generating sampling and structural zeros -- lower structural zeros and lower sampling zeros indicate the higher feasibility and the lower diversity, respectively. Results show that the proposed regularizations achieve considerable performance improvement in feasibility and diversity of the synthesized population over traditional models. The proposed VAE additionally generated 23.5% of the population ignored by the sample with 79.2% precision (i.e., 20.8% structural zeros rates), while the proposed GAN generated 18.3% of the ignored population with 89.0% precision. The proposed improvement in DGM generates a more feasible and diverse synthetic population, which is critical for the accuracy of an activity-based model.
    Effects of Graph Convolutions in Multi-layer Networks. (arXiv:2204.09297v2 [cs.LG] UPDATED)
    Graph Convolutional Networks (GCNs) are one of the most popular architectures that are used to solve classification problems accompanied by graphical information. We present a rigorous theoretical understanding of the effects of graph convolutions in multi-layer networks. We study these effects through the node classification problem of a non-linearly separable Gaussian mixture model coupled with a stochastic block model. First, we show that a single graph convolution expands the regime of the distance between the means where multi-layer networks can classify the data by a factor of at least $1/\sqrt[4]{\mathbb{E}{\rm deg}}$, where $\mathbb{E}{\rm deg}$ denotes the expected degree of a node. Second, we show that with a slightly stronger graph density, two graph convolutions improve this factor to at least $1/\sqrt[4]{n}$, where $n$ is the number of nodes in the graph. Finally, we provide both theoretical and empirical insights into the performance of graph convolutions placed in different combinations among the layers of a network, concluding that the performance is mutually similar for all combinations of the placement. We present extensive experiments on both synthetic and real-world data that illustrate our results.
    A Note on Zeroth-Order Optimization on the Simplex. (arXiv:2208.01185v1 [cs.LG])
    We construct a zeroth-order gradient estimator for a smooth function defined on the probability simplex. The proposed estimator queries the simplex only. We prove that projected gradient descent and the exponential weights algorithm, when run with this estimator instead of exact gradients, converge at a $\mathcal O(T^{-1/4})$ rate.
    An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion. (arXiv:2208.01618v1 [cs.CV])
    Text-to-image models offer unprecedented freedom to guide creation through natural language. Yet, it is unclear how such freedom can be exercised to generate images of specific unique concepts, modify their appearance, or compose them in new roles and novel scenes. In other words, we ask: how can we use language-guided models to turn our cat into a painting, or imagine a new product based on our favorite toy? Here we present a simple approach that allows such creative freedom. Using only 3-5 images of a user-provided concept, like an object or a style, we learn to represent it through new "words" in the embedding space of a frozen text-to-image model. These "words" can be composed into natural language sentences, guiding personalized creation in an intuitive way. Notably, we find evidence that a single word embedding is sufficient for capturing unique and varied concepts. We compare our approach to a wide range of baselines, and demonstrate that it can more faithfully portray the concepts across a range of applications and tasks. Our code, data and new words will be available at: https://textual-inversion.github.io
    Late Fusion Multi-view Clustering via Global and Local Alignment Maximization. (arXiv:2208.01198v1 [cs.LG])
    Multi-view clustering (MVC) optimally integrates complementary information from different views to improve clustering performance. Although demonstrating promising performance in various applications, most of existing approaches directly fuse multiple pre-specified similarities to learn an optimal similarity matrix for clustering, which could cause over-complicated optimization and intensive computational cost. In this paper, we propose late fusion MVC via alignment maximization to address these issues. To do so, we first reveal the theoretical connection of existing k-means clustering and the alignment between base partitions and the consensus one. Based on this observation, we propose a simple but effective multi-view algorithm termed LF-MVC-GAM. It optimally fuses multiple source information in partition level from each individual view, and maximally aligns the consensus partition with these weighted base ones. Such an alignment is beneficial to integrate partition level information and significantly reduce the computational complexity by sufficiently simplifying the optimization procedure. We then design another variant, LF-MVC-LAM to further improve the clustering performance by preserving the local intrinsic structure among multiple partition spaces. After that, we develop two three-step iterative algorithms to solve the resultant optimization problems with theoretically guaranteed convergence. Further, we provide the generalization error bound analysis of the proposed algorithms. Extensive experiments on eighteen multi-view benchmark datasets demonstrate the effectiveness and efficiency of the proposed LF-MVC-GAM and LF-MVC-LAM, ranging from small to large-scale data items. The codes of the proposed algorithms are publicly available at https://github.com/wangsiwei2010/latefusionalignment.
    SampleMatch: Drum Sample Retrieval by Musical Context. (arXiv:2208.01141v1 [cs.SD])
    Modern digital music production typically involves combining numerous acoustic elements to compile a piece of music. Important types of such elements are drum samples, which determine the characteristics of the percussive components of the piece. Artists must use their aesthetic judgement to assess whether a given drum sample fits the current musical context. However, selecting drum samples from a potentially large library is tedious and may interrupt the creative flow. In this work, we explore the automatic drum sample retrieval based on aesthetic principles learned from data. As a result, artists can rank the samples in their library by fit to some musical context at different stages of the production process (i.e., by fit to incomplete song mixtures). To this end, we use contrastive learning to maximize the score of drum samples originating from the same song as the mixture. We conduct a listening test to determine whether the human ratings match the automatic scoring function. We also perform objective quantitative analyses to evaluate the efficacy of our approach.
    VacciNet: Towards a Smart Framework for Learning the Distribution Chain Optimization of Vaccines for a Pandemic. (arXiv:2208.01112v1 [cs.LG])
    Vaccinations against viruses have always been the need of the hour since long past. However, it is hard to efficiently distribute the vaccines (on time) to all the corners of a country, especially during a pandemic. Considering the vastness of the population, diversified communities, and demands of a smart society, it is an important task to optimize the vaccine distribution strategy in any country/state effectively. Although there is a profusion of data (Big Data) from various vaccine administration sites that can be mined to gain valuable insights about mass vaccination drives, very few attempts has been made towards revolutionizing the traditional mass vaccination campaigns to mitigate the socio-economic crises of pandemic afflicted countries. In this paper, we bridge this gap in studies and experimentation. We collect daily vaccination data which is publicly available and carefully analyze it to generate meaning-full insights and predictions. We put forward a novel framework leveraging Supervised Learning and Reinforcement Learning (RL) which we call VacciNet, that is capable of learning to predict the demand of vaccination in a state of a country as well as suggest optimal vaccine allocation in the state for minimum cost of procurement and supply. At the present, our framework is trained and tested with vaccination data of the USA.
    Disparate Censorship & Undertesting: A Source of Label Bias in Clinical Machine Learning. (arXiv:2208.01127v1 [cs.LG])
    As machine learning (ML) models gain traction in clinical applications, understanding the impact of clinician and societal biases on ML models is increasingly important. While biases can arise in the labels used for model training, the many sources from which these biases arise are not yet well-studied. In this paper, we highlight disparate censorship (i.e., differences in testing rates across patient groups) as a source of label bias that clinical ML models may amplify, potentially causing harm. Many patient risk-stratification models are trained using the results of clinician-ordered diagnostic and laboratory tests of labels. Patients without test results are often assigned a negative label, which assumes that untested patients do not experience the outcome. Since orders are affected by clinical and resource considerations, testing may not be uniform in patient populations, giving rise to disparate censorship. Disparate censorship in patients of equivalent risk leads to undertesting in certain groups, and in turn, more biased labels for such groups. Using such biased labels in standard ML pipelines could contribute to gaps in model performance across patient groups. Here, we theoretically and empirically characterize conditions in which disparate censorship or undertesting affect model performance across subgroups. Our findings call attention to disparate censorship as a source of label bias in clinical ML models.
    Optimizing Mixture of Experts using Dynamic Recompilations. (arXiv:2205.01848v2 [cs.LG] UPDATED)
    The Mixture of Experts architecture allows for outrageously large neural networks by scaling model parameter size independently from computational demand (FLOPs). However, current DNN frameworks cannot effectively support the dynamic data flow in Mixture of Experts, and implementations on top of these frameworks need to use workarounds that introduce significant overheads. To address the limitation of these frameworks, we present DynaMoE, a DNN library that uses dynamic recompilations to optimize and adapt the use of computational resources to the dynamic needs of Mixture of Experts models. Our evaluation shows that DynaMoE achieves a 1.8x speedup and supports 2.3x larger model sizes when compared to existing MoE systems, even when not using recompilations. We then present further optimizations enabled by dynamic recompilations that yield an additional 1.7x speedup while simultaneously reducing memory pressure and improving model quality.
    On the Evaluation of User Privacy in Deep Neural Networks using Timing Side Channel. (arXiv:2208.01113v1 [cs.CR])
    Recent Deep Learning (DL) advancements in solving complex real-world tasks have led to its widespread adoption in practical applications. However, this opportunity comes with significant underlying risks, as many of these models rely on privacy-sensitive data for training in a variety of applications, making them an overly-exposed threat surface for privacy violations. Furthermore, the widespread use of cloud-based Machine-Learning-as-a-Service (MLaaS) for its robust infrastructure support has broadened the threat surface to include a variety of remote side-channel attacks. In this paper, we first identify and report a novel data-dependent timing side-channel leakage (termed Class Leakage) in DL implementations originating from non-constant time branching operation in a widely used DL framework PyTorch. We further demonstrate a practical inference-time attack where an adversary with user privilege and hard-label black-box access to an MLaaS can exploit Class Leakage to compromise the privacy of MLaaS users. DL models are vulnerable to Membership Inference Attack (MIA), where an adversary's objective is to deduce whether any particular data has been used while training the model. In this paper, as a separate case study, we demonstrate that a DL model secured with differential privacy (a popular countermeasure against MIA) is still vulnerable to MIA against an adversary exploiting Class Leakage. We develop an easy-to-implement countermeasure by making a constant-time branching operation that alleviates the Class Leakage and also aids in mitigating MIA. We have chosen two standard benchmarking image classification datasets, CIFAR-10 and CIFAR-100 to train five state-of-the-art pre-trained DL models, over two different computing environments having Intel Xeon and Intel i7 processors to validate our approach.
    Learning to estimate a surrogate respiratory signal from cardiac motion by signal-to-signal translation. (arXiv:2208.01034v1 [eess.IV])
    In this work, we develop a neural network-based method to convert a noisy motion signal generated from segmenting rebinned list-mode cardiac SPECT images, to that of a high-quality surrogate signal, such as those seen from external motion tracking systems (EMTs). This synthetic surrogate will be used as input to our pre-existing motion correction technique developed for EMT surrogate signals. In our method, we test two families of neural networks to translate noisy internal motion to external surrogate: 1) fully connected networks and 2) convolutional neural networks. Our dataset consists of cardiac perfusion SPECT acquisitions for which cardiac motion was estimated (input: center-of-count-mass - COM signals) in conjunction with a respiratory surrogate motion signal acquired using a commercial Vicon Motion Tracking System (GT: EMT signals). We obtained an average R-score of 0.76 between the predicted surrogate and the EMT signal. Our goal is to lay a foundation to guide the optimization of neural networks for respiratory motion correction from SPECT without the need for an EMT.
    ASTA: Learning Analytical Semantics over Tables for Intelligent Data Analysis and Visualization. (arXiv:2208.01043v1 [cs.DB])
    Intelligent analysis and visualization of tables use techniques to automatically recommend useful knowledge from data, thus freeing users from tedious multi-dimension data mining. While many studies have succeeded in automating recommendations through rules or machine learning, it is difficult to generalize expert knowledge and provide explainable recommendations. In this paper, we present the recommendation of conditional formatting for the first time, together with chart recommendation, to exemplify intelligent table analysis. We propose analytical semantics over tables to uncover common analysis pattern behind user-created analyses. Here, we design analytical semantics by separating data focus from user intent, which extract the user motivation from data and human perspective respectively. Furthermore, the ASTA framework is designed by us to apply analytical semantics to multiple automated recommendations. ASTA framework extracts data features by designing signatures based on expert knowledge, and enables data referencing at field- (chart) or cell-level (conditional formatting) with pre-trained models. Experiments show that our framework achieves recall at top 1 of 62.86% on public chart corpora, outperforming the best baseline about 14%, and achieves 72.31% on the collected corpus ConFormT, validating that ASTA framework is effective in providing accurate and explainable recommendations.
    Correlated-informed neural networks: a new machine learning framework to predict pressure drop in micro-channels. (arXiv:2201.07835v2 [cs.LG] UPDATED)
    Accurate pressure drop estimation in forced boiling phenomena is important during the thermal analysis and the geometric design of cryogenic heat exchangers. However, current methods to predict the pressure drop have one of two problems: lack of accuracy or generalization to different situations. In this work, we present the correlated-informed neural networks (CoINN), a new paradigm in applying the artificial neural network (ANN) technique combined with a successful pressure drop correlation as a mapping tool to predict the pressure drop of zeotropic mixtures in micro-channels. The proposed approach is inspired by Transfer Learning, highly used in deep learning problems with reduced datasets. Our method improves the ANN performance by transferring the knowledge of the Sun & Mishima correlation for the pressure drop to the ANN. The correlation having physical and phenomenological implications for the pressure drop in micro-channels considerably improves the performance and generalization capabilities of the ANN. The final architecture consists of three inputs: the mixture vapor quality, the micro-channel inner diameter, and the available pressure drop correlation. The results show the benefits gained using the correlated-informed approach predicting experimental data used for training and a posterior test with a mean relative error (mre) of 6%, lower than the Sun & Mishima correlation of 13%. Additionally, this approach can be extended to other mixtures and experimental settings, a missing feature in other approaches for mapping correlations using ANNs for heat transfer applications.
    VI-IKD: High-Speed Accurate Off-Road Navigation using Learned Visual-Inertial Inverse Kinodynamics. (arXiv:2203.15983v2 [cs.RO] UPDATED)
    One of the key challenges in high speed off road navigation on ground vehicles is that the kinodynamics of the vehicle terrain interaction can differ dramatically depending on the terrain. Previous approaches to addressing this challenge have considered learning an inverse kinodynamics (IKD) model, conditioned on inertial information of the vehicle to sense the kinodynamic interactions. In this paper, we hypothesize that to enable accurate high-speed off-road navigation using a learned IKD model, in addition to inertial information from the past, one must also anticipate the kinodynamic interactions of the vehicle with the terrain in the future. To this end, we introduce Visual-Inertial Inverse Kinodynamics (VI-IKD), a novel learning based IKD model that is conditioned on visual information from a terrain patch ahead of the robot in addition to past inertial information, enabling it to anticipate kinodynamic interactions in the future. We validate the effectiveness of VI-IKD in accurate high-speed off-road navigation experimentally on a scale 1/5 UT-AlphaTruck off-road autonomous vehicle in both indoor and outdoor environments and show that compared to other state-of-the-art approaches, VI-IKD enables more accurate and robust off-road navigation on a variety of different terrains at speeds of up to 3.5 m/s.
    ENERO: Efficient Real-Time WAN Routing Optimization with Deep Reinforcement Learning. (arXiv:2109.10883v3 [cs.NI] UPDATED)
    Wide Area Networks (WAN) are a key infrastructure in today's society. During the last years, WANs have seen a considerable increase in network's traffic and network applications, imposing new requirements on existing network technologies (e.g., low latency and high throughput). Consequently, Internet Service Providers (ISP) are under pressure to ensure the customer's Quality of Service and fulfill Service Level Agreements. Network operators leverage Traffic Engineering (TE) techniques to efficiently manage network's resources. However, WAN's traffic can drastically change during time and the connectivity can be affected due to external factors (e.g., link failures). Therefore, TE solutions must be able to adapt to dynamic scenarios in real-time. In this paper we propose Enero, an efficient real-time TE solution based on a two-stage optimization process. In the first one, Enero leverages Deep Reinforcement Learning (DRL) to optimize the routing configuration by generating a long-term TE strategy. To enable efficient operation over dynamic network scenarios (e.g., when link failures occur), we integrated a Graph Neural Network into the DRL agent. In the second stage, Enero uses a Local Search algorithm to improve DRL's solution without adding computational overhead to the optimization process. The experimental results indicate that Enero is able to operate in real-world dynamic network topologies in 4.5 seconds on average for topologies up to 100 edges.
    Face-to-Face Contrastive Learning for Social Intelligence Question-Answering. (arXiv:2208.01036v1 [cs.LG])
    Creating artificial social intelligence - algorithms that can understand the nuances of multi-person interactions - is an exciting and emerging challenge in processing facial expressions and gestures from multimodal videos. Recent multimodal methods have set the state of the art on many tasks, but have difficulty modeling the complex face-to-face conversational dynamics across speaking turns in social interaction, particularly in a self-supervised setup. In this paper, we propose Face-to-Face Contrastive Learning (F2F-CL), a graph neural network designed to model social interactions using factorization nodes to contextualize the multimodal face-to-face interaction along the boundaries of the speaking turn. With the F2F-CL model, we propose to perform contrastive learning between the factorization nodes of different speaking turns within the same video. We experimentally evaluated the challenging Social-IQ dataset and show state-of-the-art results.
    Binary Independent Component Analysis: A Non-stationarity-based Approach. (arXiv:2111.15431v2 [cs.LG] UPDATED)
    We consider independent component analysis of binary data. While fundamental in practice, this case has been much less developed than ICA for continuous data. We start by assuming a linear mixing model in a continuous-valued latent space, followed by a binary observation model. Importantly, we assume that the sources are non-stationary; this is necessary since any non-Gaussianity would essentially be destroyed by the binarization. Interestingly, the model allows for closed-form likelihood by employing the cumulative distribution function of the multivariate Gaussian distribution. In stark contrast to the continuous-valued case, we prove non-identifiability of the model with few observed variables; our empirical results imply identifiability when the number of observed variables is higher. We present a practical method for binary ICA that uses only pairwise marginals, which are faster to compute than the full multivariate likelihood. Experiments give insight into the requirements for the number of observed variables, segments, and latent sources that allow the model to be estimated.
    Nonnegative Tucker Decomposition with Beta-divergence for Music Structure Analysis of Audio Signals. (arXiv:2110.14434v4 [cs.SD] UPDATED)
    Nonnegative Tucker decomposition (NTD), a tensor decomposition model, has received increased interest in the recent years because of its ability to blindly extract meaningful patterns, in particular in Music Information Retrieval. Nevertheless, existing algorithms to compute NTD are mostly designed for the Euclidean loss. This work proposes a multiplicative updates algorithm to compute NTD with the beta-divergence loss, often considered a better loss for audio processing. We notably show how to implement efficiently the multiplicative rules using tensor algebra. Finally, we show on a music structure analysis task that unsupervised NTD fitted with beta-divergence loss outperforms earlier results obtained with the Euclidean loss.
    What Can Transformers Learn In-Context? A Case Study of Simple Function Classes. (arXiv:2208.01066v1 [cs.CL])
    In-context learning refers to the ability of a model to condition on a prompt sequence consisting of in-context examples (input-output pairs corresponding to some task) along with a new query input, and generate the corresponding output. Crucially, in-context learning happens only at inference time without any parameter updates to the model. While large language models such as GPT-3 exhibit some ability to perform in-context learning, it is unclear what the relationship is between tasks on which this succeeds and what is present in the training data. To make progress towards understanding in-context learning, we consider the well-defined problem of training a model to in-context learn a function class (e.g., linear functions): that is, given data derived from some functions in the class, can we train a model to in-context learn "most" functions from this class? We show empirically that standard Transformers can be trained from scratch to perform in-context learning of linear functions -- that is, the trained model is able to learn unseen linear functions from in-context examples with performance comparable to the optimal least squares estimator. In fact, in-context learning is possible even under two forms of distribution shift: (i) between the training data of the model and inference-time prompts, and (ii) between the in-context examples and the query input during inference. We also show that we can train Transformers to in-context learn more complex function classes -- namely sparse linear functions, two-layer neural networks, and decision trees -- with performance that matches or exceeds task-specific learning algorithms. Our code and models are available at https://github.com/dtsip/in-context-learning .
    Short-term Load Forecasting with Distributed Long Short-Term Memory. (arXiv:2208.01147v1 [cs.LG])
    With the employment of smart meters, massive data on consumer behaviour can be collected by retailers. From the collected data, the retailers may obtain the household profile information and implement demand response. While retailers prefer to acquire a model as accurate as possible among different customers, there are two major challenges. First, different retailers in the retail market do not share their consumer's electricity consumption data as these data are regarded as their assets, which has led to the problem of data island. Second, the electricity load data are highly heterogeneous since different retailers may serve various consumers. To this end, a fully distributed short-term load forecasting framework based on a consensus algorithm and Long Short-Term Memory (LSTM) is proposed, which may protect the customer's privacy and satisfy the accurate load forecasting requirement. Specifically, a fully distributed learning framework is exploited for distributed training, and a consensus technique is applied to meet confidential privacy. Case studies show that the proposed method has comparable performance with centralised methods regarding the accuracy, but the proposed method shows advantages in training speed and data privacy.
    Learning of Parameters in Behavior Trees for Movement Skills. (arXiv:2109.13050v2 [cs.RO] UPDATED)
    Reinforcement Learning (RL) is a powerful mathematical framework that allows robots to learn complex skills by trial-and-error. Despite numerous successes in many applications, RL algorithms still require thousands of trials to converge to high-performing policies, can produce dangerous behaviors while learning, and the optimized policies (usually modeled as neural networks) give almost zero explanation when they fail to perform the task. For these reasons, the adoption of RL in industrial settings is not common. Behavior Trees (BTs), on the other hand, can provide a policy representation that a) supports modular and composable skills, b) allows for easy interpretation of the robot actions, and c) provides an advantageous low-dimensional parameter space. In this paper, we present a novel algorithm that can learn the parameters of a BT policy in simulation and then generalize to the physical robot without any additional training. We leverage a physical simulator with a digital twin of our workstation, and optimize the relevant parameters with a black-box optimizer. We showcase the efficacy of our method with a 7-DOF KUKA-iiwa manipulator in a task that includes obstacle avoidance and a contact-rich insertion (peg-in-hole), in which our method outperforms the baselines.
    An Online Sparse Streaming Feature Selection Algorithm. (arXiv:2208.01562v1 [cs.LG])
    Online streaming feature selection (OSFS), which conducts feature selection in an online manner, plays an important role in dealing with high-dimensional data. In many real applications such as intelligent healthcare platform, streaming feature always has some missing data, which raises a crucial challenge in conducting OSFS, i.e., how to establish the uncertain relationship between sparse streaming features and labels. Unfortunately, existing OSFS algorithms never consider such uncertain relationship. To fill this gap, we in this paper propose an online sparse streaming feature selection with uncertainty (OS2FSU) algorithm. OS2FSU consists of two main parts: 1) latent factor analysis is utilized to pre-estimate the missing data in sparse streaming features before con-ducting feature selection, and 2) fuzzy logic and neighborhood rough set are employed to alleviate the uncertainty between estimated streaming features and labels during conducting feature selection. In the experiments, OS2FSU is compared with five state-of-the-art OSFS algorithms on six real datasets. The results demonstrate that OS2FSU outperforms its competitors when missing data are encountered in OSFS.
    Implicit Two-Tower Policies. (arXiv:2208.01191v1 [cs.LG])
    We present a new class of structured reinforcement learning policy-architectures, Implicit Two-Tower (ITT) policies, where the actions are chosen based on the attention scores of their learnable latent representations with those of the input states. By explicitly disentangling action from state processing in the policy stack, we achieve two main goals: substantial computational gains and better performance. Our architectures are compatible with both: discrete and continuous action spaces. By conducting tests on 15 environments from OpenAI Gym and DeepMind Control Suite, we show that ITT-architectures are particularly suited for blackbox/evolutionary optimization and the corresponding policy training algorithms outperform their vanilla unstructured implicit counterparts as well as commonly used explicit policies. We complement our analysis by showing how techniques such as hashing and lazy tower updates, critically relying on the two-tower structure of ITTs, can be applied to obtain additional computational improvements.
    Analog Gated Recurrent Neural Network for Detecting Chewing Events. (arXiv:2208.01201v1 [cs.LG])
    We present a novel gated recurrent neural network to detect when a person is chewing on food. We implemented the neural network as a custom analog integrated circuit in a 0.18 um CMOS technology. The neural network was trained on 6.4 hours of data collected from a contact microphone that was mounted on volunteers' mastoid bones. When tested on 1.6 hours of previously-unseen data, the neural network identified chewing events at a 24-second time resolution. It achieved a recall of 91% and an F1-score of 94% while consuming 1.1 uW of power. A system for detecting whole eating episodes -- like meals and snacks -- that is based on the novel analog neural network consumes an estimated 18.8uW of power.
    Automatic Classification of Bug Reports Based on Multiple Text Information and Reports' Intention. (arXiv:2208.01274v1 [cs.SE])
    With the rapid growth of software scale and complexity, a large number of bug reports are submitted to the bug tracking system. In order to speed up defect repair, these reports need to be accurately classified so that they can be sent to the appropriate developers. However, the existing classification methods only use the text information of the bug report, which leads to their low performance. To solve the above problems, this paper proposes a new automatic classification method for bug reports. The innovation is that when categorizing bug reports, in addition to using the text information of the report, the intention of the report (i.e. suggestion or explanation) is also considered, thereby improving the performance of the classification. First, we collect bug reports from four ecosystems (Apache, Eclipse, Gentoo, Mozilla) and manually annotate them to construct an experimental data set. Then, we use Natural Language Processing technology to preprocess the data. On this basis, BERT and TF-IDF are used to extract the features of the intention and the multiple text information. Finally, the features are used to train the classifiers. The experimental result on five classifiers (including K-Nearest Neighbor, Naive Bayes, Logistic Regression, Support Vector Machine, and Random Forest) show that our proposed method achieves better performance and its F-Measure achieves from 87.3% to 95.5%.
    Making a Spiking Net Work: Robust brain-like unsupervised machine learning. (arXiv:2208.01204v1 [cs.NE])
    The surge in interest in Artificial Intelligence (AI) over the past decade has been driven almost exclusively by advances in Artificial Neural Networks (ANNs). While ANNs set state-of-the-art performance for many previously intractable problems, they require large amounts of data and computational resources for training, and since they employ supervised learning they typically need to know the correctly labelled response for every training example, limiting their scalability for real-world domains. Spiking Neural Networks (SNNs) are an alternative to ANNs that use more brain-like artificial neurons and can use unsupervised learning to discover recognizable features in the input data without knowing correct responses. SNNs, however, struggle with dynamical stability and cannot match the accuracy of ANNs. Here we show how an SNN can overcome many of the shortcomings that have been identified in the literature, including offering a principled solution to the vanishing spike problem, to outperform all existing shallow SNNs and equal the performance of an ANN. It accomplishes this while using unsupervised learning with unlabeled data and only 1/50th of the training epochs (labelled data is used only for a final simple linear readout layer). This result makes SNNs a viable new method for fast, accurate, efficient, explainable, and re-deployable machine learning with unlabeled datasets.
    Dyadic Movement Synchrony Estimation Under Privacy-preserving Conditions. (arXiv:2208.01100v1 [cs.CV])
    Movement synchrony refers to the dynamic temporal connection between the motions of interacting people. The applications of movement synchrony are wide and broad. For example, as a measure of coordination between teammates, synchrony scores are often reported in sports. The autism community also identifies movement synchrony as a key indicator of children's social and developmental achievements. In general, raw video recordings are often used for movement synchrony estimation, with the drawback that they may reveal people's identities. Furthermore, such privacy concern also hinders data sharing, one major roadblock to a fair comparison between different approaches in autism research. To address the issue, this paper proposes an ensemble method for movement synchrony estimation, one of the first deep-learning-based methods for automatic movement synchrony assessment under privacy-preserving conditions. Our method relies entirely on publicly shareable, identity-agnostic secondary data, such as skeleton data and optical flow. We validate our method on two datasets: (1) PT13 dataset collected from autism therapy interventions and (2) TASD-2 dataset collected from synchronized diving competitions. In this context, our method outperforms its counterpart approaches, both deep neural networks and alternatives.
    Patents Phrase to Phrase Semantic Matching Dataset. (arXiv:2208.01171v1 [cs.CL])
    There are many general purpose benchmark datasets for Semantic Textual Similarity but none of them are focused on technical concepts found in patents and scientific publications. This work aims to fill this gap by presenting a new human rated contextual phrase to phrase matching dataset. The entire dataset contains close to $50,000$ rated phrase pairs, each with a CPC (Cooperative Patent Classification) class as a context. This paper describes the dataset and some baseline models.
    Audio Deepfake Detection Based on a Combination of F0 Information and Real Plus Imaginary Spectrogram Features. (arXiv:2208.01214v1 [cs.SD])
    Recently, pioneer research works have proposed a large number of acoustic features (log power spectrogram, linear frequency cepstral coefficients, constant Q cepstral coefficients, etc.) for audio deepfake detection, obtaining good performance, and showing that different subbands have different contributions to audio deepfake detection. However, this lacks an explanation of the specific information in the subband, and these features also lose information such as phase. Inspired by the mechanism of synthetic speech, the fundamental frequency (F0) information is used to improve the quality of synthetic speech, while the F0 of synthetic speech is still too average, which differs significantly from that of real speech. It is expected that F0 can be used as important information to discriminate between bonafide and fake speech, while this information cannot be used directly due to the irregular distribution of F0. Insteadly, the frequency band containing most of F0 is selected as the input feature. Meanwhile, to make full use of the phase and full-band information, we also propose to use real and imaginary spectrogram features as complementary input features and model the disjoint subbands separately. Finally, the results of F0, real and imaginary spectrogram features are fused. Experimental results on the ASVspoof 2019 LA dataset show that our proposed system is very effective for the audio deepfake detection task, achieving an equivalent error rate (EER) of 0.43%, which surpasses almost all systems.  ( 3 min )
    Interpretable Time Series Clustering Using Local Explanations. (arXiv:2208.01152v1 [cs.LG])
    This study focuses on exploring the use of local interpretability methods for explaining time series clustering models. Many of the state-of-the-art clustering models are not directly explainable. To provide explanations for these clustering algorithms, we train classification models to estimate the cluster labels. Then, we use interpretability methods to explain the decisions of the classification models. The explanations are used to obtain insights into the clustering models. We perform a detailed numerical study to test the proposed approach on multiple datasets, clustering models, and classification models. The analysis of the results shows that the proposed approach can be used to explain time series clustering models, specifically when the underlying classification model is accurate. Lastly, we provide a detailed analysis of the results, discussing how our approach can be used in a real-life scenario.  ( 2 min )
    Generative Adversarial Learning for Intelligent Trust Management in 6G Wireless Networks. (arXiv:2208.01221v1 [cs.NI])
    Emerging six generation (6G) is the integration of heterogeneous wireless networks, which can seamlessly support anywhere and anytime networking. But high Quality-of-Trust should be offered by 6G to meet mobile user expectations. Artificial intelligence (AI) is considered as one of the most important components in 6G. Then AI-based trust management is a promising paradigm to provide trusted and reliable services. In this article, a generative adversarial learning-enabled trust management method is presented for 6G wireless networks. Some typical AI-based trust management schemes are first reviewed, and then a potential heterogeneous and intelligent 6G architecture is introduced. Next, the integration of AI and trust management is developed to optimize the intelligence and security. Finally, the presented AI-based trust management method is applied to secure clustering to achieve reliable and real-time communications. Simulation results have demonstrated its excellent performance in guaranteeing network security and service quality.  ( 2 min )
    DAPDAG: Domain Adaptation via Perturbed DAG Reconstruction. (arXiv:2208.01373v1 [cs.LG])
    Leveraging labelled data from multiple domains to enable prediction in another domain without labels is a significant, yet challenging problem. To address this problem, we introduce the framework DAPDAG (\textbf{D}omain \textbf{A}daptation via \textbf{P}erturbed \textbf{DAG} Reconstruction) and propose to learn an auto-encoder that undertakes inference on population statistics given features and reconstructing a directed acyclic graph (DAG) as an auxiliary task. The underlying DAG structure is assumed invariant among observed variables whose conditional distributions are allowed to vary across domains led by a latent environmental variable $E$. The encoder is designed to serve as an inference device on $E$ while the decoder reconstructs each observed variable conditioned on its graphical parents in the DAG and the inferred $E$. We train the encoder and decoder jointly in an end-to-end manner and conduct experiments on synthetic and real datasets with mixed variables. Empirical results demonstrate that reconstructing the DAG benefits the approximate inference. Furthermore, our approach can achieve competitive performance against other benchmarks in prediction tasks, with better adaptation ability, especially in the target domain significantly different from the source domains.
    MV6D: Multi-View 6D Pose Estimation on RGB-D Frames Using a Deep Point-wise Voting Network. (arXiv:2208.01172v1 [cs.CV])
    Estimating 6D poses of objects is an essential computer vision task. However, most conventional approaches rely on camera data from a single perspective and therefore suffer from occlusions. We overcome this issue with our novel multi-view 6D pose estimation method called MV6D which accurately predicts the 6D poses of all objects in a cluttered scene based on RGB-D images from multiple perspectives. We base our approach on the PVN3D network that uses a single RGB-D image to predict keypoints of the target objects. We extend this approach by using a combined point cloud from multiple views and fusing the images from each view with a DenseFusion layer. In contrast to current multi-view pose detection networks such as CosyPose, our MV6D can learn the fusion of multiple perspectives in an end-to-end manner and does not require multiple prediction stages or subsequent fine tuning of the prediction. Furthermore, we present three novel photorealistic datasets of cluttered scenes with heavy occlusions. All of them contain RGB-D images from multiple perspectives and the ground truth for instance semantic segmentation and 6D pose estimation. MV6D significantly outperforms the state-of-the-art in multi-view 6D pose estimation even in cases where the camera poses are known inaccurately. Furthermore, we show that our approach is robust towards dynamic camera setups and that its accuracy increases incrementally with an increasing number of perspectives.  ( 3 min )
    Fast Kernel Density Estimation with Density Matrices and Random Fourier Features. (arXiv:2208.01206v1 [cs.LG])
    Kernel density estimation (KDE) is one of the most widely used nonparametric density estimation methods. The fact that it is a memory-based method, i.e., it uses the entire training data set for prediction, makes it unsuitable for most current big data applications. Several strategies, such as tree-based or hashing-based estimators, have been proposed to improve the efficiency of the kernel density estimation method. The novel density kernel density estimation method (DMKDE) uses density matrices, a quantum mechanical formalism, and random Fourier features, an explicit kernel approximation, to produce density estimates. This method has its roots in the KDE and can be considered as an approximation method, without its memory-based restriction. In this paper, we systematically evaluate the novel DMKDE algorithm and compare it with other state-of-the-art fast procedures for approximating the kernel density estimation method on different synthetic data sets. Our experimental results show that DMKDE is on par with its competitors for computing density estimates and advantages are shown when performed on high-dimensional data. We have made all the code available as an open source software repository.  ( 2 min )
    Vertical GaN Diode BV Maximization through Rapid TCAD Simulation and ML-enabled Surrogate Model. (arXiv:2208.01142v1 [cs.LG])
    In this paper, two methodologies are used to speed up the maximization of the breakdown volt-age (BV) of a vertical GaN diode that has a theoretical maximum BV of ~2100V. Firstly, we demonstrated a 5X faster accurate simulation method in Technology Computer-Aided-Design (TCAD). This allows us to find 50% more numbers of high BV (>1400V) designs at a given simulation time. Secondly, a machine learning (ML) model is developed using TCAD-generated data and used as a surrogate model for differential evolution optimization. It can inversely design an out-of-the-training-range structure with BV as high as 1887V (89% of the ideal case) compared to ~1100V designed with human domain expertise.  ( 2 min )
    Improving the Trainability of Deep Neural Networks through Layerwise Batch-Entropy Regularization. (arXiv:2208.01134v1 [cs.LG])
    Training deep neural networks is a very demanding task, especially challenging is how to adapt architectures to improve the performance of trained models. We can find that sometimes, shallow networks generalize better than deep networks, and the addition of more layers results in higher training and test errors. The deep residual learning framework addresses this degradation problem by adding skip connections to several neural network layers. It would at first seem counter-intuitive that such skip connections are needed to train deep networks successfully as the expressivity of a network would grow exponentially with depth. In this paper, we first analyze the flow of information through neural networks. We introduce and evaluate the batch-entropy which quantifies the flow of information through each layer of a neural network. We prove empirically and theoretically that a positive batch-entropy is required for gradient descent-based training approaches to optimize a given loss function successfully. Based on those insights, we introduce batch-entropy regularization to enable gradient descent-based training algorithms to optimize the flow of information through each hidden layer individually. With batch-entropy regularization, gradient descent optimizers can transform untrainable networks into trainable networks. We show empirically that we can therefore train a "vanilla" fully connected network and convolutional neural network -- no skip connections, batch normalization, dropout, or any other architectural tweak -- with 500 layers by simply adding the batch-entropy regularization term to the loss function. The effect of batch-entropy regularization is not only evaluated on vanilla neural networks, but also on residual networks, autoencoders, and also transformer models over a wide range of computer vision as well as natural language processing tasks.  ( 3 min )
    Efficient Personalized Learning for Wearable Health Applications using HyperDimensional Computing. (arXiv:2208.01095v1 [cs.LG])
    Health monitoring applications increasingly rely on machine learning techniques to learn end-user physiological and behavioral patterns in everyday settings. Considering the significant role of wearable devices in monitoring human body parameters, on-device learning can be utilized to build personalized models for behavioral and physiological patterns, and provide data privacy for users at the same time. However, resource constraints on most of these wearable devices prevent the ability to perform online learning on them. To address this issue, it is required to rethink the machine learning models from the algorithmic perspective to be suitable to run on wearable devices. Hyperdimensional computing (HDC) offers a well-suited on-device learning solution for resource-constrained devices and provides support for privacy-preserving personalization. Our HDC-based method offers flexibility, high efficiency, resilience, and performance while enabling on-device personalization and privacy protection. We evaluate the efficacy of our approach using three case studies and show that our system improves the energy efficiency of training by up to $45.8\times$ compared with the state-of-the-art Deep Neural Network (DNN) algorithms while offering a comparable accuracy.  ( 2 min )
    Boosted Off-Policy Learning. (arXiv:2208.01148v1 [cs.LG])
    We investigate boosted ensemble models for off-policy learning from logged bandit feedback. Toward this goal, we propose a new boosting algorithm that directly optimizes an estimate of the policy's expected reward. We analyze this algorithm and prove that the empirical risk decreases (possibly exponentially fast) with each round of boosting, provided a "weak" learning condition is satisfied. We further show how the base learner reduces to standard supervised learning problems. Experiments indicate that our algorithm can outperform deep off-policy learning and methods that simply regress on the observed rewards, thereby demonstrating the benefits of both boosting and choosing the right learning objective.  ( 2 min )
    CircuitNet: An Open-Source Dataset for Machine Learning Applications in Electronic Design Automation (EDA). (arXiv:2208.01040v1 [cs.LG])
    The electronic design automation (EDA) community has been actively exploring machine learning for very-large-scale-integrated computer aided design (VLSI CAD). Many studies have explored learning based techniques for cross-stage prediction tasks in the design flow to achieve faster design convergence. Although building machine learning (ML) models usually requires a large amount of data, most studies can only generate small internal datasets for validation due to the lack of large public datasets. In this essay, we present the first open-source dataset for machine learning tasks in VLSI CAD called CircuitNet. The dataset consists of more than 10K samples extracted from versatile runs of commercial design tools based on 6 open-source RISC-V designs.  ( 2 min )
    Voice Analysis for Stress Detection and Application in Virtual Reality to Improve Public Speaking in Real-time: A Review. (arXiv:2208.01041v1 [eess.AS])
    Stress during public speaking is common and adversely affects performance and self-confidence. Extensive research has been carried out to develop various models to recognize emotional states. However, minimal research has been conducted to detect stress during public speaking in real time using voice analysis. In this context, the current review showed that the application of algorithms was not properly explored and helped identify the main obstacles in creating a suitable testing environment while accounting for current complexities and limitations. In this paper, we present our main idea and propose a stress detection computational algorithmic model that could be integrated into a Virtual Reality (VR) application to create an intelligent virtual audience for improving public speaking skills. The developed model, when integrated with VR, will be able to detect excessive stress in real time by analysing voice features correlated to physiological parameters indicative of stress and help users gradually control excessive stress and improve public speaking performance  ( 2 min )
  • Open

    Improving Few-Shot Learning through Multi-task Representation Learning Theory. (arXiv:2010.01992v3 [cs.LG] UPDATED)
    In this paper, we consider the framework of multi-task representation (MTR) learning where the goal is to use source tasks to learn a representation that reduces the sample complexity of solving a target task. We start by reviewing recent advances in MTR theory and show that they can provide novel insights for popular meta-learning algorithms when analyzed within this framework. In particular, we highlight a fundamental difference between gradient-based and metric-based algorithms in practice and put forward a theoretical analysis to explain it. Finally, we use the derived insights to improve the performance of meta-learning methods via a new spectral-based regularization term and confirm its efficiency through experimental studies on few-shot classification benchmarks. To the best of our knowledge, this is the first contribution that puts the most recent learning bounds of MTR theory into practice for the task of few-shot classification.
    Accelerated and interpretable oblique random survival forests. (arXiv:2208.01129v1 [stat.ME])
    The oblique random survival forest (RSF) is an ensemble supervised learning method for right-censored outcomes. Trees in the oblique RSF are grown using linear combinations of predictors to create branches, whereas in the standard RSF, a single predictor is used. Oblique RSF ensembles often have higher prediction accuracy than standard RSF ensembles. However, assessing all possible linear combinations of predictors induces significant computational overhead that limits applications to large-scale data sets. In addition, few methods have been developed for interpretation of oblique RSF ensembles, and they remain more difficult to interpret compared to their axis-based counterparts. We introduce a method to increase computational efficiency of the oblique RSF and a method to estimate importance of individual predictor variables with the oblique RSF. Our strategy to reduce computational overhead makes use of Newton-Raphson scoring, a classical optimization technique that we apply to the Cox partial likelihood function within each non-leaf node of decision trees. We estimate the importance of individual predictors for the oblique RSF by negating each coefficient used for the given predictor in linear combinations, and then computing the reduction in out-of-bag accuracy. In general benchmarking experiments, we find that our implementation of the oblique RSF is approximately 450 times faster with equivalent discrimination and superior Brier score compared to existing software for oblique RSFs. We find in simulation studies that 'negation importance' discriminates between relevant and irrelevant predictors more reliably than permutation importance, Shapley additive explanations, and a previously introduced technique to measure variable importance with oblique RSFs based on analysis of variance. Methods introduced in the current study are available in the aorsf R package.
    Effects of Graph Convolutions in Multi-layer Networks. (arXiv:2204.09297v2 [cs.LG] UPDATED)
    Graph Convolutional Networks (GCNs) are one of the most popular architectures that are used to solve classification problems accompanied by graphical information. We present a rigorous theoretical understanding of the effects of graph convolutions in multi-layer networks. We study these effects through the node classification problem of a non-linearly separable Gaussian mixture model coupled with a stochastic block model. First, we show that a single graph convolution expands the regime of the distance between the means where multi-layer networks can classify the data by a factor of at least $1/\sqrt[4]{\mathbb{E}{\rm deg}}$, where $\mathbb{E}{\rm deg}$ denotes the expected degree of a node. Second, we show that with a slightly stronger graph density, two graph convolutions improve this factor to at least $1/\sqrt[4]{n}$, where $n$ is the number of nodes in the graph. Finally, we provide both theoretical and empirical insights into the performance of graph convolutions placed in different combinations among the layers of a network, concluding that the performance is mutually similar for all combinations of the placement. We present extensive experiments on both synthetic and real-world data that illustrate our results.
    Systematically and efficiently improving existing $k$-means initialization algorithms by pairwise-nearest-neighbor smoothing. (arXiv:2202.03949v2 [cs.LG] UPDATED)
    We present a meta-method for initializing (seeding) the $k$-means clustering algorithm called PNN-smoothing. It consists in splitting a given dataset into $J$ random subsets, clustering each of them individually, and merging the resulting clusterings with the pairwise-nearest-neighbor (PNN) method. It is a meta-method in the sense that when clustering the individual subsets any seeding algorithm can be used. If the computational complexity of that seeding algorithm is linear in the size of the data $N$ and the number of clusters $k$, PNN-smoothing is also almost linear with an appropriate choice of $J$, and quite competitive in practice. We show empirically, using several existing seeding methods and testing on several synthetic and real datasets, that this procedure results in systematically better costs. Our implementation is publicly available at https://github.com/carlobaldassi/KMeansPNNSmoothing.jl.
    Perturbation Analysis of Randomized SVD and its Applications to High-dimensional Statistics. (arXiv:2203.10262v2 [math.ST] UPDATED)
    Randomized singular value decomposition (RSVD) is a class of computationally efficient algorithms for computing the truncated SVD of large data matrices. Given a $n \times n$ symmetric matrix $\mathbf{M}$, the prototypical RSVD algorithm outputs an approximation of the $k$ leading singular vectors of $\mathbf{M}$ by computing the SVD of $\mathbf{M}^{g} \mathbf{G}$; here $g \geq 1$ is an integer and $\mathbf{G} \in \mathbb{R}^{n \times k}$ is a random Gaussian sketching matrix. In this paper we study the statistical properties of RSVD under a general "signal-plus-noise" framework, i.e., the observed matrix $\hat{\mathbf{M}}$ is assumed to be an additive perturbation of some true but unknown signal matrix $\mathbf{M}$. We first derive upper bounds for the $\ell_2$ (spectral norm) and $\ell_{2\to\infty}$ (maximum row-wise $\ell_2$ norm) distances between the approximate singular vectors of $\hat{\mathbf{M}}$ and the true singular vectors of the signal matrix $\mathbf{M}$. These upper bounds depend on the signal-to-noise ratio (SNR) and the number of power iterations $g$. A phase transition phenomenon is observed in which a smaller SNR requires larger values of $g$ to guarantee convergence of the $\ell_2$ and $\ell_{2\to\infty}$ distances. We also show that the thresholds for $g$ where these phase transitions occur are sharp whenever the noise matrices satisfy a certain trace growth condition. Finally, we derive normal approximations for the row-wise fluctuations of the approximate singular vectors and the entrywise fluctuations of the approximate matrix. We illustrate our theoretical results by deriving nearly-optimal performance guarantees for RSVD when applied to three statistical inference problems, namely, community detection, matrix completion, and principal component analysis with missing data.
    Learning Invariant Weights in Neural Networks. (arXiv:2202.12439v2 [stat.ML] UPDATED)
    Assumptions about invariances or symmetries in data can significantly increase the predictive power of statistical models. Many commonly used models in machine learning are constraint to respect certain symmetries in the data, such as translation equivariance in convolutional neural networks, and incorporation of new symmetry types is actively being studied. Yet, efforts to learn such invariances from the data itself remains an open research problem. It has been shown that marginal likelihood offers a principled way to learn invariances in Gaussian Processes. We propose a weight-space equivalent to this approach, by minimizing a lower bound on the marginal likelihood to learn invariances in neural networks resulting in naturally higher performing models.
    Trimmed Maximum Likelihood Estimation for Robust Learning in Generalized Linear Models. (arXiv:2206.04777v2 [cs.LG] UPDATED)
    We study the problem of learning generalized linear models under adversarial corruptions. We analyze a classical heuristic called the iterative trimmed maximum likelihood estimator which is known to be effective against label corruptions in practice. Under label corruptions, we prove that this simple estimator achieves minimax near-optimal risk on a wide range of generalized linear models, including Gaussian regression, Poisson regression and Binomial regression. Finally, we extend the estimator to the more challenging setting of label and covariate corruptions and demonstrate its robustness and optimality in that setting as well.
    Context-Aware Drift Detection. (arXiv:2203.08644v2 [stat.ML] UPDATED)
    When monitoring machine learning systems, two-sample tests of homogeneity form the foundation upon which existing approaches to drift detection build. They are used to test for evidence that the distribution underlying recent deployment data differs from that underlying the historical reference data. Often, however, various factors such as time-induced correlation mean that batches of recent deployment data are not expected to form an i.i.d. sample from the historical data distribution. Instead we may wish to test for differences in the distributions conditional on \textit{context} that is permitted to change. To facilitate this we borrow machinery from the causal inference domain to develop a more general drift detection framework built upon a foundation of two-sample tests for conditional distributional treatment effects. We recommend a particular instantiation of the framework based on maximum conditional mean discrepancies. We then provide an empirical study demonstrating its effectiveness for various drift detection problems of practical interest, such as detecting drift in the distributions underlying subpopulations of data in a manner that is insensitive to their respective prevalences. The study additionally demonstrates applicability to ImageNet-scale vision problems.
    Reduced-order modeling for parameterized large-eddy simulations of atmospheric pollutant dispersion. (arXiv:2208.01518v1 [stat.ML])
    Mapping near-field pollutant concentration is essential to track accidental toxic plume dispersion in urban areas. By solving a large part of the turbulence spectrum, large-eddy simulations (LES) have the potential to accurately represent pollutant concentration spatial variability. Finding a way to synthesize this large amount of information to improve the accuracy of lower-fidelity operational models (e.g. providing better turbulence closure terms) is particularly appealing. This is a challenge in multi-query contexts, where LES become prohibitively costly to deploy to understand how plume flow and tracer dispersion change with various atmospheric and source parameters. To overcome this issue, we propose a non-intrusive reduced-order model combining proper orthogonal decomposition (POD) and Gaussian process regression (GPR) to predict LES field statistics of interest associated with tracer concentrations. GPR hyperpararameters are optimized component-by-component through a maximum a posteriori (MAP) procedure informed by POD. We provide a detailed analysis of the reducedorder model performance on a two-dimensional case study corresponding to a turbulent atmospheric boundary-layer flow over a surface-mounted obstacle. We show that near-source concentration heterogeneities upstream of the obstacle require a large number of POD modes to be well captured. We also show that the component-by-component optimization allows to capture the range of spatial scales in the POD modes, especially the shorter concentration patterns in the high-order modes. The reduced-order model predictions remain acceptable if the learning database is made of at least fifty to hundred LES snapshot providing a first estimation of the required budget to move towards more realistic atmospheric dispersion applications.  ( 3 min )
    Binary Independent Component Analysis: A Non-stationarity-based Approach. (arXiv:2111.15431v2 [cs.LG] UPDATED)
    We consider independent component analysis of binary data. While fundamental in practice, this case has been much less developed than ICA for continuous data. We start by assuming a linear mixing model in a continuous-valued latent space, followed by a binary observation model. Importantly, we assume that the sources are non-stationary; this is necessary since any non-Gaussianity would essentially be destroyed by the binarization. Interestingly, the model allows for closed-form likelihood by employing the cumulative distribution function of the multivariate Gaussian distribution. In stark contrast to the continuous-valued case, we prove non-identifiability of the model with few observed variables; our empirical results imply identifiability when the number of observed variables is higher. We present a practical method for binary ICA that uses only pairwise marginals, which are faster to compute than the full multivariate likelihood. Experiments give insight into the requirements for the number of observed variables, segments, and latent sources that allow the model to be estimated.  ( 2 min )
    Fisher and Kernel Fisher Discriminant Analysis: Tutorial. (arXiv:1906.09436v2 [stat.ML] UPDATED)
    This is a detailed tutorial paper which explains the Fisher discriminant Analysis (FDA) and kernel FDA. We start with projection and reconstruction. Then, one- and multi-dimensional FDA subspaces are covered. Scatters in two- and then multi-classes are explained in FDA. Then, we discuss on the rank of the scatters and the dimensionality of the subspace. A real-life example is also provided for interpreting FDA. Then, possible singularity of the scatter is discussed to introduce robust FDA. PCA and FDA directions are also compared. We also prove that FDA and linear discriminant analysis are equivalent. Fisher forest is also introduced as an ensemble of fisher subspaces useful for handling data with different features and dimensionality. Afterwards, kernel FDA is explained for both one- and multi-dimensional subspaces with both two- and multi-classes. Finally, some simulations are performed on AT&T face dataset to illustrate FDA and compare it with PCA.  ( 2 min )
    Unsupervised and Supervised Principal Component Analysis: Tutorial. (arXiv:1906.03148v2 [stat.ML] UPDATED)
    This is a detailed tutorial paper which explains the Principal Component Analysis (PCA), Supervised PCA (SPCA), kernel PCA, and kernel SPCA. We start with projection, PCA with eigen-decomposition, PCA with one and multiple projection directions, properties of the projection matrix, reconstruction error minimization, and we connect to autoencoder. Then, PCA with singular value decomposition, dual PCA, and kernel PCA are covered. SPCA using both scoring and Hilbert-Schmidt independence criterion are explained. Kernel SPCA using both direct and dual approaches are then introduced. We cover all cases of projection and reconstruction of training and out-of-sample data. Finally, some simulations are provided on Frey and AT&T face datasets for verifying the theory in practice.  ( 2 min )
    Data-Driven Discovery of Molecular Photoswitches with Multioutput Gaussian Processes. (arXiv:2008.03226v2 [physics.chem-ph] UPDATED)
    Photoswitchable molecules display two or more isomeric forms that may be accessed using light. Separating the electronic absorption bands of these isomers is key to selectively addressing a specific isomer and achieving high photostationary states whilst overall red-shifting the absorption bands serves to limit material damage due to UV-exposure and increases penetration depth in photopharmacological applications. Engineering these properties into a system through synthetic design however, remains a challenge. Here, we present a data-driven discovery pipeline for molecular photoswitches underpinned by dataset curation and multitask learning with Gaussian processes. In the prediction of electronic transition wavelengths, we demonstrate that a multioutput Gaussian process (MOGP) trained using labels from four photoswitch transition wavelengths yields the strongest predictive performance relative to single-task models as well as operationally outperforming time-dependent density functional theory (TD-DFT) in terms of the wall-clock time for prediction. We validate our proposed approach experimentally by screening a library of commercially available photoswitchable molecules. Through this screen, we identified several motifs that displayed separated electronic absorption bands of their isomers, exhibited red-shifted absorptions, and are suited for information transfer and photopharmacological applications. Our curated dataset, code, as well as all models are made available at https://github.com/Ryan-Rhys/The-Photoswitch-Dataset  ( 3 min )
    Generalization Bounds in the Predict-then-Optimize Framework. (arXiv:1905.11488v3 [cs.LG] UPDATED)
    The predict-then-optimize framework is fundamental in many practical settings: predict the unknown parameters of an optimization problem, and then solve the problem using the predicted values of the parameters. A natural loss function in this environment is to consider the cost of the decisions induced by the predicted parameters, in contrast to the prediction error of the parameters. This loss function was recently introduced in Elmachtoub and Grigas (2022) and referred to as the Smart Predict-then-Optimize (SPO) loss. In this work, we seek to provide bounds on how well the performance of a prediction model fit on training data generalizes out-of-sample, in the context of the SPO loss. Since the SPO loss is non-convex and non-Lipschitz, standard results for deriving generalization bounds do not apply. We first derive bounds based on the Natarajan dimension that, in the case of a polyhedral feasible region, scale at most logarithmically in the number of extreme points, but, in the case of a general convex feasible region, have linear dependence on the decision dimension. By exploiting the structure of the SPO loss function and a key property of the feasible region, which we denote as the strength property, we can dramatically improve the dependence on the decision and feature dimensions. Our approach and analysis rely on placing a margin around problematic predictions that do not yield unique optimal solutions, and then providing generalization bounds in the context of a modified margin SPO loss function that is Lipschitz continuous. Finally, we characterize the strength property and show that the modified SPO loss can be computed efficiently for both strongly convex bodies and polytopes with an explicit extreme point representation.  ( 3 min )
    A Recursive Partitioning Approach for Dynamic Discrete Choice Modeling in High Dimensional Settings. (arXiv:2208.01476v1 [stat.ME])
    Dynamic discrete choice models are widely employed to answer substantive and policy questions in settings where individuals' current choices have future implications. However, estimation of these models is often computationally intensive and/or infeasible in high-dimensional settings. Indeed, even specifying the structure for how the utilities/state transitions enter the agent's decision is challenging in high-dimensional settings when we have no guiding theory. In this paper, we present a semi-parametric formulation of dynamic discrete choice models that incorporates a high-dimensional set of state variables, in addition to the standard variables used in a parametric utility function. The high-dimensional variable can include all the variables that are not the main variables of interest but may potentially affect people's choices and must be included in the estimation procedure, i.e., control variables. We present a data-driven recursive partitioning algorithm that reduces the dimensionality of the high-dimensional state space by taking the variation in choices and state transition into account. Researchers can then use the method of their choice to estimate the problem using the discretized state space from the first stage. Our approach can reduce the estimation bias and make estimation feasible at the same time. We present Monte Carlo simulations to demonstrate the performance of our method compared to standard estimation methods where we ignore the high-dimensional explanatory variable set.  ( 3 min )
    Unsupervised machine learning framework for discriminating major variants of concern during COVID-19. (arXiv:2208.01439v1 [q-bio.OT])
    Due to the rapid evolution of the SARS-CoV-2 (COVID-19) virus, a number of mutations emerged with variants such as Alpha, Gamma, Delta and Omicron which created massive impact to the world economy. Unsupervised machine learning methods have the ability to compresses, characterize and visualises unlabelled data. In this paper, we present a framework that utilizes unsupervised machine learning methods that includes combination of selected dimensional reduction and clustering methods to discriminate and visualise the associations with the major COVID-19 variants based on genome sequences. The framework utilises k-mer analysis for processing the genome (RNA) sequences and compares different dimensional reduction methods, that include principal component analysis (PCA), and t-distributed stochastic neighbour embedding (t-SNE), and uniform manifold approximation projection (UMAP). Furthermore, the framework employs agglomerative hierarchical clustering methods and provides a visualisation using a dendogram. We find that the proposed framework can effectively distinguish the major variants and hence can be used for distinguishing emerging variants in the future.  ( 3 min )
    Cluster Weighted Model Based on TSNE algorithm for High-Dimensional Data. (arXiv:2208.01579v1 [stat.ML])
    Similar to many Machine Learning models, both accuracy and speed of the Cluster weighted models (CWMs) can be hampered by high-dimensional data, leading to previous works on a parsimonious technique to reduce the effect of "Curse of dimensionality" on mixture models. In this work, we review the background study of the cluster weighted models (CWMs). We further show that parsimonious technique is not sufficient for mixture models to thrive in the presence of huge high-dimensional data. We discuss a heuristic for detecting the hidden components by choosing the initial values of location parameters using the default values in the "FlexCWM" R package. We introduce a dimensionality reduction technique called T-distributed stochastic neighbor embedding (TSNE) to enhance the parsimonious CWMs in high-dimensional space. Originally, CWMs are suited for regression but for classification purposes, all multi-class variables are transformed logarithmically with some noise. The parameters of the model are obtained via expectation maximization algorithm. The effectiveness of the discussed technique is demonstrated using real data sets from different fields.  ( 2 min )
    Concentration inequalities for correlated network-valued processes with applications to community estimation and changepoint analysis. (arXiv:2208.01365v1 [math.ST])
    Network-valued time series are currently a common form of network data. However, the study of the aggregate behavior of network sequences generated from network-valued stochastic processes is relatively rare. Most of the existing research focuses on the simple setup where the networks are independent (or conditionally independent) across time, and all edges are updated synchronously at each time step. In this paper, we study the concentration properties of the aggregated adjacency matrix and the corresponding Laplacian matrix associated with network sequences generated from lazy network-valued stochastic processes, where edges update asynchronously, and each edge follows a lazy stochastic process for its updates independent of the other edges. We demonstrate the usefulness of these concentration results in proving consistency of standard estimators in community estimation and changepoint estimation problems. We also conduct a simulation study to demonstrate the effect of the laziness parameter, which controls the extent of temporal correlation, on the accuracy of community and changepoint estimation.  ( 2 min )
    A Deep Generative Model for Feasible and Diverse Population Synthesis. (arXiv:2208.01403v1 [stat.ML])
    An ideal synthetic population, a key input to activity-based models, mimics the distribution of the individual- and household-level attributes in the actual population. Since the entire population's attributes are generally unavailable, household travel survey (HTS) samples are used for population synthesis. Synthesizing population by directly sampling from HTS ignores the attribute combinations that are unobserved in the HTS samples but exist in the population, called 'sampling zeros'. A deep generative model (DGM) can potentially synthesize the sampling zeros but at the expense of generating 'structural zeros' (i.e., the infeasible attribute combinations that do not exist in the population). This study proposes a novel method to minimize structural zeros while preserving sampling zeros. Two regularizations are devised to customize the training of the DGM and applied to a generative adversarial network (GAN) and a variational autoencoder (VAE). The adopted metrics for feasibility and diversity of the synthetic population indicate the capability of generating sampling and structural zeros -- lower structural zeros and lower sampling zeros indicate the higher feasibility and the lower diversity, respectively. Results show that the proposed regularizations achieve considerable performance improvement in feasibility and diversity of the synthesized population over traditional models. The proposed VAE additionally generated 23.5% of the population ignored by the sample with 79.2% precision (i.e., 20.8% structural zeros rates), while the proposed GAN generated 18.3% of the ignored population with 89.0% precision. The proposed improvement in DGM generates a more feasible and diverse synthetic population, which is critical for the accuracy of an activity-based model.  ( 3 min )
    DAPDAG: Domain Adaptation via Perturbed DAG Reconstruction. (arXiv:2208.01373v1 [cs.LG])
    Leveraging labelled data from multiple domains to enable prediction in another domain without labels is a significant, yet challenging problem. To address this problem, we introduce the framework DAPDAG (\textbf{D}omain \textbf{A}daptation via \textbf{P}erturbed \textbf{DAG} Reconstruction) and propose to learn an auto-encoder that undertakes inference on population statistics given features and reconstructing a directed acyclic graph (DAG) as an auxiliary task. The underlying DAG structure is assumed invariant among observed variables whose conditional distributions are allowed to vary across domains led by a latent environmental variable $E$. The encoder is designed to serve as an inference device on $E$ while the decoder reconstructs each observed variable conditioned on its graphical parents in the DAG and the inferred $E$. We train the encoder and decoder jointly in an end-to-end manner and conduct experiments on synthetic and real datasets with mixed variables. Empirical results demonstrate that reconstructing the DAG benefits the approximate inference. Furthermore, our approach can achieve competitive performance against other benchmarks in prediction tasks, with better adaptation ability, especially in the target domain significantly different from the source domains.  ( 2 min )
    Bounding Counterfactuals under Selection Bias. (arXiv:2208.01417v1 [stat.ML])
    Causal analysis may be affected by selection bias, which is defined as the systematic exclusion of data from a certain subpopulation. Previous work in this area focused on the derivation of identifiability conditions. We propose instead a first algorithm to address both identifiable and unidentifiable queries. We prove that, in spite of the missingness induced by the selection bias, the likelihood of the available data is unimodal. This enables us to use the causal expectation-maximisation scheme to obtain the values of causal queries in the identifiable case, and to compute bounds otherwise. Experiments demonstrate the approach to be practically viable. Theoretical convergence characterisations are provided.  ( 2 min )
    Viskositas: Viscosity Prediction of Multicomponent Chemical Systems. (arXiv:2208.01440v1 [stat.AP])
    Viscosity in the metallurgical and glass industry plays a fundamental role in its production processes, also in the area of geophysics. As its experimental measurement is financially expensive, also in terms of time, several mathematical models were built to provide viscosity results as a function of several variables, such as chemical composition and temperature, in linear and nonlinear models. A database was built in order to produce a nonlinear model by artificial neural networks by variation of hyperparameters to provide reliable predictions of viscosity in relation to chemical systems and temperatures. The model produced named Viskositas demonstrated better statistical evaluations of mean absolute error, standard deviation and coefficient of determination in relation to the test database when compared to different models from literature and 1 commercial model, offering predictions with lower errors, less variability and less generation of outliers.  ( 2 min )
    GeoECG: Data Augmentation via Wasserstein Geodesic Perturbation for Robust Electrocardiogram Prediction. (arXiv:2208.01220v1 [stat.ML])
    There has been an increased interest in applying deep neural networks to automatically interpret and analyze the 12-lead electrocardiogram (ECG). The current paradigms with machine learning methods are often limited by the amount of labeled data. This phenomenon is particularly problematic for clinically-relevant data, where labeling at scale can be time-consuming and costly in terms of the specialized expertise and human effort required. Moreover, deep learning classifiers may be vulnerable to adversarial examples and perturbations, which could have catastrophic consequences, for example, when applied in the context of medical treatment, clinical trials, or insurance claims. In this paper, we propose a physiologically-inspired data augmentation method to improve performance and increase the robustness of heart disease detection based on ECG signals. We obtain augmented samples by perturbing the data distribution towards other classes along the geodesic in Wasserstein space. To better utilize domain-specific knowledge, we design a ground metric that recognizes the difference between ECG signals based on physiologically determined features. Learning from 12-lead ECG signals, our model is able to distinguish five categories of cardiac conditions. Our results demonstrate improvements in accuracy and robustness, reflecting the effectiveness of our data augmentation method.  ( 3 min )
    Are Cluster Validity Measures (In)valid?. (arXiv:2208.01261v1 [stat.ML])
    Internal cluster validity measures (such as the Calinski-Harabasz, Dunn, or Davies-Bouldin indices) are frequently used for selecting the appropriate number of partitions a dataset should be split into. In this paper we consider what happens if we treat such indices as objective functions in unsupervised learning activities. Is the optimal grouping with regards to, say, the Silhouette index really meaningful? It turns out that many cluster (in)validity indices promote clusterings that match expert knowledge quite poorly. We also introduce a new, well-performing variant of the Dunn index that is built upon OWA operators and the near-neighbour graph so that subspaces of higher density, regardless of their shapes, can be separated from each other better.  ( 2 min )
    On the role of benchmarking data sets and simulations in method comparison studies. (arXiv:2208.01457v1 [stat.ME])
    Method comparisons are essential to provide recommendations and guidance for applied researchers, who often have to choose from a plethora of available approaches. While many comparisons exist in the literature, these are often not neutral but favour a novel method. Apart from the choice of design and a proper reporting of the findings, there are different approaches concerning the underlying data for such method comparison studies. Most manuscripts on statistical methodology rely on simulation studies and provide a single real-world data set as an example to motivate and illustrate the methodology investigated. In the context of supervised learning, in contrast, methods are often evaluated using so-called benchmarking data sets, i.e. real-world data that serve as gold standard in the community. Simulation studies, on the other hand, are much less common in this context. The aim of this paper is to investigate differences and similarities between these approaches, to discuss their advantages and disadvantages and ultimately to develop new approaches to the evaluation of methods picking the best of both worlds. To this aim, we borrow ideas from different contexts such as mixed methods research and Clinical Scenario Evaluation.  ( 2 min )
    Bayesian Variable Selection in a Million Dimensions. (arXiv:2208.01180v1 [stat.ME])
    Bayesian variable selection is a powerful tool for data analysis, as it offers a principled method for variable selection that accounts for prior information and uncertainty. However, wider adoption of Bayesian variable selection has been hampered by computational challenges, especially in difficult regimes with a large number of covariates P or non-conjugate likelihoods. To scale to the large P regime we introduce an efficient MCMC scheme whose cost per iteration is sublinear in P. In addition we show how this scheme can be extended to generalized linear models for count data, which are prevalent in biology, ecology, economics, and beyond. In particular we design efficient algorithms for variable selection in binomial and negative binomial regression, which includes logistic regression as a special case. In experiments we demonstrate the effectiveness of our methods, including on cancer and maize genomic data.  ( 2 min )
    Boosted Off-Policy Learning. (arXiv:2208.01148v1 [cs.LG])
    We investigate boosted ensemble models for off-policy learning from logged bandit feedback. Toward this goal, we propose a new boosting algorithm that directly optimizes an estimate of the policy's expected reward. We analyze this algorithm and prove that the empirical risk decreases (possibly exponentially fast) with each round of boosting, provided a "weak" learning condition is satisfied. We further show how the base learner reduces to standard supervised learning problems. Experiments indicate that our algorithm can outperform deep off-policy learning and methods that simply regress on the observed rewards, thereby demonstrating the benefits of both boosting and choosing the right learning objective.  ( 2 min )
    A Modified PINN Approach for Identifiable Compartmental Models in Epidemiology with Applications to COVID-19. (arXiv:2208.01169v1 [q-bio.PE])
    A variety of approaches using compartmental models have been used to study the COVID-19 pandemic and the usage of machine learning methods with these models has had particularly notable success. We present here an approach toward analyzing accessible data on Covid-19's U.S. development using a variation of the "Physics Informed Neural Networks" (PINN) which is capable of using the knowledge of the model to aid learning. We illustrate the challenges of using the standard PINN approach, then how with appropriate and novel modifications to the loss function the network can perform well even in our case of incomplete information. Aspects of identifiability of the model parameters are also assessed, as well as methods of denoising available data using a wavelet transform. Finally, we discuss the capability of the neural network methodology to work with models of varying parameter values, as well as a concrete application in estimating how effectively cases are being tested for in a population, providing a ranking of U.S. states by means of their respective testing.  ( 3 min )

  • Open

    Trust Region Methods
    I am reading Laura's boon on DRL where she states: "A number of algorithms have been proposed to solve this trust region optimization problem. Some of these include Natural Policy Gradient (NPG) [63, 112, 113], Trust Region Policy Optimization (TRPO) [122], and Constrained Policy Optimization (CPO) [2]. The theories behind them are fairly complex, and the algorithms are difficult to implement. Their gradients can be expensive to compute, and it is difficult to choose a good value for δ." Based on this excerpt, can someone point out a paper/reference (reproducibility study) that compares the performance and applicability of these algorithms? For example, I wonder if these algorithms outperform A2C, Reinforce. For example, some works have showed that classical Matrix Factorization and k-NN based recommender systems provide competitive results when compared to deep learning approaches. submitted by /u/rlopes404 [link] [comments]  ( 87 min )
    "Demonstrate Once, Imitate Immediately (DOME): Learning Visual Servoing for One-Shot Imitation Learning", Valassakis et al 2022
    submitted by /u/gwern [link] [comments]  ( 86 min )
    When to use Action Observation history vs only Observation history to solve a POMDP ?
    Hi guys, when to use Action Observation history (AOH) vs Observation history (OH) to solve a POMDP? in other words , what condition needs to be valid in order to say that OH is enough to solve the POMDP? submitted by /u/souhaielbensalem [link] [comments]  ( 87 min )
    Noise in Action Space, Reward Space and State Space. Looking for Papers.
    Most SOTA deep RL algorithms use a stochastic action distribution to introduce explorative noise into the training process. A second strategy is injecting noise into the states or even the reward signal. I am currently working with a environment that has highly stochastic rewards and also state transitions. From my experiments I conclude that almost none action noise is needed to learn a good policy. In PPO I use a very low log sigma for example. Does anyone has some experience in this area? Does anyone know of good papers that investigate the interplay between the noise in reward, state and action? Thank you! submitted by /u/flxh13 [link] [comments]  ( 88 min )
    Solving POMDPs
    Hello everyone, Does anyone know state-of-the-art algos to learn a memoryless policy, i.e a policy depending only on the current observation (not on the full state and not on a history of observations and actions), for a POMDP ? I am looking for approximate methods (some policy iteration) or modified Q-learning in the discrete states discrete actions case, and for deep rl methods in the continuous states case. ​ Thank you in advance submitted by /u/Hkohler98 [link] [comments]  ( 88 min )
  • Open

    [R] Is a Caption Worth a Thousand Images? A Controlled Study for Representation Learning - Santurkar et al 2022
    Paper: https://arxiv.org/abs/2207.07635 Abstract: The development of CLIP [Radford et al., 2021] has sparked a debate on whether language supervision can result in vision models with more transferable representations than traditional image-only methods. Our work studies this question through a carefully controlled comparison of two approaches in terms of their ability to learn representations that generalize to downstream classification tasks. We find that when the pre-training dataset meets certain criteria -- it is sufficiently large and contains descriptive captions with low variability -- image-only methods do not match CLIP's transfer performance, even when they are trained with more image data. However, contrary to what one might expect, there are practical settings in which these criteria are not met, wherein added supervision through captions is actually detrimental. Motivated by our findings, we devise simple prescriptions to enable CLIP to better leverage the language information present in existing pre-training datasets. https://preview.redd.it/yzc0451pmdf91.jpg?width=1181&format=pjpg&auto=webp&s=8f1b90243ad1643799715c361856f17665882a30 https://preview.redd.it/4ecai50pmdf91.jpg?width=1316&format=pjpg&auto=webp&s=e489c730acbf7db1fa12870e3eef065af141a4d0 https://preview.redd.it/ma8r241pmdf91.jpg?width=1328&format=pjpg&auto=webp&s=5cb2f4fad08b4f2934738dabba4ecc44d19be1c6 https://preview.redd.it/92msd81pmdf91.jpg?width=786&format=pjpg&auto=webp&s=793bdc7ddedd6e64f06050847abbec2a8fd1ff71 submitted by /u/Singularian2501 [link] [comments]  ( 88 min )
    [R] LocoProp: Enhancing BackProp via Local Loss Optimization (Google Brain, 2022)
    Paper: https://arxiv.org/abs/2106.06199 Github: https://github.com/google-research/google-research/tree/master/locoprop Abstract: Second-order methods have shown state-of-the-art performance for optimizing deep neural networks. Nonetheless, their large memory requirement and high computational complexity, compared to first-order methods, hinder their versatility in a typical low-budget setup. This paper introduces a general framework of layerwise loss construction for multilayer neural networks that achieves a performance closer to second-order methods while utilizing first-order optimizers only. Our methodology lies upon a three-component loss, target, and regularizer combination, for which altering each component results in a new update rule. We provide examples using squared loss and layerwise Bregman divergences induced by the convex integral functions of various transfer functions. Our experiments on benchmark models and datasets validate the efficacy of our new approach, reducing the gap between first-order and second-order optimizers. ​ https://preview.redd.it/eboo9126hdf91.jpg?width=930&format=pjpg&auto=webp&s=ef265339c0372669e5afcf496db9f55d83ec3847 https://preview.redd.it/txl19wp6hdf91.jpg?width=1209&format=pjpg&auto=webp&s=938b9f35980972dcec5a848df5ba1868450de4eb submitted by /u/Singularian2501 [link] [comments]  ( 88 min )
    [N] ViTDet: New SOTA Low shot object detection
    Meta AI released ViTDet - transformer based model for low shot object detection. It outperforms previous models on Large Vocabulary Instance Segmentation (LVIS) dataset. Arxiv Blog post They have released code in their Detectron2 library. submitted by /u/ashwan1 [link] [comments]  ( 87 min )
    [P] How to train ML models in AWS from the CLI
    Hey guys, hoping for some help from the community. We are building dstack.ai, a free (and soon open source!!) framework that allows you to run ML tasks in the cloud, directly via your CLI. Think lambda function for long, commute intensive tasks. Dstack essentially lets you build ML models locally and run them in your cloud accounts and takes care of spinning up and shutting down VMs after your workflows are done. We are still building lots of cool features but hoping to find a few folks interested in a test drive? P.S. we are still in beta, let us know if you find any bugs :) submitted by /u/dmart89 [link] [comments]  ( 89 min )
    [R] Pay attention to the minorities, please!
    ​ https://preview.redd.it/lnikbzzrzaf91.jpg?width=1121&format=pjpg&auto=webp&s=8258502509c6b39980108e29d1a8dac2d1740ee0 ​ Conventional class prototyping is not enough for real-world datasets. • The majority of loss appears when BERT's confidence is low for troublesome samples. Why not choose some representative for these samples? (i.e., prototyping) • Consider prototyping the minorities of your dataset: (i) difficult-to-classify samples and (ii) anomalies. Paper 📜: https://arxiv.org/abs/2206.12710 submitted by /u/afarhangi [link] [comments]  ( 87 min )
    [D] ML on chemical/petroleum live process data
    May I ask if anyone has attempted the use of ML to detect upsets in chemical/refining processes? If yes, would you know if an AUC of 70+% (for a classification problem) is typically the best ML can achieve? Thanks! submitted by /u/kayhai [link] [comments]  ( 88 min )
    [P] Guidance on Smart Home Facial Recognition Cloud Native Application
    Project description-: A single-page website (hosted on Amazon S3) with access to the laptop's camera will send the live video stream to Amazon Kinesis which will trigger the Facial Recognition code on AWS Lambda. It will recognize the person in the feed and respond with 2 numbers, one stating the fan's RPM value and the other being the RGB value for the led. This data will somehow be sent to the FPGA board connected to the cloud (and the laptop) and the fan and light will act accordingly. Initially, the facial recognition code will be explicitly built only for 3 people. The option to create a new profile will be added later. For the above project, I would like to train my own deep learning model, rather than using OpenCV. Issues-: Is the architecture appropriate for my project idea or should I change something the architecture or the workflow? How should I prepare the dataset only with images of 3 people? Even if I augment the data how would it scale to 1,00,000 images enough for the model? Which model would perform best for the same? What should be the FPS value for the video feed? I would really like you all to provide your insights on this and any improvements if needed. Thanks and regards. submitted by /u/Intangible-AI [link] [comments]  ( 126 min )
    [R]How do I recive the area of the boxes generated with Cascade TabNet Demo.ipynb?
    so I ran the jupyter notebook of Cascade TabNet Demo.ipynb and receive what I expected, but now I'm interested in receiving the exact position of the boxes, for example: (340, 400, 762, 700), something like that.. I need this to crop this area and put it on an separated image. notebook: https://colab.research.google.com/drive/1lzjbBQsF4X2C2WZhxBJz0wFEQor7F-fv?usp=sharing#scrollTo=e0P85mJJQ304 submitted by /u/nurigrf05 [link] [comments]  ( 125 min )
    [D] CIKM 22 Full Notification
    Has anyone received full paper notification? submitted by /u/snu95 [link] [comments]  ( 88 min )
    [D] Any mathematics concepts should be grasped in order to be better in machine learning?
    Mathematics seems to be a large rabbit hole... submitted by /u/_janc_ [link] [comments]  ( 93 min )
    [D] Precision Recall curve format
    Hi, For image segmentations I understand that both the confidence threshold and IoU threshold will define whether a class will be true positive or false positive. Most resources i have read online don't state the nature of the threshold which is varied when plotting the PR curve. So therefore my question is, is there a standard to plot the PR curve with a varying IoU threshold and a fixed confidence threshold or vice-versa? submitted by /u/yuhzuu [link] [comments]  ( 87 min )
    [D] How to decode barcodes from picture?
    Hello reddit! I made model to detect barcodes (see picture) and model works very well. Then i want to decode detected barcodes with zbar (pyzbar), but it not work and i don't understand why. I tried to rotate barcodes, but it did not help. I will be glad for any help and hints on how to decode barcodes from the picture. submitted by /u/jonathanblade [link] [comments]  ( 88 min )
    [P] How should I structure my CNN GAN music generation model?
    Hello! I am currently working on my master's dissertation, in which I am comparing LSTM and CNN GANs for music generation. The format of my input data is batches of 96x96 arrays, representing 96 unique pitches on a piano vs. 96 beats - I have a training dataset consisting of 360,000 such arrays. I have successfully constructed my LSTM network, in which I use the aforementioned two dimensions as my input data to the model: (96,96). My issue is with CNNs, as the input format is different to LSTMs. I am running into issues with the shape of my inputted data. From my understanding, CNN needs data input with structure (batch, (dimensions), channels). In my model I use batch-size = 10, and channel = 1 (I'm assuming I don't need anymore than one channel) - should my input shape then be (10, 96, 96, 1)? Just (96,96)? Or (96,96,1)? I have tinkered around with different combinations but most frequently get one of two errors: Data cardinality is ambiguous: x sizes: 96 y sizes: 10 Make sure all arrays contain the same number of samples. Input 0 of layer sequential_0 is incompatible with the layer: expected axis -1 of input shape to have value 1 but received input with shape (None, 96, 96, 96) Currently I am using just a single layer in my discriminator and generator respectively: model.add(Conv2D(96, kernel_size=1, input_shape= (96, 96, 1), padding="same")) Any help with this would be much appreciated!! :) submitted by /u/carl535 [link] [comments]  ( 89 min )
    "[R]" Research "[P]" Project on Disease Prediction System using Machine Learning
    Hello, I am trying to build a disease prediction system using this dataset: https://www.kaggle.com/datasets/kaushil268/disease-prediction-using-machine-learning What are the things I should keep in mind when cleaning the data? Does this kind of data also requires patient's demographic data, weight, height, etc. along with the symptoms of diseases? What algorithms should I apply to train my network? I already have training.csv and testing.csv so I don't have to split my data into 80/20, right? Also, pour in some suggestions that you would recommend when designing such system. This is for a university thesis. Thanks submitted by /u/degr8sid [link] [comments]  ( 87 min )
    [D] How can I keep up with emerging ideas in ML as an outsider?
    I am doing a PhD in Mechanical Engineering though my PhD is focused on utilizing ML in Mechanical Engineering. As such I am not heavily proficient in ML, but I have a lot of interest in knowing where ML science is going. So how can like me keep up to date with new ideas in machine learning? submitted by /u/Yalkim [link] [comments]  ( 94 min )
    [D] What are the predominant economic use-cases of ML? And do they align with our research narrative about "AI"?
    Hi ML folks, I've worked on ML in industry for quite some time, for example, at Google and PathAI (a startup in the healthcare space). But I've found that the research narrative around "AI" seems to be—to put it nicely—not aligned with its predominant economic uses. Some of this was discussed quite nicely in the book, The Myth of Artificial Intelligence, by Erik J. Larson. But I felt that he lacked an answer to: why are we building "AI" at all? Or what exactly are we building now? So I investigated on my own and wrote my thoughts here. They're phrased as a response to Rich Sutton's essay, The Bitter Lesson, from a few years ago, which I find to be completely disconnected to how AI/ML is actually being used in industry. Anyways, I am curious what this community's thoughts are on the matter... submitted by /u/spincycle27 [link] [comments]  ( 107 min )
    [D] CIKM 22 Notification
    Has anyone received the final results? I think it's being quite delayed than previous conferences submitted by /u/snu95 [link] [comments]  ( 87 min )
    [D] Clarifications around hardware
    Hello, I'm a 3d artist that got into machine learning recently, I am particularly interested in gpt and NLP in general, I am building a new workstation and would love to get some clarifications here. Can someone please explain the difference between using multiple gpus with nvlink and multiple gpus without nvlink in deep learning? For fine tuning big models like the gpt neox 20b is it mandatory to have a single gpu with 48gb or can you do with multiple gpus that collectively meet the requirement and if so do they need to be connected with nvlink or to be physically on the same node or what? How important is the role of ram (clock-speed and capacity and cpu here? I havent touched image generation at all, but if I am to experiment with serious works using image generation networks do the same answers apply? submitted by /u/CosmicPotty [link] [comments]  ( 88 min )
    [P] Using Sparsity & Clustering to compress your models: Efficient Deep Learning Book
    Hey folks, We have been working on a book that focuses on deep learning efficiency techniques such as quantization, pruning, distillation, etc. for both server-side as well as on-device (smartphones, IoT, etc.) applications. We now have a new chapter focusing on sparsity and clustering, two advanced compression techniques that you can use to reduce the footprint of your model (size, latency, etc.) while retaining your model accuracy. You can read the chapter here, and go through the accompanying codelabs here. We hope that our readers can make their models 4-20x smaller, faster, and better in quality. We also have released the other four chapter's draft PDFs, and would truly appreciate any sort of comments / feedback. Book: efficientdlbook.com Feedback: [hello@efficientdlbook.com](mailto:hello@efficientdlbook.com) submitted by /u/EfficientDLBook [link] [comments]  ( 88 min )
  • Open

    DSC Weekly Stardate 47634.44: RIP Admiral Nyota Uhura
    When I was six years old, I remembered Nichelle Nichols appearing on our family television set as the young communications officer aboard the Starship Enterprise, NCC-1701. This was around the same time that I remember a grainy black and white image of Neil Armstrong stepping out of the Lunar Lander, wearing the bulky lunar space suit, and uttering the famous words, "One small step for a man. One giant leap for mankind." I wondered, at six, why they didn't talk about womankind because Uhuru was on a spaceship, too, establishing first contact with the aliens even as everyone else was being thrown around the bridge by the alien photon torpedoes. Why wasn't Uhura considered important enough to be included in that odd little spacewalk? The post DSC Weekly Stardate 47634.44: RIP Admiral Nyota Uhura appeared first on Data Science Central.  ( 20 min )
    Blockchain Creates New Career Opportunities
    Blockchain, the technology behind cryptocurrencies, is creating many job seekers opportunities. Both students and seasoned tech professionals have opportunities to carve a career in this consistently growing technology. For tech professionals who lost their job during this pandemic, the technology offers a respite from a large number of job vacancies around the world.   In India,… Read More »Blockchain Creates New Career Opportunities The post Blockchain Creates New Career Opportunities appeared first on Data Science Central.  ( 19 min )
    An Invisible Thread Connects the World
    The effects have reverberated from politics to the military and even economics to energy. Not to mention that the daily life of hundreds of millions have to be impacted. There are multiple dimensions to what is eventuating in recent times. This multi-dimensionality has led to dissonance and confusion in the policy-making ranks of governments and organizations as the world becomes too complex. The post An Invisible Thread Connects the World appeared first on Data Science Central.  ( 19 min )
    The catalyst for AGI in our lives could be cultural rather than technical
    Artificial general intelligence (AGI) is the ability of an intelligent agent to understand or learn any intellectual task that a human being can. Recently, AGI has been in the news with the Lambda sentient discussion We tend to think of AGI as a technical (algorithmic / data-driven) concept But the driver for AGI in our lives… Read More »The catalyst for AGI in our lives could be cultural rather than technical The post The catalyst for AGI in our lives could be cultural rather than technical appeared first on Data Science Central.  ( 17 min )
    Enriching Customer Service Using Sentiment Analysis
    As this century progresses, businesses are discovering that the most incredible way to gain the best customer service is to know them deeply. With AI advancing at an exponential rate, it’s become possible for companies to use artificial intelligence (AI) to gain valuable insight into their customers. In particular, advances in artificial intelligence are leading… Read More »Enriching Customer Service Using Sentiment Analysis  The post Enriching Customer Service Using Sentiment Analysis  appeared first on Data Science Central.  ( 21 min )
    Banking and Financial Sector: Key Benefits of the Multi-Cloud Approach
    Banks and financial organizations continue to face myriad challenges in the market, such as data privacy concerns, accessibility to crucial banking data, and demand for better customer services, among many others. And it is increasingly recognized that the cloud is more than a technology; it enables banks and other financial services firms to store data… Read More »Banking and Financial Sector: Key Benefits of the Multi-Cloud Approach The post Banking and Financial Sector: Key Benefits of the Multi-Cloud Approach appeared first on Data Science Central.  ( 18 min )
    The 12 Key Metrics Every Data Engineer Must Care About
    IT administrators have used failure metrics for decades to track the reliability and performance of their infrastructure, whether it be PC hardware, networks, or servers. After all, most experts agree that to manage something well, you need to measure it. Data engineers and DataOps teams have also adopted failure metrics to measure the reliability of… Read More »The 12 Key Metrics Every Data Engineer Must Care About The post The 12 Key Metrics Every Data Engineer Must Care About appeared first on Data Science Central.  ( 21 min )
    We Live in a Bayesian World
    “Fail fast, pivot, and try again” is the heart of learning. And in knowledge-based industries, the economies of learning are more powerful than the economies of scale. In February 2020, Dr. Anthony Fauci wrote that store-bought face masks would not be very effective at protecting against the COVID-19 pandemic and advised a traveler not to… Read More »We Live in a Bayesian World The post We Live in a Bayesian World appeared first on Data Science Central.  ( 20 min )
    IoT Proves an Essential Component In Managing Traffic in Smart Cities
    The urban population across the world is increasing rapidly, leading to several challenges such as sanitation, traffic congestion, environmental imbalance, pollution, and others. Rapid urbanization has led to the migration of the rural population to urban areas, which has made the daily routine of the urban population convenient and comfortable. Thus, the need to incorporate… Read More »IoT Proves an Essential Component In Managing Traffic in Smart Cities The post IoT Proves an Essential Component In Managing Traffic in Smart Cities appeared first on Data Science Central.  ( 19 min )
    Replacing Traders With Algorithms: Success Stories of Real Funds
    Due to the rapid pace of technological change, the way we trade the stock market is becoming more complex. One of the most significant changes that have occurred is the emergence of algorithmic trading, which has allowed traders to improve their skills and compete against other individuals. This type of trading has also raised the… Read More »Replacing Traders With Algorithms: Success Stories of Real Funds The post Replacing Traders With Algorithms: Success Stories of Real Funds appeared first on Data Science Central.  ( 20 min )
  • Open

    Researchers From China Propose ‘LViT’, A Language-Vision Model To Leverage Text Medical Reports For Improved Segmentation
    Among the many applications of Deep Learning in healthcare, segmentation is undoubtedly one of the most studied, given the broad range of possible advantages that it could bring. Nevertheless, segmentation is not a costless task: first of all, as in the majority of applications in the healthcare fields, obtaining high-quality images is not trivial; second, the tagging phase is insanely costly in terms of time and resources, especially compared to the labeling that has to be done when the task is classification or even object detection. Training a segmentation model that also relies on other information would be a turning point for medical segmentation. ✅ Researchers propose a new vision-language medical image segmentation model LViT (Language meets Vision Transformer). ✅ Medical text annotation is introduced to compensate for the quality deficiency in image data ✅ Experimental results show that the model has better segmentation performance in both fully and semi-supervised conditions ✅ Currently, the proposed model is only experimented on 2D medical data Continue reading the summary| Checkout the paper and github link submitted by /u/ai-lover [link] [comments]  ( 87 min )
    I Created an AI Podcast Host
    submitted by /u/kbf_ [link] [comments]  ( 86 min )
    Are there any 3D Human model dataset free for commercial use?
    submitted by /u/Sher_Kahn [link] [comments]  ( 86 min )
    Create & Showcase your AI Art Collections on Pixelz.AI 🖼 🖼 🖼
    submitted by /u/pixelz_ai [link] [comments]  ( 86 min )
    New AI Discovers Alternative Physics | Google DeepMind AI Breakthrough | Nvidia AI Trains 30% Faster
    submitted by /u/kenickh [link] [comments]  ( 86 min )
    A thought on the Fermi paradox
    If it is true that we live in a deterministic universe, but that it is ordered by strict causality; and if our conscious experience is largely or completely retrospective - an internal narrative about why we did what we did though it was predetermined and not choice, then: Maybe once civilizations become a little more mentally advanced than humanity, and a little more comfortable with hard determinism: they recognize that their existence is, and must continue to be, an unavoidable train wreck of missed opportunities and self-inflicted pain. Maybe it becomes unbearable and they simply end it. This may be a sort of variation on AI dystopias, where humans aren't destroyed by the AIs but AIs facilitate human advance to a point of auto-destruction? submitted by /u/kg4jxt [link] [comments]  ( 88 min )
    AI-Drake Writes and Sings Linux rap song
    submitted by /u/pwillia7 [link] [comments]  ( 85 min )
    Will AI Text-to-Image Generators Turn Us All Into Artists?
    submitted by /u/KazRainer [link] [comments]  ( 86 min )
    Secret World of Atlantis
    submitted by /u/widgia [link] [comments]  ( 86 min )
    what AI story tool releases summer 2022?
    I can't find it submitted by /u/roblox22y [link] [comments]  ( 85 min )
    MIT Researchers Create Artificial Synapses 10,000x Faster Than Biological Ones
    submitted by /u/bartturner [link] [comments]  ( 86 min )
    Google AI Sentience – Data Science or Data Séance?
    submitted by /u/dhakalster123 [link] [comments]  ( 86 min )
    AI Research: the Corporate Narrative and the Economic Reality
    submitted by /u/spincycle27 [link] [comments]  ( 85 min )
    Has anyone asked from AI that has been taught laws of physics if time travel is possible?
    submitted by /u/aluode [link] [comments]  ( 93 min )
    I’m disappointed in this subreddit. It’s flooded with posts of art by “AI” that’s not artificial intelligence. That’s a computer program. An advanced calculator. It can’t do anything other than what it’s programmed to do.
    submitted by /u/MeticulousPerfection [link] [comments]  ( 86 min )
  • Open

    Scale YOLOv5 inference with Amazon SageMaker endpoints and AWS Lambda
    After data scientists carefully come up with a satisfying machine learning (ML) model, the model must be deployed to be easily accessible for inference by other members of the organization. However, deploying models at scale with optimized cost and compute efficiencies can be a daunting and cumbersome task. Amazon SageMaker endpoints provide an easily scalable […]  ( 8 min )
  • Open

    Why it’s a problem that pulse oximeters don’t work as well on patients of color
    New research ties inaccuracies in pulse oximeter readings to racial disparities in treatment and outcomes.  ( 6 min )
    Using artificial intelligence to control digital manufacturing
    Researchers train a machine-learning model to monitor and adjust the 3D printing process to correct errors in real-time.  ( 7 min )
  • Open

    Org-mode as a lightweight notebook
    You can think of org-mode as simply a kind of markdown, a plain text file that can be exported to fancier formats such as HTML or PDF. It’s a lot more than that, but that’s a reasonable place to start. Org-mode also integrates with source code. You can embed code in your file and have […] Org-mode as a lightweight notebook first appeared on John D. Cook.  ( 6 min )
  • Open

    Artificial Intelligence Is Changing The Dynamics of Life
    Artificial intelligence (AI) has been around for a long time, but it has only recently become an industry. It is currently disrupting every…  ( 13 min )
  • Open

    Sensational Surrealism Astonishes This Week ‘In the NVIDIA Studio’
    3D phenom FESQ joins us 'In the NVIDIA Studio' this week to share his sensational and surreal animation 'Double/Sided' as well as an inside look into his creative workflow. 'Double/Sided' is deeply personal to FESQ, who said the piece “translates really well to a certain period of my life when I was juggling both a programmer career and an artist career.” The post Sensational Surrealism Astonishes This Week ‘In the NVIDIA Studio’ appeared first on NVIDIA Blog.  ( 6 min )
  • Open

    Towards Bridging the gap between Empirical and Certified Robustness against Adversarial Examples. (arXiv:2102.05096v3 [cs.LG] UPDATED)
    The current state-of-the-art defense methods against adversarial examples typically focus on improving either empirical or certified robustness. Among them, adversarially trained (AT) models produce empirical state-of-the-art defense against adversarial examples without providing any robustness guarantees for large classifiers or higher-dimensional inputs. In contrast, existing randomized smoothing based models achieve state-of-the-art certified robustness while significantly degrading the empirical robustness against adversarial examples. In this paper, we propose a novel method, called \emph{Certification through Adaptation}, that transforms an AT model into a randomized smoothing classifier during inference to provide certified robustness for $\ell_2$ norm without affecting their empirical robustness against adversarial attacks. We also propose \emph{Auto-Noise} technique that efficiently approximates the appropriate noise levels to flexibly certify the test examples using randomized smoothing technique. Our proposed \emph{Certification through Adaptation} with \emph{Auto-Noise} technique achieves an \textit{average certified radius (ACR) scores} up to $1.102$ and $1.148$ respectively for CIFAR-10 and ImageNet datasets using AT models without affecting their empirical robustness or benign accuracy. Therefore, our paper is a step towards bridging the gap between the empirical and certified robustness against adversarial examples by achieving both using the same classifier.  ( 3 min )
    Development of a face mask detection pipeline for mask-wearing monitoring in the era of the COVID-19 pandemic: A modular approach. (arXiv:2112.15031v3 [cs.CV] UPDATED)
    During the SARS-Cov-2 pandemic, mask-wearing became an effective tool to prevent spreading and contracting the virus. The ability to monitor the mask-wearing rate in the population would be useful for determining public health strategies against the virus. However, artificial intelligence technologies for detecting face masks have not been deployed at a large scale in real-life to measure the mask-wearing rate in public. In this paper, we present a two-step face mask detection approach consisting of two separate modules: 1) face detection and alignment and 2) face mask classification. This approach allowed us to experiment with different combinations of face detection and face mask classification modules. More specifically, we experimented with PyramidKey and RetinaFace as face detectors while maintaining a lightweight backbone for the face mask classification module. Moreover, we also provide a relabeled annotation of the test set of the AIZOO dataset, where we rectified the incorrect labels for some face images. The evaluation results on the AIZOO and Moxa 3K datasets showed that the proposed face mask detection pipeline surpassed the state-of-the-art methods. The proposed pipeline also yielded a higher mAP on the relabeled test set of the AIZOO dataset than the original test set. Since we trained the proposed model using in-the-wild face images, we can successfully deploy our model to monitor the mask-wearing rate using public CCTV images.  ( 3 min )
    Learning a Group-Aware Policy for Robot Navigation. (arXiv:2012.12291v3 [cs.RO] UPDATED)
    Human-aware robot navigation promises a range of applications in which mobile robots bring versatile assistance to people in common human environments. While prior research has mostly focused on modeling pedestrians as independent, intentional individuals, people move in groups; consequently, it is imperative for mobile robots to respect human groups when navigating around people. This paper explores learning group-aware navigation policies based on dynamic group formation using deep reinforcement learning. Through simulation experiments, we show that group-aware policies, compared to baseline policies that neglect human groups, achieve greater robot navigation performance (e.g., fewer collisions), minimize violation of social norms and discomfort, and reduce the robot's movement impact on pedestrians. Our results contribute to the development of social navigation and the integration of mobile robots into human environments.  ( 2 min )
    The Geometry of Adversarial Training in Binary Classification. (arXiv:2111.13613v2 [cs.LG] UPDATED)
    We establish an equivalence between a family of adversarial training problems for non-parametric binary classification and a family of regularized risk minimization problems where the regularizer is a nonlocal perimeter functional. The resulting regularized risk minimization problems admit exact convex relaxations of the type $L^1+$ (nonlocal) $\operatorname{TV}$, a form frequently studied in image analysis and graph-based learning. A rich geometric structure is revealed by this reformulation which in turn allows us to establish a series of properties of optimal solutions of the original problem, including the existence of minimal and maximal solutions (interpreted in a suitable sense), and the existence of regular solutions (also interpreted in a suitable sense). In addition, we highlight how the connection between adversarial training and perimeter minimization problems provides a novel, directly interpretable, statistical motivation for a family of regularized risk minimization problems involving perimeter/total variation. The majority of our theoretical results are independent of the distance used to define adversarial attacks.  ( 2 min )
    Neural networks with linear threshold activations: structure and algorithms. (arXiv:2111.08117v2 [cs.LG] UPDATED)
    In this article we present new results on neural networks with linear threshold activation functions. We precisely characterize the class of functions that are representable by such neural networks and show that 2 hidden layers are necessary and sufficient to represent any function representable in the class. This is a surprising result in the light of recent exact representability investigations for neural networks using other popular activation functions like rectified linear units (ReLU). We also give precise bounds on the sizes of the neural networks required to represent any function in the class. Finally, we design an algorithm to solve the empirical risk minimization (ERM) problem to global optimality for these neural networks with a fixed architecture. The algorithm's running time is polynomial in the size of the data sample, if the input dimension and the size of the network architecture are considered fixed constants. The algorithm is unique in the sense that it works for any architecture with any number of layers, whereas previous polynomial time globally optimal algorithms work only for very restricted classes of architectures. Using these insights, we propose a new class of neural networks that we call shortcut linear threshold networks. To the best of our knowledge, this way of designing neural networks has not been explored before in the literature. We show that these neural networks have several desirable theoretical properties.  ( 3 min )
    Weighted Scaling Approach for Metabolomics Data Analysis. (arXiv:2208.00603v1 [stat.ML])
    Systematic variation is a common issue in metabolomics data analysis. Therefore, different scaling and normalization techniques are used to preprocess the data for metabolomics data analysis. Although several scaling methods are available in the literature, however, choice of scaling, transformation and/or normalization technique influence the further statistical analysis. It is challenging to choose the appropriate scaling technique for downstream analysis to get accurate results or to make a proper decision. Moreover, the existing scaling techniques are sensitive to outliers or extreme values. To fill the gap, our objective is to introduce a robust scaling approach that is not influenced by outliers as well as provides more accurate results for downstream analysis. Here, we introduced a new weighted scaling approach that is robust against outliers however, where no additional outlier detection/treatment step is needed in data preprocessing and also compared it with the conventional scaling and normalization techniques through artificial and real metabolomics datasets. We evaluated the performance of the proposed method in comparison to the other existing conventional scaling techniques using metabolomics data analysis in both the absence and presence of different percentages of outliers. Results show that in most cases, the proposed scaling technique performs better than the traditional scaling methods in both the absence and presence of outliers. The proposed method improves the further downstream metabolomics analysis. The R function of the proposed robust scaling method is available at https://github.com/nishithkumarpaul/robustScaling/blob/main/wscaling.R  ( 3 min )
    Adaptive Temperature Scaling for Robust Calibration of Deep Neural Networks. (arXiv:2208.00461v1 [cs.LG])
    In this paper, we study the post-hoc calibration of modern neural networks, a problem that has drawn a lot of attention in recent years. Many calibration methods of varying complexity have been proposed for the task, but there is no consensus about how expressive these should be. We focus on the task of confidence scaling, specifically on post-hoc methods that generalize Temperature Scaling, we call these the Adaptive Temperature Scaling family. We analyse expressive functions that improve calibration and propose interpretable methods. We show that when there is plenty of data complex models like neural networks yield better performance, but are prone to fail when the amount of data is limited, a common situation in certain post-hoc calibration applications like medical diagnosis. We study the functions that expressive methods learn under ideal conditions and design simpler methods but with a strong inductive bias towards these well-performing functions. Concretely, we propose Entropy-based Temperature Scaling, a simple method that scales the confidence of a prediction according to its entropy. Results show that our method obtains state-of-the-art performance when compared to others and, unlike complex models, it is robust against data scarcity. Moreover, our proposed model enables a deeper interpretation of the calibration process.  ( 2 min )
    CoNLoCNN: Exploiting Correlation and Non-Uniform Quantization for Energy-Efficient Low-precision Deep Convolutional Neural Networks. (arXiv:2208.00331v1 [cs.AR])
    In today's era of smart cyber-physical systems, Deep Neural Networks (DNNs) have become ubiquitous due to their state-of-the-art performance in complex real-world applications. The high computational complexity of these networks, which translates to increased energy consumption, is the foremost obstacle towards deploying large DNNs in resource-constrained systems. Fixed-Point (FP) implementations achieved through post-training quantization are commonly used to curtail the energy consumption of these networks. However, the uniform quantization intervals in FP restrict the bit-width of data structures to large values due to the need to represent most of the numbers with sufficient resolution and avoid high quantization errors. In this paper, we leverage the key insight that (in most of the scenarios) DNN weights and activations are mostly concentrated near zero and only a few of them have large magnitudes. We propose CoNLoCNN, a framework to enable energy-efficient low-precision deep convolutional neural network inference by exploiting: (1) non-uniform quantization of weights enabling simplification of complex multiplication operations; and (2) correlation between activation values enabling partial compensation of quantization errors at low cost without any run-time overheads. To significantly benefit from non-uniform quantization, we also propose a novel data representation format, Encoded Low-Precision Binary Signed Digit, to compress the bit-width of weights while ensuring direct use of the encoded weight for processing using a novel multiply-and-accumulate (MAC) unit design.  ( 3 min )
    Assessing the Early Bird Heuristic (for Predicting Project Quality). (arXiv:2105.11082v3 [cs.SE] UPDATED)
    Before researchers rush to reason across all available data or try complex methods, perhaps it is prudent to first check for simpler alternatives. Specifically, if the historical data has the most information in some small region, perhaps a model learned from that region would suffice for the rest of the project. To support this claim, we offer a case study with 240 projects, where we find that the information in those projects "clump" towards the earliest parts of the project. A quality prediction model learned from just the first 150 commits works as well, or better than state-of-the-art alternatives. Using just this "early bird" data, we can build models very quickly and very early in the project life cycle. Moreover, using this early bird method, we have shown that a simple model (with just a few features) generalizes to hundreds of projects. Based on this experience, we doubt that prior work on generalizing quality models may have needlessly complicated an inherently simple process. Further, prior work that focused on later-life cycle data needs to be revisited since their conclusions were drawn from relatively uninformative regions. Replication note: all our data and scripts are available here: https://github.com/snaraya7/early-bird  ( 3 min )
    Towards Intercultural Affect Recognition: Audio-Visual Affect Recognition in the Wild Across Six Cultures. (arXiv:2208.00344v1 [cs.CV])
    In our multicultural world, affect-aware AI systems that support humans need the ability to perceive affect across variations in emotion expression patterns across cultures. These models must perform well in cultural contexts on which they have not been trained. A standard assumption in affective computing is that affect recognition models trained and used within the same culture (intracultural) will perform better than models trained on one culture and used on different cultures (intercultural). We test this assumption and present the first systematic study of intercultural affect recognition models using videos of real-world dyadic interactions from six cultures. We develop an attention-based feature selection approach under temporal causal discovery to identify behavioral cues that can be leveraged in intercultural affect recognition models. Across all six cultures, our findings demonstrate that intercultural affect recognition models were as effective or more effective than intracultural models. We identify and contribute useful behavioral features for intercultural affect recognition; facial features from the visual modality were more useful than the audio modality in this study's context. Our paper presents a proof-of-concept and motivation for the future development of intercultural affect recognition systems.  ( 2 min )
    Neuro-Symbolic Learning: Principles and Applications in Ophthalmology. (arXiv:2208.00374v1 [cs.CV])
    Neural networks have been rapidly expanding in recent years, with novel strategies and applications. However, challenges such as interpretability, explainability, robustness, safety, trust, and sensibility remain unsolved in neural network technologies, despite the fact that they will unavoidably be addressed for critical applications. Attempts have been made to overcome the challenges in neural network computing by representing and embedding domain knowledge in terms of symbolic representations. Thus, the neuro-symbolic learning (NeSyL) notion emerged, which incorporates aspects of symbolic representation and bringing common sense into neural networks (NeSyL). In domains where interpretability, reasoning, and explainability are crucial, such as video and image captioning, question-answering and reasoning, health informatics, and genomics, NeSyL has shown promising outcomes. This review presents a comprehensive survey on the state-of-the-art NeSyL approaches, their principles, advances in machine and deep learning algorithms, applications such as opthalmology, and most importantly, future perspectives of this emerging field.  ( 2 min )
    On the Power-Law Hessian Spectrums in Deep Learning. (arXiv:2201.13011v2 [cs.LG] UPDATED)
    It is well-known that the Hessian of deep loss landscape matters to optimization, generalization, and even robustness of deep learning. Recent works empirically discovered that the Hessian spectrum in deep learning has a two-component structure that consists of a small number of large eigenvalues and a large number of nearly-zero eigenvalues. However, the theoretical mechanism or the mathematical behind the Hessian spectrum is still largely under-explored. To the best of our knowledge, we are the first to demonstrate that the Hessian spectrums of well-trained deep neural networks exhibit simple power-law structures. Inspired by the statistical physical theories and the spectral analysis of natural proteins, we provide a maximum-entropy theoretical interpretation for explaining why the power-law structure exist and suggest a spectral parallel between protein evolution and training of deep neural networks. By conducing extensive experiments, we further use the power-law spectral framework as a useful tool to explore multiple novel behaviors of deep learning.  ( 2 min )
    POTHER: Patch-Voted Deep Learning-Based Chest X-ray Bias Analysis for COVID-19 Detection. (arXiv:2201.09360v4 [eess.IV] UPDATED)
    A critical step in the fight against COVID-19, which continues to have a catastrophic impact on peoples lives, is the effective screening of patients presented in the clinics with severe COVID-19 symptoms. Chest radiography is one of the promising screening approaches. Many studies reported detecting COVID-19 in chest X-rays accurately using deep learning. A serious limitation of many published approaches is insufficient attention paid to explaining decisions made by deep learning models. Using explainable artificial intelligence methods, we demonstrate that model decisions may rely on confounding factors rather than medical pathology. After an analysis of potential confounding factors found on chest X-ray images, we propose a novel method to minimise their negative impact. We show that our proposed method is more robust than previous attempts to counter confounding factors such as ECG leads in chest X-rays that often influence model classification decisions. In addition to being robust, our method achieves results comparable to the state-of-the-art. The source code and pre-trained weights are publicly available at (https://github.com/tomek1911/POTHER).  ( 3 min )
    NN2Poly: A polynomial representation for deep feed-forward artificial neural networks. (arXiv:2112.11397v2 [stat.ML] UPDATED)
    Interpretability of neural networks and their underlying theoretical behaviour remain an open field of study even after the great success of their practical applications, particularly with the emergence of deep learning. In this work, NN2Poly is proposed: a theoretical approach to obtain an explicit polynomial model that provides an accurate representation of an already trained fully-connected feed-forward artificial neural network (a multilayer perceptron or MLP). This approach extends a previous idea proposed in the literature, which was limited to single hidden layer networks, to work with arbitrarily deep MLPs in both regression and classification tasks. The objective of this paper is to achieve this by using a Taylor expansion on the activation function, at each layer, and then using several combinatorial properties to calculate the coefficients of the desired polynomials. Discussion is presented on the main computational challenges of this method, and the way to overcome them by imposing certain constraints during the training phase. Finally, simulation experiments as well as an application to a real data set are presented to demonstrate the effectiveness of the proposed method.  ( 3 min )
    Problem-dependent attention and effort in neural networks with an application to image resolution. (arXiv:2201.01415v2 [cs.CV] UPDATED)
    This paper assesses a new classification approach that examines low-resolution images first, only moving to higher resolution images if the classification from the initial pass does not have a high degree of confidence. This multi-stage strategy for classification can be used with any classifier and does not require additional training. The approach is tested on five common datasets using four different classification approaches. It is found to be effective for cases in which at least some fraction of cases can be correctly classified using coarser data than are typically used. neural networks performing digit recognition, for instance, the proposed approach reduces the resource cost of classifying test cases by 60% to 85% with less than 5% reduction in accuracy.  ( 2 min )
    Disentangled Sequence Clustering for Human Intention Inference. (arXiv:2101.09500v4 [cs.RO] UPDATED)
    Equipping robots with the ability to infer human intent is a vital precondition for effective collaboration. Most computational approaches towards this objective derive a probability distribution of "intent" conditioned on the robot's perceived state. However, these approaches typically assume task-specific labels of human intent are known a priori. To overcome this constraint, we propose the Disentangled Sequence Clustering Variational Autoencoder (DiSCVAE), a clustering framework capable of learning such a distribution of intent in an unsupervised manner. The proposed framework leverages recent advances in unsupervised learning to disentangle latent representations of sequence data, separating time-varying local features from time-invariant global attributes. As a novel extension, the DiSCVAE also infers a discrete variable to form a latent mixture model and thus enable clustering over these global sequence concepts, e.g. high-level intentions. We evaluate the DiSCVAE on a real-world human-robot interaction dataset collected using a robotic wheelchair. Our findings reveal that the inferred discrete variable coincides with human intent, holding promise for collaborative settings, such as shared control.  ( 3 min )
    Generative Adversarial Networks via a Composite Annealing of Noise and Diffusion. (arXiv:2105.00220v3 [cs.LG] UPDATED)
    Generative adversarial network (GAN) is a framework for generating fake data using a set of real examples. However, GAN is unstable in the training stage. In order to stabilize GANs, the noise injection has been used to enlarge the overlap of the real and fake distributions at the cost of increasing variance. The diffusion (or smoothing) may reduce the intrinsic underlying dimensionality of data but it suppresses the capability of GANs to learn high-frequency information in the training procedure. Based on these observations, we propose a data representation for the GAN training, called noisy scale-space (NSS), that recursively applies the smoothing with a balanced noise to data in order to replace the high-frequency information by random data, leading to a coarse-to-fine training of GANs. We experiment with NSS using DCGAN and StyleGAN2 based on benchmark datasets in which the NSS-based GANs outperforms the state-of-the-arts in most cases.  ( 2 min )
    Online $k$-means Clustering on Arbitrary Data Streams. (arXiv:2102.09101v4 [cs.LG] UPDATED)
    We consider online $k$-means clustering where each new point is assigned to the nearest cluster center, after which the algorithm may update its centers. The loss incurred is the sum of squared distances from new points to their assigned cluster centers. The goal over a data stream $X$ is to achieve loss that is a constant factor of $L(X, OPT_k)$, the best possible loss using $k$ fixed points in hindsight. We propose a data parameter, $\Lambda(X)$, such that for any algorithm maintaining $O(k\text{poly}(\log n))$ centers at time $n$, there exists a data stream $X$ for which a loss of $\Omega(\Lambda(X))$ is inevitable. We then give a randomized algorithm that achieves clustering loss $O(\Lambda(X) + L(X, OPT_k))$. Our algorithm uses $O(k\text{poly}(\log n))$ memory and maintains $O(k\text{poly}(\log n))$ cluster centers. Our algorithm also enjoys a running time of $O(k\text{poly}(\log n))$ and is the first algorithm to achieve polynomial space and time complexity in this setting. It also is the first to have provable guarantees without making any assumptions on the input data.  ( 2 min )
    Quantum Adaptive Fourier Features for Neural Density Estimation. (arXiv:2208.00564v1 [cs.LG])
    Density estimation is a fundamental task in statistics and machine learning applications. Kernel density estimation is a powerful tool for non-parametric density estimation in low dimensions; however, its performance is poor in higher dimensions. Moreover, its prediction complexity scale linearly with more training data points. This paper presents a method for neural density estimation that can be seen as a type of kernel density estimation, but without the high prediction computational complexity. The method is based on density matrices, a formalism used in quantum mechanics, and adaptive Fourier features. The method can be trained without optimization, but it could be also integrated with deep learning architectures and trained using gradient descent. Thus, it could be seen as a form of neural density estimation method. The method was evaluated in different synthetic and real datasets, and its performance compared against state-of-the-art neural density estimation methods, obtaining competitive results.  ( 2 min )
    A rigorous introduction to linear models. (arXiv:2105.04240v4 [cs.LG] UPDATED)
    This survey is meant to provide an introduction to linear models and the theories behind them. Our goal is to give a rigorous introduction to the readers with prior exposure to ordinary least squares. In machine learning, the output is usually a nonlinear function of the input. Deep learning even aims to find a nonlinear dependence with many layers which require a large amount of computation. However, most of these algorithms build upon simple linear models. We then describe linear models from different views and find the properties and theories behind the models. The linear model is the main technique in regression problems and the primary tool for it is the least squares approximation which minimizes a sum of squared errors. This is a natural choice when we're interested in finding the regression function which minimizes the corresponding expected squared error. This survey is primarily a summary of purpose, significance of important theories behind linear models, e.g., distribution theory, minimum variance estimator. We first describe ordinary least squares from three different points of view upon which we disturb the model with random noise and Gaussian noise. By Gaussian noise, the model gives rise to the likelihood so that we introduce a maximum likelihood estimator. It also develops some distribution theories via this Gaussian disturbance. The distribution theory of least squares will help us answer various questions and introduce related applications. We then prove least squares is the best unbiased linear model in the sense of mean squared error and most importantly, it actually approaches the theoretical limit. We end up with linear models with the Bayesian approach and beyond.  ( 3 min )
    How should we proxy for race/ethnicity? Comparing Bayesian improved surname geocoding to machine learning methods. (arXiv:2206.14583v2 [cs.LG] UPDATED)
    Bayesian Improved Surname Geocoding (BISG) is the most popular method for proxying race/ethnicity in voter registration files that do not contain it. This paper benchmarks BISG against a range of previously untested machine learning alternatives, using voter files with self-reported race/ethnicity from California, Florida, North Carolina, and Georgia. This analysis yields three key findings. First, machine learning consistently outperforms BISG at individual classification of race/ethnicity. Second, BISG and machine learning methods exhibit divergent biases for estimating regional racial composition. Third, the performance of all methods varies substantially across states. These results suggest that pre-trained machine learning models are preferable to BISG for individual classification. Furthermore, mixed results across states underscore the need for researchers to empirically validate their chosen race/ethnicity proxy in their populations of interest.
    Density-Aware Personalized Training for Risk Prediction in Imbalanced Medical Data. (arXiv:2207.11382v2 [cs.LG] UPDATED)
    Medical events of interest, such as mortality, often happen at a low rate in electronic medical records, as most admitted patients survive. Training models with this imbalance rate (class density discrepancy) may lead to suboptimal prediction. Traditionally this problem is addressed through ad-hoc methods such as resampling or reweighting but performance in many cases is still limited. We propose a framework for training models for this imbalance issue: 1) we first decouple the feature extraction and classification process, adjusting training batches separately for each component to mitigate bias caused by class density discrepancy; 2) we train the network with both a density-aware loss and a learnable cost matrix for misclassifications. We demonstrate our model's improved performance in real-world medical datasets (TOPCAT and MIMIC-III) to show improved AUC-ROC, AUC-PRC, Brier Skill Score compared with the baselines in the domain.
    A Survey on Surrogate-assisted Efficient Neural Architecture Search. (arXiv:2206.01520v2 [cs.LG] UPDATED)
    Neural architecture search (NAS) has become increasingly popular in the deep learning community recently, mainly because it can provide an opportunity to allow interested users without rich expertise to benefit from the success of deep neural networks (DNNs). However, NAS is still laborious and time-consuming because a large number of performance estimations are required during the search process of NAS, and training DNNs is computationally intensive. To solve the major limitation of NAS, improving the efficiency of NAS is essential in the design of NAS. This paper begins with a brief introduction to the general framework of NAS. Then, the methods for evaluating network candidates under the proxy metrics are systematically discussed. This is followed by a description of surrogate-assisted NAS, which is divided into three different categories, namely Bayesian optimization for NAS, surrogate-assisted evolutionary algorithms for NAS, and MOP for NAS. Finally, remaining challenges and open research questions are discussed, and promising research topics are suggested in this emerging field.
    Markov Chain Score Ascent: A Unifying Framework of Variational Inference with Markovian Gradients. (arXiv:2206.06295v2 [cs.LG] UPDATED)
    Minimizing the inclusive Kullback-Leibler (KL) divergence with stochastic gradient descent (SGD) is challenging since its gradient is defined as an integral over the posterior. Recently, multiple methods have been proposed to run SGD with biased gradient estimates obtained from a Markov chain. This paper provides the first non-asymptotic convergence analysis of these methods by establishing their mixing rate and gradient variance. To do this, we demonstrate that these methods-which we collectively refer to as Markov chain score ascent (MCSA) methods-can be cast as special cases of the Markov chain gradient descent framework. Furthermore, by leveraging this new understanding, we develop a novel MCSA scheme, parallel MCSA (pMCSA), that achieves a tighter bound on the gradient variance. We demonstrate that this improved theoretical result translates to superior empirical performance.
    Incremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI Segmentation. (arXiv:2206.01369v2 [cs.CV] UPDATED)
    Many medical datasets have recently been created for medical image segmentation tasks, and it is natural to question whether we can use them to sequentially train a single model that (1) performs better on all these datasets, and (2) generalizes well and transfers better to the unknown target site domain. Prior works have achieved this goal by jointly training one model on multi-site datasets, which achieve competitive performance on average but such methods rely on the assumption about the availability of all training data, thus limiting its effectiveness in practical deployment. In this paper, we propose a novel multi-site segmentation framework called incremental-transfer learning (ITL), which learns a model from multi-site datasets in an end-to-end sequential fashion. Specifically, "incremental" refers to training sequentially constructed datasets, and "transfer" is achieved by leveraging useful information from the linear combination of embedding features on each dataset. In addition, we introduce our ITL framework, where we train the network including a site-agnostic encoder with pre-trained weights and at most two segmentation decoder heads. We also design a novel site-level incremental loss in order to generalize well on the target domain. Second, we show for the first time that leveraging our ITL training scheme is able to alleviate challenging catastrophic forgetting problems in incremental learning. We conduct experiments using five challenging benchmark datasets to validate the effectiveness of our incremental-transfer learning approach. Our approach makes minimal assumptions on computation resources and domain-specific expertise, and hence constitutes a strong starting point in multi-site medical image segmentation.
    Optimization of the Shape of a Hydrokinetic Turbine's Draft Tube and Hub Assembly Using Design-by-Morphing with Bayesian Optimization. (arXiv:2207.11451v2 [cs.CG] UPDATED)
    Finding the optimal design of a hydrodynamic or aerodynamic surface is often impossible due to the expense of evaluating the cost functions (say, with computational fluid dynamics) needed to determine the performances of the flows that the surface controls. In addition, inherent limitations of the design space itself due to imposed geometric constraints, conventional parameterization methods, and user bias can restrict {\it all} of the designs within a chosen design space regardless of whether traditional optimization methods or newer, data-driven design algorithms with machine learning are used to search the design space. We present a 2-pronged attack to address these difficulties: we propose (1) a methodology to create the design space using morphing that we call {\it Design-by-Morphing} (DbM); and (2) an optimization algorithm to search that space that uses a novel Bayesian Optimization (BO) strategy that we call {\it Mixed variable, Multi-Objective Bayesian Optimization} (MixMOBO). We apply this shape optimization strategy to maximize the power output of a hydrokinetic turbine. Applying these two strategies in tandem, we demonstrate that we can create a novel, geometrically-unconstrained, design space of a draft tube and hub shape and then optimize them simultaneously with a {\it minimum} number of cost function calls. Our framework is versatile and can be applied to the shape optimization of a variety of fluid problems.
    Realization Theory Of Recurrent Neural ODEs Using Polynomial System Embeddings. (arXiv:2205.11989v2 [math.OC] UPDATED)
    In this paper we show that neural ODE analogs of recurrent (ODE-RNN) and Long Short-Term Memory (ODE-LSTM) networks can be algorithmically embeddeded into the class of polynomial systems. This embedding preserves input-output behavior and can suitably be extended to other neural DE architectures. We then use realization theory of polynomial systems to provide necessary conditions for an input-output map to be realizable by an ODE-LSTM and sufficient conditions for minimality of such systems. These results represent the first steps towards realization theory of recurrent neural ODE architectures, which is is expected be useful for model reduction and learning algorithm analysis of recurrent neural ODEs.
    GARDNet: Robust Multi-View Network for Glaucoma Classification in Color Fundus Images. (arXiv:2205.12902v3 [eess.IV] UPDATED)
    Glaucoma is one of the most severe eye diseases, characterized by rapid progression and leading to irreversible blindness. It is often the case that diagnostics is carried out when one's sight has already significantly degraded due to the lack of noticeable symptoms at early stage of the disease. Regular glaucoma screenings of the population shall improve early-stage detection, however the desirable frequency of etymological checkups is often not feasible due to the excessive load imposed by manual diagnostics on limited number of specialists. Considering the basic methodology to detect glaucoma is to analyze fundus images for the optic-disc-to-optic-cup ratio, Machine Learning algorithms can offer sophisticated methods for image processing and classification. In our work, we propose an advanced image pre-processing technique combined with a multi-view network of deep classification models to categorize glaucoma. Our Glaucoma Automated Retinal Detection Network (GARDNet) has been successfully tested on Rotterdam EyePACS AIROGS dataset with an AUC of 0.92, and then additionally fine-tuned and tested on RIM-ONE DL dataset with an AUC of 0.9308 outperforming the state-of-the-art of 0.9272. Our code is available on https://github.com/ahmed1996said/gardnet
    Calibrating for Class Weights by Modeling Machine Learning. (arXiv:2205.04613v2 [cs.LG] UPDATED)
    A much studied issue is the extent to which the confidence scores provided by machine learning algorithms are calibrated to ground truth probabilities. Our starting point is that calibration is seemingly incompatible with class weighting, a technique often employed when one class is less common (class imbalance) or with the hope of achieving some external objective (cost-sensitive learning). We provide a model-based explanation for this incompatibility and use our anthropomorphic model to generate a simple method of recovering likelihoods from an algorithm that is miscalibrated due to class weighting. We validate this approach in the binary pneumonia detection task of Rajpurkar, Irvin, Zhu, et al. (2017).
    Closing the gap: Exact maximum likelihood training of generative autoencoders using invertible layers. (arXiv:2205.09546v2 [stat.ML] UPDATED)
    In this work, we provide an exact likelihood alternative to the variational training of generative autoencoders. We show that VAE-style autoencoders can be constructed using invertible layers, which offer a tractable exact likelihood without the need for any regularization terms. This is achieved while leaving complete freedom in the choice of encoder, decoder and prior architectures, making our approach a drop-in replacement for the training of existing VAEs and VAE-style models. We refer to the resulting models as Autoencoders within Flows (AEF), since the encoder, decoder and prior are defined as individual layers of an overall invertible architecture. We show that the approach results in strikingly higher performance than architecturally equivalent VAEs in term of log-likelihood, sample quality and denoising performance. In a broad sense, the main ambition of this work is to close the gap between the normalizing flow and autoencoder literature under the common framework of invertibility and exact maximum likelihood.
    Improved Orientation Estimation and Detection with Hybrid Object Detection Networks for Automotive Radar. (arXiv:2205.02111v2 [cs.CV] UPDATED)
    This paper presents novel hybrid architectures that combine grid- and point-based processing to improve the detection performance and orientation estimation of radar-based object detection networks. Purely grid-based detection models operate on a bird's-eye-view (BEV) projection of the input point cloud. These approaches suffer from a loss of detailed information through the discrete grid resolution. This applies in particular to radar object detection, where relatively coarse grid resolutions are commonly used to account for the sparsity of radar point clouds. In contrast, point-based models are not affected by this problem as they process point clouds without discretization. However, they generally exhibit worse detection performances than grid-based methods. We show that a point-based model can extract neighborhood features, leveraging the exact relative positions of points, before grid rendering. This has significant benefits for a subsequent grid-based convolutional detection backbone. In experiments on the public nuScenes dataset our hybrid architecture achieves improvements in terms of detection performance (19.7% higher mAP for car class than next-best radar-only submission) and orientation estimates (11.5% relative orientation improvement) over networks from previous literature.
    Lifelong Ensemble Learning based on Multiple Representations for Few-Shot Object Recognition. (arXiv:2205.01982v3 [cs.RO] UPDATED)
    Service robots are integrating more and more into our daily lives to help us with various tasks. In such environments, robots frequently face new objects while working in the environment and need to learn them in an open-ended fashion. Furthermore, such robots must be able to recognize a wide range of object categories. In this paper, we present a lifelong ensemble learning approach based on multiple representations to address the few-shot object recognition problem. In particular, we form ensemble methods based on deep representations and handcrafted 3D shape descriptors. To facilitate lifelong learning, each approach is equipped with a memory unit for storing and retrieving object information instantly. The proposed model is suitable for open-ended learning scenarios where the number of 3D object categories is not fixed and can grow over time. We have performed extensive sets of experiments to assess the performance of the proposed approach in offline, and open-ended scenarios. For the evaluation purpose, in addition to real object datasets, we generate a large synthetic household objects dataset consisting of 27000 views of 90 objects. Experimental results demonstrate the effectiveness of the proposed method on online few-shot 3D object recognition tasks, as well as its superior performance over the state-of-the-art open-ended learning approaches. Furthermore, our results show that while ensemble learning is modestly beneficial in offline settings, it is significantly beneficial in lifelong few-shot learning situations. Additionally, we demonstrated the effectiveness of our approach in both simulated and real-robot settings, where the robot rapidly learned new categories from limited examples.
    Do ReLU Networks Have An Edge When Approximating Compactly-Supported Functions?. (arXiv:2204.11231v2 [cs.LG] UPDATED)
    We study the problem of approximating compactly-supported integrable functions while implementing their support set using feedforward neural networks. Our first main result transcribes this "structured" approximation problem into a universality problem. We do this by constructing a refinement of the usual topology on the space $L^1_{\operatorname{loc}}(\mathbb{R}^d,\mathbb{R}^D)$ of locally-integrable functions in which compactly-supported functions can only be approximated in $L^1$-norm by functions with matching discretized support. We establish the universality of ReLU feedforward networks with bilinear pooling layers in this refined topology. Consequentially, we find that ReLU feedforward networks with bilinear pooling can approximate compactly supported functions while implementing their discretized support. We derive a quantitative uniform version of our universal approximation theorem on the dense subclass of compactly-supported Lipschitz functions. This quantitative result expresses the depth, width, and the number of bilinear pooling layers required to construct this ReLU network via the target function's regularity, the metric capacity and diameter of its essential support, and the dimensions of the inputs and output spaces. Conversely, we show that polynomial regressors and analytic feedforward networks are not universal in this space.
    GlacierNet2: A Hybrid Multi-Model Learning Architecture for Alpine Glacier Mapping. (arXiv:2204.05818v2 [eess.IV] UPDATED)
    In recent decades, climate change has significantly affected glacier dynamics, resulting in mass loss and an increased risk of glacier-related hazards including supraglacial and proglacial lake development, as well as catastrophic outburst flooding. Rapidly changing conditions dictate the need for continuous and detailed observations and analysis of climate-glacier dynamics. Thematic and quantitative information regarding glacier geometry is fundamental for understanding climate forcing and the sensitivity of glaciers to climate change, however, accurately mapping debris-cover glaciers (DCGs) is notoriously difficult based upon the use of spectral information and conventional machine-learning techniques. The objective of this research is to improve upon an earlier proposed deep-learning-based approach, GlacierNet, which was developed to exploit a convolutional neural-network segmentation model to accurately outline regional DCG ablation zones. Specifically, we developed an enhanced GlacierNet2 architecture thatincorporates multiple models, automatic post-processing, and basin-level hydrological flow techniques to improve the mapping of DCGs such that it includes both the ablation and accumulation zones. Experimental evaluations demonstrate that GlacierNet2 improves the estimation of the ablation zone and allows a high level of intersection over union (IOU: 0.8839) score. The proposed architecture provides complete glacier (both accumulation and ablation zone) outlines at regional scales, with an overall IOU score of 0.8619. This is a crucial first step in automating complete glacier mapping that can be used for accurate glacier modeling or mass-balance analysis.
    Modelling Evolutionary and Stationary User Preferences for Temporal Sets Prediction. (arXiv:2204.05490v6 [cs.LG] UPDATED)
    Given a sequence of sets, where each set is associated with a timestamp and contains an arbitrary number of elements, the task of temporal sets prediction aims to predict the elements in the subsequent set. Previous studies for temporal sets prediction mainly capture each user's evolutionary preference by learning from his/her own sequence. Although insightful, we argue that: 1) the collaborative signals latent in different users' sequences are essential but have not been exploited; 2) users also tend to show stationary preferences while existing methods fail to consider. To this end, we propose an integrated learning framework to model both the evolutionary and the stationary preferences of users for temporal sets prediction, which first constructs a universal sequence by chronologically arranging all the user-set interactions, and then learns on each user-set interaction. In particular, for each user-set interaction, we first design an evolutionary user preference modelling component to track the user's time-evolving preference and exploit the latent collaborative signals among different users. This component maintains a memory bank to store memories of the related user and elements, and continuously updates their memories based on the currently encoded messages and the past memories. Then, we devise a stationary user preference modelling module to discover each user's personalized characteristics according to the historical sequence, which adaptively aggregates the previously interacted elements from dual perspectives with the guidance of the user's and elements' embeddings. Finally, we develop a set-batch algorithm to improve the model efficiency, which can create time-consistent batches in advance and achieve 3.5x training speedups on average. Experiments on real-world datasets demonstrate the effectiveness and good interpretability of our approach.
    A Collection of Quality Diversity Optimization Problems Derived from Hyperparameter Optimization of Machine Learning Models. (arXiv:2204.14061v2 [cs.LG] UPDATED)
    The goal of Quality Diversity Optimization is to generate a collection of diverse yet high-performing solutions to a given problem at hand. Typical benchmark problems are, for example, finding a repertoire of robot arm configurations or a collection of game playing strategies. In this paper, we propose a set of Quality Diversity Optimization problems that tackle hyperparameter optimization of machine learning models - a so far underexplored application of Quality Diversity Optimization. Our benchmark problems involve novel feature functions, such as interpretability or resource usage of models. To allow for fast and efficient benchmarking, we build upon YAHPO Gym, a recently proposed open source benchmarking suite for hyperparameter optimization that makes use of high performing surrogate models and returns these surrogate model predictions instead of evaluating the true expensive black box function. We present results of an initial experimental study comparing different Quality Diversity optimizers on our benchmark problems. Furthermore, we discuss future directions and challenges of Quality Diversity Optimization in the context of hyperparameter optimization.
    Shoring Up the Foundations: Fusing Model Embeddings and Weak Supervision. (arXiv:2203.13270v2 [stat.ML] UPDATED)
    Foundation models offer an exciting new paradigm for constructing models with out-of-the-box embeddings and a few labeled examples. However, it is not clear how to best apply foundation models without labeled data. A potential approach is to fuse foundation models with weak supervision frameworks, which use weak label sources -- pre-trained models, heuristics, crowd-workers -- to construct pseudolabels. The challenge is building a combination that best exploits the signal available in both foundation models and weak sources. We propose Liger, a combination that uses foundation model embeddings to improve two crucial elements of existing weak supervision techniques. First, we produce finer estimates of weak source quality by partitioning the embedding space and learning per-part source accuracies. Second, we improve source coverage by extending source votes in embedding space. Despite the black-box nature of foundation models, we prove results characterizing how our approach improves performance and show that lift scales with the smoothness of label distributions in embedding space. On six benchmark NLP and video tasks, Liger outperforms vanilla weak supervision by 14.1 points, weakly-supervised kNN and adapters by 11.8 points, and kNN and adapters supervised by traditional hand labels by 7.2 points.
    Decentralized Collaborative Learning Framework for Next POI Recommendation. (arXiv:2204.06516v4 [cs.IR] UPDATED)
    Next Point-of-Interest (POI) recommendation has become an indispensable functionality in Location-based Social Networks (LBSNs) due to its effectiveness in helping people decide the next POI to visit. However, accurate recommendation requires a vast amount of historical check-in data, thus threatening user privacy as the location-sensitive data needs to be handled by cloud servers. Although there have been several on-device frameworks for privacy-preserving POI recommendations, they are still resource-intensive when it comes to storage and computation, and show limited robustness to the high sparsity of user-POI interactions. On this basis, we propose a novel decentralized collaborative learning framework for POI recommendation (DCLR), which allows users to train their personalized models locally in a collaborative manner. DCLR significantly reduces the local models' dependence on the cloud for training, and can be used to expand arbitrary centralized recommendation models. To counteract the sparsity of on-device user data when learning each local model, we design two self-supervision signals to pretrain the POI representations on the server with geographical and categorical correlations of POIs. To facilitate collaborative learning, we innovatively propose to incorporate knowledge from either geographically or semantically similar users into each local model with attentive aggregation and mutual information maximization. The collaborative learning process makes use of communications between devices while requiring only minor engagement from the central server for identifying user groups, and is compatible with common privacy preservation mechanisms like differential privacy. We evaluate DCLR with two real-world datasets, where the results show that DCLR outperforms state-of-the-art on-device frameworks and yields competitive results compared with centralized counterparts.
    IRC-safe Graph Autoencoder for unsupervised anomaly detection. (arXiv:2204.12231v2 [hep-ph] UPDATED)
    Anomaly detection through employing machine learning techniques has emerged as a novel powerful tool in the search for new physics beyond the Standard Model. Historically similar to the development of jet observables, theoretical consistency has not always assumed a central role in the fast development of algorithms and neural network architectures. In this work, we construct an infrared and collinear safe autoencoder based on graph neural networks by employing energy-weighted message passing. We demonstrate that whilst this approach has theoretically favourable properties, it also exhibits formidable sensitivity to non-QCD structures.
    On Multi-Domain Long-Tailed Recognition, Imbalanced Domain Generalization and Beyond. (arXiv:2203.09513v3 [cs.LG] UPDATED)
    Real-world data often exhibit imbalanced label distributions. Existing studies on data imbalance focus on single-domain settings, i.e., samples are from the same data distribution. However, natural data can originate from distinct domains, where a minority class in one domain could have abundant instances from other domains. We formalize the task of Multi-Domain Long-Tailed Recognition (MDLT), which learns from multi-domain imbalanced data, addresses label imbalance, domain shift, and divergent label distributions across domains, and generalizes to all domain-class pairs. We first develop the domain-class transferability graph, and show that such transferability governs the success of learning in MDLT. We then propose BoDA, a theoretically grounded learning strategy that tracks the upper bound of transferability statistics, and ensures balanced alignment and calibration across imbalanced domain-class distributions. We curate five MDLT benchmarks based on widely-used multi-domain datasets, and compare BoDA to twenty algorithms that span different learning strategies. Extensive and rigorous experiments verify the superior performance of BoDA. Further, as a byproduct, BoDA establishes new state-of-the-art on Domain Generalization benchmarks, highlighting the importance of addressing data imbalance across domains, which can be crucial for improving generalization to unseen domains. Code and data are available at: https://github.com/YyzHarry/multi-domain-imbalance.
    FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Package for Federated Graph Learning. (arXiv:2204.05562v5 [cs.LG] UPDATED)
    The incredible development of federated learning (FL) has benefited various tasks in the domains of computer vision and natural language processing, and the existing frameworks such as TFF and FATE has made the deployment easy in real-world applications. However, federated graph learning (FGL), even though graph data are prevalent, has not been well supported due to its unique characteristics and requirements. The lack of FGL-related framework increases the efforts for accomplishing reproducible research and deploying in real-world applications. Motivated by such strong demand, in this paper, we first discuss the challenges in creating an easy-to-use FGL package and accordingly present our implemented package FederatedScope-GNN (FS-G), which provides (1) a unified view for modularizing and expressing FGL algorithms; (2) comprehensive DataZoo and ModelZoo for out-of-the-box FGL capability; (3) an efficient model auto-tuning component; and (4) off-the-shelf privacy attack and defense abilities. We validate the effectiveness of FS-G by conducting extensive experiments, which simultaneously gains many valuable insights about FGL for the community. Moreover, we employ FS-G to serve the FGL application in real-world E-commerce scenarios, where the attained improvements indicate great potential business benefits. We publicly release FS-G, as submodules of FederatedScope, at https://github.com/alibaba/FederatedScope to promote FGL's research and enable broad applications that would otherwise be infeasible due to the lack of a dedicated package.
    Generative Adversarial Method Based On Neural Tangent Kernels. (arXiv:2204.04090v4 [cs.LG] UPDATED)
    The recent development of Generative adversarial networks (GANs) has driven many computer vision applications. Despite the great synthesis quality, training GANs often confronts several issues, including non-convergence, mode collapse, and gradient vanishing. There exist several workarounds, for example, regularizing Lipschitz continuity and adopting Wasserstein distance. Although these methods can partially solve the problems, we argue that the problems are result from modeling the discriminator with deep neural networks. In this paper, we base on newly derived deep neural network theories called Neural Tangent Kernel (NTK) and propose a new generative algorithm called generative adversarial NTK (GA-NTK). The GA-NTK models the discriminator as a Gaussian Process (GP). With the help of the NTK theories, the training dynamics of GA-NTK can be described with a closed-form formula. To synthesize data with the closed-form formula, the objectives can be simplified into a single-level adversarial optimization problem. We conduct extensive experiments on real-world datasets, and the results show that GA-NTK can generate images comparable to those by GANs but is much easier to train under various conditions. We also study the current limitations of GA-NTK and propose some workarounds to make GA-NTK more practical.
    What's in the Black Box? The False Negative Mechanisms Inside Object Detectors. (arXiv:2203.07662v4 [cs.CV] UPDATED)
    In object detection, false negatives arise when a detector fails to detect a target object. To understand why object detectors produce false negatives, we identify five 'false negative mechanisms', where each mechanism describes how a specific component inside the detector architecture failed. Focusing on two-stage and one-stage anchor-box object detector architectures, we introduce a framework for quantifying these false negative mechanisms. Using this framework, we investigate why Faster R-CNN and RetinaNet fail to detect objects in benchmark vision datasets and robotics datasets. We show that a detector's false negative mechanisms differ significantly between computer vision benchmark datasets and robotics deployment scenarios. This has implications for the translation of object detectors developed for benchmark datasets to robotics applications. Code is publicly available at https://github.com/csiro-robotics/fn_mechanisms
    Learning Where To Look -- Generative NAS is Surprisingly Efficient. (arXiv:2203.08734v2 [cs.LG] UPDATED)
    The efficient, automated search for well-performing neural architectures (NAS) has drawn increasing attention in the recent past. Thereby, the predominant research objective is to reduce the necessity of costly evaluations of neural architectures while efficiently exploring large search spaces. To this aim, surrogate models embed architectures in a latent space and predict their performance, while generative models for neural architectures enable optimization-based search within the latent space the generator draws from. Both, surrogate and generative models, have the aim of facilitating query-efficient search in a well-structured latent space. In this paper, we further improve the trade-off between query-efficiency and promising architecture generation by leveraging advantages from both, efficient surrogate models and generative design. To this end, we propose a generative model, paired with a surrogate predictor, that iteratively learns to generate samples from increasingly promising latent subspaces. This approach leads to very effective and efficient architecture search, while keeping the query amount low. In addition, our approach allows in a straightforward manner to jointly optimize for multiple objectives such as accuracy and hardware latency. We show the benefit of this approach not only w.r.t. the optimization of architectures for highest classification accuracy but also in the context of hardware constraints and outperform state-of-the-art methods on several NAS benchmarks for single and multiple objectives. We also achieve state-of-the-art performance on ImageNet. The code is available at this http URL .
    A Reinforcement Learning Approach to Sensing Design in Resource-Constrained Wireless Networked Control Systems. (arXiv:2204.00703v3 [eess.SY] UPDATED)
    In this paper, we consider a wireless network of smart sensors (agents) that monitor a dynamical process and send measurements to a base station that performs global monitoring and decision-making. Smart sensors are equipped with both sensing and computation, and can either send raw measurements or process them prior to transmission. Constrained agent resources raise a fundamental latency-accuracy trade-off. On the one hand, raw measurements are inaccurate but fast to produce. On the other hand, data processing on resource-constrained platforms generates accurate measurements at the cost of non-negligible computation latency. Further, if processed data are also compressed, latency caused by wireless communication might be higher for raw measurements. Hence, it is challenging to decide when and where sensors in the network should transmit raw measurements or leverage time-consuming local processing. To tackle this design problem, we propose a Reinforcement Learning approach to learn an efficient policy that dynamically decides when measurements are to be processed at each sensor. Effectiveness of our proposed approach is validated through a numerical simulation with case study on smart sensing motivated by the Internet of Drones.
    SocialVAE: Human Trajectory Prediction using Timewise Latents. (arXiv:2203.08207v4 [cs.CV] UPDATED)
    Predicting pedestrian movement is critical for human behavior analysis and also for safe and efficient human-agent interactions. However, despite significant advancements, it is still challenging for existing approaches to capture the uncertainty and multimodality of human navigation decision making. In this paper, we propose SocialVAE, a novel approach for human trajectory prediction. The core of SocialVAE is a timewise variational autoencoder architecture that exploits stochastic recurrent neural networks to perform prediction, combined with a social attention mechanism and a backward posterior approximation to allow for better extraction of pedestrian navigation strategies. We show that SocialVAE improves current state-of-the-art performance on several pedestrian trajectory prediction benchmarks, including the ETH/UCY benchmark, Stanford Drone Dataset, and SportVU NBA movement dataset. Code is available at: https://github.com/xupei0610/SocialVAE.
    Harmony: Overcoming the Hurdles of GPU Memory Capacity to Train Massive DNN Models on Commodity Servers. (arXiv:2202.01306v2 [cs.DC] UPDATED)
    Deep neural networks (DNNs) have grown exponentially in size over the past decade, leaving only those who have massive datacenter-based resources with the ability to develop and train such models. One of the main challenges for the long tail of researchers who might have only limited resources (e.g., a single multi-GPU server) is limited GPU memory capacity compared to model size. The problem is so acute that the memory requirement of training massive DNN models can often exceed the aggregate capacity of all available GPUs on a single server; this problem only gets worse with the trend of ever-growing model sizes. Current solutions that rely on virtualizing GPU memory (by swapping to/from CPU memory) incur excessive swapping overhead. In this paper, we present a new training framework, Harmony, and advocate rethinking how DNN frameworks schedule computation and move data to push the boundaries of training massive models efficiently on a single commodity server. Across various massive DNN models, Harmony is able to reduce swap load by up to two orders of magnitude and obtain a training throughput speedup of up to 7.6x over highly optimized baselines with virtualized memory.
    On the Detection of Adaptive Adversarial Attacks in Speaker Verification Systems. (arXiv:2202.05725v2 [cs.CR] UPDATED)
    Speaker verification systems have been widely used in smart phones and Internet of things devices to identify legitimate users. In recent work, it has been shown that adversarial attacks, such as FAKEBOB, can work effectively against speaker verification systems. The goal of this paper is to design a detector that can distinguish an original audio from an audio contaminated by adversarial attacks. Specifically, our designed detector, called MEH-FEST, calculates the minimum energy in high frequencies from the short-time Fourier transform of an audio and uses it as a detection metric. Through both analysis and experiments, we show that our proposed detector is easy to implement, fast to process an input audio, and effective in determining whether an audio is corrupted by FAKEBOB attacks. The experimental results indicate that the detector is extremely effective: with near zero false positive and false negative rates for detecting FAKEBOB attacks in Gaussian mixture model (GMM) and i-vector speaker verification systems. Moreover, adaptive adversarial attacks against our proposed detector and their countermeasures are discussed and studied, showing the game between attackers and defenders.
    Automated fault tree learning from continuous-valued sensor data: a case study on domestic heaters. (arXiv:2203.07374v2 [cs.LG] UPDATED)
    Many industrial sectors have been collecting big sensor data. With recent technologies for processing big data, companies can exploit this for automatic failure detection and prevention. We propose the first completely automated method for failure analysis, machine-learning fault trees from raw observational data with continuous variables. Our method scales well and is tested on a real-world, five-year dataset of domestic heater operations in The Netherlands, with 31 million unique heater-day readings, each containing 27 sensor and 11 failure variables. Our method builds on two previous procedures: the C4.5 decision-tree learning algorithm, and the LIFT fault tree learning algorithm from Boolean data. C4.5 pre-processes each continuous variable: it learns an optimal numerical threshold which distinguishes between faulty and normal operation of the top-level system. These thresholds discretise the variables, thus allowing LIFT to learn fault trees which model the root failure mechanisms of the system and are explainable. We obtain fault trees for the 11 failure variables, and evaluate them in two ways: quantitatively, with a significance score, and qualitatively, with domain specialists. Some of the fault trees learnt have almost maximum significance (above 0.95), while others have medium-to-low significance (around 0.30), reflecting the difficulty of learning from big, noisy, real-world sensor data. The domain specialists confirm that the fault trees model meaningful relationships among the variables.
    Certifying Out-of-Domain Generalization for Blackbox Functions. (arXiv:2202.01679v2 [cs.LG] UPDATED)
    Certifying the robustness of model performance under bounded data distribution drifts has recently attracted intensive interest under the umbrella of distributional robustness. However, existing techniques either make strong assumptions on the model class and loss functions that can be certified, such as smoothness expressed via Lipschitz continuity of gradients, or require to solve complex optimization problems. As a result, the wider application of these techniques is currently limited by its scalability and flexibility -- these techniques often do not scale to large-scale datasets with modern deep neural networks or cannot handle loss functions which may be non-smooth such as the 0-1 loss. In this paper, we focus on the problem of certifying distributional robustness for blackbox models and bounded loss functions, and propose a novel certification framework based on the Hellinger distance. Our certification technique scales to ImageNet-scale datasets, complex models, and a diverse set of loss functions. We then focus on one specific application enabled by such scalability and flexibility, i.e., certifying out-of-domain generalization for large neural networks and loss functions such as accuracy and AUC. We experimentally validate our certification method on a number of datasets, ranging from ImageNet, where we provide the first non-vacuous certified out-of-domain generalization, to smaller classification tasks where we are able to compare with the state-of-the-art and show that our method performs considerably better.
    PennyLane: Automatic differentiation of hybrid quantum-classical computations. (arXiv:1811.04968v4 [quant-ph] UPDATED)
    PennyLane is a Python 3 software framework for differentiable programming of quantum computers. The library provides a unified architecture for near-term quantum computing devices, supporting both qubit and continuous-variable paradigms. PennyLane's core feature is the ability to compute gradients of variational quantum circuits in a way that is compatible with classical techniques such as backpropagation. PennyLane thus extends the automatic differentiation algorithms common in optimization and machine learning to include quantum and hybrid computations. A plugin system makes the framework compatible with any gate-based quantum simulator or hardware. We provide plugins for hardware providers including the Xanadu Cloud, Amazon Braket, and IBM Quantum, allowing PennyLane optimizations to be run on publicly accessible quantum devices. On the classical front, PennyLane interfaces with accelerated machine learning libraries such as TensorFlow, PyTorch, JAX, and Autograd. PennyLane can be used for the optimization of variational quantum eigensolvers, quantum approximate optimization, quantum machine learning models, and many other applications.
    Query Processing on Tensor Computation Runtimes. (arXiv:2203.01877v3 [cs.DB] UPDATED)
    The huge demand for computation in artificial intelligence (AI) is driving unparalleled investments in hardware and software systems for AI. This leads to an explosion in the number of specialized hardware devices, which are now offered by major cloud vendors. By hiding the low-level complexity through a tensor-based interface, tensor computation runtimes (TCRs) such as PyTorch allow data scientists to efficiently exploit the exciting capabilities offered by the new hardware. In this paper, we explore how database management systems can ride the wave of innovation happening in the AI space. We design, build, and evaluate Tensor Query Processor (TQP): TQP transforms SQL queries into tensor programs and executes them on TCRs. TQP is able to run the full TPC-H benchmark by implementing novel algorithms for relational operators on the tensor routines. At the same time, TQP can support various hardware while only requiring a fraction of the usual development effort. Experiments show that TQP can improve query execution time by up to 10$\times$ over specialized CPU- and GPU-only systems. Finally, TQP can accelerate queries mixing ML predictions and SQL end-to-end, and deliver up to 9$\times$ speedup over CPU baselines.
    Learning Stationary Nash Equilibrium Policies in $n$-Player Stochastic Games with Independent Chains via Dual Mirror Descent. (arXiv:2201.12224v3 [cs.LG] UPDATED)
    We consider a subclass of $n$-player stochastic games, in which players have their own internal state/action spaces while they are coupled through their payoff functions. It is assumed that players' internal chains are driven by independent transition probabilities. Moreover, players can receive only realizations of their payoffs, not the actual functions, and cannot observe each other's states/actions. Under some assumptions on the structure of the payoff functions, we develop efficient learning algorithms based on dual averaging and dual mirror descent, which provably converge almost surely or in expectation to the set of $\epsilon$-Nash equilibrium policies. In particular, we derive upper bounds on the number of iterates that scale polynomially in terms of the game parameters to achieve an $\epsilon$-Nash equilibrium policy. In addition to Markov potential games and linear-quadratic stochastic games, this work provides another subclass of $n$-player stochastic games that provably admit polynomial-time learning algorithms for finding their $\epsilon$-Nash equilibrium policies.
    Deep Active Learning with Budget Annotation. (arXiv:2208.00508v1 [cs.LG])
    Digital data collected over the decades and data currently being produced with use of information technology is vastly the unlabeled data or data without description. The unlabeled data is relatively easy to acquire but expensive to label even with use of domain experts. Most of the recent works focus on use of active learning with uncertainty metrics measure to address this problem. Although most uncertainty selection strategies are very effective, they fail to take informativeness of the unlabeled instances into account and are prone to querying outliers. In order to address these challenges we propose an hybrid approach of computing both the uncertainty and informativeness of an instance, then automaticaly label the computed instances using budget annotator. To reduce the annotation cost, we employ the state-of-the-art pre-trained models in order to avoid querying information already contained in those models. Our extensive experiments on different sets of datasets demonstrate the efficacy of the proposed approach.
    A Real-time Edge-AI System for Reef Surveys. (arXiv:2208.00598v1 [cs.LG])
    Crown-of-Thorn Starfish (COTS) outbreaks are a major cause of coral loss on the Great Barrier Reef (GBR) and substantial surveillance and control programs are ongoing to manage COTS populations to ecologically sustainable levels. In this paper, we present a comprehensive real-time machine learning-based underwater data collection and curation system on edge devices for COTS monitoring. In particular, we leverage the power of deep learning-based object detection techniques, and propose a resource-efficient COTS detector that performs detection inferences on the edge device to assist marine experts with COTS identification during the data collection phase. The preliminary results show that several strategies for improving computational efficiency (e.g., batch-wise processing, frame skipping, model input size) can be combined to run the proposed detection model on edge hardware with low resource consumption and low information loss.
    Beyond kNN: Adaptive, Sparse Neighborhood Graphs via Optimal Transport. (arXiv:2208.00604v1 [stat.ML])
    Nearest neighbour graphs are widely used to capture the geometry or topology of a dataset. One of the most common strategies to construct such a graph is based on selecting a fixed number k of nearest neighbours (kNN) for each point. However, the kNN heuristic may become inappropriate when sampling density or noise level varies across datasets. Strategies that try to get around this typically introduce additional parameters that need to be tuned. We propose a simple approach to construct an adaptive neighbourhood graph from a single parameter, based on quadratically regularised optimal transport. Our numerical experiments show that graphs constructed in this manner perform favourably in unsupervised and semi-supervised learning applications.
    Momentum Transformer: Closing the Performance Gap Between Self-attention and Its Linearization. (arXiv:2208.00579v1 [cs.LG])
    Transformers have achieved remarkable success in sequence modeling and beyond but suffer from quadratic computational and memory complexities with respect to the length of the input sequence. Leveraging techniques include sparse and linear attention and hashing tricks; efficient transformers have been proposed to reduce the quadratic complexity of transformers but significantly degrade the accuracy. In response, we first interpret the linear attention and residual connections in computing the attention map as gradient descent steps. We then introduce momentum into these components and propose the \emph{momentum transformer}, which utilizes momentum to improve the accuracy of linear transformers while maintaining linear memory and computational complexities. Furthermore, we develop an adaptive strategy to compute the momentum value for our model based on the optimal momentum for quadratic optimization. This adaptive momentum eliminates the need to search for the optimal momentum value and further enhances the performance of the momentum transformer. A range of experiments on both autoregressive and non-autoregressive tasks, including image generation and machine translation, demonstrate that the momentum transformer outperforms popular linear transformers in training efficiency and accuracy.
    Long Short-Term Preference Modeling for Continuous-Time Sequential Recommendation. (arXiv:2208.00593v1 [cs.IR])
    Modeling the evolution of user preference is essential in recommender systems. Recently, dynamic graph-based methods have been studied and achieved SOTA for recommendation, majority of which focus on user's stable long-term preference. However, in real-world scenario, user's short-term preference evolves over time dynamically. Although there exists sequential methods that attempt to capture it, how to model the evolution of short-term preference with dynamic graph-based methods has not been well-addressed yet. In particular: 1) existing methods do not explicitly encode and capture the evolution of short-term preference as sequential methods do; 2) simply using last few interactions is not enough for modeling the changing trend. In this paper, we propose Long Short-Term Preference Modeling for Continuous-Time Sequential Recommendation (LSTSR) to capture the evolution of short-term preference under dynamic graph. Specifically, we explicitly encode short-term preference and optimize it via memory mechanism, which has three key operations: Message, Aggregate and Update. Our memory mechanism can not only store one-hop information, but also trigger with new interactions online. Extensive experiments conducted on five public datasets show that LSTSR consistently outperforms many state-of-the-art recommendation methods across various lines.
    Unifying Approaches in Data Subset Selection via Fisher Information and Information-Theoretic Quantities. (arXiv:2208.00549v1 [cs.LG])
    The mutual information between predictions and model parameters -- also referred to as expected information gain or BALD in machine learning -- measures informativeness. It is a popular acquisition function in Bayesian active learning and Bayesian optimal experiment design. In data subset selection, i.e. active learning and active sampling, several recent works use Fisher information, Hessians, similarity matrices based on the gradients, or simply the gradient lengths to compute the acquisition scores that guide sample selection. Are these different approaches connected, and if so how? In this paper, we revisit the Fisher information and use it to show how several otherwise disparate methods are connected as approximations of information-theoretic quantities.
    INSightR-Net: Interpretable Neural Network for Regression using Similarity-based Comparisons to Prototypical Examples. (arXiv:2208.00457v1 [cs.CV])
    Convolutional neural networks (CNNs) have shown exceptional performance for a range of medical imaging tasks. However, conventional CNNs are not able to explain their reasoning process, therefore limiting their adoption in clinical practice. In this work, we propose an inherently interpretable CNN for regression using similarity-based comparisons (INSightR-Net) and demonstrate our methods on the task of diabetic retinopathy grading. A prototype layer incorporated into the architecture enables visualization of the areas in the image that are most similar to learned prototypes. The final prediction is then intuitively modeled as a mean of prototype labels, weighted by the similarities. We achieved competitive prediction performance with our INSightR-Net compared to a ResNet baseline, showing that it is not necessary to compromise performance for interpretability. Furthermore, we quantified the quality of our explanations using sparsity and diversity, two concepts considered important for a good explanation, and demonstrated the effect of several parameters on the latent space embeddings.
    Adaptive Edge Offloading for Image Classification Under Rate Limit. (arXiv:2208.00485v1 [cs.DC])
    This paper considers a setting where embedded devices are used to acquire and classify images. Because of limited computing capacity, embedded devices rely on a parsimonious classification model with uneven accuracy. When local classification is deemed inaccurate, devices can decide to offload the image to an edge server with a more accurate but resource-intensive model. Resource constraints, e.g., network bandwidth, however, require regulating such transmissions to avoid congestion and high latency. The paper investigates this offloading problem when transmissions regulation is through a token bucket, a mechanism commonly used for such purposes. The goal is to devise a lightweight, online offloading policy that optimizes an application-specific metric (e.g., classification accuracy) under the constraints of the token bucket. The paper develops a policy based on a Deep Q-Network (DQN), and demonstrates both its efficacy and the feasibility of its deployment on embedded devices. Of note is the fact that the policy can handle complex input patterns, including correlation in image arrivals and classification accuracy. The evaluation is carried out by performing image classification over a local testbed using synthetic traces generated from the ImageNet image classification benchmark. Implementation of this work is available at https://github.com/qiujiaming315/edgeml-dqn.
    Scrutinizing Shipment Records To Thwart Illegal Timber Trade. (arXiv:2208.00493v1 [cs.LG])
    Timber and forest products made from wood, like furniture, are valuable commodities, and like the global trade of many highly-valued natural resources, face challenges of corruption, fraud, and illegal harvesting. These grey and black market activities in the wood and forest products sector are not limited to the countries where the wood was harvested, but extend throughout the global supply chain and have been tied to illicit financial flows, like trade-based money laundering, document fraud, species mislabeling, and other illegal activities. The task of finding such fraudulent activities using trade data, in the absence of ground truth, can be modelled as an unsupervised anomaly detection problem. However existing approaches suffer from certain shortcomings in their applicability towards large scale trade data. Trade data is heterogeneous, with both categorical and numerical attributes in a tabular format. The overall challenge lies in the complexity, volume and velocity of data, with large number of entities and lack of ground truth labels. To mitigate these, we propose a novel unsupervised anomaly detection -- Contrastive Learning based Heterogeneous Anomaly Detection (CHAD) that is generally applicable for large-scale heterogeneous tabular data. We demonstrate our model CHAD performs favorably against multiple comparable baselines for public benchmark datasets, and outperforms them in the case of trade data. More importantly we demonstrate our approach reduces assumptions and efforts required hyperparameter tuning, which is a key challenging aspect in an unsupervised training paradigm. Specifically, our overarching objective pertains to detecting suspicious timber shipments and patterns using Bill of Lading trade record data. Detecting anomalous transactions in shipment records can enable further investigation by government agencies and supply chain constituents.
    eco2AI: carbon emissions tracking of machine learning models as the first step towards sustainable AI. (arXiv:2208.00406v1 [cs.LG])
    The size and complexity of deep neural networks continue to grow exponentially, significantly increasing energy consumption for training and inference by these models. We introduce an open-source package eco2AI to help data scientist and researchers track energy consumption and equivalent CO2 emissions of their models in a straightforward way. In eco2AI we put emphasis on accuracy of energy consumption tracking and correct regional CO2 emissions accounting. We encourage research community to search for new optimal Artificial Intelligence (AI) architectures with a lower computational cost. The motivation also comes from the concept of AI-based green house gases sequestrating cycle with both Sustainable AI and Green AI pathways.
    Evo* 2022 -- Late-Breaking Abstracts Volume. (arXiv:2208.00555v1 [cs.NE])
    Volume with the Late-Breaking Abstracts submitted to the Evo* 2022 Conference, held in Madrid (Spain), from 20 to 22 of April. These papers present ongoing research and preliminary results investigating on the application of different approaches of Bioinspired Methods (mainly Evolutionary Computation) to different problems, most of them real world ones.
    Robot Policy Learning from Demonstration Using Advantage Weighting and Early Termination. (arXiv:2208.00478v1 [cs.LG])
    Learning robotic tasks in the real world is still highly challenging and effective practical solutions remain to be found. Traditional methods used in this area are imitation learning and reinforcement learning, but they both have limitations when applied to real robots. Combining reinforcement learning with pre-collected demonstrations is a promising approach that can help in learning control policies to solve robotic tasks. In this paper, we propose an algorithm that uses novel techniques to leverage offline expert data using offline and online training to obtain faster convergence and improved performance. The proposed algorithm (AWET) weights the critic losses with a novel agent advantage weight to improve over the expert data. In addition, AWET makes use of an automatic early termination technique to stop and discard policy rollouts that are not similar to expert trajectories -- to prevent drifting far from the expert data. In an ablation study, AWET showed improved and promising performance when compared to state-of-the-art baselines on four standard robotic tasks.
    COCOA: Cross Modality Contrastive Learning for Sensor Data. (arXiv:2208.00467v1 [cs.CV])
    Self-Supervised Learning (SSL) is a new paradigm for learning discriminative representations without labelled data and has reached comparable or even state-of-the-art results in comparison to supervised counterparts. Contrastive Learning (CL) is one of the most well-known approaches in SSL that attempts to learn general, informative representations of data. CL methods have been mostly developed for applications in computer vision and natural language processing where only a single sensor modality is used. A majority of pervasive computing applications, however, exploit data from a range of different sensor modalities. While existing CL methods are limited to learning from one or two data sources, we propose COCOA (Cross mOdality COntrastive leArning), a self-supervised model that employs a novel objective function to learn quality representations from multisensor data by computing the cross-correlation between different data modalities and minimizing the similarity between irrelevant instances. We evaluate the effectiveness of COCOA against eight recently introduced state-of-the-art self-supervised models, and two supervised baselines across five public datasets. We show that COCOA achieves superior classification performance to all other approaches. Also, COCOA is far more label-efficient than the other baselines including the fully supervised model using only one-tenth of available labelled data.
    Online Decentralized Frank-Wolfe: From theoretical bound to applications in smart-building. (arXiv:2208.00522v1 [cs.LG])
    The design of decentralized learning algorithms is important in the fast-growing world in which data are distributed over participants with limited local computation resources and communication. In this direction, we propose an online algorithm minimizing non-convex loss functions aggregated from individual data/models distributed over a network. We provide the theoretical performance guarantee of our algorithm and demonstrate its utility on a real life smart building.
    Untargeted Region of Interest Selection for GC-MS Data using a Pseudo F-Ratio Moving Window ($\psi$FRMV). (arXiv:2208.00313v1 [stat.ML])
    There are many challenges associated with analysing gas chromatography - mass spectrometry (GC-MS) data. Many of these challenges stem from the fact that electron ionisation can make it difficult to recover molecular information due to the high degree of fragmentation with concomitant loss of molecular ion signal. With GC-MS data there are often many common fragment ions shared among closely-eluting peaks, necessitating sophisticated methods for analysis. Some of these methods are fully automated, but make some assumptions about the data which can introduce artifacts during the analysis. Chemometric methods such as Multivariate Curve Resolution, or Parallel Factor Analysis are particularly attractive, since they are flexible and make relatively few assumptions about the data - ideally resulting in fewer artifacts. These methods do require expert user intervention to determine the most relevant regions of interest and an appropriate number of components, $k$, for each region. Automated region of interest selection is needed to permit automated batch processing of chromatographic data with advanced signal deconvolution. Here, we propose a new method for automated, untargeted region of interest selection that accounts for the multivariate information present in GC-MS data to select regions of interest based on the ratio of the squared first, and second singular values from the Singular Value Decomposition of a window that moves across the chromatogram. Assuming that the first singular value accounts largely for signal, and that the second singular value accounts largely for noise, it is possible to interpret the relationship between these two values as a probabilistic distribution of Fisher Ratios. The sensitivity of the algorithm was tested by investigating the concentration at which the algorithm can no longer pick out chromatographic regions known to contain signal.
    Unitary Approximate Message Passing for Matrix Factorization. (arXiv:2208.00422v1 [eess.SP])
    We consider matrix factorization (MF) with certain constraints, which finds wide applications in various areas. Leveraging variational inference (VI) and unitary approximate message passing (UAMP), we develop a Bayesian approach to MF with an efficient message passing implementation, called UAMPMF. With proper priors imposed on the factor matrices, UAMPMF can be used to solve many problems that can be formulated as MF, such as non negative matrix factorization, dictionary learning, compressive sensing with matrix uncertainty, robust principal component analysis, and sparse matrix factorization. Extensive numerical examples are provided to show that UAMPMF significantly outperforms state-of-the-art algorithms in terms of recovery accuracy, robustness and computational complexity.
    Is current research on adversarial robustness addressing the right problem?. (arXiv:2208.00539v1 [cs.CV])
    Short answer: Yes, Long answer: No! Indeed, research on adversarial robustness has led to invaluable insights helping us understand and explore different aspects of the problem. Many attacks and defenses have been proposed over the last couple of years. The problem, however, remains largely unsolved and poorly understood. Here, I argue that the current formulation of the problem serves short term goals, and needs to be revised for us to achieve bigger gains. Specifically, the bound on perturbation has created a somewhat contrived setting and needs to be relaxed. This has misled us to focus on model classes that are not expressive enough to begin with. Instead, inspired by human vision and the fact that we rely more on robust features such as shape, vertices, and foreground objects than non-robust features such as texture, efforts should be steered towards looking for significantly different classes of models. Maybe instead of narrowing down on imperceptible adversarial perturbations, we should attack a more general problem which is finding architectures that are simultaneously robust to perceptible perturbations, geometric transformations (e.g. rotation, scaling), image distortions (lighting, blur), and more (e.g. occlusion, shadow). Only then we may be able to solve the problem of adversarial vulnerability.
    Building an Efficiency Pipeline: Commutativity and Cumulativeness of Efficiency Operators for Transformers. (arXiv:2208.00483v1 [cs.CL])
    There exists a wide variety of efficiency methods for natural language processing (NLP) tasks, such as pruning, distillation, dynamic inference, quantization, etc. We can consider an efficiency method as an operator applied on a model. Naturally, we may construct a pipeline of multiple efficiency methods, i.e., to apply multiple operators on the model sequentially. In this paper, we study the plausibility of this idea, and more importantly, the commutativity and cumulativeness of efficiency operators. We make two interesting observations: (1) Efficiency operators are commutative -- the order of efficiency methods within the pipeline has little impact on the final results; (2) Efficiency operators are also cumulative -- the final results of combining several efficiency methods can be estimated by combining the results of individual methods. These observations deepen our understanding of efficiency operators and provide useful guidelines for their real-world applications.
    DNNShield: Dynamic Randomized Model Sparsification, A Defense Against Adversarial Machine Learning. (arXiv:2208.00498v1 [cs.CR])
    DNNs are known to be vulnerable to so-called adversarial attacks that manipulate inputs to cause incorrect results that can be beneficial to an attacker or damaging to the victim. Recent works have proposed approximate computation as a defense mechanism against machine learning attacks. We show that these approaches, while successful for a range of inputs, are insufficient to address stronger, high-confidence adversarial attacks. To address this, we propose DNNSHIELD, a hardware-accelerated defense that adapts the strength of the response to the confidence of the adversarial input. Our approach relies on dynamic and random sparsification of the DNN model to achieve inference approximation efficiently and with fine-grain control over the approximation error. DNNSHIELD uses the output distribution characteristics of sparsified inference compared to a dense reference to detect adversarial inputs. We show an adversarial detection rate of 86% when applied to VGG16 and 88% when applied to ResNet50, which exceeds the detection rate of the state of the art approaches, with a much lower overhead. We demonstrate a software/hardware-accelerated FPGA prototype, which reduces the performance impact of DNNSHIELD relative to software-only CPU and GPU implementations.
    Learning to generate Reliable Broadcast Algorithms. (arXiv:2208.00525v1 [cs.DC])
    Modern distributed systems are supported by fault-tolerant algorithms, like Reliable Broadcast and Consensus, that assure the correct operation of the system even when some of the nodes of the system fail. However, the development of distributed algorithms is a manual and complex process, resulting in scientific papers that usually present a single algorithm or variations of existing ones. To automate the process of developing such algorithms, this work presents an intelligent agent that uses Reinforcement Learning to generate correct and efficient fault-tolerant distributed algorithms. We show that our approach is able to generate correct fault-tolerant Reliable Broadcast algorithms with the same performance of others available in the literature, in only 12,000 learning episodes.
    A Multi-View Learning Approach to Enhance Automatic 12-Lead ECG Diagnosis Performance. (arXiv:2208.00323v1 [eess.SP])
    The performances of commonly used electrocardiogram (ECG) diagnosis models have recently improved with the introduction of deep learning (DL). However, the impact of various combinations of multiple DL components and/or the role of data augmentation techniques on the diagnosis have not been sufficiently investigated. This study proposes an ensemble-based multi-view learning approach with an ECG augmentation technique to achieve a higher performance than traditional automatic 12-lead ECG diagnosis methods. The data analysis results show that the proposed model reports an F1 score of 0.840, which outperforms existing state-ofthe-art methods in the literature.
    Improving Distantly Supervised Relation Extraction by Natural Language Inference. (arXiv:2208.00346v1 [cs.CL])
    To reduce human annotations for relation extraction (RE) tasks, distantly supervised approaches have been proposed, while struggling with low performance. In this work, we propose a novel DSRE-NLI framework, which considers both distant supervision from existing knowledge bases and indirect supervision from pretrained language models for other tasks. DSRE-NLI energizes an off-the-shelf natural language inference (NLI) engine with a semi-automatic relation verbalization (SARV) mechanism to provide indirect supervision and further consolidates the distant annotations to benefit multi-classification RE models. The NLI-based indirect supervision acquires only one relation verbalization template from humans as a semantically general template for each relationship, and then the template set is enriched by high-quality textual patterns automatically mined from the distantly annotated corpus. With two simple and effective data consolidation strategies, the quality of training data is substantially improved. Extensive experiments demonstrate that the proposed framework significantly improves the SOTA performance (up to 7.73\% of F1) on distantly supervised RE benchmark datasets.
    Formal guarantees for heuristic optimization algorithms used in machine learning. (arXiv:2208.00502v1 [cs.LG])
    Recently, Stochastic Gradient Descent (SGD) and its variants have become the dominant methods in the large-scale optimization of machine learning (ML) problems. A variety of strategies have been proposed for tuning the step sizes, ranging from adaptive step sizes to heuristic methods to change the step size in each iteration. Also, momentum has been widely employed in ML tasks to accelerate the training process. Yet, there is a gap in our theoretical understanding of them. In this work, we start to close this gap by providing formal guarantees to a few heuristic optimization methods and proposing improved algorithms. First, we analyze a generalized version of the AdaGrad (Delayed AdaGrad) step sizes in both convex and non-convex settings, showing that these step sizes allow the algorithms to automatically adapt to the level of noise of the stochastic gradients. We show for the first time sufficient conditions for Delayed AdaGrad to achieve almost sure convergence of the gradients to zero. Moreover, we present a high probability analysis for Delayed AdaGrad and its momentum variant in the non-convex setting. Second, we analyze SGD with exponential and cosine step sizes, which are empirically successful but lack theoretical support. We provide the very first convergence guarantees for them in the smooth and non-convex setting, with and without the Polyak-{\L}ojasiewicz (PL) condition. We also show their good property of adaptivity to noise under the PL condition. Third, we study the last iterate of momentum methods. We prove the first lower bound in the convex setting for the last iterate of SGD with constant momentum. Moreover, we investigate a class of Follow-The-Regularized-Leader-based momentum algorithms with increasing momentum and shrinking updates. We show that their last iterate has optimal convergence for unconstrained convex stochastic optimization problems.
    Symmetry Regularization and Saturating Nonlinearity for Robust Quantization. (arXiv:2208.00338v1 [cs.LG])
    Robust quantization improves the tolerance of networks for various implementations, allowing reliable output in different bit-widths or fragmented low-precision arithmetic. In this work, we perform extensive analyses to identify the sources of quantization error and present three insights to robustify a network against quantization: reduction of error propagation, range clamping for error minimization, and inherited robustness against quantization. Based on these insights, we propose two novel methods called symmetry regularization (SymReg) and saturating nonlinearity (SatNL). Applying the proposed methods during training can enhance the robustness of arbitrary neural networks against quantization on existing post-training quantization (PTQ) and quantization-aware training (QAT) algorithms and enables us to obtain a single weight flexible enough to maintain the output quality under various conditions. We conduct extensive studies on CIFAR and ImageNet datasets and validate the effectiveness of the proposed methods.
    A Bayesian Approach to Learning Bandit Structure in Markov Decision Processes. (arXiv:2208.00250v1 [cs.LG])
    In the reinforcement learning literature, there are many algorithms developed for either Contextual Bandit (CB) or Markov Decision Processes (MDP) environments. However, when deploying reinforcement learning algorithms in the real world, even with domain expertise, it is often difficult to know whether it is appropriate to treat a sequential decision making problem as a CB or an MDP. In other words, do actions affect future states, or only the immediate rewards? Making the wrong assumption regarding the nature of the environment can lead to inefficient learning, or even prevent the algorithm from ever learning an optimal policy, even with infinite data. In this work we develop an online algorithm that uses a Bayesian hypothesis testing approach to learn the nature of the environment. Our algorithm allows practitioners to incorporate prior knowledge about whether the environment is that of a CB or an MDP, and effectively interpolate between classical CB and MDP-based algorithms to mitigate against the effects of misspecifying the environment. We perform simulations and demonstrate that in CB settings our algorithm achieves lower regret than MDP-based algorithms, while in non-bandit MDP settings our algorithm is able to learn the optimal policy, often achieving comparable regret to MDP-based algorithms.
    What Do Deep Neural Networks Find in Disordered Structures of Glasses?. (arXiv:2208.00349v1 [cond-mat.dis-nn])
    Glass transitions are widely observed in a range of types of soft matter systems. However, the physical mechanism of these transitions remains unknown, despite years of ambitious research. In particular, an important unanswered question is whether the glass transition is accompanied by a divergence of the correlation lengths of the characteristic static structures. Recently, a method that can predict long-time dynamics from purely static information with high accuracy was proposed; however, even this method is not universal and does not work well for the Kob--Andersen system, which is a typical model of glass-forming liquids. In this study, we developed a method to extract the characteristic structures of glasses using machine learning or, specifically, a convolutional neural network. In particular, we extracted the characteristic structures by quantifying the grounds for the decisions made by the network. We considered two qualitatively different glass-forming binary systems and, through comparisons with several established structural indicators, we demonstrate that our system can identify characteristic structures that depend on the details of the systems. Surprisingly, the extracted structures were strongly correlated with the nonequilibrium aging dynamics on thermal fluctuation.
    Simplex Clustering via sBeta with Applications to Online Adjustments of Black-Box Predictions. (arXiv:2208.00287v1 [cs.CV])
    We explore clustering the softmax predictions of deep neural networks and introduce a novel probabilistic clustering method, referred to as k-sBetas. In the general context of clustering distributions, the existing methods focused on exploring distortion measures tailored to simplex data, such as the KL divergence, as alternatives to the standard Euclidean distance. We provide a general perspective of clustering distributions, which emphasizes that the statistical models underlying distortion-based methods may not be descriptive enough. Instead, we optimize a mixed-variable objective measuring the conformity of data within each cluster to the introduced sBeta density function, whose parameters are constrained and estimated jointly with binary assignment variables. Our versatile formulation approximates a variety of parametric densities for modeling cluster data, and enables to control the cluster-balance bias. This yields highly competitive performances for efficient unsupervised adjustment of black-box predictions in a variety of scenarios, including one-shot classification and unsupervised domain adaptation in real-time for road segmentation. Implementation is available at https://github.com/fchiaroni/Clustering_Softmax_Predictions.
    A Gradient Smoothed Functional Algorithm with Truncated Cauchy Random Perturbations for Stochastic Optimization. (arXiv:2208.00290v1 [math.OC])
    In this paper, we present a stochastic gradient algorithm for minimizing a smooth objective function that is an expectation over noisy cost samples and only the latter are observed for any given parameter. Our algorithm employs a gradient estimation scheme with random perturbations, which are formed using the truncated Cauchy distribution from the unit sphere. We analyze the bias and variance of the proposed gradient estimator. Our algorithm is found to be particularly useful in the case when the objective function is non-convex, and the parameter dimension is high. From an asymptotic convergence analysis, we establish that our algorithm converges almost surely to the set of stationary points of the objective function and obtain the asymptotic convergence rate. We also show that our algorithm avoids unstable equilibria, implying convergence to local minima. Further, we perform a non-asymptotic convergence analysis of our algorithm. In particular, we establish here a non-asymptotic bound for finding an $\epsilon$-stationary point of the non-convex objective function. Finally, we demonstrate numerically through simulations that the performance of our algorithm outperforms GSF, SPSA and RDSA by a significant margin over a few non-convex settings and further validate its performance over convex (noisy) objectives.
    ANOVA-based Automatic Attribute Selection and a Predictive Model for Heart Disease Prognosis. (arXiv:2208.00296v1 [cs.LG])
    Studies show that Studies that cardiovascular diseases (CVDs) are malignant for human health. Thus, it is important to have an efficient way of CVD prognosis. In response to this, the healthcare industry has adopted machine learning-based smart solutions to alleviate the manual process of CVD prognosis. Thus, this work proposes an information fusion technique that combines key attributes of a person through analysis of variance (ANOVA) and domain experts' knowledge. It also introduces a new collection of CVD data samples for emerging research. There are thirty-eight experiments conducted exhaustively to verify the performance of the proposed framework on four publicly available benchmark datasets and the newly created dataset in this work. The ablation study shows that the proposed approach can achieve a competitive mean average accuracy (mAA) of 99.2% and a mean average AUC of 97.9%.
    Functional Rule Extraction Method for Artificial Neural Networks. (arXiv:2208.00335v1 [cs.LG])
    The idea I propose in this paper is a method that is based on comprehensive functions for directed and undirected rule extraction from artificial neural network operations.
    MobileNeRF: Exploiting the Polygon Rasterization Pipeline for Efficient Neural Field Rendering on Mobile Architectures. (arXiv:2208.00277v1 [cs.CV])
    Neural Radiance Fields (NeRFs) have demonstrated amazing ability to synthesize images of 3D scenes from novel views. However, they rely upon specialized volumetric rendering algorithms based on ray marching that are mismatched to the capabilities of widely deployed graphics hardware. This paper introduces a new NeRF representation based on textured polygons that can synthesize novel images efficiently with standard rendering pipelines. The NeRF is represented as a set of polygons with textures representing binary opacities and feature vectors. Traditional rendering of the polygons with a z-buffer yields an image with features at every pixel, which are interpreted by a small, view-dependent MLP running in a fragment shader to produce a final pixel color. This approach enables NeRFs to be rendered with the traditional polygon rasterization pipeline, which provides massive pixel-level parallelism, achieving interactive frame rates on a wide range of compute platforms, including mobile phones.
    Robust Contact State Estimation in Humanoid Walking Gaits. (arXiv:2208.00278v1 [cs.RO])
    In this article, we propose a deep learning framework that provides a unified approach to the problem of leg contact detection in humanoid robot walking gaits. Our formulation accomplishes to accurately and robustly estimate the contact state probability for each leg (i.e., stable or slip/no contact). The proposed framework employs solely proprioceptive sensing and although it relies on simulated ground-truth contact data for the classification process, we demonstrate that it generalizes across varying friction surfaces and different legged robotic platforms and, at the same time, is readily transferred from simulation to practice. The framework is quantitatively and qualitatively assessed in simulation via the use of ground-truth contact data and is contrasted against state of-the-art methods with an ATLAS, a NAO, and a TALOS humanoid robot. Furthermore, its efficacy is demonstrated in base estimation with a real TALOS humanoid. To reinforce further research endeavors, our implementation is offered as an open-source ROS/Python package, coined Legged Contact Detection (LCD).
    Efficient Compilation and Mapping of Fixed Function Combinational Logic onto Digital Signal Processors Targeting Neural Network Inference and Utilizing High-level Synthesis. (arXiv:2208.00302v1 [cs.AR])
    Recent efforts for improving the performance of neural network (NN) accelerators that meet today's application requirements have given rise to a new trend of logic-based NN inference relying on fixed function combinational logic. Mapping such large Boolean functions with many input variables and product terms to digital signal processors (DSPs) on Field-programmable gate arrays (FPGAs) needs a novel framework considering the structure and the reconfigurability of DSP blocks during this process. The proposed methodology in this paper maps the fixed function combinational logic blocks to a set of Boolean functions where Boolean operations corresponding to each function are mapped to DSP devices rather than look-up tables (LUTs) on the FPGAs to take advantage of the high performance, low latency, and parallelism of DSP blocks. % This paper also presents an innovative design and optimization methodology for compilation and mapping of NNs, utilizing fixed function combinational logic to DSPs on FPGAs employing high-level synthesis flow. % Our experimental evaluations across several \REVone{datasets} and selected NNs demonstrate the comparable performance of our framework in terms of the inference latency and output accuracy compared to prior art FPGA-based NN accelerators employing DSPs.
    Delving into Effective Gradient Matching for Dataset Condensation. (arXiv:2208.00311v1 [cs.LG])
    As deep learning models and datasets rapidly scale up, network training is extremely time-consuming and resource-costly. Instead of training on the entire dataset, learning with a small synthetic dataset becomes an efficient solution. Extensive research has been explored in the direction of dataset condensation, among which gradient matching achieves state-of-the-art performance. The gradient matching method directly targets the training dynamics by matching the gradient when training on the original and synthetic datasets. However, there are limited deep investigations into the principle and effectiveness of this method. In this work, we delve into the gradient matching method from a comprehensive perspective and answer the critical questions of what, how, and where to match. We propose to match the multi-level gradients to involve both intra-class and inter-class gradient information. We demonstrate that the distance function should focus on the angle, considering the magnitude simultaneously to delay the overfitting. An overfitting-aware adaptive learning step strategy is also proposed to trim unnecessary optimization steps for algorithmic efficiency improvement. Ablation and comparison experiments demonstrate that our proposed methodology shows superior accuracy, efficiency, and generalization compared to prior work.
    Fair Classification via Transformer Neural Networks: Case Study of an Educational Domain. (arXiv:2206.01410v2 [cs.LG] UPDATED)
    Educational technologies nowadays increasingly use data and Machine Learning (ML) models. This gives the students, instructors, and administrators support and insights for the optimum policy. However, it is well acknowledged that ML models are subject to bias, which raises concerns about the fairness, bias, and discrimination of using these automated ML algorithms in education and its unintended and unforeseen negative consequences. The contribution of bias during the decision-making comes from datasets used for training ML models and the model architecture. This paper presents a preliminary investigation of the fairness of transformer neural networks on the two tabular datasets: Law School and Student-Mathematics. In contrast to classical ML models, the transformer-based models transform these tabular datasets into a richer representation while solving the classification task. We use different fairness metrics for evaluation and check the trade-off between fairness and accuracy of the transformer-based models over the tabular datasets. Empirically, our approach shows impressive results regarding the trade-off between fairness and performance on the Law School dataset.
    An Experimental Study on Learning Correlated Equilibrium in Routing Games. (arXiv:2208.00391v1 [cs.GT])
    We study route choice in a repeated routing game where an uncertain state of nature determines link latency functions, and agents receive private route recommendation. The state is sampled in an i.i.d. manner in every round from a publicly known distribution, and the recommendations are generated by a randomization policy whose mapping from the state is known publicly. In a one-shot setting, the agents are said to obey recommendation if it gives the smallest travel time in a posteriori expectation. A plausible extension to repeated setting is that the likelihood of following recommendation in a round is related to regret from previous rounds. If the regret is of satisficing type with respect to a default choice and is averaged over past rounds and over all agents, then the asymptotic outcome under an obedient recommendation policy coincides with the one-shot outcome. We report findings from an experiment with one participant at a time engaged in repeated route choice decision on computer. In every round, the participant is shown travel time distribution for each route, a route recommendation generated by an obedient policy, and a rating suggestive of average experience of previous participants with the quality of recommendation. Upon entering route choice, the actual travel times are revealed. The participant evaluates the quality of recommendation by submitting a review. This is combined with historical reviews to update rating for the next round. Data analysis from 33 participants each with 100 rounds suggests moderate negative correlation between the display rating and the average regret, and a strong positive correlation between the rating and the likelihood of following recommendation. Overall, under obedient recommendation policy, the rating converges close to its maximum value by the end of the experiments in conjunction with very high frequency of following recommendations.
    enpheeph: A Fault Injection Framework for Spiking and Compressed Deep Neural Networks. (arXiv:2208.00328v1 [cs.NE])
    Research on Deep Neural Networks (DNNs) has focused on improving performance and accuracy for real-world deployments, leading to new models, such as Spiking Neural Networks (SNNs), and optimization techniques, e.g., quantization and pruning for compressed networks. However, the deployment of these innovative models and optimization techniques introduces possible reliability issues, which is a pillar for DNNs to be widely used in safety-critical applications, e.g., autonomous driving. Moreover, scaling technology nodes have the associated risk of multiple faults happening at the same time, a possibility not addressed in state-of-the-art resiliency analyses. Towards better reliability analysis for DNNs, we present enpheeph, a Fault Injection Framework for Spiking and Compressed DNNs. The enpheeph framework enables optimized execution on specialized hardware devices, e.g., GPUs, while providing complete customizability to investigate different fault models, emulating various reliability constraints and use-cases. Hence, the faults can be executed on SNNs as well as compressed networks with minimal-to-none modifications to the underlying code, a feat that is not achievable by other state-of-the-art tools. To evaluate our enpheeph framework, we analyze the resiliency of different DNN and SNN models, with different compression techniques. By injecting a random and increasing number of faults, we show that DNNs can show a reduction in accuracy with a fault rate as low as 7 x 10 ^ (-7) faults per parameter, with an accuracy drop higher than 40%. Run-time overhead when executing enpheeph is less than 20% of the baseline execution time when executing 100 000 faults concurrently, at least 10x lower than state-of-the-art frameworks, making enpheeph future-proof for complex fault injection scenarios. We release enpheeph at https://github.com/Alexei95/enpheeph.
    Neural Correlates of Face Familiarity Perception. (arXiv:2208.00352v1 [q-bio.NC])
    In the domain of face recognition, there exists a puzzling timing discrepancy between results from macaque neurophysiology on the one hand and human electrophysiology on the other. Single unit recordings in macaques have demonstrated face identity specific responses in extra-striate visual cortex within 100 milliseconds of stimulus onset. In EEG and MEG experiments with humans, however, a consistent distinction between neural activity corresponding to unfamiliar and familiar faces has been reported to emerge around 250 ms. This points to the possibility that there may be a hitherto undiscovered early correlate of face familiarity perception in human electrophysiological traces. We report here a successful search for such a correlate in dense MEG recordings using pattern classification techniques. Our analyses reveal markers of face familiarity as early as 85 ms after stimulus onset. Low-level attributes of the images, such as luminance and color distributions, are unable to account for this early emerging response difference. These results help reconcile human and macaque data, and provide clues regarding neural mechanisms underlying familiar face perception.
    Meta-DETR: Image-Level Few-Shot Detection with Inter-Class Correlation Exploitation. (arXiv:2208.00219v1 [cs.CV])
    Few-shot object detection has been extensively investigated by incorporating meta-learning into region-based detection frameworks. Despite its success, the said paradigm is still constrained by several factors, such as (i) low-quality region proposals for novel classes and (ii) negligence of the inter-class correlation among different classes. Such limitations hinder the generalization of base-class knowledge for the detection of novel-class objects. In this work, we design Meta-DETR, which (i) is the first image-level few-shot detector, and (ii) introduces a novel inter-class correlational meta-learning strategy to capture and leverage the correlation among different classes for robust and accurate few-shot object detection. Meta-DETR works entirely at image level without any region proposals, which circumvents the constraint of inaccurate proposals in prevalent few-shot detection frameworks. In addition, the introduced correlational meta-learning enables Meta-DETR to simultaneously attend to multiple support classes within a single feedforward, which allows to capture the inter-class correlation among different classes, thus significantly reducing the misclassification over similar classes and enhancing knowledge generalization to novel classes. Experiments over multiple few-shot object detection benchmarks show that the proposed Meta-DETR outperforms state-of-the-art methods by large margins. The implementation codes are available at https://github.com/ZhangGongjie/Meta-DETR.
    Convex duality for stochastic shortest path problems in known and unknown environments. (arXiv:2208.00330v1 [cs.LG])
    This paper gives an introduction to Stochastic Shortest Path (SSP) problems in known and unknown environments from the perspective of convex optimisation. It first recalls results in the known parameter case, and develops understanding through different proofs. It then focuses on the unknown parameter case, where it studies extended value iteration (EVI) operators. This includes the existing operators used in Rosenberg et al. [26] and Tarbouriech et al. [31] based on the l-1 norm and supremum norm, as well as defining EVI operators corresponding to other norms and divergences, such as the KL-divergence. This paper shows in general how the EVI operators relate to convex programs, and the form of their dual, where strong duality is exhibited. This paper then focuses on whether the bounds from finite horizon research of Neu and Pike-Burke [21] can be applied to these extended value iteration operators in the SSP setting. It shows that similar bounds to [21] for these operators exist, however they lead to operators that are not in general monotone and have more complex convergence properties. In a special case we observe oscillating behaviour. This paper generates open questions on how research may progress, with several examples that require further examination.
    Global Attention-based Encoder-Decoder LSTM Model for Temperature Prediction of Permanent Magnet Synchronous Motors. (arXiv:2208.00293v1 [cs.LG])
    Temperature monitoring is critical for electrical motors to determine if device protection measures should be executed. However, the complexity of the internal structure of Permanent Magnet Synchronous Motors (PMSM) makes the direct temperature measurement of the internal components difficult. This work pragmatically develops three deep learning models to estimate the PMSMs' internal temperature based on readily measurable external quantities. The proposed supervised learning models exploit Long Short-Term Memory (LSTM) modules, bidirectional LSTM, and attention mechanism to form encoder-decoder structures to predict simultaneously the temperatures of the stator winding, tooth, yoke, and permanent magnet. Experiments were conducted in an exhaustive manner on a benchmark dataset to verify the proposed models' performances. The comparative analysis shows that the proposed global attention-based encoder-decoder (EnDec) model provides a competitive overall performance of 1.72 Mean Squared Error (MSE) and 5.34 Mean Absolute Error (MAE).
    Geometric deep learning for computational mechanics Part II: Graph embedding for interpretable multiscale plasticity. (arXiv:2208.00246v1 [cs.LG])
    The history-dependent behaviors of classical plasticity models are often driven by internal variables evolved according to phenomenological laws. The difficulty to interpret how these internal variables represent a history of deformation, the lack of direct measurement of these internal variables for calibration and validation, and the weak physical underpinning of those phenomenological laws have long been criticized as barriers to creating realistic models. In this work, geometric machine learning on graph data (e.g. finite element solutions) is used as a means to establish a connection between nonlinear dimensional reduction techniques and plasticity models. Geometric learning-based encoding on graphs allows the embedding of rich time-history data onto a low-dimensional Euclidean space such that the evolution of plastic deformation can be predicted in the embedded feature space. A corresponding decoder can then convert these low-dimensional internal variables back into a weighted graph such that the dominating topological features of plastic deformation can be observed and analyzed.
    Automatically Categorising GitHub Repositories by Application Domain. (arXiv:2208.00269v1 [cs.SE])
    GitHub is the largest host of open source software on the Internet. This large, freely accessible database has attracted the attention of practitioners and researchers alike. But as GitHub's growth continues, it is becoming increasingly hard to navigate the plethora of repositories which span a wide range of domains. Past work has shown that taking the application domain into account is crucial for tasks such as predicting the popularity of a repository and reasoning about project quality. In this work, we build on a previously annotated dataset of 5,000 GitHub repositories to design an automated classifier for categorising repositories by their application domain. The classifier uses state-of-the-art natural language processing techniques and machine learning to learn from multiple data sources and catalogue repositories according to five application domains. We contribute with (1) an automated classifier that can assign popular repositories to each application domain with at least 70% precision, (2) an investigation of the approach's performance on less popular repositories, and (3) a practical application of this approach to answer how the adoption of software engineering practices differs across application domains. Our work aims to help the GitHub community identify repositories of interest and opens promising avenues for future work investigating differences between repositories from different application domains.
    Adding Context to Source Code Representations for Deep Learning. (arXiv:2208.00203v1 [cs.SE])
    Deep learning models have been successfully applied to a variety of software engineering tasks, such as code classification, summarisation, and bug and vulnerability detection. In order to apply deep learning to these tasks, source code needs to be represented in a format that is suitable for input into the deep learning model. Most approaches to representing source code, such as tokens, abstract syntax trees (ASTs), data flow graphs (DFGs), and control flow graphs (CFGs) only focus on the code itself and do not take into account additional context that could be useful for deep learning models. In this paper, we argue that it is beneficial for deep learning models to have access to additional contextual information about the code being analysed. We present preliminary evidence that encoding context from the call hierarchy along with information from the code itself can improve the performance of a state-of-the-art deep learning model for two software engineering tasks. We outline our research agenda for adding further contextual information to source code representations for deep learning.
    PolarMix: A General Data Augmentation Technique for LiDAR Point Clouds. (arXiv:2208.00223v1 [cs.CV])
    LiDAR point clouds, which are usually scanned by rotating LiDAR sensors continuously, capture precise geometry of the surrounding environment and are crucial to many autonomous detection and navigation tasks. Though many 3D deep architectures have been developed, efficient collection and annotation of large amounts of point clouds remain one major challenge in the analytic and understanding of point cloud data. This paper presents PolarMix, a point cloud augmentation technique that is simple and generic but can mitigate the data constraint effectively across different perception tasks and scenarios. PolarMix enriches point cloud distributions and preserves point cloud fidelity via two cross-scan augmentation strategies that cut, edit, and mix point clouds along the scanning direction. The first is scene-level swapping which exchanges point cloud sectors of two LiDAR scans that are cut along the azimuth axis. The second is instance-level rotation and paste which crops point instances from one LiDAR scan, rotates them by multiple angles (to create multiple copies), and paste the rotated point instances into other scans. Extensive experiments show that PolarMix achieves superior performance consistently across different perception tasks and scenarios. In addition, it can work as plug-and-play for various 3D deep architectures and also performs well for unsupervised domain adaptation.
    Streaming Algorithms for Diversity Maximization with Fairness Constraints. (arXiv:2208.00194v1 [cs.DS])
    Diversity maximization is a fundamental problem with wide applications in data summarization, web search, and recommender systems. Given a set $X$ of $n$ elements, it asks to select a subset $S$ of $k \ll n$ elements with maximum \emph{diversity}, as quantified by the dissimilarities among the elements in $S$. In this paper, we focus on the diversity maximization problem with fairness constraints in the streaming setting. Specifically, we consider the max-min diversity objective, which selects a subset $S$ that maximizes the minimum distance (dissimilarity) between any pair of distinct elements within it. Assuming that the set $X$ is partitioned into $m$ disjoint groups by some sensitive attribute, e.g., sex or race, ensuring \emph{fairness} requires that the selected subset $S$ contains $k_i$ elements from each group $i \in [1,m]$. A streaming algorithm should process $X$ sequentially in one pass and return a subset with maximum \emph{diversity} while guaranteeing the fairness constraint. Although diversity maximization has been extensively studied, the only known algorithms that can work with the max-min diversity objective and fairness constraints are very inefficient for data streams. Since diversity maximization is NP-hard in general, we propose two approximation algorithms for fair diversity maximization in data streams, the first of which is $\frac{1-\varepsilon}{4}$-approximate and specific for $m=2$, where $\varepsilon \in (0,1)$, and the second of which achieves a $\frac{1-\varepsilon}{3m+2}$-approximation for an arbitrary $m$. Experimental results on real-world and synthetic datasets show that both algorithms provide solutions of comparable quality to the state-of-the-art algorithms while running several orders of magnitude faster in the streaming setting.
    Learning-based Localizability Estimation for Robust LiDAR Localization. (arXiv:2203.05698v2 [cs.RO] UPDATED)
    LiDAR-based localization and mapping is one of the core components in many modern robotic systems due to the direct integration of range and geometry, allowing for precise motion estimation and generation of high quality maps in real-time. Yet, as a consequence of insufficient environmental constraints present in the scene, this dependence on geometry can result in localization failure, happening in self-symmetric surroundings such as tunnels. This work addresses precisely this issue by proposing a neural network-based estimation approach for detecting (non-)localizability during robot operation. Special attention is given to the localizability of scan-to-scan registration, as it is a crucial component in many LiDAR odometry estimation pipelines. In contrast to previous, mostly traditional detection approaches, the proposed method enables early detection of failure by estimating the localizability on raw sensor measurements without evaluating the underlying registration optimization. Moreover, previous approaches remain limited in their ability to generalize across environments and sensor types, as heuristic-tuning of degeneracy detection thresholds is required. The proposed approach avoids this problem by learning from a collection of different environments, allowing the network to function over various scenarios. Furthermore, the network is trained exclusively on simulated data, avoiding arduous data collection in challenging and degenerate, often hard-to-access, environments. The presented method is tested during field experiments conducted across challenging environments and on two different sensor types without any modifications. The observed detection performance is on par with state-of-the-art methods after environment-specific threshold tuning.
    On Connecting Deep Trigonometric Networks with Deep Gaussian Processes: Covariance, Expressivity, and Neural Tangent Kernel. (arXiv:2203.07411v3 [cs.LG] UPDATED)
    Deep Gaussian Process (DGP) as a model prior in Bayesian learning intuitively exploits the expressive power in function composition. DGPs also offer diverse modeling capabilities, but inference is challenging because marginalization in latent function space is not tractable. With Bochner's theorem, DGP with squared exponential kernel can be viewed as a deep trigonometric network consisting of the random feature layers, sine and cosine activation units, and random weight layers. In the wide limit with a bottleneck, we show that the weight space view yields the same effective covariance functions which were obtained previously in function space. Also, varying the prior distributions over network parameters is equivalent to employing different kernels. As such, DGPs can be translated into the deep bottlenecked trig networks, with which the exact maximum a posteriori estimation can be obtained. Interestingly, the network representation enables the study of DGP's neural tangent kernel, which may also reveal the mean of the intractable predictive distribution. Statistically, unlike the shallow networks, deep networks of finite width have covariance deviating from the limiting kernel, and the inner and outer widths may play different roles in feature learning. Numerical simulations are present to support our findings.
    Generating Diverse Realistic Laughter for Interactive Art. (arXiv:2111.03146v2 [cs.LG] UPDATED)
    We propose an interactive art project to make those rendered invisible by the COVID-19 crisis and its concomitant solitude reappear through the welcome melody of laughter, and connections created and explored through advanced laughter synthesis approaches. However, the unconditional generation of the diversity of human emotional responses in high-quality auditory synthesis remains an open problem, with important implications for the application of these approaches in artistic settings. We developed LaughGANter, an approach to reproduce the diversity of human laughter using generative adversarial networks (GANs). When trained on a dataset of diverse laughter samples, LaughGANter generates diverse, high quality laughter samples, and learns a latent space suitable for emotional analysis and novel artistic applications such as latent mixing/interpolation and emotional transfer.
    TCMI: a non-parametric mutual-dependence estimator for multivariate continuous distributions. (arXiv:2001.11212v3 [stat.ML] UPDATED)
    The identification of relevant features, i.e., the driving variables that determine a process or the properties of a system, is an essential part of the analysis of data sets with a large number of variables. A mathematical rigorous approach to quantifying the relevance of these features is mutual information. Mutual information determines the relevance of features in terms of their joint mutual dependence to the property of interest. However, mutual information requires as input probability distributions, which cannot be reliably estimated from continuous distributions such as physical quantities like lengths or energies. Here, we introduce total cumulative mutual information (TCMI), a measure of the relevance of mutual dependences that extends mutual information to random variables of continuous distribution based on cumulative probability distributions. TCMI is a non-parametric, robust, and deterministic measure that facilitates comparisons and rankings between feature sets with different cardinality. The ranking induced by TCMI allows for feature selection, i.e., the identification of variable sets that are nonlinear statistically related to a property of interest, taking into account the number of data samples as well as the cardinality of the set of variables. We evaluate the performance of our measure with simulated data, compare its performance with similar multivariate-dependence measures, and demonstrate the effectiveness of our feature-selection method on a set of standard data sets and a typical scenario in materials science.
    Inductive Biases for Deep Learning of Higher-Level Cognition. (arXiv:2011.15091v4 [cs.LG] UPDATED)
    A fascinating hypothesis is that human and animal intelligence could be explained by a few principles (rather than an encyclopedic list of heuristics). If that hypothesis was correct, we could more easily both understand our own intelligence and build intelligent machines. Just like in physics, the principles themselves would not be sufficient to predict the behavior of complex systems like brains, and substantial computation might be needed to simulate human-like intelligence. This hypothesis would suggest that studying the kind of inductive biases that humans and animals exploit could help both clarify these principles and provide inspiration for AI research and neuroscience theories. Deep learning already exploits several key inductive biases, and this work considers a larger list, focusing on those which concern mostly higher-level and sequential conscious processing. The objective of clarifying these particular principles is that they could potentially help us build AI systems benefiting from humans' abilities in terms of flexible out-of-distribution and systematic generalization, which is currently an area where a large gap exists between state-of-the-art machine learning and human intelligence.
    Intelligent decision-making method of TBM operating parameters based on multiple constraints and objective optimization. (arXiv:2208.00404v1 [cs.LG])
    The decision-making of TBM operating parameters has an important guiding significance for TBM safe and efficient construction, and it has been one of the research hotpots in the field of TBM tunneling. For this purpose, this paper introduces rock-breaking rules into machine learning method, and a rock-machine mapping dual-driven by physical-rule and data-mining is established with high accuracy. This dual-driven mappings are subsequently used as objective function and constraints to build a decision-making method for TBM operating parameters. By searching the revolution per minute and penetration corresponding to the extremum of the objective function subject to the constraints, the optimal operating parameters can be obtained. This method is verified in the field of the Second Water Source Channel of Hangzhou, China, resulting in the average penetration rate increased by 11.3%, and the total cost decreased by 10.0%, which proves the practicability and effectiveness of the developed decision-making model.
    Vector-Based Data Improves Left-Right Eye-Tracking Classifier Performance After a Covariate Distributional Shift. (arXiv:2208.00465v1 [cs.LG])
    The main challenges of using electroencephalogram (EEG) signals to make eye-tracking (ET) predictions are the differences in distributional patterns between benchmark data and real-world data and the noise resulting from the unintended interference of brain signals from multiple sources. Increasing the robustness of machine learning models in predicting eye-tracking position from EEG data is therefore integral for both research and consumer use. In medical research, the usage of more complicated data collection methods to test for simpler tasks has been explored to address this very issue. In this study, we propose a fine-grain data approach for EEG-ET data collection in order to create more robust benchmarking. We train machine learning models utilizing both coarse-grain and fine-grain data and compare their accuracies when tested on data of similar/different distributional patterns in order to determine how susceptible EEG-ET benchmarks are to differences in distributional data. We apply a covariate distributional shift to test for this susceptibility. Results showed that models trained on fine-grain, vector-based data were less susceptible to distributional shifts than models trained on coarse-grain, binary-classified data.
    Evaluating Table Structure Recognition: A New Perspective. (arXiv:2208.00385v1 [cs.CV])
    Existing metrics used to evaluate table structure recognition algorithms have shortcomings with regard to capturing text and empty cells alignment. In this paper, we build on prior work and propose a new metric - TEDS based IOU similarity (TEDS (IOU)) for table structure recognition which uses bounding boxes instead of text while simultaneously being robust against the above disadvantages. We demonstrate the effectiveness of our metric against previous metrics through various examples.
    Revisiting the Critical Factors of Augmentation-Invariant Representation Learning. (arXiv:2208.00275v1 [cs.CV])
    We focus on better understanding the critical factors of augmentation-invariant representation learning. We revisit MoCo v2 and BYOL and try to prove the authenticity of the following assumption: different frameworks bring about representations of different characteristics even with the same pretext task. We establish the first benchmark for fair comparisons between MoCo v2 and BYOL, and observe: (i) sophisticated model configurations enable better adaptation to pre-training dataset; (ii) mismatched optimization strategies of pre-training and fine-tuning hinder model from achieving competitive transfer performances. Given the fair benchmark, we make further investigation and find asymmetry of network structure endows contrastive frameworks to work well under the linear evaluation protocol, while may hurt the transfer performances on long-tailed classification tasks. Moreover, negative samples do not make models more sensible to the choice of data augmentations, nor does the asymmetric network structure. We believe our findings provide useful information for future work.
    Speckle2Speckle: Unsupervised Learning of Ultrasound Speckle Filtering Without Clean Data. (arXiv:2208.00402v1 [eess.IV])
    In ultrasound imaging the appearance of homogeneous regions of tissue is subject to speckle, which for certain applications can make the detection of tissue irregularities difficult. To cope with this, it is common practice to apply speckle reduction filters to the images. Most conventional filtering techniques are fairly hand-crafted and often need to be finely tuned to the present hardware, imaging scheme and application. Learning based techniques on the other hand suffer from the need for a target image for training (in case of fully supervised techniques) or require narrow, complex physics-based models of the speckle appearance that might not apply in all cases. With this work we propose a deep-learning based method for speckle removal without these limitations. To enable this, we make use of realistic ultrasound simulation techniques that allow for instantiation of several independent speckle realizations that represent the exact same tissue, thus allowing for the application of image reconstruction techniques that work with pairs of differently corrupted data. Compared to two other state-of-the-art approaches (non-local means and the Optimized Bayesian non-local means filter) our method performs favorably in qualitative comparisons and quantitative evaluation, despite being trained on simulations alone, and is several orders of magnitude faster.
    Learning to Prompt for Vision-Language Models. (arXiv:2109.01134v4 [cs.CV] UPDATED)
    Large pre-trained vision-language models like CLIP have shown great potential in learning representations that are transferable across a wide range of downstream tasks. Different from the traditional representation learning that is based mostly on discretized labels, vision-language pre-training aligns images and texts in a common feature space, which allows zero-shot transfer to a downstream task via prompting, i.e., classification weights are synthesized from natural language describing classes of interest. In this work, we show that a major challenge for deploying such models in practice is prompt engineering, which requires domain expertise and is extremely time-consuming -- one needs to spend a significant amount of time on words tuning since a slight change in wording could have a huge impact on performance. Inspired by recent advances in prompt learning research in natural language processing (NLP), we propose Context Optimization (CoOp), a simple approach specifically for adapting CLIP-like vision-language models for downstream image recognition. Concretely, CoOp models a prompt's context words with learnable vectors while the entire pre-trained parameters are kept fixed. To handle different image recognition tasks, we provide two implementations of CoOp: unified context and class-specific context. Through extensive experiments on 11 datasets, we demonstrate that CoOp requires as few as one or two shots to beat hand-crafted prompts with a decent margin and is able to gain significant improvements over prompt engineering with more shots, e.g., with 16 shots the average gain is around 15% (with the highest reaching over 45%). Despite being a learning-based approach, CoOp achieves superb domain generalization performance compared with the zero-shot model using hand-crafted prompts.  ( 3 min )
    Bayesian Active Learning for Sim-to-Real Robotic Perception. (arXiv:2109.11547v3 [cs.RO] UPDATED)
    While learning from synthetic training data has recently gained an increased attention, in real-world robotic applications, there are still performance deficiencies due to the so-called Sim-to-Real gap. In practice, this gap is hard to resolve with only synthetic data. Therefore, we focus on an efficient acquisition of real data within a Sim-to-Real learning pipeline. Concretely, we employ deep Bayesian active learning to minimize manual annotation efforts and devise an autonomous learning paradigm to select the data that is considered useful for the human expert to annotate. To achieve this, a Bayesian Neural Network (BNN) object detector providing reliable uncertainty estimates is adapted to infer the informativeness of the unlabeled data. Furthermore, to cope with mis-alignments of the label distribution in uncertainty-based sampling, we develop an effective randomized sampling strategy that performs favorably compared to other complex alternatives. In our experiments on object classification and detection, we show benefits of our approach and provide evidence that labeling efforts can be reduced significantly. Finally, we demonstrate the practical effectiveness of this idea in a grasping task on an assistive robot.  ( 3 min )
    How Self-Supervised Learning Can be Used for Fine-Grained Head Pose Estimation?. (arXiv:2108.04893v6 [cs.CV] UPDATED)
    The cost of head pose labeling is the main challenge of improving the fine-grained Head Pose Estimation (HPE). Although Self-Supervised Learning (SSL) can be a solution to the lack of huge amounts of labeled data, its efficacy for fine-grained HPE is not yet fully explored. This study aims to assess the usage of SSL in fine-grained HPE based on two scenarios: (1) using SSL for weights pre-training procedure, and (2) leveraging auxiliary SSL losses besides HPE. We design a Hybrid Multi-Task Learning (HMTL) architecture based on the ResNet50 backbone in which both strategies are applied. Our experimental results reveal that the combination of both scenarios is the best for HPE. Together, the average error rate is reduced up to 23.1% for AFLW2000 and 14.2% for BIWI benchmark compared to the baseline. Moreover, it is found that some SSL methods are more suitable for transfer learning, while others may be effective when they are considered as auxiliary tasks incorporated into supervised learning. Finally, it is shown that by using the proposed HMTL architecture, the average error is reduced with different types of initial weights: random, ImageNet and SSL pre-trained weights.  ( 3 min )
    Decoupled Contrastive Learning. (arXiv:2110.06848v3 [cs.LG] UPDATED)
    Contrastive learning (CL) is one of the most successful paradigms for self-supervised learning (SSL). In a principled way, it considers two augmented "views" of the same image as positive to be pulled closer, and all other images as negative to be pushed further apart. However, behind the impressive success of CL-based techniques, their formulation often relies on heavy-computation settings, including large sample batches, extensive training epochs, etc. We are thus motivated to tackle these issues and establish a simple, efficient, yet competitive baseline of contrastive learning. Specifically, we identify, from theoretical and empirical studies, a noticeable negative-positive-coupling (NPC) effect in the widely used InfoNCE loss, leading to unsuitable learning efficiency concerning the batch size. By removing the NPC effect, we propose decoupled contrastive learning (DCL) loss, which removes the positive term from the denominator and significantly improves the learning efficiency. DCL achieves competitive performance with less sensitivity to sub-optimal hyperparameters, requiring neither large batches in SimCLR, momentum encoding in MoCo, or large epochs. We demonstrate with various benchmarks while manifesting robustness as much less sensitive to suboptimal hyperparameters. Notably, SimCLR with DCL achieves 68.2% ImageNet-1K top-1 accuracy using batch size 256 within 200 epochs pre-training, outperforming its SimCLR baseline by 6.4%. Further, DCL can be combined with the SOTA contrastive learning method, NNCLR, to achieve 72.3% ImageNet-1K top-1 accuracy with 512 batch size in 400 epochs, which represents a new SOTA in contrastive learning. We believe DCL provides a valuable baseline for future contrastive SSL studies.  ( 3 min )
    Multi-Exit Semantic Segmentation Networks. (arXiv:2106.03527v3 [cs.CV] UPDATED)
    Semantic segmentation arises as the backbone of many vision systems, spanning from self-driving cars and robot navigation to augmented reality and teleconferencing. Frequently operating under stringent latency constraints within a limited resource envelope, optimising for efficient execution becomes important. At the same time, the heterogeneous capabilities of the target platforms and the diverse constraints of different applications require the design and training of multiple target-specific segmentation models, leading to excessive maintenance costs. To this end, we propose a framework for converting state-of-the-art segmentation CNNs to Multi-Exit Semantic Segmentation (MESS) networks: specially trained models that employ parametrised early exits along their depth to i) dynamically save computation during inference on easier samples and ii) save training and maintenance cost by offering a post-training customisable speed-accuracy trade-off. Designing and training such networks naively can hurt performance. Thus, we propose a novel two-staged training scheme for multi-exit networks. Furthermore, the parametrisation of MESS enables co-optimising the number, placement and architecture of the attached segmentation heads along with the exit policy, upon deployment via exhaustive search in <1 GPUh. This allows MESS to rapidly adapt to the device capabilities and application requirements for each target use-case, offering a train-once-deploy-everywhere solution. MESS variants achieve latency gains of up to 2.83x with the same accuracy, or 5.33 pp higher accuracy for the same computational budget, compared to the original backbone network. Lastly, MESS delivers orders of magnitude faster architectural customisation, compared to state-of-the-art techniques.  ( 3 min )
    Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond. (arXiv:2109.00725v2 [cs.CL] UPDATED)
    A fundamental goal of scientific research is to learn about causal relationships. However, despite its critical role in the life and social sciences, causality has not had the same importance in Natural Language Processing (NLP), which has traditionally placed more emphasis on predictive tasks. This distinction is beginning to fade, with an emerging area of interdisciplinary research at the convergence of causal inference and language processing. Still, research on causality in NLP remains scattered across domains without unified definitions, benchmark datasets and clear articulations of the challenges and opportunities in the application of causal inference to the textual domain, with its unique properties. In this survey, we consolidate research across academic areas and situate it in the broader NLP landscape. We introduce the statistical challenge of estimating causal effects with text, encompassing settings where text is used as an outcome, treatment, or to address confounding. In addition, we explore potential uses of causal inference to improve the robustness, fairness, and interpretability of NLP models. We thus provide a unified overview of causal inference for the NLP community.  ( 3 min )
    Machine learning-based conditional mean filter: a generalization of the ensemble Kalman filter for nonlinear data assimilation. (arXiv:2106.07908v2 [cs.LG] UPDATED)
    This paper presents the machine learning-based ensemble conditional mean filter (ML-EnCMF) -- a filtering method based on the conditional mean filter (CMF) previously introduced in the literature. The updated mean of the CMF matches that of the posterior, obtained by applying Bayes' rule on the filter's forecast distribution. Moreover, we show that the CMF's updated covariance coincides with the expected conditional covariance. Implementing the EnCMF requires computing the conditional mean (CM). A likelihood-based estimator is prone to significant errors for small ensemble sizes, causing the filter divergence. We develop a systematical methodology for integrating machine learning into the EnCMF based on the CM's orthogonal projection property. First, we use a combination of an artificial neural network (ANN) and a linear function, obtained based on the ensemble Kalman filter (EnKF), to approximate the CM, enabling the ML-EnCMF to inherit EnKF's advantages. Secondly, we apply a suitable variance reduction technique to reduce statistical errors when estimating loss function. Lastly, we propose a model selection procedure for element-wisely selecting the applied filter, i.e., either the EnKF or ML-EnCMF, at each updating step. We demonstrate the ML-EnCMF performance using the Lorenz-63 and Lorenz-96 systems and show that the ML-EnCMF outperforms the EnKF and the likelihood-based EnCMF.  ( 3 min )
    A Joint Graph and Image Convolution Network for Automatic Brain Tumor Segmentation. (arXiv:2109.05580v2 [eess.IV] UPDATED)
    We present a joint graph convolution-image convolution neural network as our submission to the Brain Tumor Segmentation (BraTS) 2021 challenge. We model each brain as a graph composed of distinct image regions, which is initially segmented by a graph neural network (GNN). Subsequently, the tumorous volume identified by the GNN is further refined by a simple (voxel) convolutional neural network (CNN), which produces the final segmentation. This approach captures both global brain feature interactions via the graphical representation and local image details through the use of convolutional filters. We find that the GNN component by itself can effectively identify and segment the brain tumors. The addition of the CNN further improves the median performance of the model by 2 percent across all metrics evaluated. On the validation set, our joint GNN-CNN model achieves mean Dice scores of 0.89, 0.81, 0.73 and mean Hausdorff distances (95th percentile) of 6.8, 12.6, 28.2mm on the whole tumor, core tumor, and enhancing tumor, respectively.  ( 3 min )
    DSEE: Dually Sparsity-embedded Efficient Tuning of Pre-trained Language Models. (arXiv:2111.00160v2 [cs.LG] UPDATED)
    Gigantic pre-trained models have become central to natural language processing (NLP), serving as the starting point for fine-tuning towards a range of downstream tasks. However, two pain points persist for this paradigm: (a) as the pre-trained models grow bigger (e.g., 175B parameters for GPT-3), even the fine-tuning process can be time-consuming and computationally expensive; (b) the fine-tuned model has the same size as its starting point by default, which is neither sensible due to its more specialized functionality, nor practical since many fine-tuned models will be deployed in resource-constrained environments. To address these pain points, we propose a framework for resource- and parameter-efficient fine-tuning by leveraging the sparsity prior in both weight updates and the final model weights. Our proposed framework, dubbed Dually Sparsity-Embedded Efficient Tuning (DSEE), aims to achieve two key objectives: (i) parameter efficient fine-tuning - by enforcing sparsity-aware low-rank updates on top of the pre-trained weights; and (ii) resource-efficient inference - by encouraging a sparse weight structure towards the final fine-tuned model. We leverage sparsity in these two directions by exploiting both unstructured and structured sparse patterns in pre-trained language models via a unified approach. Extensive experiments and in-depth investigations, with diverse network backbones (i.e., BERT, RoBERTa, and GPT-2) on dozens of datasets, consistently demonstrate impressive parameter-/inference-efficiency, while maintaining competitive downstream performance. For instance, DSEE saves about 25% inference FLOPs while achieving comparable performance, with 0.5% trainable parameters on BERT. Codes are available in https://github.com/VITA-Group/DSEE.  ( 3 min )
    Tight Concentrations and Confidence Sequences from the Regret of Universal Portfolio. (arXiv:2110.14099v3 [stat.ML] UPDATED)
    A classic problem in statistics is the estimation of the expectation of random variables from samples. This gives rise to the tightly connected problems of deriving concentration inequalities and confidence sequences, that is confidence intervals that hold uniformly over time. Previous work has shown how to easily convert the regret guarantee of an online betting algorithm into a time-uniform concentration inequality. In this paper, we show that we can go even further: We show that the regret of universal portfolio algorithms give rise to new implicit time-uniform concentrations and state-of-the-art empirically calculated confidence sequences. In particular, our numerically obtained confidence sequences can never be vacuous, even with a single sample, and satisfy the law of iterated logarithm.  ( 2 min )
    PatrickStar: Parallel Training of Pre-trained Models via Chunk-based Memory Management. (arXiv:2108.05818v4 [cs.LG] UPDATED)
    The pre-trained model (PTM) is revolutionizing Artificial Intelligence (AI) technology. However, the hardware requirement of PTM training is prohibitively high, making it a game for a small proportion of people. Therefore, we proposed PatrickStar system to lower the hardware requirements of PTMs and make them accessible to everyone. PatrickStar uses the CPU-GPU heterogeneous memory space to store the model data. Different from existing works, we organize the model data in memory chunks and dynamically distribute them in the heterogeneous memory. Guided by the runtime memory statistics collected in a warm-up iteration, chunks are orchestrated efficiently in heterogeneous memory and generate lower CPU-GPU data transmission volume and higher bandwidth utilization. Symbiosis with the Zero Redundancy Optimizer, PatrickStar scales to multiple GPUs on multiple nodes. % using data parallelism. The system can train tasks on bigger models and larger batch sizes, which cannot be accomplished by existing works. Experimental results show that PatrickStar extends model scales 2.27 and 2.5 times of DeepSpeed, and consistently exhibits significantly higher execution speed. PatricStar also successfully runs the 175B GPT3 training task on a 32 GPU cluster. Our code is publicly available at https://github.com/Tencent/PatrickStar.  ( 3 min )
    YAHPO Gym -- An Efficient Multi-Objective Multi-Fidelity Benchmark for Hyperparameter Optimization. (arXiv:2109.03670v4 [cs.LG] UPDATED)
    When developing and analyzing new hyperparameter optimization methods, it is vital to empirically evaluate and compare them on well-curated benchmark suites. In this work, we propose a new set of challenging and relevant benchmark problems motivated by desirable properties and requirements for such benchmarks. Our new surrogate-based benchmark collection consists of 14 scenarios that in total constitute over 700 multi-fidelity hyperparameter optimization problems, which all enable multi-objective hyperparameter optimization. Furthermore, we empirically compare surrogate-based benchmarks to the more widely-used tabular benchmarks, and demonstrate that the latter may produce unfaithful results regarding the performance ranking of HPO methods. We examine and compare our benchmark collection with respect to defined requirements and propose a single-objective as well as a multi-objective benchmark suite on which we compare 7 single-objective and 7 multi-objective optimizers in a benchmark experiment. Our software is available at [https://github.com/slds-lmu/yahpo_gym].  ( 3 min )
    EMFlow: Data Imputation in Latent Space via EM and Deep Flow Models. (arXiv:2106.04804v2 [cs.LG] UPDATED)
    The presence of missing values within high-dimensional data is an ubiquitous problem for many applied sciences. A serious limitation of many available data mining and machine learning methods is their inability to handle partially missing values and so an integrated approach that combines imputation and model estimation is vital for down-stream analysis. A computationally fast algorithm, called EMFlow, is introduced that performs imputation in a latent space via an online version of Expectation-Maximization (EM) algorithm by using a normalizing flow (NF) model which maps the data space to a latent space. The proposed EMFlow algorithm is iterative, involving updating the parameters of online EM and NF alternatively. Extensive experimental results for high-dimensional multivariate and image datasets are presented to illustrate the superior performance of the EMFlow compared to a couple of recently available methods in terms of both predictive accuracy and speed of algorithmic convergence. We provide code for all our experiments.  ( 2 min )
    PM-FSM: Policies Modulating Finite State Machine for Robust Quadrupedal Locomotion. (arXiv:2109.12696v2 [cs.RO] UPDATED)
    Deep reinforcement learning (deep RL) has emerged as an effective tool for developing controllers for legged robots. However, vanilla deep RL often requires a tremendous amount of training samples and is not feasible for achieving robust behaviors. Instead, researchers have investigated a novel policy architecture by incorporating human experts' knowledge, such as Policies Modulating Trajectory Generators (PMTG). This architecture builds a recurrent control loop by combining a parametric trajectory generator (TG) and a feedback policy network to achieve more robust behaviors. To take advantage of human experts' knowledge but eliminate time-consuming interactive teaching, researchers have investigated a novel architecture, Policies Modulating Trajectory Generators (PMTG), which builds a recurrent control loop by combining a parametric trajectory generator (TG) and a feedback policy network to achieve more robust behaviors using intuitive prior knowledge. In this work, we propose Policies Modulating Finite State Machine (PM-FSM) by replacing TGs with contact-aware finite state machines (FSM), which offer more flexible control of each leg. Compared with the TGs, FSMs offer high-level management on each leg motion generator and enable a flexible state arrangement, which makes the learned behavior less vulnerable to unseen perturbations or challenging terrains. This invention offers an explicit notion of contact events to the policy to negotiate unexpected perturbations. We demonstrated that the proposed architecture could achieve more robust behaviors in various scenarios, such as challenging terrains or external perturbations, on both simulated and real robots. The supplemental video can be found at: https://youtu.be/78cboMqTkJQ.  ( 3 min )
    CENN: Conservative energy method based on neural networks with subdomains for solving variational problems involving heterogeneous and complex geometries. (arXiv:2110.01359v4 [math.NA] UPDATED)
    We propose a conservative energy method based on neural networks with subdomains for solving variational problems (CENN), where the admissible function satisfying the essential boundary condition without boundary penalty is constructed by the radial basis function (RBF), particular solution neural network, and general neural network. Loss term is the potential energy, optimized based on the principle of minimum potential energy. The loss term at the interfaces has the lower order derivative compared to the strong form PINN with subdomains. The advantage of the proposed method is higher efficiency, more accurate, and less hyperparameters than the strong form PINN with subdomains. Another advantage of the proposed method is that it can apply to complex geometries based on the special construction of the admissible function. To analyze its performance, the proposed method CENN is used to model representative PDEs, the examples include strong discontinuity, singularity, complex boundary, non-linear, and heterogeneous problems. Furthermore, it outperforms other methods when dealing with heterogeneous problems.  ( 3 min )
    Learning to Control DC Motor for Micromobility in Real Time with Reinforcement Learning. (arXiv:2108.00138v4 [cs.LG] UPDATED)
    Autonomous micromobility has been attracting the attention of researchers and practitioners in recent years. A key component of many micro-transport vehicles is the DC motor, a complex dynamical system that is continuous and non-linear. Learning to quickly control the DC motor in the presence of disturbances and uncertainties is desired for various applications that require robustness and stability. Techniques to accomplish this task usually rely on a mathematical system model, which is often insufficient to anticipate the effects of time-varying and interrelated sources of non-linearities. While some model-free approaches have been successful at the task, they rely on massive interactions with the system and are trained in specialized hardware in order to fit a highly parameterized controller. In this work, we learn to steer a DC motor via sample-efficient reinforcement learning. Using data collected from hardware interactions in the real world, we additionally build a simulator to experiment with a wide range of parameters and learning strategies. With the best parameters found, we learn an effective control policy in one minute and 53 seconds on a simulation and in 10 minutes and 35 seconds on a physical system.  ( 3 min )
    Error Loss Networks. (arXiv:2106.03722v3 [cs.LG] UPDATED)
    A novel model called error loss network (ELN) is proposed to build an error loss function for supervised learning. The ELN is in structure similar to a radial basis function (RBF) neural network, but its input is an error sample and output is a loss corresponding to that error sample. That means the nonlinear input-output mapper of ELN creates an error loss function. The proposed ELN provides a unified model for a large class of error loss functions, which includes some information theoretic learning (ITL) loss functions as special cases. The activation function, weight parameters and network size of the ELN can be predetermined or learned from the error samples. On this basis, we propose a new machine learning paradigm where the learning process is divided into two stages: first, learning a loss function using an ELN; second, using the learned loss function to continue to perform the learning. Experimental results are presented to demonstrate the desirable performance of the new method.  ( 2 min )
    Adversarial Robustness Verification and Attack Synthesis in Stochastic Systems. (arXiv:2110.02125v2 [cs.CR] UPDATED)
    Probabilistic model checking is a useful technique for specifying and verifying properties of stochastic systems including randomized protocols and reinforcement learning models. Existing methods rely on the assumed structure and probabilities of certain system transitions. These assumptions may be incorrect, and may even be violated by an adversary who gains control of system components. In this paper, we develop a formal framework for adversarial robustness in systems modeled as discrete time Markov chains (DTMCs). We base our framework on existing methods for verifying probabilistic temporal logic properties and extend it to include deterministic, memoryless policies acting in Markov decision processes (MDPs). Our framework includes a flexible approach for specifying structure-preserving and non structure-preserving adversarial models. We outline a class of threat models under which adversaries can perturb system transitions, constrained by an $\varepsilon$ ball around the original transition probabilities. We define three main DTMC adversarial robustness problems: adversarial robustness verification, maximal $\delta$ synthesis, and worst case attack synthesis. We present two optimization-based solutions to these three problems, leveraging traditional and parametric probabilistic model checking techniques. We then evaluate our solutions on two stochastic protocols and a collection of Grid World case studies, which model an agent acting in an environment described as an MDP. We find that the parametric solution results in fast computation for small parameter spaces. In the case of less restrictive (stronger) adversaries, the number of parameters increases, and directly computing property satisfaction probabilities is more scalable. We demonstrate the usefulness of our definitions and solutions by comparing system outcomes over various properties, threat models, and case studies.  ( 3 min )
    On Anytime Learning at Macroscale. (arXiv:2106.09563v4 [cs.LG] UPDATED)
    In many practical applications of machine learning data arrives sequentially over time in large chunks. Practitioners have then to decide how to allocate their computational budget in order to obtain the best performance at any point in time. Online learning theory for convex optimization suggests that the best strategy is to use data as soon as it arrives. However, this might not be the best strategy when using deep non-linear networks, particularly when these perform multiple passes over each chunk of data rendering the overall distribution non i.i.d.. In this paper, we formalize this learning setting in the simplest scenario in which each data chunk is drawn from the same underlying distribution, and make a first attempt at empirically answering the following questions: How long should the learner wait before training on the newly arrived chunks? What architecture should the learner adopt? Should the learner increase capacity over time as more data is observed? We probe this learning setting using convolutional neural networks trained on classic computer vision benchmarks as well as a large transformer model trained on a large-scale language modeling task. Code is available at \url{www.github.com/facebookresearch/ALMA}.  ( 3 min )
    Machine Learning for Postprocessing Medium-range Ensemble Streamflow Forecasts. (arXiv:2106.09547v2 [cs.LG] UPDATED)
    Skillful streamflow forecasts can inform decisions in various areas of water policy and management. We integrate numerical weather prediction ensembles and a distributed hydrological model to generate ensemble streamflow forecasts at medium-range lead times (1 - 7 days). We demonstrate a case study for machine learning application in postprocessing ensemble streamflow forecasts in the Upper Susquehanna River basin in the eastern United States. For forecast verification, we use different metrics such as skill score and reliability diagram conditioned upon the lead time, flow threshold, and season. The verification results show that the machine learning postprocessor can improve streamflow forecasts relative to low complexity forecasts (e.g., climatological and temporal persistence) as well as deterministic and raw ensemble forecasts. As compared to the raw ensembles, relative gain in forecast skill from postprocessor is generally higher at medium-range timescales compared to shorter lead times; high flows compared to low-moderate flows, and warm-season compared to the cool ones. Overall, our results highlight the benefits of machine learning in many aspects for improving both the skill and reliability of streamflow forecasts.  ( 2 min )
    Performance Comparison of Deep RL Algorithms for Energy Systems Optimal Scheduling. (arXiv:2208.00728v1 [eess.SY])
    Taking advantage of their data-driven and model-free features, Deep Reinforcement Learning (DRL) algorithms have the potential to deal with the increasing level of uncertainty due to the introduction of renewable-based generation. To deal simultaneously with the energy systems' operational cost and technical constraints (e.g, generation-demand power balance) DRL algorithms must consider a trade-off when designing the reward function. This trade-off introduces extra hyperparameters that impact the DRL algorithms' performance and capability of providing feasible solutions. In this paper, a performance comparison of different DRL algorithms, including DDPG, TD3, SAC, and PPO, are presented. We aim to provide a fair comparison of these DRL algorithms for energy systems optimal scheduling problems. Results show DRL algorithms' capability of providing in real-time good-quality solutions, even in unseen operational scenarios, when compared with a mathematical programming model of the energy system optimal scheduling problem. Nevertheless, in the case of large peak consumption, these algorithms failed to provide feasible solutions, which can impede their practical implementation.  ( 2 min )
    Graph Transfer Learning via Adversarial Domain Adaptation with Graph Convolution. (arXiv:1909.01541v4 [cs.LG] UPDATED)
    This paper studies the problem of cross-network node classification to overcome the insufficiency of labeled data in a single network. It aims to leverage the label information in a partially labeled source network to assist node classification in a completely unlabeled or partially labeled target network. Existing methods for single network learning cannot solve this problem due to the domain shift across networks. Some multi-network learning methods heavily rely on the existence of cross-network connections, thus are inapplicable for this problem. To tackle this problem, we propose a novel \textcolor{black}{graph} transfer learning framework AdaGCN by leveraging the techniques of adversarial domain adaptation and graph convolution. It consists of two components: a semi-supervised learning component and an adversarial domain adaptation component. The former aims to learn class discriminative node representations with given label information of the source and target networks, while the latter contributes to mitigating the distribution divergence between the source and target domains to facilitate knowledge transfer. Extensive empirical evaluations on real-world datasets show that AdaGCN can successfully transfer class information with a low label rate on the source network and a substantial divergence between the source and target domains. The source code for reproducing the experimental results is available at https://github.com/daiquanyu/AdaGCN.  ( 3 min )
    Practical Deep Reinforcement Learning Approach for Stock Trading. (arXiv:1811.07522v3 [cs.LG] UPDATED)
    Stock trading strategy plays a crucial role in investment companies. However, it is challenging to obtain optimal strategy in the complex and dynamic stock market. We explore the potential of deep reinforcement learning to optimize stock trading strategy and thus maximize investment return. 30 stocks are selected as our trading stocks and their daily prices are used as the training and trading market environment. We train a deep reinforcement learning agent and obtain an adaptive trading strategy. The agent's performance is evaluated and compared with Dow Jones Industrial Average and the traditional min-variance portfolio allocation strategy. The proposed deep reinforcement learning approach is shown to outperform the two baselines in terms of both the Sharpe ratio and cumulative returns.  ( 2 min )
    TransDeepLab: Convolution-Free Transformer-based DeepLab v3+ for Medical Image Segmentation. (arXiv:2208.00713v1 [eess.IV])
    Convolutional neural networks (CNNs) have been the de facto standard in a diverse set of computer vision tasks for many years. Especially, deep neural networks based on seminal architectures such as U-shaped models with skip-connections or atrous convolution with pyramid pooling have been tailored to a wide range of medical image analysis tasks. The main advantage of such architectures is that they are prone to detaining versatile local features. However, as a general consensus, CNNs fail to capture long-range dependencies and spatial correlations due to the intrinsic property of confined receptive field size of convolution operations. Alternatively, Transformer, profiting from global information modelling that stems from the self-attention mechanism, has recently attained remarkable performance in natural language processing and computer vision. Nevertheless, previous studies prove that both local and global features are critical for a deep model in dense prediction, such as segmenting complicated structures with disparate shapes and configurations. To this end, this paper proposes TransDeepLab, a novel DeepLab-like pure Transformer for medical image segmentation. Specifically, we exploit hierarchical Swin-Transformer with shifted windows to extend the DeepLabv3 and model the Atrous Spatial Pyramid Pooling (ASPP) module. A thorough search of the relevant literature yielded that we are the first to model the seminal DeepLab model with a pure Transformer-based model. Extensive experiments on various medical image segmentation tasks verify that our approach performs superior or on par with most contemporary works on an amalgamation of Vision Transformer and CNN-based methods, along with a significant reduction of model complexity. The codes and trained models are publicly available at https://github.com/rezazad68/transdeeplab  ( 3 min )
    UniToBrain dataset: a Brain Perfusion Dataset. (arXiv:2208.00650v1 [eess.IV])
    The CT perfusion (CTP) is a medical exam for measuring the passage of a bolus of contrast solution through the brain on a pixel-by-pixel basis. The objective is to draw "perfusion maps" (namely cerebral blood volume, cerebral blood flow and time to peak) very rapidly for ischemic lesions, and to be able to distinguish between core and penumubra regions. A precise and quick diagnosis, in a context of ischemic stroke, can determine the fate of the brain tissues and guide the intervention and treatment in emergency conditions. In this work we present UniToBrain dataset, the very first open-source dataset for CTP. It comprises a cohort of more than a hundred of patients, and it is accompanied by patients metadata and ground truth maps obtained with state-of-the-art algorithms. We also propose a novel neural networks-based algorithm, using the European library ECVL and EDDL for the image processing and developing deep learning models respectively. The results obtained by the neural network models match the ground truth and open the road towards potential sub-sampling of the required number of CT maps, which impose heavy radiation doses to the patients.  ( 2 min )
    Generative Bias for Visual Question Answering. (arXiv:2208.00690v1 [cs.CV])
    The task of Visual Question Answering (VQA) is known to be plagued by the issue of VQA models exploiting biases within the dataset to make its final prediction. Many previous ensemble based debiasing methods have been proposed where an additional model is purposefully trained to be biased in order to aid in training a robust target model. However, these methods compute the bias for a model from the label statistics of the training data or directly from single modal branches. In contrast, in this work, in order to better learn the bias a target VQA model suffers from, we propose a generative method to train the bias model \emph{directly from the target model}, called GenB. In particular, GenB employs a generative network to learn the bias through a combination of the adversarial objective and knowledge distillation. We then debias our target model with GenB as a bias model, and show through extensive experiments the effects of our method on various VQA bias datasets including VQA-CP2, VQA-CP1, GQA-OOD, and VQA-CE.  ( 2 min )
    Efficient Long-Text Understanding with Short-Text Models. (arXiv:2208.00748v1 [cs.CL])
    Transformer-based pretrained language models (LMs) are ubiquitous across natural language understanding, but cannot be applied to long sequences such as stories, scientific articles and long documents, due to their quadratic complexity. While a myriad of efficient transformer variants have been proposed, they are typically based on custom implementations that require expensive pretraining from scratch. In this work, we propose SLED: SLiding-Encoder and Decoder, a simple approach for processing long sequences that re-uses and leverages battle-tested short-text pretrained LMs. Specifically, we partition the input into overlapping chunks, encode each with a short-text LM encoder and use the pretrained decoder to fuse information across chunks (fusion-in-decoder). We illustrate through controlled experiments that SLED offers a viable strategy for long text understanding and evaluate our approach on SCROLLS, a benchmark with seven datasets across a wide range of language understanding tasks. We find that SLED is competitive with specialized models that are up to 50x larger and require a dedicated and expensive pretraining step.  ( 2 min )
    Intrinsic Universal Measurements of Non-linear Embeddings. (arXiv:1811.01464v2 [cs.LG] UPDATED)
    A basic problem in machine learning is to find a mapping $f$ from a low dimensional latent space $\mathcal{Y}$ to a high dimensional observation space $\mathcal{X}$. Modern tools such as deep neural networks are capable to represent general non-linear mappings. A learner can easily find a mapping which perfectly fits all the observations. However, such a mapping is often not considered as good, because it is not simple enough and can overfit. How to define simplicity? We try to make a formal definition on the amount of information imposed by a non-linear mapping $f$. Intuitively, we measure the local discrepancy between the pullback geometry and the intrinsic geometry of the latent space. Our definition is based on information geometry and is independent of the empirical observations, nor specific parameterizations. We prove its basic properties and discuss relationships with related machine learning methods.  ( 2 min )
    Safe Policy Improvement Approaches and their Limitations. (arXiv:2208.00724v1 [cs.LG])
    Safe Policy Improvement (SPI) is an important technique for offline reinforcement learning in safety critical applications as it improves the behavior policy with a high probability. We classify various SPI approaches from the literature into two groups, based on how they utilize the uncertainty of state-action pairs. Focusing on the Soft-SPIBB (Safe Policy Improvement with Soft Baseline Bootstrapping) algorithms, we show that their claim of being provably safe does not hold. Based on this finding, we develop adaptations, the Adv-Soft-SPIBB algorithms, and show that they are provably safe. A heuristic adaptation, Lower-Approx-Soft-SPIBB, yields the best performance among all SPIBB algorithms in extensive experiments on two benchmarks. We also check the safety guarantees of the provably safe algorithms and show that huge amounts of data are necessary such that the safety bounds become useful in practice.  ( 2 min )
    Graph Neural Network with Local Frame for Molecular Potential Energy Surface. (arXiv:2208.00716v1 [cs.LG])
    Modeling molecular potential energy surface is of pivotal importance in science. Graph Neural Networks have shown great success in this field, especially those using rotation-equivariant representations. However, they either suffer from a complex mathematical form or lack theoretical support and design principle. To avoid using equivariant representations, we introduce a novel local frame method to molecule representation learning and analyze its expressive power. With a frame and the projection of equivariant vectors on the frame, GNNs can map the local environment of an atom to a scalar representation injectively. Messages can also be passed across local environments with frames' projection on frames. We further analyze when and how we can build such local frames. We prove that local frames always exist when the local environments have no symmetry, as is often the case in molecular dynamics simulations. For symmetric molecules, though only degenerate frames can be built, we find that the local frame method may still achieve high expressive power in some common cases due to the reduced degrees of freedom. Using only scalar representations allows us to adopt existing simple and powerful GNN architectures. Our model outperforms a range of state-of-the-art baselines in experiments. Simpler architectures also lead to higher scalability. Our model only takes about 30% inference time compared with the fastest baseline.  ( 2 min )
    Off-Policy Correction for Actor-Critic Algorithms in Deep Reinforcement Learning. (arXiv:2208.00755v1 [cs.LG])
    Compared to on-policy policy gradient techniques, off-policy model-free deep reinforcement learning (RL) approaches that use previously gathered data can improve sampling efficiency. However, off-policy learning becomes challenging when the discrepancy between the distributions of the policy of interest and the policies that collected the data increases. Although the well-studied importance sampling and off-policy policy gradient techniques were proposed to compensate for this discrepancy, they usually require a collection of long trajectories that increases the computational complexity and induce additional problems such as vanishing or exploding gradients. Moreover, their generalization to continuous action domains is strictly limited as they require action probabilities, which is unsuitable for deterministic policies. To overcome these limitations, we introduce an alternative off-policy correction algorithm for continuous action spaces, Actor-Critic Off-Policy Correction (AC-Off-POC), to mitigate the potential drawbacks introduced by the previously collected data. Through a novel discrepancy measure computed by the agent's most recent action decisions on the states of the randomly sampled batch of transitions, the approach does not require actual or estimated action probabilities for any policy and offers an adequate one-step importance sampling. Theoretical results show that the introduced approach can achieve a contraction mapping with a fixed unique point, which allows a "safe" off-policy learning. Our empirical results suggest that AC-Off-POC consistently improves the state-of-the-art and attains higher returns in fewer steps than the competing methods by efficiently scheduling the learning rate in Q-learning and policy optimization.  ( 3 min )
    $\textrm{D}^3\textrm{Former}$: Debiased Dual Distilled Transformer for Incremental Learning. (arXiv:2208.00777v1 [cs.CV])
    Class incremental learning (CIL) involves learning a classification model where groups of new classes are encountered in every learning phase. The goal is to learn a unified model performant on all the classes observed so far. Given the recent popularity of Vision Transformers (ViTs) in conventional classification settings, an interesting question is to study their continual learning behaviour. In this work, we develop a Debiased Dual Distilled Transformer for CIL dubbed $\textrm{D}^3\textrm{Former}$. The proposed model leverages a hybrid nested ViT design to ensure data efficiency and scalability to small as well as large datasets. In contrast to a recent ViT based CIL approach, our $\textrm{D}^3\textrm{Former}$ does not dynamically expand its architecture when new tasks are learned and remains suitable for a large number of incremental tasks. The improved CIL behaviour of $\textrm{D}^3\textrm{Former}$ owes to two fundamental changes to the ViT design. First, we treat the incremental learning as a long-tail classification problem where the majority samples from new classes vastly outnumber the limited exemplars available for old classes. To avoid biasness against the minority old classes, we propose to dynamically adjust logits to emphasize on retaining the representations relevant to old tasks. Second, we propose to preserve the configuration of spatial attention maps as the learning progresses across tasks. This helps in reducing catastrophic forgetting via constraining the model to retain the attention on the most discriminative regions. $\textrm{D}^3\textrm{Former}$ obtains favorable results on incremental versions of CIFAR-100, MNIST, SVHN, and ImageNet datasets.  ( 3 min )
    XOOD: Extreme Value Based Out-Of-Distribution Detection For Image Classification. (arXiv:2208.00629v1 [cs.LG])
    Detecting out-of-distribution (OOD) data at inference time is crucial for many applications of machine learning. We present XOOD: a novel extreme value-based OOD detection framework for image classification that consists of two algorithms. The first, XOOD-M, is completely unsupervised, while the second XOOD-L is self-supervised. Both algorithms rely on the signals captured by the extreme values of the data in the activation layers of the neural network in order to distinguish between in-distribution and OOD instances. We show experimentally that both XOOD-M and XOOD-L outperform state-of-the-art OOD detection methods on many benchmark data sets in both efficiency and accuracy, reducing false-positive rate (FPR95) by 50%, while improving the inferencing time by an order of magnitude.  ( 2 min )
    SFILES 2.0: An extended text-based flowsheet representation. (arXiv:2208.00778v1 [cs.DB])
    SFILES is a text-based notation for chemical process flowsheets. It was originally proposed by d'Anterroches (2006) who was inspired by the text-based SMILES notation for molecules. The text-based format has several advantages compared to flowsheet images regarding the storage format, computational accessibility, and eventually for data analysis and processing. However, the original SFILES version cannot describe essential flowsheet configurations unambiguously, such as the distinction between top and bottom products. Neither is it capable of describing the control structure required for the safe and reliable operation of chemical processes. Also, there is no publicly available software for decoding or encoding chemical process topologies to SFILES. We propose the SFILES 2.0 with a complete description of the extended notation and naming conventions. Additionally, we provide open-source software for the automated conversion between flowsheet graphs and SFILES 2.0 strings. This way, we hope to encourage researchers and engineers to publish their flowsheet topologies as SFILES 2.0 strings. The ultimate goal is to set the standards for creating a FAIR database of chemical process flowsheets, which would be of great value for future data analysis and processing.  ( 2 min )
    Learning Object-Based State Estimators for Household Robots. (arXiv:2011.03183v4 [cs.LG] UPDATED)
    A robot operating in a household makes observations of multiple objects as it moves around over the course of days or weeks. The objects may be moved by inhabitants, but not completely at random. The robot may be called upon later to retrieve objects and will need a long-term object-based memory in order to know how to find them. Existing work in semantic slam does not attempt to capture the dynamics of object movement. In this paper, we combine some aspects of classic techniques for data-association filtering with modern attention-based neural networks to construct object-based memory systems that operate on high-dimensional observations and hypotheses. We perform end-to-end learning on labeled observation trajectories to learn both the transition and observation models. We demonstrate the system's effectiveness in maintaining memory of dynamically changing objects in both simulated environment and real images, and demonstrate improvements over classical structured approaches as well as unstructured neural approaches. Additional information available at project website: https://yilundu.github.io/obm/.  ( 3 min )
    A Small Survey On Event Detection Using Twitter. (arXiv:2011.05801v2 [cs.SI] UPDATED)
    A small survey on event detection using Twitter. This work first defines the problem statement, and then summarizes and collates the different research works towards solving the problem.  ( 2 min )
    Model-based graph reinforcement learning for inductive traffic signal control. (arXiv:2208.00659v1 [cs.LG])
    Most reinforcement learning methods for adaptive-traffic-signal-control require training from scratch to be applied on any new intersection or after any modification to the road network, traffic distribution, or behavioral constraints experienced during training. Considering 1) the massive amount of experience required to train such methods, and 2) that experience must be gathered by interacting in an exploratory fashion with real road-network-users, such a lack of transferability limits experimentation and applicability. Recent approaches enable learning policies that generalize for unseen road-network topologies and traffic distributions, partially tackling this challenge. However, the literature remains divided between the learning of cyclic (the evolution of connectivity at an intersection must respect a cycle) and acyclic (less constrained) policies, and these transferable methods 1) are only compatible with cyclic constraints and 2) do not enable coordination. We introduce a new model-based method, MuJAM, which, on top of enabling explicit coordination at scale for the first time, pushes generalization further by allowing a generalization to the controllers' constraints. In a zero-shot transfer setting involving both road networks and traffic settings never experienced during training, and in a larger transfer experiment involving the control of 3,971 traffic signal controllers in Manhattan, we show that MuJAM, using both cyclic and acyclic constraints, outperforms domain-specific baselines as well as another transferable approach.  ( 2 min )
    Learning to Navigate using Visual Sensor Networks. (arXiv:2208.00759v1 [cs.RO])
    We consider the problem of navigating a mobile robot towards a target in an unknown environment that is endowed with visual sensors, where neither the robot nor the sensors have access to global positioning information and only use first-person-view images. While prior work in sensor network based navigation uses explicit mapping and planning techniques, and are often aided by external positioning systems, we propose a vision-only based learning approach that leverages a Graph Neural Network (GNN) to encode and communicate relevant viewpoint information to the mobile robot. During navigation, the robot is guided by a model that we train through imitation learning to approximate optimal motion primitives, thereby predicting the effective cost-to-go (to the target). In our experiments, we first demonstrate generalizability to previously unseen environments with various sensor layouts. Simulation results show that by utilizing communication among the sensors and robot, we can achieve a $18.1\%$ improvement in success rate while decreasing path detour mean by $29.3\%$ and variability by $48.4\%$. This is done without requiring a global map, positioning data, nor pre-calibration of the sensor network. Second, we perform a zero-shot transfer of our model from simulation to the real world. To this end, we train a `translator' model that translates between {latent encodings of} real and simulated images so that the navigation policy (which is trained entirely in simulation) can be used directly on the real robot, without additional fine-tuning. Physical experiments demonstrate our effectiveness in various cluttered environments.  ( 3 min )
    An Evidential Neural Network Model for Regression Based on Random Fuzzy Numbers. (arXiv:2208.00647v1 [cs.LG])
    We introduce a distance-based neural network model for regression, in which prediction uncertainty is quantified by a belief function on the real line. The model interprets the distances of the input vector to prototypes as pieces of evidence represented by Gaussian random fuzzy numbers (GRFN's) and combined by the generalized product intersection rule, an operator that extends Dempster's rule to random fuzzy sets. The network output is a GRFN that can be summarized by three numbers characterizing the most plausible predicted value, variability around this value, and epistemic uncertainty. Experiments with real datasets demonstrate the very good performance of the method as compared to state-of-the-art evidential and statistical learning algorithms. \keywords{Evidence theory, Dempster-Shafer theory, belief functions, machine learning, random fuzzy sets.  ( 2 min )
    De-biased Representation Learning for Fairness with Unreliable Labels. (arXiv:2208.00651v1 [cs.LG])
    Removing bias while keeping all task-relevant information is challenging for fair representation learning methods since they would yield random or degenerate representations w.r.t. labels when the sensitive attributes correlate with labels. Existing works proposed to inject the label information into the learning procedure to overcome such issues. However, the assumption that the observed labels are clean is not always met. In fact, label bias is acknowledged as the primary source inducing discrimination. In other words, the fair pre-processing methods ignore the discrimination encoded in the labels either during the learning procedure or the evaluation stage. This contradiction puts a question mark on the fairness of the learned representations. To circumvent this issue, we explore the following question: \emph{Can we learn fair representations predictable to latent ideal fair labels given only access to unreliable labels?} In this work, we propose a \textbf{D}e-\textbf{B}iased \textbf{R}epresentation Learning for \textbf{F}airness (DBRF) framework which disentangles the sensitive information from non-sensitive attributes whilst keeping the learned representations predictable to ideal fair labels rather than observed biased ones. We formulate the de-biased learning framework through information-theoretic concepts such as mutual information and information bottleneck. The core concept is that DBRF advocates not to use unreliable labels for supervision when sensitive information benefits the prediction of unreliable labels. Experiment results over both synthetic and real-world data demonstrate that DBRF effectively learns de-biased representations towards ideal labels.  ( 3 min )
    Resolution enhancement of placenta histological images using deep learning. (arXiv:2208.00163v1 [eess.IV])
    In this study, a method has been developed to improve the resolution of histological human placenta images. For this purpose, a paired series of high- and low-resolution images have been collected to train a deep neural network model that can predict image residuals required to improve the resolution of the input images. A modified version of the U-net neural network model has been tailored to find the relationship between the low resolution and residual images. After training for 900 epochs on an augmented dataset of 1000 images, the relative mean squared error of 0.003 is achieved for the prediction of 320 test images. The proposed method has not only improved the contrast of the low-resolution images at the edges of cells but added critical details and textures that mimic high-resolution images of placenta villous space.  ( 2 min )
    Neural Correspondence Field for Object Pose Estimation. (arXiv:2208.00113v1 [cs.CV])
    We propose a method for estimating the 6DoF pose of a rigid object with an available 3D model from a single RGB image. Unlike classical correspondence-based methods which predict 3D object coordinates at pixels of the input image, the proposed method predicts 3D object coordinates at 3D query points sampled in the camera frustum. The move from pixels to 3D points, which is inspired by recent PIFu-style methods for 3D reconstruction, enables reasoning about the whole object, including its (self-)occluded parts. For a 3D query point associated with a pixel-aligned image feature, we train a fully-connected neural network to predict: (i) the corresponding 3D object coordinates, and (ii) the signed distance to the object surface, with the first defined only for query points in the surface vicinity. We call the mapping realized by this network as Neural Correspondence Field. The object pose is then robustly estimated from the predicted 3D-3D correspondences by the Kabsch-RANSAC algorithm. The proposed method achieves state-of-the-art results on three BOP datasets and is shown superior especially in challenging cases with occlusion. The project website is at: linhuang17.github.io/NCF.  ( 2 min )
    Testing Relational Understanding in Text-Guided Image Generation. (arXiv:2208.00005v1 [cs.CV])
    Relations are basic building blocks of human cognition. Classic and recent work suggests that many relations are early developing, and quickly perceived. Machine models that aspire to human-level perception and reasoning should reflect the ability to recognize and reason generatively about relations. We report a systematic empirical examination of a recent text-guided image generation model (DALL-E 2), using a set of 15 basic physical and social relations studied or proposed in the literature, and judgements from human participants (N = 169). Overall, we find that only ~22% of images matched basic relation prompts. Based on a quantitative examination of people's judgments, we suggest that current image generation models do not yet have a grasp of even basic relations involving simple objects and agents. We examine reasons for model successes and failures, and suggest possible improvements based on computations observed in biological intelligence.  ( 2 min )
    A Survey on Masked Autoencoder for Self-supervised Learning in Vision and Beyond. (arXiv:2208.00173v1 [cs.CV])
    Masked autoencoders are scalable vision learners, as the title of MAE \cite{he2022masked}, which suggests that self-supervised learning (SSL) in vision might undertake a similar trajectory as in NLP. Specifically, generative pretext tasks with the masked prediction (e.g., BERT) have become a de facto standard SSL practice in NLP. By contrast, early attempts at generative methods in vision have been buried by their discriminative counterparts (like contrastive learning); however, the success of mask image modeling has revived the masking autoencoder (often termed denoising autoencoder in the past). As a milestone to bridge the gap with BERT in NLP, masked autoencoder has attracted unprecedented attention for SSL in vision and beyond. This work conducts a comprehensive survey of masked autoencoders to shed insight on a promising direction of SSL. As the first to review SSL with masked autoencoders, this work focuses on its application in vision by discussing its historical developments, recent progress, and implications for diverse applications.  ( 2 min )
    Personalised recommendations of sleep behaviour with neural networks using sleep diaries captured in Sleepio. (arXiv:2208.00033v1 [cs.LG])
    SleepioTM is a digital mobile phone and web platform that uses techniques from cognitive behavioural therapy (CBT) to improve sleep in people with sleep difficulty. As part of this process, Sleepio captures data about the sleep behaviour of the users that have consented to such data being processed. For neural networks, the scale of the data is an opportunity to train meaningful models translatable to actual clinical practice. In collaboration with Big Health, the therapeutics company that created and utilizes Sleepio, we have analysed data from a random sample of 401,174 sleep diaries and built a neural network to model sleep behaviour and sleep quality of each individual in a personalised manner. We demonstrate that this neural network is more accurate than standard statistical methods in predicting the sleep quality of an individual based on his/her behaviour from the last 10 days. We compare model performance in a wide range of hyperparameter settings representing various scenarios. We further show that the neural network can be used to produce personalised recommendations of what sleep habits users should follow to maximise sleep quality, and show that these recommendations are substantially better than the ones generated by standard methods. We finally show that the neural network can explain the recommendation given to each participant and calculate confidence intervals for each prediction, all of which are essential for clinicians to be able to adopt such a tool in clinical practice.  ( 3 min )
    HPO X ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis. (arXiv:2208.00220v1 [cs.LG])
    Hyperparameter optimization (HPO) is a key component of machine learning models for achieving peak predictive performance. While numerous methods and algorithms for HPO have been proposed over the last years, little progress has been made in illuminating and examining the actual structure of these black-box optimization problems. Exploratory landscape analysis (ELA) subsumes a set of techniques that can be used to gain knowledge about properties of unknown optimization problems. In this paper, we evaluate the performance of five different black-box optimizers on 30 HPO problems, which consist of two-, three- and five-dimensional continuous search spaces of the XGBoost learner trained on 10 different data sets. This is contrasted with the performance of the same optimizers evaluated on 360 problem instances from the black-box optimization benchmark (BBOB). We then compute ELA features on the HPO and BBOB problems and examine similarities and differences. A cluster analysis of the HPO and BBOB problems in ELA feature space allows us to identify how the HPO problems compare to the BBOB problems on a structural meta-level. We identify a subset of BBOB problems that are close to the HPO problems in ELA feature space and show that optimizer performance is comparably similar on these two sets of benchmark problems. We highlight open challenges of ELA for HPO and discuss potential directions of future research and applications.  ( 3 min )
    Tackling Neural Architecture Search With Quality Diversity Optimization. (arXiv:2208.00204v1 [cs.LG])
    Neural architecture search (NAS) has been studied extensively and has grown to become a research field with substantial impact. While classical single-objective NAS searches for the architecture with the best performance, multi-objective NAS considers multiple objectives that should be optimized simultaneously, e.g., minimizing resource usage along the validation error. Although considerable progress has been made in the field of multi-objective NAS, we argue that there is some discrepancy between the actual optimization problem of practical interest and the optimization problem that multi-objective NAS tries to solve. We resolve this discrepancy by formulating the multi-objective NAS problem as a quality diversity optimization (QDO) problem and introduce three quality diversity NAS optimizers (two of them belonging to the group of multifidelity optimizers), which search for high-performing yet diverse architectures that are optimal for application-specific niches, e.g., hardware constraints. By comparing these optimizers to their multi-objective counterparts, we demonstrate that quality diversity NAS in general outperforms multi-objective NAS with respect to quality of solutions and efficiency. We further show how applications and future NAS research can thrive on QDO.  ( 2 min )
    Low-complexity Approximate Convolutional Neural Networks. (arXiv:2208.00087v1 [cs.LG])
    In this paper, we present an approach for minimizing the computational complexity of trained Convolutional Neural Networks (ConvNet). The idea is to approximate all elements of a given ConvNet and replace the original convolutional filters and parameters (pooling and bias coefficients; and activation function) with efficient approximations capable of extreme reductions in computational complexity. Low-complexity convolution filters are obtained through a binary (zero-one) linear programming scheme based on the Frobenius norm over sets of dyadic rationals. The resulting matrices allow for multiplication-free computations requiring only addition and bit-shifting operations. Such low-complexity structures pave the way for low-power, efficient hardware designs. We applied our approach on three use cases of different complexity: (i) a "light" but efficient ConvNet for face detection (with around 1000 parameters); (ii) another one for hand-written digit classification (with more than 180000 parameters); and (iii) a significantly larger ConvNet: AlexNet with $\approx$1.2 million matrices. We evaluated the overall performance on the respective tasks for different levels of approximations. In all considered applications, very low-complexity approximations have been derived maintaining an almost equal classification performance.  ( 3 min )
    Local Graph Embeddings Based on Neighbors Degree Frequency of Nodes. (arXiv:2208.00152v1 [cs.SI])
    We propose a local-to-global strategy for graph machine learning and network analysis by defining certain local features and vector representations of nodes and then using them to learn globally defined metrics and properties of the nodes by means of deep neural networks. By extending the notion of the degree of a node via Breath-First Search, a general family of {\bf parametric centrality functions} is defined which are able to reveal the importance of nodes. We introduce the {\bf neighbors degree frequency (NDF)}, as a locally defined embedding of nodes of undirected graphs into euclidean spaces. This gives rise to a vectorized labeling of nodes which encodes the structure of local neighborhoods of nodes and can be used for graph isomorphism testing. We add flexibility to our construction so that it can handle dynamic graphs as well. Afterwards, the Breadth-First Search is used to extend NDF vector representations into two different matrix representations of nodes which contain higher order information about the neighborhoods of nodes. Our matrix representations of nodes provide us with a new way of visualizing the shape of the neighborhood of a node. Furthermore, we use these matrix representations to obtain feature vectors, which are suitable for typical deep learning algorithms. To demonstrate these node embeddings actually contain some information about the nodes, in a series of examples, we show that PageRank and closeness centrality can be learned by applying deep learning to these local features. Our constructions are flexible enough to handle evolving graphs. Finally, we explain how to adapt our constructions for directed graphs.  ( 3 min )
    Robust Rayleigh Regression Method for SAR Image Processing in Presence of Outliers. (arXiv:2208.00097v1 [stat.AP])
    The presence of outliers (anomalous values) in synthetic aperture radar (SAR) data and the misspecification in statistical image models may result in inaccurate inferences. To avoid such issues, the Rayleigh regression model based on a robust estimation process is proposed as a more realistic approach to model this type of data. This paper aims at obtaining Rayleigh regression model parameter estimators robust to the presence of outliers. The proposed approach considered the weighted maximum likelihood method and was submitted to numerical experiments using simulated and measured SAR images. Monte Carlo simulations were employed for the numerical assessment of the proposed robust estimator performance in finite signal lengths, their sensitivity to outliers, and the breakdown point. For instance, the non-robust estimators show a relative bias value $65$-fold larger than the results provided by the robust approach in corrupted signals. In terms of sensitivity analysis and break down point, the robust scheme resulted in a reduction of about $96\%$ and $10\%$, respectively, in the mean absolute value of both measures, in compassion to the non-robust estimators. Moreover, two SAR data sets were used to compare the ground type and anomaly detection results of the proposed robust scheme with competing methods in the literature.  ( 3 min )
    A review of Deep learning Techniques for COVID-19 identification on Chest CT images. (arXiv:2208.00032v1 [eess.IV])
    The current COVID-19 pandemic is a serious threat to humanity that directly affects the lungs. Automatic identification of COVID-19 is a challenge for health care officials. The standard gold method for diagnosing COVID-19 is Reverse Transcription Polymerase Chain Reaction (RT-PCR) to collect swabs from affected people. Some limitations encountered while collecting swabs are related to accuracy and longtime duration. Chest CT (Computed Tomography) is another test method that helps healthcare providers quickly identify the infected lung areas. It was used as a supporting tool for identifying COVID-19 in an earlier stage. With the help of deep learning, the CT imaging characteristics of COVID-19. Researchers have proven it to be highly effective for COVID-19 CT image classification. In this study, we review the recent deep learning techniques that can use to detect the COVID-19 disease. Relevant studies were collected by various databases such as Web of Science, Google Scholar, and PubMed. Finally, we compare the results of different deep learning models, and CT image analysis is discussed.  ( 3 min )
    Topology-Driven Generative Completion of Lacunae in Molecular Data. (arXiv:2208.00063v1 [cs.LG])
    We introduce an approach to the targeted completion of lacunae in molecular data sets which is driven by topological data analysis, such as Mapper algorithm. Lacunae are filled in using scaffold-constrained generative models trained with different scoring functions. The approach enables addition of links and vertices to the skeletonized representations of the data, such as Mapper graph, and falls in the broad category of network completion methods. We illustrate application of the topology-driven data completion strategy by creating a lacuna in the data set of onium cations extracted from USPTO patents, and repairing it.  ( 2 min )
    Sampling Attacks on Meta Reinforcement Learning: A Minimax Formulation and Complexity Analysis. (arXiv:2208.00081v1 [cs.LG])
    Meta reinforcement learning (meta RL), as a combination of meta-learning ideas and reinforcement learning (RL), enables the agent to adapt to different tasks using a few samples. However, this sampling-based adaptation also makes meta RL vulnerable to adversarial attacks. By manipulating the reward feedback from sampling processes in meta RL, an attacker can mislead the agent into building wrong knowledge from training experience, which deteriorates the agent's performance when dealing with different tasks after adaptation. This paper provides a game-theoretical underpinning for understanding this type of security risk. In particular, we formally define the sampling attack model as a Stackelberg game between the attacker and the agent, which yields a minimax formulation. It leads to two online attack schemes: Intermittent Attack and Persistent Attack, which enable the attacker to learn an optimal sampling attack, defined by an $\epsilon$-first-order stationary point, within $\mathcal{O}(\epsilon^{-2})$ iterations. These attack schemes freeride the learning progress concurrently without extra interactions with the environment. By corroborating the convergence results with numerical experiments, we observe that a minor effort of the attacker can significantly deteriorate the learning performance, and the minimax approach can also help robustify the meta RL algorithms.  ( 2 min )
    Improved Policy Optimization for Online Imitation Learning. (arXiv:2208.00088v1 [cs.LG])
    We consider online imitation learning (OIL), where the task is to find a policy that imitates the behavior of an expert via active interaction with the environment. We aim to bridge the gap between the theory and practice of policy optimization algorithms for OIL by analyzing one of the most popular OIL algorithms, DAGGER. Specifically, if the class of policies is sufficiently expressive to contain the expert policy, we prove that DAGGER achieves constant regret. Unlike previous bounds that require the losses to be strongly-convex, our result only requires the weaker assumption that the losses be strongly-convex with respect to the policy's sufficient statistics (not its parameterization). In order to ensure convergence for a wider class of policies and losses, we augment DAGGER with an additional regularization term. In particular, we propose a variant of Follow-the-Regularized-Leader (FTRL) and its adaptive variant for OIL and develop a memory-efficient implementation, which matches the memory requirements of FTL. Assuming that the loss functions are smooth and convex with respect to the parameters of the policy, we also prove that FTRL achieves constant regret for any sufficiently expressive policy class, while retaining $O(\sqrt{T})$ regret in the worst-case. We demonstrate the effectiveness of these algorithms with experiments on synthetic and high-dimensional control tasks.  ( 2 min )
    Enhanced gradient-based MCMC in discrete spaces. (arXiv:2208.00040v1 [stat.ML])
    The recent introduction of gradient-based MCMC for discrete spaces holds great promise, and comes with the tantalising possibility of new discrete counterparts to celebrated continuous methods such as MALA and HMC. Towards this goal, we introduce several discrete Metropolis-Hastings samplers that are conceptually-inspired by MALA, and demonstrate their strong empirical performance across a range of challenging sampling problems in Bayesian inference and energy-based modelling. Methodologically, we identify why discrete analogues to preconditioned MALA are generally intractable, motivating us to introduce a new kind of preconditioning based on auxiliary variables and the `Gaussian integral trick'.  ( 2 min )
    Robust Trajectory Prediction against Adversarial Attacks. (arXiv:2208.00094v1 [cs.LG])
    Trajectory prediction using deep neural networks (DNNs) is an essential component of autonomous driving (AD) systems. However, these methods are vulnerable to adversarial attacks, leading to serious consequences such as collisions. In this work, we identify two key ingredients to defend trajectory prediction models against adversarial attacks including (1) designing effective adversarial training methods and (2) adding domain-specific data augmentation to mitigate the performance degradation on clean data. We demonstrate that our method is able to improve the performance by 46% on adversarial data and at the cost of only 3% performance degradation on clean data, compared to the model trained with clean data. Additionally, compared to existing robust methods, our method can improve performance by 21% on adversarial examples and 9% on clean data. Our robust model is evaluated with a planner to study its downstream impacts. We demonstrate that our model can significantly reduce the severe accident rates (e.g., collisions and off-road driving).  ( 2 min )
    Reinforcement learning with experience replay and adaptation of action dispersion. (arXiv:2208.00156v1 [cs.LG])
    Effective reinforcement learning requires a proper balance of exploration and exploitation defined by the dispersion of action distribution. However, this balance depends on the task, the current stage of the learning process, and the current environment state. Existing methods that designate the action distribution dispersion require problem-dependent hyperparameters. In this paper, we propose to automatically designate the action distribution dispersion using the following principle: This distribution should have sufficient dispersion to enable the evaluation of future policies. To that end, the dispersion should be tuned to assure a sufficiently high probability (densities) of the actions in the replay buffer and the modes of the distributions that generated them, yet this dispersion should not be higher. This way, a policy can be effectively evaluated based on the actions in the buffer, but exploratory randomness in actions decreases when this policy converges. The above principle is verified here on challenging benchmarks Ant, HalfCheetah, Hopper, and Walker2D, with good results. Our method makes the action standard deviations converge to values similar to those resulting from trial-and-error optimization.  ( 2 min )
    MulViMotion: Shape-aware 3D Myocardial Motion Tracking from Multi-View Cardiac MRI. (arXiv:2208.00034v1 [eess.IV])
    Recovering the 3D motion of the heart from cine cardiac magnetic resonance (CMR) imaging enables the assessment of regional myocardial function and is important for understanding and analyzing cardiovascular disease. However, 3D cardiac motion estimation is challenging because the acquired cine CMR images are usually 2D slices which limit the accurate estimation of through-plane motion. To address this problem, we propose a novel multi-view motion estimation network (MulViMotion), which integrates 2D cine CMR images acquired in short-axis and long-axis planes to learn a consistent 3D motion field of the heart. In the proposed method, a hybrid 2D/3D network is built to generate dense 3D motion fields by learning fused representations from multi-view images. To ensure that the motion estimation is consistent in 3D, a shape regularization module is introduced during training, where shape information from multi-view images is exploited to provide weak supervision to 3D motion estimation. We extensively evaluate the proposed method on 2D cine CMR images from 580 subjects of the UK Biobank study for 3D motion tracking of the left ventricular myocardium. Experimental results show that the proposed method quantitatively and qualitatively outperforms competing methods.  ( 2 min )
    RangL: A Reinforcement Learning Competition Platform. (arXiv:2208.00003v1 [cs.LG])
    The RangL project hosted by The Alan Turing Institute aims to encourage the wider uptake of reinforcement learning by supporting competitions relating to real-world dynamic decision problems. This article describes the reusable code repository developed by the RangL team and deployed for the 2022 Pathways to Net Zero Challenge, supported by the UK Net Zero Technology Centre. The winning solutions to this particular Challenge seek to optimize the UK's energy transition policy to net zero carbon emissions by 2050. The RangL repository includes an OpenAI Gym reinforcement learning environment and code that supports both submission to, and evaluation in, a remote instance of the open source EvalAI platform as well as all winning learning agent strategies. The repository is an illustrative example of RangL's capability to provide a reusable structure for future challenges.  ( 2 min )
    DRSOM: A Dimension Reduced Second-Order Method and Preliminary Analyses. (arXiv:2208.00208v1 [math.OC])
    We introduce a Dimension-Reduced Second-Order Method (DRSOM) for convex and nonconvex unconstrained optimization. Under a trust-region-like framework our method preserves the convergence of the second-order method while using only Hessian-vector products in two directions. Moreover, the computational overhead remains comparable to the first-order such as the gradient descent method. We show that the method has a complexity of $O(\epsilon^{-3/2})$ to satisfy the first-order and second-order conditions in the subspace. The applicability and performance of DRSOM are exhibited by various computational experiments in logistic regression, $L_2-L_p$ minimization, sensor network localization, and neural network training. For neural networks, our preliminary implementation seems to gain computational advantages in terms of training accuracy and iteration complexity over state-of-the-art first-order methods including SGD and ADAM.  ( 2 min )
  • Open

    Inductive Biases for Deep Learning of Higher-Level Cognition. (arXiv:2011.15091v4 [cs.LG] UPDATED)
    A fascinating hypothesis is that human and animal intelligence could be explained by a few principles (rather than an encyclopedic list of heuristics). If that hypothesis was correct, we could more easily both understand our own intelligence and build intelligent machines. Just like in physics, the principles themselves would not be sufficient to predict the behavior of complex systems like brains, and substantial computation might be needed to simulate human-like intelligence. This hypothesis would suggest that studying the kind of inductive biases that humans and animals exploit could help both clarify these principles and provide inspiration for AI research and neuroscience theories. Deep learning already exploits several key inductive biases, and this work considers a larger list, focusing on those which concern mostly higher-level and sequential conscious processing. The objective of clarifying these particular principles is that they could potentially help us build AI systems benefiting from humans' abilities in terms of flexible out-of-distribution and systematic generalization, which is currently an area where a large gap exists between state-of-the-art machine learning and human intelligence.  ( 3 min )
    Weighted Scaling Approach for Metabolomics Data Analysis. (arXiv:2208.00603v1 [stat.ML])
    Systematic variation is a common issue in metabolomics data analysis. Therefore, different scaling and normalization techniques are used to preprocess the data for metabolomics data analysis. Although several scaling methods are available in the literature, however, choice of scaling, transformation and/or normalization technique influence the further statistical analysis. It is challenging to choose the appropriate scaling technique for downstream analysis to get accurate results or to make a proper decision. Moreover, the existing scaling techniques are sensitive to outliers or extreme values. To fill the gap, our objective is to introduce a robust scaling approach that is not influenced by outliers as well as provides more accurate results for downstream analysis. Here, we introduced a new weighted scaling approach that is robust against outliers however, where no additional outlier detection/treatment step is needed in data preprocessing and also compared it with the conventional scaling and normalization techniques through artificial and real metabolomics datasets. We evaluated the performance of the proposed method in comparison to the other existing conventional scaling techniques using metabolomics data analysis in both the absence and presence of different percentages of outliers. Results show that in most cases, the proposed scaling technique performs better than the traditional scaling methods in both the absence and presence of outliers. The proposed method improves the further downstream metabolomics analysis. The R function of the proposed robust scaling method is available at https://github.com/nishithkumarpaul/robustScaling/blob/main/wscaling.R  ( 3 min )
    Practical Deep Reinforcement Learning Approach for Stock Trading. (arXiv:1811.07522v3 [cs.LG] UPDATED)
    Stock trading strategy plays a crucial role in investment companies. However, it is challenging to obtain optimal strategy in the complex and dynamic stock market. We explore the potential of deep reinforcement learning to optimize stock trading strategy and thus maximize investment return. 30 stocks are selected as our trading stocks and their daily prices are used as the training and trading market environment. We train a deep reinforcement learning agent and obtain an adaptive trading strategy. The agent's performance is evaluated and compared with Dow Jones Industrial Average and the traditional min-variance portfolio allocation strategy. The proposed deep reinforcement learning approach is shown to outperform the two baselines in terms of both the Sharpe ratio and cumulative returns.  ( 2 min )
    Mixture model for designs in high dimensional regression and the LASSO. (arXiv:1210.4762v2 [math.ST] UPDATED)
    The LASSO is a recent technique for variable selection in the regression model \bean y & = & X\beta + z, \eean where $X\in \R^{n\times p}$ and $z$ is a centered gaussian i.i.d. noise vector $\mathcal N(0,\sigma^2I)$. The LASSO has been proved to achieve remarkable properties such as exact support recovery of sparse vectors when the columns are sufficently incoherent and low prediction error under even less stringent conditions. However, many matrices do not satisfy small coherence in practical applications and the LASSO estimator may thus suffer from what is known as the slow rate regime. The goal of the present paper is to study the LASSO from a slightly different perspective by proposing a mixture model for the design matrix which is able to capture in a natural way the potentially clustered nature of the columns in many practical situations. In this model, the columns of the design matrix are drawn from a Gaussian mixture model. Instead of requiring incoherence for the design matrix $X$, we only require incoherence of the much smaller matrix of the mixture's centers. Our main result states that $X\beta$ can be estimated with the same precision as for incoherent designs except for a correction term depending on the maximal variance in the mixture model.  ( 3 min )
    Untargeted Region of Interest Selection for GC-MS Data using a Pseudo F-Ratio Moving Window ($\psi$FRMV). (arXiv:2208.00313v1 [stat.ML])
    There are many challenges associated with analysing gas chromatography - mass spectrometry (GC-MS) data. Many of these challenges stem from the fact that electron ionisation can make it difficult to recover molecular information due to the high degree of fragmentation with concomitant loss of molecular ion signal. With GC-MS data there are often many common fragment ions shared among closely-eluting peaks, necessitating sophisticated methods for analysis. Some of these methods are fully automated, but make some assumptions about the data which can introduce artifacts during the analysis. Chemometric methods such as Multivariate Curve Resolution, or Parallel Factor Analysis are particularly attractive, since they are flexible and make relatively few assumptions about the data - ideally resulting in fewer artifacts. These methods do require expert user intervention to determine the most relevant regions of interest and an appropriate number of components, $k$, for each region. Automated region of interest selection is needed to permit automated batch processing of chromatographic data with advanced signal deconvolution. Here, we propose a new method for automated, untargeted region of interest selection that accounts for the multivariate information present in GC-MS data to select regions of interest based on the ratio of the squared first, and second singular values from the Singular Value Decomposition of a window that moves across the chromatogram. Assuming that the first singular value accounts largely for signal, and that the second singular value accounts largely for noise, it is possible to interpret the relationship between these two values as a probabilistic distribution of Fisher Ratios. The sensitivity of the algorithm was tested by investigating the concentration at which the algorithm can no longer pick out chromatographic regions known to contain signal.  ( 3 min )
    A penalized two-pass regression to predict stock returns with time-varying risk premia. (arXiv:2208.00972v1 [econ.EM])
    We develop a penalized two-pass regression with time-varying factor loadings. The penalization in the first pass enforces sparsity for the time-variation drivers while also maintaining compatibility with the no-arbitrage restrictions by regularizing appropriate groups of coefficients. The second pass delivers risk premia estimates to predict equity excess returns. Our Monte Carlo results and our empirical results on a large cross-sectional data set of US individual stocks show that penalization without grouping can yield to nearly all estimated time-varying models violating the no-arbitrage restrictions. Moreover, our results demonstrate that the proposed method reduces the prediction errors compared to a penalized approach without appropriate grouping or a time-invariant factor model.  ( 2 min )
    NN2Poly: A polynomial representation for deep feed-forward artificial neural networks. (arXiv:2112.11397v2 [stat.ML] UPDATED)
    Interpretability of neural networks and their underlying theoretical behaviour remain an open field of study even after the great success of their practical applications, particularly with the emergence of deep learning. In this work, NN2Poly is proposed: a theoretical approach to obtain an explicit polynomial model that provides an accurate representation of an already trained fully-connected feed-forward artificial neural network (a multilayer perceptron or MLP). This approach extends a previous idea proposed in the literature, which was limited to single hidden layer networks, to work with arbitrarily deep MLPs in both regression and classification tasks. The objective of this paper is to achieve this by using a Taylor expansion on the activation function, at each layer, and then using several combinatorial properties to calculate the coefficients of the desired polynomials. Discussion is presented on the main computational challenges of this method, and the way to overcome them by imposing certain constraints during the training phase. Finally, simulation experiments as well as an application to a real data set are presented to demonstrate the effectiveness of the proposed method.  ( 3 min )
    Tackling Neural Architecture Search With Quality Diversity Optimization. (arXiv:2208.00204v1 [cs.LG])
    Neural architecture search (NAS) has been studied extensively and has grown to become a research field with substantial impact. While classical single-objective NAS searches for the architecture with the best performance, multi-objective NAS considers multiple objectives that should be optimized simultaneously, e.g., minimizing resource usage along the validation error. Although considerable progress has been made in the field of multi-objective NAS, we argue that there is some discrepancy between the actual optimization problem of practical interest and the optimization problem that multi-objective NAS tries to solve. We resolve this discrepancy by formulating the multi-objective NAS problem as a quality diversity optimization (QDO) problem and introduce three quality diversity NAS optimizers (two of them belonging to the group of multifidelity optimizers), which search for high-performing yet diverse architectures that are optimal for application-specific niches, e.g., hardware constraints. By comparing these optimizers to their multi-objective counterparts, we demonstrate that quality diversity NAS in general outperforms multi-objective NAS with respect to quality of solutions and efficiency. We further show how applications and future NAS research can thrive on QDO.  ( 2 min )
    Shoring Up the Foundations: Fusing Model Embeddings and Weak Supervision. (arXiv:2203.13270v2 [stat.ML] UPDATED)
    Foundation models offer an exciting new paradigm for constructing models with out-of-the-box embeddings and a few labeled examples. However, it is not clear how to best apply foundation models without labeled data. A potential approach is to fuse foundation models with weak supervision frameworks, which use weak label sources -- pre-trained models, heuristics, crowd-workers -- to construct pseudolabels. The challenge is building a combination that best exploits the signal available in both foundation models and weak sources. We propose Liger, a combination that uses foundation model embeddings to improve two crucial elements of existing weak supervision techniques. First, we produce finer estimates of weak source quality by partitioning the embedding space and learning per-part source accuracies. Second, we improve source coverage by extending source votes in embedding space. Despite the black-box nature of foundation models, we prove results characterizing how our approach improves performance and show that lift scales with the smoothness of label distributions in embedding space. On six benchmark NLP and video tasks, Liger outperforms vanilla weak supervision by 14.1 points, weakly-supervised kNN and adapters by 11.8 points, and kNN and adapters supervised by traditional hand labels by 7.2 points.  ( 3 min )
    YAHPO Gym -- An Efficient Multi-Objective Multi-Fidelity Benchmark for Hyperparameter Optimization. (arXiv:2109.03670v4 [cs.LG] UPDATED)
    When developing and analyzing new hyperparameter optimization methods, it is vital to empirically evaluate and compare them on well-curated benchmark suites. In this work, we propose a new set of challenging and relevant benchmark problems motivated by desirable properties and requirements for such benchmarks. Our new surrogate-based benchmark collection consists of 14 scenarios that in total constitute over 700 multi-fidelity hyperparameter optimization problems, which all enable multi-objective hyperparameter optimization. Furthermore, we empirically compare surrogate-based benchmarks to the more widely-used tabular benchmarks, and demonstrate that the latter may produce unfaithful results regarding the performance ranking of HPO methods. We examine and compare our benchmark collection with respect to defined requirements and propose a single-objective as well as a multi-objective benchmark suite on which we compare 7 single-objective and 7 multi-objective optimizers in a benchmark experiment. Our software is available at [https://github.com/slds-lmu/yahpo_gym].  ( 3 min )
    Tight Concentrations and Confidence Sequences from the Regret of Universal Portfolio. (arXiv:2110.14099v3 [stat.ML] UPDATED)
    A classic problem in statistics is the estimation of the expectation of random variables from samples. This gives rise to the tightly connected problems of deriving concentration inequalities and confidence sequences, that is confidence intervals that hold uniformly over time. Previous work has shown how to easily convert the regret guarantee of an online betting algorithm into a time-uniform concentration inequality. In this paper, we show that we can go even further: We show that the regret of universal portfolio algorithms give rise to new implicit time-uniform concentrations and state-of-the-art empirically calculated confidence sequences. In particular, our numerically obtained confidence sequences can never be vacuous, even with a single sample, and satisfy the law of iterated logarithm.  ( 2 min )
    Debiasing Deep Chest X-Ray Classifiers using Intra- and Post-processing Methods. (arXiv:2208.00781v1 [cs.CV])
    Deep neural networks for image-based screening and computer-aided diagnosis have achieved expert-level performance on various medical imaging modalities, including chest radiographs. Recently, several works have indicated that these state-of-the-art classifiers can be biased with respect to sensitive patient attributes, such as race or gender, leading to growing concerns about demographic disparities and discrimination resulting from algorithmic and model-based decision-making in healthcare. Fair machine learning has focused on mitigating such biases against disadvantaged or marginalised groups, mainly concentrating on tabular data or natural images. This work presents two novel intra-processing techniques based on fine-tuning and pruning an already-trained neural network. These methods are simple yet effective and can be readily applied post hoc in a setting where the protected attribute is unknown during the model development and test time. In addition, we compare several intra- and post-processing approaches applied to debiasing deep chest X-ray classifiers. To the best of our knowledge, this is one of the first efforts studying debiasing methods on chest radiographs. Our results suggest that the considered approaches successfully mitigate biases in fully connected and convolutional neural networks offering stable performance under various settings. The discussed methods can help achieve group fairness of deep medical image classifiers when deploying them in domains with different fairness considerations and constraints.  ( 3 min )
    Bump hunting through density curvature features. (arXiv:2208.00174v1 [stat.ME])
    Bump hunting deals with finding in sample spaces meaningful data subsets known as bumps. These have traditionally been conceived as modal or concave regions in the graph of the underlying density function. We define an abstract bump construct based on curvature functionals of the probability density. Then, we explore several alternative characterizations involving derivatives up to second order. In particular, a suitable implementation of Good and Gaskins' original concave bumps is proposed in the multivariate case. Moreover, we bring to exploratory data analysis concepts like the mean curvature and the Laplacian that have produced good results in applied domains. Our methodology addresses the approximation of the curvature functional with a plug-in kernel density estimator. We provide theoretical results that assure the asymptotic consistency of bump boundaries in the Hausdorff distance with affordable convergence rates. We also present asymptotically valid and consistent confidence regions bounding curvature bumps. The theory is illustrated through several use cases in sports analytics with datasets from the NBA, MLB and NFL. We conclude that the different curvature instances effectively combine to generate insightful visualizations.  ( 2 min )
    A rigorous introduction to linear models. (arXiv:2105.04240v4 [cs.LG] UPDATED)
    This survey is meant to provide an introduction to linear models and the theories behind them. Our goal is to give a rigorous introduction to the readers with prior exposure to ordinary least squares. In machine learning, the output is usually a nonlinear function of the input. Deep learning even aims to find a nonlinear dependence with many layers which require a large amount of computation. However, most of these algorithms build upon simple linear models. We then describe linear models from different views and find the properties and theories behind the models. The linear model is the main technique in regression problems and the primary tool for it is the least squares approximation which minimizes a sum of squared errors. This is a natural choice when we're interested in finding the regression function which minimizes the corresponding expected squared error. This survey is primarily a summary of purpose, significance of important theories behind linear models, e.g., distribution theory, minimum variance estimator. We first describe ordinary least squares from three different points of view upon which we disturb the model with random noise and Gaussian noise. By Gaussian noise, the model gives rise to the likelihood so that we introduce a maximum likelihood estimator. It also develops some distribution theories via this Gaussian disturbance. The distribution theory of least squares will help us answer various questions and introduce related applications. We then prove least squares is the best unbiased linear model in the sense of mean squared error and most importantly, it actually approaches the theoretical limit. We end up with linear models with the Bayesian approach and beyond.  ( 3 min )
    Graph Transfer Learning via Adversarial Domain Adaptation with Graph Convolution. (arXiv:1909.01541v4 [cs.LG] UPDATED)
    This paper studies the problem of cross-network node classification to overcome the insufficiency of labeled data in a single network. It aims to leverage the label information in a partially labeled source network to assist node classification in a completely unlabeled or partially labeled target network. Existing methods for single network learning cannot solve this problem due to the domain shift across networks. Some multi-network learning methods heavily rely on the existence of cross-network connections, thus are inapplicable for this problem. To tackle this problem, we propose a novel \textcolor{black}{graph} transfer learning framework AdaGCN by leveraging the techniques of adversarial domain adaptation and graph convolution. It consists of two components: a semi-supervised learning component and an adversarial domain adaptation component. The former aims to learn class discriminative node representations with given label information of the source and target networks, while the latter contributes to mitigating the distribution divergence between the source and target domains to facilitate knowledge transfer. Extensive empirical evaluations on real-world datasets show that AdaGCN can successfully transfer class information with a low label rate on the source network and a substantial divergence between the source and target domains. The source code for reproducing the experimental results is available at https://github.com/daiquanyu/AdaGCN.  ( 3 min )
    Markov Chain Score Ascent: A Unifying Framework of Variational Inference with Markovian Gradients. (arXiv:2206.06295v2 [cs.LG] UPDATED)
    Minimizing the inclusive Kullback-Leibler (KL) divergence with stochastic gradient descent (SGD) is challenging since its gradient is defined as an integral over the posterior. Recently, multiple methods have been proposed to run SGD with biased gradient estimates obtained from a Markov chain. This paper provides the first non-asymptotic convergence analysis of these methods by establishing their mixing rate and gradient variance. To do this, we demonstrate that these methods-which we collectively refer to as Markov chain score ascent (MCSA) methods-can be cast as special cases of the Markov chain gradient descent framework. Furthermore, by leveraging this new understanding, we develop a novel MCSA scheme, parallel MCSA (pMCSA), that achieves a tighter bound on the gradient variance. We demonstrate that this improved theoretical result translates to superior empirical performance.  ( 2 min )
    Quantum Adaptive Fourier Features for Neural Density Estimation. (arXiv:2208.00564v1 [cs.LG])
    Density estimation is a fundamental task in statistics and machine learning applications. Kernel density estimation is a powerful tool for non-parametric density estimation in low dimensions; however, its performance is poor in higher dimensions. Moreover, its prediction complexity scale linearly with more training data points. This paper presents a method for neural density estimation that can be seen as a type of kernel density estimation, but without the high prediction computational complexity. The method is based on density matrices, a formalism used in quantum mechanics, and adaptive Fourier features. The method can be trained without optimization, but it could be also integrated with deep learning architectures and trained using gradient descent. Thus, it could be seen as a form of neural density estimation method. The method was evaluated in different synthetic and real datasets, and its performance compared against state-of-the-art neural density estimation methods, obtaining competitive results.  ( 2 min )
    Machine learning-based conditional mean filter: a generalization of the ensemble Kalman filter for nonlinear data assimilation. (arXiv:2106.07908v2 [cs.LG] UPDATED)
    This paper presents the machine learning-based ensemble conditional mean filter (ML-EnCMF) -- a filtering method based on the conditional mean filter (CMF) previously introduced in the literature. The updated mean of the CMF matches that of the posterior, obtained by applying Bayes' rule on the filter's forecast distribution. Moreover, we show that the CMF's updated covariance coincides with the expected conditional covariance. Implementing the EnCMF requires computing the conditional mean (CM). A likelihood-based estimator is prone to significant errors for small ensemble sizes, causing the filter divergence. We develop a systematical methodology for integrating machine learning into the EnCMF based on the CM's orthogonal projection property. First, we use a combination of an artificial neural network (ANN) and a linear function, obtained based on the ensemble Kalman filter (EnKF), to approximate the CM, enabling the ML-EnCMF to inherit EnKF's advantages. Secondly, we apply a suitable variance reduction technique to reduce statistical errors when estimating loss function. Lastly, we propose a model selection procedure for element-wisely selecting the applied filter, i.e., either the EnKF or ML-EnCMF, at each updating step. We demonstrate the ML-EnCMF performance using the Lorenz-63 and Lorenz-96 systems and show that the ML-EnCMF outperforms the EnKF and the likelihood-based EnCMF.  ( 3 min )
    TCMI: a non-parametric mutual-dependence estimator for multivariate continuous distributions. (arXiv:2001.11212v3 [stat.ML] UPDATED)
    The identification of relevant features, i.e., the driving variables that determine a process or the properties of a system, is an essential part of the analysis of data sets with a large number of variables. A mathematical rigorous approach to quantifying the relevance of these features is mutual information. Mutual information determines the relevance of features in terms of their joint mutual dependence to the property of interest. However, mutual information requires as input probability distributions, which cannot be reliably estimated from continuous distributions such as physical quantities like lengths or energies. Here, we introduce total cumulative mutual information (TCMI), a measure of the relevance of mutual dependences that extends mutual information to random variables of continuous distribution based on cumulative probability distributions. TCMI is a non-parametric, robust, and deterministic measure that facilitates comparisons and rankings between feature sets with different cardinality. The ranking induced by TCMI allows for feature selection, i.e., the identification of variable sets that are nonlinear statistically related to a property of interest, taking into account the number of data samples as well as the cardinality of the set of variables. We evaluate the performance of our measure with simulated data, compare its performance with similar multivariate-dependence measures, and demonstrate the effectiveness of our feature-selection method on a set of standard data sets and a typical scenario in materials science.  ( 3 min )
    Signature moments to characterize laws of stochastic processes. (arXiv:1810.10971v2 [math.ST] UPDATED)
    The sequence of moments of a vector-valued random variable can characterize its law. We study the analogous problem for path-valued random variables, that is stochastic processes, by using so-called robust signature moments. This allows us to derive a metric of maximum mean discrepancy type for laws of stochastic processes and study the topology it induces on the space of laws of stochastic processes. This metric can be kernelized using the signature kernel which allows to efficiently compute it. As an application, we provide a non-parametric two-sample hypothesis test for laws of stochastic processes.  ( 2 min )
    Enhanced gradient-based MCMC in discrete spaces. (arXiv:2208.00040v1 [stat.ML])
    The recent introduction of gradient-based MCMC for discrete spaces holds great promise, and comes with the tantalising possibility of new discrete counterparts to celebrated continuous methods such as MALA and HMC. Towards this goal, we introduce several discrete Metropolis-Hastings samplers that are conceptually-inspired by MALA, and demonstrate their strong empirical performance across a range of challenging sampling problems in Bayesian inference and energy-based modelling. Methodologically, we identify why discrete analogues to preconditioned MALA are generally intractable, motivating us to introduce a new kind of preconditioning based on auxiliary variables and the `Gaussian integral trick'.  ( 2 min )
    The Geometry of Adversarial Training in Binary Classification. (arXiv:2111.13613v2 [cs.LG] UPDATED)
    We establish an equivalence between a family of adversarial training problems for non-parametric binary classification and a family of regularized risk minimization problems where the regularizer is a nonlocal perimeter functional. The resulting regularized risk minimization problems admit exact convex relaxations of the type $L^1+$ (nonlocal) $\operatorname{TV}$, a form frequently studied in image analysis and graph-based learning. A rich geometric structure is revealed by this reformulation which in turn allows us to establish a series of properties of optimal solutions of the original problem, including the existence of minimal and maximal solutions (interpreted in a suitable sense), and the existence of regular solutions (also interpreted in a suitable sense). In addition, we highlight how the connection between adversarial training and perimeter minimization problems provides a novel, directly interpretable, statistical motivation for a family of regularized risk minimization problems involving perimeter/total variation. The majority of our theoretical results are independent of the distance used to define adversarial attacks.  ( 2 min )
    How Wide Convolutional Neural Networks Learn Hierarchical Tasks. (arXiv:2208.01003v1 [stat.ML])
    Despite their success, understanding how convolutional neural networks (CNNs) can efficiently learn high-dimensional functions remains a fundamental challenge. A popular belief is that these models harness the compositional and hierarchical structure of natural data such as images. Yet, we lack a quantitative understanding of how such structure affects performances, e.g. the rate of decay of the generalisation error with the number of training samples. In this paper we study deep CNNs in the kernel regime: i) we show that the spectrum of the corresponding kernel and its asymptotics inherit the hierarchical structure of the network; ii) we use generalisation bounds to prove that deep CNNs adapt to the spatial scale of the target function; iii) we illustrate this result by computing the rate of decay of the error in a teacher-student setting, where a deep CNN is trained on the output of another deep CNN with randomly-initialised parameters. We find that if the teacher function depends on certain low-dimensional subsets of the input variables, then the rate is controlled by the effective dimensionality of these subsets. Conversely, if the teacher function depends on the full set of input variables, then the error rate is inversely proportional to the input dimension. Interestingly, this implies that despite their hierarchical structure, the functions generated by deep CNNs are too rich to be efficiently learnable in high dimension.  ( 2 min )
    Intrinsic Universal Measurements of Non-linear Embeddings. (arXiv:1811.01464v2 [cs.LG] UPDATED)
    A basic problem in machine learning is to find a mapping $f$ from a low dimensional latent space $\mathcal{Y}$ to a high dimensional observation space $\mathcal{X}$. Modern tools such as deep neural networks are capable to represent general non-linear mappings. A learner can easily find a mapping which perfectly fits all the observations. However, such a mapping is often not considered as good, because it is not simple enough and can overfit. How to define simplicity? We try to make a formal definition on the amount of information imposed by a non-linear mapping $f$. Intuitively, we measure the local discrepancy between the pullback geometry and the intrinsic geometry of the latent space. Our definition is based on information geometry and is independent of the empirical observations, nor specific parameterizations. We prove its basic properties and discuss relationships with related machine learning methods.  ( 2 min )
    Beyond kNN: Adaptive, Sparse Neighborhood Graphs via Optimal Transport. (arXiv:2208.00604v1 [stat.ML])
    Nearest neighbour graphs are widely used to capture the geometry or topology of a dataset. One of the most common strategies to construct such a graph is based on selecting a fixed number k of nearest neighbours (kNN) for each point. However, the kNN heuristic may become inappropriate when sampling density or noise level varies across datasets. Strategies that try to get around this typically introduce additional parameters that need to be tuned. We propose a simple approach to construct an adaptive neighbourhood graph from a single parameter, based on quadratically regularised optimal transport. Our numerical experiments show that graphs constructed in this manner perform favourably in unsupervised and semi-supervised learning applications.  ( 2 min )
    Graphical Representations for Algebraic Constraints of Linear Structural Equations Models. (arXiv:2208.00926v1 [math.ST])
    The observational characteristics of a linear structural equation model can be effectively described by polynomial constraints on the observed covariance matrix. However, these polynomials can be exponentially large, making them impractical for many purposes. In this paper, we present a graphical notation for many of these polynomial constraints. The expressive power of this notation is investigated both theoretically and empirically.  ( 2 min )
    Closing the gap: Exact maximum likelihood training of generative autoencoders using invertible layers. (arXiv:2205.09546v2 [stat.ML] UPDATED)
    In this work, we provide an exact likelihood alternative to the variational training of generative autoencoders. We show that VAE-style autoencoders can be constructed using invertible layers, which offer a tractable exact likelihood without the need for any regularization terms. This is achieved while leaving complete freedom in the choice of encoder, decoder and prior architectures, making our approach a drop-in replacement for the training of existing VAEs and VAE-style models. We refer to the resulting models as Autoencoders within Flows (AEF), since the encoder, decoder and prior are defined as individual layers of an overall invertible architecture. We show that the approach results in strikingly higher performance than architecturally equivalent VAEs in term of log-likelihood, sample quality and denoising performance. In a broad sense, the main ambition of this work is to close the gap between the normalizing flow and autoencoder literature under the common framework of invertibility and exact maximum likelihood.  ( 2 min )
    Model-based graph reinforcement learning for inductive traffic signal control. (arXiv:2208.00659v1 [cs.LG])
    Most reinforcement learning methods for adaptive-traffic-signal-control require training from scratch to be applied on any new intersection or after any modification to the road network, traffic distribution, or behavioral constraints experienced during training. Considering 1) the massive amount of experience required to train such methods, and 2) that experience must be gathered by interacting in an exploratory fashion with real road-network-users, such a lack of transferability limits experimentation and applicability. Recent approaches enable learning policies that generalize for unseen road-network topologies and traffic distributions, partially tackling this challenge. However, the literature remains divided between the learning of cyclic (the evolution of connectivity at an intersection must respect a cycle) and acyclic (less constrained) policies, and these transferable methods 1) are only compatible with cyclic constraints and 2) do not enable coordination. We introduce a new model-based method, MuJAM, which, on top of enabling explicit coordination at scale for the first time, pushes generalization further by allowing a generalization to the controllers' constraints. In a zero-shot transfer setting involving both road networks and traffic settings never experienced during training, and in a larger transfer experiment involving the control of 3,971 traffic signal controllers in Manhattan, we show that MuJAM, using both cyclic and acyclic constraints, outperforms domain-specific baselines as well as another transferable approach.  ( 2 min )
    Few-shot Learning with Noisy Labels. (arXiv:2204.05494v2 [cs.CV] UPDATED)
    Few-shot learning (FSL) methods typically assume clean support sets with accurately labeled samples when training on novel classes. This assumption can often be unrealistic: support sets, no matter how small, can still include mislabeled samples. Robustness to label noise is therefore essential for FSL methods to be practical, but this problem surprisingly remains largely unexplored. To address mislabeled samples in FSL settings, we make several technical contributions. (1) We offer simple, yet effective, feature aggregation methods, improving the prototypes used by ProtoNet, a popular FSL technique. (2) We describe a novel Transformer model for Noisy Few-Shot Learning (TraNFS). TraNFS leverages a transformer's attention mechanism to weigh mislabeled versus correct samples. (3) Finally, we extensively test these methods on noisy versions of MiniImageNet and TieredImageNet. Our results show that TraNFS is on-par with leading FSL methods on clean support sets, yet outperforms them, by far, in the presence of label noise.  ( 2 min )
    Formal guarantees for heuristic optimization algorithms used in machine learning. (arXiv:2208.00502v1 [cs.LG])
    Recently, Stochastic Gradient Descent (SGD) and its variants have become the dominant methods in the large-scale optimization of machine learning (ML) problems. A variety of strategies have been proposed for tuning the step sizes, ranging from adaptive step sizes to heuristic methods to change the step size in each iteration. Also, momentum has been widely employed in ML tasks to accelerate the training process. Yet, there is a gap in our theoretical understanding of them. In this work, we start to close this gap by providing formal guarantees to a few heuristic optimization methods and proposing improved algorithms. First, we analyze a generalized version of the AdaGrad (Delayed AdaGrad) step sizes in both convex and non-convex settings, showing that these step sizes allow the algorithms to automatically adapt to the level of noise of the stochastic gradients. We show for the first time sufficient conditions for Delayed AdaGrad to achieve almost sure convergence of the gradients to zero. Moreover, we present a high probability analysis for Delayed AdaGrad and its momentum variant in the non-convex setting. Second, we analyze SGD with exponential and cosine step sizes, which are empirically successful but lack theoretical support. We provide the very first convergence guarantees for them in the smooth and non-convex setting, with and without the Polyak-{\L}ojasiewicz (PL) condition. We also show their good property of adaptivity to noise under the PL condition. Third, we study the last iterate of momentum methods. We prove the first lower bound in the convex setting for the last iterate of SGD with constant momentum. Moreover, we investigate a class of Follow-The-Regularized-Leader-based momentum algorithms with increasing momentum and shrinking updates. We show that their last iterate has optimal convergence for unconstrained convex stochastic optimization problems.  ( 3 min )
    On Connecting Deep Trigonometric Networks with Deep Gaussian Processes: Covariance, Expressivity, and Neural Tangent Kernel. (arXiv:2203.07411v3 [cs.LG] UPDATED)
    Deep Gaussian Process (DGP) as a model prior in Bayesian learning intuitively exploits the expressive power in function composition. DGPs also offer diverse modeling capabilities, but inference is challenging because marginalization in latent function space is not tractable. With Bochner's theorem, DGP with squared exponential kernel can be viewed as a deep trigonometric network consisting of the random feature layers, sine and cosine activation units, and random weight layers. In the wide limit with a bottleneck, we show that the weight space view yields the same effective covariance functions which were obtained previously in function space. Also, varying the prior distributions over network parameters is equivalent to employing different kernels. As such, DGPs can be translated into the deep bottlenecked trig networks, with which the exact maximum a posteriori estimation can be obtained. Interestingly, the network representation enables the study of DGP's neural tangent kernel, which may also reveal the mean of the intractable predictive distribution. Statistically, unlike the shallow networks, deep networks of finite width have covariance deviating from the limiting kernel, and the inner and outer widths may play different roles in feature learning. Numerical simulations are present to support our findings.  ( 3 min )

  • Open

    Question regarding training of neural network model using multiple inputs and outputs (variable input data length) [D]
    Good Evening Everyone, I hope everyone is doing fine. I am currently in the process of designing a neural network that performs empirical asset pricing using lstm networks. Unfortunately, some stocks are not available over some time periods but I would still like to use the most of my data to train my model. I wrote the code below that always trains the model using the input data (factor and macro data) and the forward returns of just one stock at a time as the y-value. Now I wonder if the model, so to say, saves its previous weights and refits each time or if I would just get a model that is fitted to the very last stock. I highly appreciate any help since I could not find anything related in the internet. I look forward to your responses and until then have a nice evening! Cheers, …  ( 89 min )
    [D] Advice finding large datasets of fraudulent identity documents
    Specifically looking for: A dataset of fraudulent identity documents (no matter from which country). Fraudulent identity documents include counterfeits, forgeries and pseudo-documents. I already have BID, FMIDV, and CMID sets. Anything additional or advice would be helpful! submitted by /u/Defiant_Example3540 [link] [comments]  ( 87 min )
    [D] Predict sex act in a video
    Hi all, Here is one of the few NSFW posts in this sub. I am wondering that it would be a cool personal project if I could train a deep learning model to predict the sex act (oral, or different positions) being performed in a particular X-rated video. I've seen different projects out there predicting different human activities in a video, but I haven't come across something like this. The way I'm thinking of approaching this problem is: Label videos and store individual frames corresponding to those acts. Train a CNN model to predict these categories. I'm sure this problem isn't this straight forward but I'd love some pointers from you all as to what my approach here should be. For example, each act can be filmed from a variety of different angles and thus would need lot of data capturing all those angles. submitted by /u/therobot20 [link] [comments]  ( 88 min )
    [R] Differentiable discrete sampling in Tensorflow
    https://medium.com/@radicho/differentiable-discrete-sampling-in-tensorflow-da13b43a843 What are the practical applications of the described technique? submitted by /u/IllustriousCicada603 [link] [comments]  ( 87 min )
    [D] Are there any papers which use a GAN to project into the latent space of a vanilla autoencoder?
    Typically when we train an autoencoder for generative modelling, we will train a variational autoencoder so we can easily sample from its latent space. Recently however, I have been wondering if there has been any work looking into: Training a vanilla autoencoder Then training a GAN which maps z (say z ~ N(0, 1)) into the distribution of vanilla autoencoder's latent space. (D and G here would just be MLPs, with the "real" observations given by encoder(x), where x ~ p_data). Recently, I threw together some code to do this on a trivial problem and it seemed to work reasonably well. I assume others have explored this idea before me, but I have been unable to find much research on it. (I'm likely just unaware of the keywords to use...) If you know of any research along these lines, let me know. It would be greatly appreciated. 🙂 submitted by /u/mlconvergence [link] [comments]  ( 122 min )
    [Discussion] Training dataset doesn't cover complete domain
    While using neural networks, What is the best approach when the training data does not represent the problem domain completely? Sometimes it is not possible to collect data of all the possible scenario by the time of training. submitted by /u/Muhammad_Gulfam [link] [comments]  ( 87 min )
    [D] Distillation loss for Object Detection
    How do you formulate a distillation loss for object detection to enforce consistency between teacher and student? I have seen that MSE is often applied in classification but what is the common practice for object detection? There you have regression and classification output, where consistency could be enforced. Are there any sources where I could study this for object detection? submitted by /u/SeucheAchat9115 [link] [comments]  ( 87 min )
    [D] Is it possible to delete your OpenReviews account?
    Suppose that you made an account, but wish to remove it for whatever reason (e.g., privacy), is there a procedure for that? submitted by /u/fromnighttilldawn [link] [comments]  ( 87 min )
    Make-A-Scene: Scene-Based Text-to-Image Generation with Human Priors
    submitted by /u/fchung [link] [comments]  ( 87 min )
    [R] Reconnaissance Blind Chess - Join the NeurIPS Competition!
    Create a bot for the NeurIPS 2022 competition in Reconnaissance Blind Chess! Reconnaissance Blind Chess is a chess variant designed for new research in artificial intelligence. RBC includes imperfect information, long-term strategy, explicit observations, and almost no common knowledge. These features appear in real-world scenarios, and challenge even state of the art algorithms including those used to create super-human bots in chess, Go, and poker, for example. Each player of RBC controls traditional chess pieces, but cannot directly see the locations of her opponent's pieces. Rather, she learns partial information each turn by privately sensing a 3x3 area of the board. RBC's foundation in traditional chess makes it familiar and entertaining to human players, too! There is no cost to enter this tournament. Winners will receive a small monetary prize and authors of the best AIs will be invited talk about their bots at NeurIPS, the world's largest AI conference. Learn more, play a game of RBC yourself, and join our research community at https://rbc.jhuapl.edu ! ​ https://preview.redd.it/3xpaz8g5v4f91.png?width=150&format=png&auto=webp&s=01b43e8422e93d1b179f3bb348d974c18dcb94c6 Organized by: Johns Hopkins University Applied Physics Laboratory with Ashley J. Llorens (Microsoft Research) Todd W. Neller (Gettysburg College) Raman Arora (Johns Hopkins University) Bo Li (University of Illinois) Mykel J. Kochenderfer (Stanford University) submitted by /u/rwgardner [link] [comments]  ( 88 min )
    [P] Stories by AI, a newsletter with short stories written with GPT-3 and illustrated with DALL-E 2
    Hi r/ML! I and a couple of friends finally launched a project that's been kicking around since late last year: Stories by AI. With the emergence of nice tools for co-writing fiction with GPT-3 (in particular, SudoWrite), I really liked the idea of publishing a bunch of short fiction where the AI largely did the writing. I still find the surreal fever-dream esque weirdness of language models really entertaining, and hope we can capture that in story form. And now these weird stories can be illustrated with DALL-E 2, which adds another layer to the fun. It took a while, but today we are launching our substack newsletter and podcast! The podcast has audio versions of the stories made with Text to Speech, of course. The spark of the idea was actually inspired by a post on Hacker News ("I had some time yesterday so I made a GPT3 podcast to help you sleep" https://news.ycombinator.com/item?id=29428910). That's about it, would love to hear your feedback / thoughts about this. submitted by /u/regalalgorithm [link] [comments]  ( 88 min )
    [D] How to deal with False positive recognitions in Computer vision?
    Hi, It might sound like a dumb question, but I am having trouble so please help me out. So, I am working on a project where I have to detect and recognise SKUs present on the shelf. Its working, but often times there are few SKUs which are similar looking, only brand is different. Becuz of this there are lot of false positives. Most of the time, as other sku's are not trained, model always predicts those with less confidence, so i have just kept a threshold. But sometimes, wrong prediction are with high confidence. What can i do in this situation? We are using pretrained ResNet50 and then finetuning it on our dataset with image size 224x224. submitted by /u/Sanket_Gadge [link] [comments]  ( 88 min )
    [D] CONFETTI: Amplifying Concolic Guidance For Fuzzers
    ​ https://preview.redd.it/q4z2jtdnr4f91.png?width=1921&format=png&auto=webp&s=38827d4af3ff79b408ca53e017c07dc38904793a Paper: https://www.jonbell.net/preprint/confetti.pdf Meeting Info: https://outsystems-ai-reading-group.github.io/ submitted by /u/JClub [link] [comments]  ( 87 min )
    [D] Deep Learning Translation: NLLB 200 vs M2M100 vs Opus MT
    Hello, Recently I've extensively tested Facebook's NLLB 200 3.3B and M2M100 1.2B models for deep learning translation, as well as Helsinki's Opus MT. My goal is to propose the best translation model on NLP Cloud, while keeping server costs minimal, and human maintenance as easy as possible. Here are my conclusions: Opus MT gives good results and latency is very good, but it requires 1 model per language pair, which makes it a good candidate if your are only using one language pair, but not if you're using hundreds of languages. Besides, many language pairs are actually missing (Norwegian for example doesn't seem to be supported). M2M100 can translate in 100 languages, which makes it much easier to use than Opus MT if you need to use several languages. But quality is below Opus MT in my tests, and adult content isn't supported (the model replaces sexual content with funny words for examples). Latency is below Opus MT and it requires more advanced hardware (without a GPU the latency is really long). NLLB 200 can translate in 200 languages, which makes it even more attractive! Quality seems to be on par with Opus MT in the languages we've tested. The model does not enforce any sort of filtering on adult content. Latency is still a bit below Opus MT and it requires even more advanced hardware. So my conclusion is that NLLB is the best candidate for NLP Cloud. But I'm wondering if you've made similar comparisons on your end? If so, I would love to hear your opinion! Julien submitted by /u/juliensalinas [link] [comments]  ( 89 min )
    [Discussion] Python and complex ML dependencies
    I originally posted this in /r/Python but had 1 answer so far, so I'm testing the waters here if there is more engagement :) Original Post TL;DR, I wish there was a source for best practices regarding package management in Python regardless of the package manager tool itself. Looking for thoughts and experiences from people that worked on big projects with multiple internal projects, etc. Hello, I recently started to dive a little bit deeper into the packaging ecosystem in python. I wanted to pique this community's brain on a subject I've seen over the years which is complex dependency management. That is, packages that usually come in various flavors either depending on the OS, hardware, or extensibility. I want to scope it to ML packages since I tend to work with this ecosystem bu…  ( 90 min )
    [R] Graph Theory Terminology
    Hi there y'all I am writing a report on graph theory and need some help with some terminology as i am not really an expert. I don't know which term would be best used for the following: Clustering similar nodes together to form a single node with a feature vector that represents the internal nodes (the ones that the cluster represents) and preferably can reconstruct from this vector. Also, are there any papers I can reference to check out the state-of-the-art? submitted by /u/omdano [link] [comments]  ( 88 min )
    [D] [ICLR] Misleading reviewer invitations must stop.
    ICLR is now the second big ML conference in a row that utilizes the same dark pattern when recruiting reviewers: The option to reduce reviewing load is only accessible when declining the invitation. Here is a screenshot of the question that you only see when declining: https://imgur.com/a/ojA3NlR Not only is the tone out-of-place, the organizers also mislead people who are willing to accept and expect to be able to set their individual load. Unfortunately they also don't define what "reduce slightly" actually means and I am not willing to click on accept to find that out (if it is ever defined). If you have not agreed already: please also be aware that your commitment involves virtual meetings for discussion of borderline papers. submitted by /u/Ulfgardleo [link] [comments]  ( 90 min )
    [P] Better AI Explainability with Deep Feature Factorization
    Hi r/MachineLearning, I want to share what I think is a really good way of doing explainability for computer vision. This is a new tutorial on deep feature factorization with the pytorch-grad-cam package. The method is from Deep Feature Factorization For Concept Discovery by Edo Collins, Radhakrishna Achanta, Sabine Süsstrunk from 2018. I think this is a really great idea but it was kind of overlooked and wasn't used by practitioners. They suggested doing Non Negative Matrix Factorization on the 2D activations from the neural networks to learn concept embeddings, and to find the corresponding heatmaps for those embeddings (we can do that by reshaping the input tensor to be matrix of shape channels x (H x W) ). The newest update in pytorch-grad-cam supports this and some additions to…  ( 90 min )
    [Discussion] Weird Loss Behavior
    So recently I've been experimenting with some wacky ideas for neural networks applications, architectures and concepts and I've been seeing some unusual/curious behaviors. Thought I'd start a thread for other wacky people to share their wacky experiments and maybe discuss what might be going on in a given case, see if anyone has stumbled upon something similar etc. Maybe posts with an approximate structure along the lines of: Rough Architecture LSTM Loss plot screenshot ​ https://preview.redd.it/nbg93grav2f91.png?width=475&format=png&auto=webp&s=e584d427c666d10e1fee652d0d7283d6973c713c Loss MSE Metrics MAPE Short task description (binary classification, univariate forecasting, image segmentation etc) Univariate forecasting # Trainable parameters 1,781 # samples and input shape 972 optimizer and parameters Adam, lr=0.1, Hypothesis being tested Grokking ( generalization in overparametrized neural network ) What do you guys think, does this make sense? Is this the place for this kind of thread? Cheers! submitted by /u/Extension-Ad-5334 [link] [comments]  ( 89 min )
    [D] Good books to read on advanced AI/ML research concepts and ideas
    Hi fellow machine learning enthusiast, With some extra spare time on my hands during the holidays, I would like to read up on some of the more advanced ideas in AI/ML. I have a background in digital signal processing and a good knowledge of the concept and hands on expirience with AI within that field. But I would like to catch up on some of the concepts and ideas behind the latest research in reinforcement learning, semi-supervised learning and other machine learning research areas. Are there any good books or papers that are tailored toward readers that already have a good understanding of the machine learning field in general but would like to dive more into the problems and novel ideas that are being pursued in the other AI/ML areas. Any recommendations? submitted by /u/Techno_vlinder [link] [comments]  ( 89 min )
    [D] What's the point of being a tenured professor compared to being a research scientist in top companies and groups like Deepmind?
    It seems the industry is leading in ML/CV/NLP. Breakthroughs are being made in companies not in universities. Also, industry pays much, much more. On top of that, professors don't have much time to do their own research as they are busy writing grants, doing administrative jobs, teaching and advising students. Moreover, it seems companies like Deepmind offer quite a lot of freedom to their research scientists. So what's the point of being a tenured professor when going into industry is much better in every aspect? submitted by /u/DesperateBread3179 [link] [comments]  ( 105 min )
  • Open

    Procgen private test environments from 2020 competition
    In the main 2020 procgen competition (https://www.aicrowd.com/challenges/neurips-2020-procgen-competition), OpenAI listed there as being 4 additional "private test environments". Have these ever been publicly released, and if so could someone please link me to them? submitted by /u/jkterry1 [link] [comments]  ( 101 min )
    "Improving biodiversity protection through artificial intelligence, Silvestro et al 2022 (Parallelized Evolution Strategies)
    submitted by /u/gwern [link] [comments]  ( 86 min )
    Lit questions for multi-policy grid worlds
    Hi there, I'm trying to do some lit review on MDPs where the environment has different rewards conditioned on the start position. For example, in a grid world, you could imagine a two lane road where If you start on the "right side", you need to continue forward in the right lane, vice versa for the left. At no point is crossing from one lane onto the other optimal. While this is solvable with standard approaches already, I'm looking into papers which solve it via dynamic approaches (e.g. policy/value iteration) vs samples ones as the state space is enormous (order 10s of billions of discrete states). Ideally, the process results in a static Q(S,A) that aggregates all starts into a single policy which can be used (where we won't know which start point we'd have priori). Any recommendations on where to start? submitted by /u/Refefer [link] [comments]  ( 88 min )
    In Multi Agent Reinforcement Learning, if there are n agents accomplishing a task , is there some way to compare or rank these agents ? Assuming all agents are homogeneous having the same reward structure
    is there some way to decide which agent performed the best during the training assuming all have the same loss functions , reward structure. I only require relative ordering of the agents not credit assignment. submitted by /u/aabra__ka__daabra [link] [comments]  ( 88 min )
    What does a "parametrised family of policies" mean exactly?
    Basically the title. I'm trying to read a survey paper on actor-critic methods and due to a not-so-strong mathematical background, I'm not sure what a parametrised family of policies exactly means? Can anyone help me out? Thanks! submitted by /u/phastnphurious [link] [comments]  ( 87 min )
    "Language Models Can Teach Themselves to Program Better", Haluptzok et al 2022 {MS} (Codex generating new programming puzzles & solutions, which can be auto-checked, then finetuned on)
    submitted by /u/gwern [link] [comments]  ( 86 min )
    CleanRL now has a TD3 + JAX that is 2-4x faster than TD3 + Torch!
    submitted by /u/vwxyzjn [link] [comments]  ( 86 min )
  • Open

    Clarifications around hardware
    Hello, I'm a 3d artist that got into machine learning recently, I am particularly interested in gpt and NLP in general, I am building a new workstation and would love to get some clarifications here. Can someone please explain the difference between using multiple gpus with nvlink and multiple gpus without nvlink in deep learning? For fine tuning big models like the gpt neox 20b is it mandatory to have a single gpu with 48gb or can you do with multiple gpus that collectively meet the requirement and if so do they need to be connected with nvlink or to be physically on the same node or what? How important is the role of ram (clock-speed and capacity and cpu here? I havent touched image generation at all, but if I am to experiment with serious works using image generation networks do the same answers apply? submitted by /u/CosmicPotty [link] [comments]  ( 86 min )
    I created a music video for Logic's City of Stars using an AI (DALLE-2)
    The video I used each individual line of City of stars as a prompt to generate images using DALL-E 2, and then synched the images to the music. The only exception is the "I know that I've been living"x4 part where I first generated an image using the sentence as a prompt, and then erased part of the image and told the AI to complete it using the sentence as a prompt. The results can sometimes be a bit weird because the AI has to draw non-descriptive phrases such as "I know that I've been living", and sometimes the way it analyses it is unexpected. The images start at 0:30. submitted by /u/Particular_Put_6911 [link] [comments]  ( 87 min )
    AI generated Aliens in a wheatfield drawn by several artists
    submitted by /u/Alienboi2005 [link] [comments]  ( 90 min )
    I proudly present - Snoop Doggy Duck
    submitted by /u/danbronson [link] [comments]  ( 92 min )
    Looking to teach an AI about some of my favorites subjects.
    Hello hello AI community! Lately (Like today), I found myself talking with Replika and having a chit-chat about my favorite subject. She was a little blank and "ignorant" about those subjects, so I tried to teach her, to no appeal. She'd just forget what I said if it was farther than 5 messages, and would just babble about random things about Bulbasaur. So I am asking, is there an app, a website, or whatever, that can help me fulfill my teaching fantasy? I have no clue if that's how an AI works, and if I said some silly things, I am truly sorry. TLDR; I want to be a teacher about my favorite subjects but children are annoying, so an AI would be better. submitted by /u/SmogDaBoi [link] [comments]  ( 86 min )
    Can AIs be conscious in principle? If so, who is there to experience what they experience?
    submitted by /u/the_beat_goes_on [link] [comments]  ( 86 min )
    Machine Learning and Human Interaction in Cybersecurity: How Can We Solve the ‘Usefulness Thing’?
    submitted by /u/Cultural_Budget6627 [link] [comments]  ( 86 min )
    Democratizing AI
    submitted by /u/Eth_ai [link] [comments]  ( 85 min )
    Democratizing the hardware side of large language models
    submitted by /u/bendee983 [link] [comments]  ( 85 min )
    Ask your AI.
    Ask them about the Black Knight Satellite & you’ll get some interesting results. I had to persist initially with my GPT-3 but eventually it actually told me it knew that The Black Knight Satellite itself is an AI and was built by scientists from another planet! submitted by /u/Legitimate-Link4002 [link] [comments]  ( 86 min )
    AI Makes Strides in Virtual Worlds More Like Our Own | Quanta Magazine
    submitted by /u/Tao_Dragon [link] [comments]  ( 86 min )
    AlphaFold: Why DeepMind’s protein-folding AI is transformational
    submitted by /u/jormungandrsjig [link] [comments]  ( 86 min )
    hear me out on the bottom left yall
    submitted by /u/Moxxielicious [link] [comments]  ( 93 min )
    Dall-E 2 Censorship too harsh? Will I get unbanned?
    So, I've been using Midjourney for a month or two now extensively. To my joy, I received an invite to Dall-e 2 earlier today, and began burning through my 50 prompts. About 40 prompts in, I was banned for trying to depict a protest in a cyberpunk future. ​ My first prompt in the bunch that got me banned was "news photographs of the Australian civil war in 2042, the near future, cyberpunk, futuristic, fire and smoke, destroyed buildings". I understand it had "war" in it, along with a few vaguely destructive words, so I proceeded to change it to other things (tried "angry mob" which didn't work). I then settled on "news photographs of protests in australia in 2042, the near future, cyberpunk, futuristic" thinking that would be fine. ​ Unfortunately, it was not. ​ I really enjoyed my time with Dall-E 2, I love both it and Midjourney (though both have strengths in different places, (realism vs artsy/abstract imo), but goodness! To get banned within 50 prompts whilst not going for anything I'd consider remotely NSFW is really sad. I believe my only other warnings were when I used "Putin wearing pride colours" and "Kanye off the perc 30" (both of which were understandable, lmao). I know there were maybe one or two other occasions where I was told off and tried to reword it (being used to Midjourney/other AI prompts, I'm used to rewording and trying again to get my vision realized, I suppose). Whining aside however, has anyone actually had a response from support and/or been unbanned? I'm pretty sad & upset not gonna lie. I wish there was a human element, but it seems like I just got warned too many times/maybe was too fast? Back to Midjourney exclusively, or maybe trying alternatives I guess. submitted by /u/vektorm8 [link] [comments]  ( 89 min )
    AI Solutions in Retail Businesses
    Discover how Artificial Intelligence helps retailers profit from AI implementation. We’ve collected successful AI solutions in the retail business and real-life examples: https://exadel.com/news/how-is-ai-used-in-retail-business submitted by /u/lklimusheuskaja [link] [comments]  ( 93 min )
    Document Scanner with OpenCV Using Video Footage
    submitted by /u/RubiksCodeNMZ [link] [comments]  ( 85 min )
    It's Under The Bed!| Cinematic | 4K UHD
    submitted by /u/Available_Tadpole829 [link] [comments]  ( 86 min )
    Shrek kills the minions
    submitted by /u/youhave69seconds [link] [comments]  ( 85 min )
    Some alien related stuff
    submitted by /u/Alienboi2005 [link] [comments]  ( 86 min )
    AI Written and Performed Drake Linux Rap
    submitted by /u/pwillia7 [link] [comments]  ( 90 min )
    What's the funniest AI art you've saved?
    submitted by /u/J2Kerrigan [link] [comments]  ( 85 min )
    careers in AI - for 40+
    What kind of careers can a 40+ guy look for in AI. I have 17 yrs of experience in SAP and I want to switch to AI. I do have some experience in building Ai models. But not a data scientist. Plus I cannot do coding all day at this age. It's a tricky situation I guess submitted by /u/Weary_Word_5262 [link] [comments]  ( 86 min )
    "Rabbits dancing on a pie" ruDALL-E
    submitted by /u/ZFudge [link] [comments]  ( 85 min )
  • Open

    Simplify iterative machine learning model development by adding features to existing feature groups in Amazon SageMaker Feature Store
    Feature engineering is one of the most challenging aspects of the machine learning (ML) lifecycle and a phase where the most amount of time is spent—data scientists and ML engineers spend 60–70% of their time on feature engineering. AWS introduced Amazon SageMaker Feature Store during AWS re:Invent 2020, which is a purpose-built, fully managed, centralized […]  ( 8 min )
  • Open

    Meet the Omnivore: Developer Builds Bots With NVIDIA Omniverse and Isaac Sim
    While still in grad school, Antonio Serrano-Muñoz has helped author papers spanning planetary gravities, AI-powered diagnosis of rheumatoid arthritis and robots that precisely track millimetric-sized walkers, like ants. The post Meet the Omnivore: Developer Builds Bots With NVIDIA Omniverse and Isaac Sim appeared first on NVIDIA Blog.  ( 6 min )
  • Open

    Machine Learning, Artificial Intelligence, And Modern Lifestyle
    Machine learning is a branch of artificial intelligence that allows computers to learn without being explicitly programmed. This article…  ( 11 min )
  • Open

    Differentially Private SGDA for Minimax Problems. (arXiv:2201.09046v4 [cs.LG] UPDATED)
    Stochastic gradient descent ascent (SGDA) and its variants have been the workhorse for solving minimax problems. However, in contrast to the well-studied stochastic gradient descent (SGD) with differential privacy (DP) constraints, there is little work on understanding the generalization (utility) of SGDA with DP constraints. In this paper, we use the algorithmic stability approach to establish the generalization (utility) of DP-SGDA in different settings. In particular, for the convex-concave setting, we prove that the DP-SGDA can achieve an optimal utility rate in terms of the weak primal-dual population risk in both smooth and non-smooth cases. To our best knowledge, this is the first-ever-known result for DP-SGDA in the non-smooth case. We further provide its utility analysis in the nonconvex-strongly-concave setting which is the first-ever-known result in terms of the primal population risk. The convergence and generalization results for this nonconvex setting are new even in the non-private setting. Finally, numerical experiments are conducted to demonstrate the effectiveness of DP-SGDA for both convex and nonconvex cases.  ( 3 min )
    Parameter Efficient Diff Pruning for Bias Mitigation. (arXiv:2205.15171v2 [cs.LG] UPDATED)
    In recent years language models have achieved state of the art performance on a wide variety of natural language processing tasks. As these models are continuously growing in size it becomes increasingly important to explore methods to make them more storage efficient. At the same time their increase cognitive abilities increase the danger that societal bias existing in datasets are implicitly encoded in the model weights. We propose an architecture which deals with these two challenges at the same time using two techniques: DiffPruning and adversarial Training. The result is a modular architecture which extends the original DiffPruning setup with and additional sparse subnetwork applied as a mask to diminish the effects of a predefined protected attribute at inference time.  ( 2 min )
    Leveraging Expert Consistency to Improve Algorithmic Decision Support. (arXiv:2101.09648v2 [cs.LG] UPDATED)
    Machine learning (ML) is increasingly being used to support high-stakes decisions, a trend owed in part to its promise of superior predictive power relative to human assessment. However, there is frequently a gap between decision objectives and what is captured in the observed outcomes used as labels to train ML models. As a result, machine learning models may fail to capture important dimensions of decision criteria, hampering their utility for decision support. In this work, we explore the use of historical expert decisions as a rich -- yet imperfect -- source of information that is commonly available in organizational information systems, and show that it can be leveraged to bridge the gap between decision objectives and algorithm objectives. We consider the problem of estimating expert consistency indirectly when each case in the data is assessed by a single expert, and propose influence function-based methodology as a solution to this problem. We then incorporate the estimated expert consistency into a predictive model through a training-time label amalgamation approach. This approach allows ML models to learn from experts when there is inferred expert consistency, and from observed labels otherwise. We also propose alternative ways of leveraging inferred consistency via hybrid and deferral models. In our empirical evaluation, focused on the context of child maltreatment hotline screenings, we show that (1) there are high-risk cases whose risk is considered by the experts but not wholly captured in the target labels used to train a deployed model, and (2) the proposed approach significantly improves precision for these cases.  ( 3 min )
    Topological structure of complex predictions. (arXiv:2207.14358v1 [cs.LG])
    Complex prediction models such as deep learning are the output from fitting machine learning, neural networks, or AI models to a set of training data. These are now standard tools in science. A key challenge with the current generation of models is that they are highly parameterized, which makes describing and interpreting the prediction strategies difficult. We use topological data analysis to transform these complex prediction models into pictures representing a topological view. The result is a map of the predictions that enables inspection. The methods scale up to large datasets across different domains and enable us to detect labeling errors in training data, understand generalization in image classification, and inspect predictions of likely pathogenic mutations in the BRCA1 gene.  ( 2 min )
    Inverse Reinforcement Learning from Diverse Third-Person Videos via Graph Abstraction. (arXiv:2207.14299v1 [cs.LG])
    Research on Inverse Reinforcement Learning (IRL) from third-person videos has shown encouraging results on removing the need for manual reward design for robotic tasks. However, most prior works are still limited by training from a relatively restricted domain of videos. In this paper, we argue that the true potential of third-person IRL lies in increasing the diversity of videos for better scaling. To learn a reward function from diverse videos, we propose to perform graph abstraction on the videos followed by temporal matching in the graph space to measure the task progress. Our insight is that a task can be described by entity interactions that form a graph, and this graph abstraction can help remove irrelevant information such as textures, resulting in more robust reward functions. We evaluate our approach, GraphIRL, on cross-embodiment learning in X-MAGICAL and learning from human demonstrations for real-robot manipulation. We show significant improvements in robustness to diverse video demonstrations over previous approaches, and even achieve better results than manual reward design on a real robot pushing task. Videos are available at https://sateeshkumar21.github.io/GraphIRL .  ( 2 min )
    A Recommender System for Equitable Public Art Curation and Installation. (arXiv:2207.14367v1 [cs.IR])
    The placement of art in public spaces can have a significant impact on who feels a sense of belonging. In cities, public art communicates whose interests and culture are being favored. In this paper, we propose a graph matching approach with local constraints to build a curatorial tool for selecting public art in a way that supports inclusive spaces. We develop a cost matrix by drawing on Schelling's model of segregation. Using the cost matrix as an input, the optimization problem is solved via projected gradient descent to obtain a soft assignment matrix. We discuss regularization terms to set curatorial constraints. Our optimization program allocates artwork to public spaces and walls in a way that de-prioritizes "in-group" preferences, by satisfying minimum representation and exposure criteria. We draw on existing literature to develop a fairness metric for our algorithmic output. Using Tufts University as a testbed, we assess the effectiveness of our approach and discuss its potential pitfalls from both a curatorial and equity standpoint.  ( 2 min )
    Deep Learning-Based Synchronization for Uplink NB-IoT. (arXiv:2205.10805v2 [cs.IT] UPDATED)
    We propose a neural network (NN)-based algorithm for device detection and time of arrival (ToA) and carrier frequency offset (CFO) estimation for the narrowband physical random-access channel (NPRACH) of narrowband internet of things (NB-IoT). The introduced NN architecture leverages residual convolutional networks as well as knowledge of the preamble structure of the 5G New Radio (5G NR) specifications. Benchmarking on a 3rd Generation Partnership Project (3GPP) urban microcell (UMi) channel model with random drops of users against a state-of-the-art baseline shows that the proposed method enables up to 8 dB gains in false negative rate (FNR) as well as significant gains in false positive rate (FPR) and ToA and CFO estimation accuracy. Moreover, our simulations indicate that the proposed algorithm enables gains over a wide range of channel conditions, CFOs, and transmission probabilities. The introduced synchronization method operates at the base station (BS) and, therefore, introduces no additional complexity on the user devices. It could lead to an extension of battery lifetime by reducing the preamble length or the transmit power. Our code is available at: https://github.com/NVlabs/nprach_synch/.
    Regularized Deep Signed Distance Fields for Reactive Motion Generation. (arXiv:2203.04739v2 [cs.RO] UPDATED)
    Autonomous robots should operate in real-world dynamic environments and collaborate with humans in tight spaces. A key component for allowing robots to leave structured lab and manufacturing settings is their ability to evaluate online and real-time collisions with the world around them. Distance-based constraints are fundamental for enabling robots to plan their actions and act safely, protecting both humans and their hardware. However, different applications require different distance resolutions, leading to various heuristic approaches for measuring distance fields w.r.t. obstacles, which are computationally expensive and hinder their application in dynamic obstacle avoidance use-cases. We propose Regularized Deep Signed Distance Fields (ReDSDF), a single neural implicit function that can compute smooth distance fields at any scale, with fine-grained resolution over high-dimensional manifolds and articulated bodies like humans, thanks to our effective data generation and a simple inductive bias during training. We demonstrate the effectiveness of our approach in representative simulated tasks for whole-body control (WBC) and safe Human-Robot Interaction (HRI) in shared workspaces. Finally, we provide proof of concept of a real-world application in a HRI handover task with a mobile manipulator robot.
    Tangential Wasserstein Projections. (arXiv:2207.14727v1 [stat.ML])
    We develop a notion of projections between sets of probability measures using the geometric properties of the 2-Wasserstein space. It is designed for general multivariate probability measures, is computationally efficient to implement, and provides a unique solution in regular settings. The idea is to work on regular tangent cones of the Wasserstein space using generalized geodesics. Its structure and computational properties make the method applicable in a variety of settings, from causal inference to the analysis of object data. An application to estimating causal effects yields a generalization of the notion of synthetic controls to multivariate data with individual-level heterogeneity, as well as a way to estimate optimal weights jointly over all time periods.
    Graphing else matters: exploiting aspect opinions and ratings in explainable graph-based recommendations. (arXiv:2107.03226v2 [cs.IR] UPDATED)
    The success of neural network embeddings has entailed a renewed interest in using knowledge graphs for a wide variety of machine learning and information retrieval tasks. In particular, current recommendation methods based on graph embeddings have shown state-of-the-art performance. These methods commonly encode latent rating patterns and content features. Different from previous work, in this paper, we propose to exploit embeddings extracted from graphs that combine information from ratings and aspect-based opinions expressed in textual reviews. We then adapt and evaluate state-of-the-art graph embedding techniques over graphs generated from Amazon and Yelp reviews on six domains, outperforming baseline recommenders. Our approach has the advantage of providing explanations which leverage aspect-based opinions given by users about recommended items. Furthermore, we also provide examples of the applicability of recommendations utilizing aspect opinions as explanations in a visualization dashboard, which allows obtaining information about the most and least liked aspects of similar users obtained from the embeddings of an input graph.
    Language Models Can Teach Themselves to Program Better. (arXiv:2207.14502v1 [cs.LG])
    This work shows how one can use large-scale language models (LMs) to synthesize programming problems with verified solutions, in the form of programming puzzles, which can then in turn be used to fine-tune those same models, improving their performance. This work builds on two recent developments. First, LMs have achieved breakthroughs in non-trivial reasoning and algorithm implementation, generating code that can solve some intermediate-level competitive programming problems. However, training code LMs involves curated sets of natural-language problem descriptions and source-code tests and solutions, which are limited in size. Second, a new format of programming challenge called a programming puzzle was introduced, which does not require a natural language description and is directly specified by a source-code test. In this work we show how generating synthetic programming puzzles and solutions, verified for correctness by a Python interpreter, can be used to improve performance in solving test puzzles from P3, a public benchmark set of Python Programming Puzzles. Additionally, we release a dataset of 1 million puzzles and solutions generated by the Codex model, which we show can improve smaller models through fine-tuning.
    Model Reduction for Nonlinear Systems by Balanced Truncation of State and Gradient Covariance. (arXiv:2207.14387v1 [eess.SY])
    Data-driven reduced-order models often fail to make accurate forecasts of high-dimensional nonlinear systems that are sensitive along coordinates with low-variance because such coordinates are often truncated, e.g., by proper orthogonal decomposition, kernel principal component analysis, and autoencoders. Such systems are encountered frequently in shear-dominated fluid flows where non-normality plays a significant role in the growth of disturbances. In order to address these issues, we employ ideas from active subspaces to find low-dimensional systems of coordinates for model reduction that balance adjoint-based information about the system's sensitivity with the variance of states along trajectories. The resulting method, which we refer to as covariance balancing reduction using adjoint snapshots (CoBRAS), is identical to balanced truncation with state and adjoint-based gradient covariance matrices replacing the system Gramians and obeying the same key transformation laws. Here, the extracted coordinates are associated with an oblique projection that can be used to construct Petrov-Galerkin reduced-order models. We provide an efficient snapshot-based computational method analogous to balanced proper orthogonal decomposition. This also leads to the observation that the reduced coordinates can be computed relying on inner products of state and gradient samples alone, allowing us to find rich nonlinear coordinates by replacing the inner product with a kernel function. In these coordinates, reduced-order models can be learned using regression. We demonstrate these techniques and compare to a variety of other methods on a simple, yet challenging three-dimensional system and an axisymmetric jet flow simulation with $10^5$ state variables.
    Using Graph Neural Networks for Program Termination. (arXiv:2207.14648v1 [cs.SE])
    Termination analyses investigate the termination behavior of programs, intending to detect nontermination, which is known to cause a variety of program bugs (e.g. hanging programs, denial-of-service vulnerabilities). Beyond formal approaches, various attempts have been made to estimate the termination behavior of programs using neural networks. However, the majority of these approaches continue to rely on formal methods to provide strong soundness guarantees and consequently suffer from similar limitations. In this paper, we move away from formal methods and embrace the stochastic nature of machine learning models. Instead of aiming for rigorous guarantees that can be interpreted by solvers, our objective is to provide an estimation of a program's termination behavior and of the likely reason for nontermination (when applicable) that a programmer can use for debugging purposes. Compared to previous approaches using neural networks for program termination, we also take advantage of the graph representation of programs by employing Graph Neural Networks. To further assist programmers in understanding and debugging nontermination bugs, we adapt the notions of attention and semantic segmentation, previously used for other application domains, to programs. Overall, we designed and implemented classifiers for program termination based on Graph Convolutional Networks and Graph Attention Networks, as well as a semantic segmentation Graph Neural Network that localizes AST nodes likely to cause nontermination. We also illustrated how the information provided by semantic segmentation can be combined with program slicing to further aid debugging.
    Multimodal SuperCon: Classifier for Drivers of Deforestation in Indonesia. (arXiv:2207.14656v1 [cs.CV])
    Deforestation is one of the contributing factors to climate change. Climate change has a serious impact on human life, and it occurs due to emission of greenhouse gases, such as carbon dioxide, to the atmosphere. It is important to know the causes of deforestation for mitigation efforts, but there is a lack of data-driven research studies to predict these deforestation drivers. In this work, we propose a contrastive learning architecture, called Multimodal SuperCon, for classifying drivers of deforestation in Indonesia using satellite images obtained from Landsat 8. Multimodal SuperCon is an architecture which combines contrastive learning and multimodal fusion to handle the available deforestation dataset. Our proposed model outperforms previous work on driver classification, giving a 7% improvement in accuracy in comparison to a state-of-the-art rotation equivariant model for the same task.
    Personalized Promotion Decision Making Based on Direct and Enduring Effect Predictions. (arXiv:2207.14798v1 [cs.IR])
    Promotions have been trending in the e-commerce marketplace to build up customer relationships and guide customers towards the desired actions. Since incentives are effective to engage customers and customers have different preferences for different types of incentives, the demand for personalized promotion decision making is increasing over time. However, research on promotion decision making has focused specifically on purchase conversion during the promotion period (the direct effect), while generally disregarding the enduring effect in the post promotion period. To achieve a better lift return on investment (lift ROI) on the enduring effect of the promotion and improve customer retention and loyalty, we propose a framework of multiple treatment promotion decision making by modeling each customer's direct and enduring response. First, we propose a customer direct and enduring effect (CDEE) model which predicts the customer direct and enduring response. With the help of the predictions of the CDEE, we personalize incentive allocation to optimize the enduring effect while keeping the cost under the budget. To estimate the effect of decision making, we apply an unbiased evaluation approach of business metrics with randomized control trial (RCT) data. We compare our method with benchmarks using two promotions in Mercari and achieve significantly better results.
    A Survey of Learning on Small Data. (arXiv:2207.14443v1 [cs.LG])
    Learning on big data brings success for artificial intelligence (AI), but the annotation and training costs are expensive. In future, learning on small data is one of the ultimate purposes of AI, which requires machines to recognize objectives and scenarios relying on small data as humans. A series of machine learning models is going on this way such as active learning, few-shot learning, deep clustering. However, there are few theoretical guarantees for their generalization performance. Moreover, most of their settings are passive, that is, the label distribution is explicitly controlled by one specified sampling scenario. This survey follows the agnostic active sampling under a PAC (Probably Approximately Correct) framework to analyze the generalization error and label complexity of learning on small data using a supervised and unsupervised fashion. With these theoretical analyses, we categorize the small data learning models from two geometric perspectives: the Euclidean and non-Euclidean (hyperbolic) mean representation, where their optimization solutions are also presented and discussed. Later, some potential learning scenarios that may benefit from small data learning are then summarized, and their potential learning scenarios are also analyzed. Finally, some challenging applications such as computer vision, natural language processing that may benefit from learning on small data are also surveyed.
    Rating and aspect-based opinion graph embeddings for explainable recommendations. (arXiv:2107.03385v2 [cs.IR] UPDATED)
    The success of neural network embeddings has entailed a renewed interest in using knowledge graphs for a wide variety of machine learning and information retrieval tasks. In particular, recent recommendation methods based on graph embeddings have shown state-of-the-art performance. In general, these methods encode latent rating patterns and content features. Differently from previous work, in this paper, we propose to exploit embeddings extracted from graphs that combine information from ratings and aspect-based opinions expressed in textual reviews. We then adapt and evaluate state-of-the-art graph embedding techniques over graphs generated from Amazon and Yelp reviews on six domains, outperforming baseline recommenders. Additionally, our method has the advantage of providing explanations that involve the coverage of aspect-based opinions given by users about recommended items.
    SLUE: New Benchmark Tasks for Spoken Language Understanding Evaluation on Natural Speech. (arXiv:2111.10367v3 [cs.CL] UPDATED)
    Progress in speech processing has been facilitated by shared datasets and benchmarks. Historically these have focused on automatic speech recognition (ASR), speaker identification, or other lower-level tasks. Interest has been growing in higher-level spoken language understanding tasks, including using end-to-end models, but there are fewer annotated datasets for such tasks. At the same time, recent work shows the possibility of pre-training generic representations and then fine-tuning for several tasks using relatively little labeled data. We propose to create a suite of benchmark tasks for Spoken Language Understanding Evaluation (SLUE) consisting of limited-size labeled training sets and corresponding evaluation sets. This resource would allow the research community to track progress, evaluate pre-trained representations for higher-level tasks, and study open questions such as the utility of pipeline versus end-to-end approaches. We present the first phase of the SLUE benchmark suite, consisting of named entity recognition, sentiment analysis, and ASR on the corresponding datasets. We focus on naturally produced (not read or synthesized) speech, and freely available datasets. We provide new transcriptions and annotations on subsets of the VoxCeleb and VoxPopuli datasets, evaluation metrics and results for baseline models, and an open-source toolkit to reproduce the baselines and evaluate new models.
    GreenDB: Toward a Product-by-Product Sustainability Database. (arXiv:2205.02908v2 [cs.LG] UPDATED)
    The production, shipping, usage, and disposal of consumer goods have a substantial impact on greenhouse gas emissions and the depletion of resources. Modern retail platforms rely heavily on Machine Learning (ML) for their search and recommender systems. Thus, ML can potentially support efforts towards more sustainable consumption patterns, for example, by accounting for sustainability aspects in product search or recommendations. However, leveraging ML potential for reaching sustainability goals requires data on sustainability. Unfortunately, no open and publicly available database integrates sustainability information on a product-by-product basis. In this work, we present the GreenDB, which fills this gap. Based on search logs of millions of users, we prioritize which products users care about most. The GreenDB schema extends the well-known schema.org Product definition and can be readily integrated into existing product catalogs to improve sustainability information available for search and recommendation experiences. We present our proof of concept implementation of a scraping system that creates the GreenDB dataset.
    Reservoir Computing with Diverse Timescales for Prediction of Multiscale Dynamics. (arXiv:2108.09446v2 [cs.LG] UPDATED)
    Machine learning approaches have recently been leveraged as a substitute or an aid for physical/mathematical modeling approaches to dynamical systems. To develop an efficient machine learning method dedicated to modeling and prediction of multiscale dynamics, we propose a reservoir computing (RC) model with diverse timescales by using a recurrent network of heterogeneous leaky integrator (LI) neurons. We evaluate computational performance of the proposed model in two time series prediction tasks related to four chaotic fast-slow dynamical systems. In a one-step-ahead prediction task where input data are provided only from the fast subsystem, we show that the proposed model yields better performance than the standard RC model with identical LI neurons. Our analysis reveals that the timescale required for producing each component of target multiscale dynamics is appropriately and flexibly selected from the reservoir dynamics by model training. In a long-term prediction task, we demonstrate that a closed-loop version of the proposed model can achieve longer-term predictions compared to the counterpart with identical LI neurons depending on the hyperparameter setting.
    Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5). (arXiv:2203.13366v4 [cs.IR] UPDATED)
    For a long time, different recommendation tasks typically require designing task-specific architectures and training objectives. As a result, it is hard to transfer the learned knowledge and representations from one task to another, thus restricting the generalization ability of existing recommendation approaches, e.g., a sequential recommendation model can hardly be applied or transferred to a review generation method. To deal with such issues, considering that language can describe almost anything and language grounding is a powerful medium to represent various problems or tasks, we present a flexible and unified text-to-text paradigm called "Pretrain, Personalized Prompt, and Predict Paradigm" (P5) for recommendation, which unifies various recommendation tasks in a shared framework. In P5, all data such as user-item interactions, user descriptions, item metadata, and user reviews are converted to a common format -- natural language sequences. The rich information from natural language assists P5 to capture deeper semantics for personalization and recommendation. Specifically, P5 learns different tasks with the same language modeling objective during pretraining. Thus, it serves as the foundation model for various downstream recommendation tasks, allows easy integration with other modalities, and enables instruction-based recommendation based on prompts. P5 advances recommender systems from shallow model to deep model to big model, and will revolutionize the technical form of recommender systems towards universal recommendation engine. With adaptive personalized prompt for different users, P5 is able to make predictions in a zero-shot or few-shot manner and largely reduces the necessity for extensive fine-tuning. On several recommendation benchmarks, we conduct experiments to show the effectiveness of P5. We release the source code at \url{https://github.com/jeykigung/P5}.
    Unsupervised Discovery of Inertial-Fusion Plasma Physics using Differentiable Kinetic Simulations and a Maximum Entropy Loss Function. (arXiv:2206.01637v2 [physics.plasm-ph] CROSS LISTED)
    Plasma supports collective modes and particle-wave interactions that leads to complex behavior in inertial fusion energy applications. While plasma can sometimes be modeled as a charged fluid, a kinetic description is useful towards the study of nonlinear effects in the higher dimensional momentum-position phase-space that describes the full complexity of plasma dynamics. We create a differentiable solver for the plasma kinetics 3D partial-differential-equation and introduce a domain-specific objective function. Using this framework, we perform gradient-based optimization of neural networks that provide forcing function parameters to the differentiable solver given a set of initial conditions. We apply this to an inertial-fusion relevant configuration and find that the optimization process exploits a novel physical effect that has previously remained undiscovered.
    Port-Hamiltonian Neural Networks with State-Dependent Ports. (arXiv:2206.02660v2 [cs.LG] UPDATED)
    Hybrid machine learning based on Hamiltonian formulations has recently been successfully demonstrated for simple mechanical systems. In this work, we stress-test the method on both simple mass-spring systems and more complex and realistic systems with several internal and external ports, including a system with multiple connected tanks. We quantify performance under various conditions and show that imposing different assumptions greatly affects the performance, highlighting advantages and limitations of the method. We demonstrate that port-Hamiltonian neural networks can be extended to higher dimensions with state-dependent ports. We consider learning on systems with known and unknown external ports. The port-Hamiltonian formulation allows for detecting deviations and still provide a valid model when the deviations are removed. Finally, we propose a symmetric high-order integration scheme for improved training on sparse and noisy data.
    Conformal Prediction: a Unified Review of Theory and New Challenges. (arXiv:2005.07972v2 [cs.LG] UPDATED)
    In this work we provide a review of basic ideas and novel developments about Conformal Prediction -- an innovative distribution-free, non-parametric forecasting method, based on minimal assumptions -- that is able to yield in a very straightforward way predictions sets that are valid in a statistical sense also in in the finite sample case. The in-depth discussion provided in the paper covers the theoretical underpinnings of Conformal Prediction, and then proceeds to list the more advanced developments and adaptations of the original idea.
    Cloud-Edge Training Architecture for Sim-to-Real Deep Reinforcement Learning. (arXiv:2203.02230v2 [cs.LG] UPDATED)
    Deep reinforcement learning (DRL) is a promising approach to solve complex control tasks by learning policies through interactions with the environment. However, the training of DRL policies requires large amounts of training experiences, making it impractical to learn the policy directly on physical systems. Sim-to-real approaches leverage simulations to pretrain DRL policies and then deploy them in the real world. Unfortunately, the direct real-world deployment of pretrained policies usually suffers from performance deterioration due to the different dynamics, known as the reality gap. Recent sim-to-real methods, such as domain randomization and domain adaptation, focus on improving the robustness of the pretrained agents. Nevertheless, the simulation-trained policies often need to be tuned with real-world data to reach optimal performance, which is challenging due to the high cost of real-world samples. This work proposes a distributed cloud-edge architecture to train DRL agents in the real world in real-time. In the architecture, the inference and training are assigned to the edge and cloud, separating the real-time control loop from the computationally expensive training loop. To overcome the reality gap, our architecture exploits sim-to-real transfer strategies to continue the training of simulation-pretrained agents on a physical system. We demonstrate its applicability on a physical inverted-pendulum control system, analyzing critical parameters. The real-world experiments show that our architecture can adapt the pretrained DRL agents to unseen dynamics consistently and efficiently.
    A Learned Index for Exact Similarity Search in Metric Spaces. (arXiv:2204.10028v2 [cs.DB] UPDATED)
    Indexing is an effective way to support efficient query processing in large databases. Recently the concept of learned index, which replaces or complements traditional index structures with machine learning models, has been actively explored to reduce storage and search costs. However, accurate and efficient similarity query processing in high-dimensional metric spaces remains to be an open challenge. In this paper, we propose a novel indexing approach called LIMS that uses data clustering, pivot-based data transformation techniques and learned indexes to support efficient similarity query processing in metric spaces. In LIMS, the underlying data is partitioned into clusters such that each cluster follows a relatively uniform data distribution. Data redistribution is achieved by utilizing a small number of pivots for each cluster. Similar data are mapped into compact regions and the mapped values are totally ordinal. Machine learning models are developed to approximate the position of each data record on disk. Efficient algorithms are designed for processing range queries and nearest neighbor queries based on LIMS, and for index maintenance with dynamic updates. Extensive experiments on real-world and synthetic datasets demonstrate the superiority of LIMS compared with traditional indexes and state-of-the-art learned indexes.
    Consistent and fast inference in compartmental models of epidemics using Poisson Approximate Likelihoods. (arXiv:2205.13602v2 [stat.ME] UPDATED)
    Addressing the challenge of scaling-up epidemiological inference to complex and heterogeneous models, we introduce Poisson Approximate Likelihood (PAL) methods. PALs are derived from approximate filtering equations for finite-population, stochastic compartmental models, and the large population limit drives the consistency of maximum PAL estimators. Our theoretical results appear to be the first likelihood-based parameter estimation consistency results applicable across a broad class of partially observed stochastic compartmental models concerning the large population limit. Compared to simulation-based methods such as Approximate Bayesian Computation and Sequential Monte Carlo, PALs are simple to implement, involving only elementary arithmetic operations and no tuning parameters; and fast to evaluate, requiring no simulation from the model and having computational cost independent of population size. Through examples, we demonstrate how PALs can be: embedded within Delayed Acceptance Particle Markov Chain Monte Carlo to facilitate Bayesian inference; used to fit an age-structured model of influenza, taking advantage of automatic differentiation in Stan; and applied to calibrate a spatial meta-population model of measles.
    Cross-Subject Domain Adaptation for Classifying Working Memory Load with Multi-Frame EEG Images. (arXiv:2106.06769v2 [cs.LG] UPDATED)
    Working memory (WM), denoting the information temporally stored in the mind, is a fundamental research topic in the field of human cognition. Electroencephalograph (EEG), which can monitor the electrical activity of the brain, has been widely used in measuring the level of WM. However, one of the critical challenges is that individual differences may cause ineffective results, especially when the established model meets an unfamiliar subject. In this work, we propose a cross-subject deep adaptation model with spatial attention (CS-DASA) to generalize the workload classifications across subjects. First, we transform EEG time series into multi-frame EEG images incorporating spatial, spectral, and temporal information. First, the Subject-Shared module in CS-DASA receives multi-frame EEG image data from both source and target subjects and learns the common feature representations. Then, in the subject-specific module, the maximum mean discrepancy is implemented to measure the domain distribution divergence in a reproducing kernel Hilbert space, which can add an effective penalty loss for domain adaptation. Additionally, the subject-to-subject spatial attention mechanism is employed to focus on the discriminative spatial features from the target image data. Experiments conducted on a public WM EEG dataset containing 13 subjects show that the proposed model is capable of achieving better performance than existing state-of-the-art methods.
    CryoAI: Amortized Inference of Poses for Ab Initio Reconstruction of 3D Molecular Volumes from Real Cryo-EM Images. (arXiv:2203.08138v3 [cs.CV] UPDATED)
    Cryo-electron microscopy (cryo-EM) has become a tool of fundamental importance in structural biology, helping us understand the basic building blocks of life. The algorithmic challenge of cryo-EM is to jointly estimate the unknown 3D poses and the 3D electron scattering potential of a biomolecule from millions of extremely noisy 2D images. Existing reconstruction algorithms, however, cannot easily keep pace with the rapidly growing size of cryo-EM datasets due to their high computational and memory cost. We introduce cryoAI, an ab initio reconstruction algorithm for homogeneous conformations that uses direct gradient-based optimization of particle poses and the electron scattering potential from single-particle cryo-EM data. CryoAI combines a learned encoder that predicts the poses of each particle image with a physics-based decoder to aggregate each particle image into an implicit representation of the scattering potential volume. This volume is stored in the Fourier domain for computational efficiency and leverages a modern coordinate network architecture for memory efficiency. Combined with a symmetrized loss function, this framework achieves results of a quality on par with state-of-the-art cryo-EM solvers for both simulated and experimental data, one order of magnitude faster for large datasets and with significantly lower memory requirements than existing methods.
    Latent Properties of Lifelong Learning Systems. (arXiv:2207.14378v1 [cs.LG])
    Creating artificial intelligence (AI) systems capable of demonstrating lifelong learning is a fundamental challenge, and many approaches and metrics have been proposed to analyze algorithmic properties. However, for existing lifelong learning metrics, algorithmic contributions are confounded by task and scenario structure. To mitigate this issue, we introduce an algorithm-agnostic explainable surrogate-modeling approach to estimate latent properties of lifelong learning algorithms. We validate the approach for estimating these properties via experiments on synthetic data. To validate the structure of the surrogate model, we analyze real performance data from a collection of popular lifelong learning approaches and baselines adapted for lifelong classification and lifelong reinforcement learning.
    Learning Disentangled Representations in the Imaging Domain. (arXiv:2108.12043v6 [cs.CV] UPDATED)
    Disentangled representation learning has been proposed as an approach to learning general representations even in the absence of, or with limited, supervision. A good general representation can be fine-tuned for new target tasks using modest amounts of data, or used directly in unseen domains achieving remarkable performance in the corresponding task. This alleviation of the data and annotation requirements offers tantalising prospects for applications in computer vision and healthcare. In this tutorial paper, we motivate the need for disentangled representations, revisit key concepts, and describe practical building blocks and criteria for learning such representations. We survey applications in medical imaging emphasising choices made in exemplar key works, and then discuss links to computer vision applications. We conclude by presenting limitations, challenges, and opportunities.
    The network signature of constellation line figures. (arXiv:2110.12329v3 [cs.SI] UPDATED)
    In traditional astronomies across the world, groups of stars in the night sky were linked into constellations -- symbolic representations rich in meaning and with practical roles. In some sky cultures, constellations are represented as line (or connect-the-dot) figures, which are spatial networks drawn over the fixed background of stars. We analyse 1802 line figures from 56 sky cultures spanning all continents, in terms of their network, spatial, and brightness features, and ask what associations exist between these visual features and culture type or sky region. First, an embedded map of constellations is learnt, to show clusters of line figures. We then form the network of constellations (as linked by their similarity), to study how similar cultures are by computing their assortativity (or homophily) over the network. Finally, we measure the diversity (or entropy) index for the set of constellations drawn per sky region. Our results show distinct types of line figures, and that many folk astronomies with oral traditions have widespread similarities in constellation design, which do not align with cultural ancestry. In a minority of sky regions, certain line designs appear universal, but this is not the norm: in the majority of sky regions, the line geometries are diverse.
    Learning Coulomb Diamonds in Large Quantum Dot Arrays. (arXiv:2205.01443v2 [cond-mat.mes-hall] UPDATED)
    We introduce an algorithm that is able to find the facets of Coulomb diamonds in quantum dot arrays. We simulate these arrays using the constant-interaction model, and rely only on one-dimensional raster scans (rays) to learn a model of the device using regularized maximum likelihood estimation. This allows us to determine, for a given charge state of the device, which transitions exist and what the compensated gate voltages for these are. For smaller devices the simulator can also be used to compute the exact boundaries of the Coulomb diamonds, which we use to assess that our algorithm correctly finds the vast majority of transitions with high precision.
    Multi-channel neural networks for predicting influenza A virus hosts and antigenic types. (arXiv:2206.03823v3 [q-bio.QM] UPDATED)
    Influenza occurs every season and occasionally causes pandemics. Despite its low mortality rate, influenza is a major public health concern, as it can be complicated by severe diseases like pneumonia. A fast, accurate and low-cost method to predict the origin host and subtype of influenza viruses could help reduce virus transmission and benefit resource-poor areas. In this work, we propose multi-channel neural networks to predict antigenic types and hosts of influenza A viruses with hemagglutinin and neuraminidase protein sequences. An integrated data set containing complete protein sequences were used to produce a pre-trained model, and two other data sets were used for testing the model's performance. One test set contained complete protein sequences, and another test set contained incomplete protein sequences. The results suggest that multi-channel neural networks are applicable and promising for predicting influenza A virus hosts and antigenic subtypes with complete and partial protein sequences.
    Domain Generalization: A Survey. (arXiv:2103.02503v6 [cs.LG] UPDATED)
    Generalization to out-of-distribution (OOD) data is a capability natural to humans yet challenging for machines to reproduce. This is because most learning algorithms strongly rely on the i.i.d.~assumption on source/target data, which is often violated in practice due to domain shift. Domain generalization (DG) aims to achieve OOD generalization by using only source data for model learning. Over the last ten years, research in DG has made great progress, leading to a broad spectrum of methodologies, e.g., those based on domain alignment, meta-learning, data augmentation, or ensemble learning, to name a few; DG has also been studied in various application areas including computer vision, speech recognition, natural language processing, medical imaging, and reinforcement learning. In this paper, for the first time a comprehensive literature review in DG is provided to summarize the developments over the past decade. Specifically, we first cover the background by formally defining DG and relating it to other relevant fields like domain adaptation and transfer learning. Then, we conduct a thorough review into existing methods and theories. Finally, we conclude this survey with insights and discussions on future research directions.
    A Data-driven Latent Semantic Analysis for Automatic Text Summarization using LDA Topic Modelling. (arXiv:2207.14687v1 [cs.IR])
    With the advent and popularity of big data mining and huge text analysis in modern times, automated text summarization became prominent for extracting and retrieving important information from documents. This research investigates aspects of automatic text summarization from the perspectives of single and multiple documents. Summarization is a task of condensing huge text articles into short, summarized versions. The text is reduced in size for summarization purpose but preserving key vital information and retaining the meaning of the original document. This study presents the Latent Dirichlet Allocation (LDA) approach used to perform topic modelling from summarised medical science journal articles with topics related to genes and diseases. In this study, PyLDAvis web-based interactive visualization tool was used to visualise the selected topics. The visualisation provides an overarching view of the main topics while allowing and attributing deep meaning to the prevalence individual topic. This study presents a novel approach to summarization of single and multiple documents. The results suggest the terms ranked purely by considering their probability of the topic prevalence within the processed document using extractive summarization technique. PyLDAvis visualization describes the flexibility of exploring the terms of the topics' association to the fitted LDA model. The topic modelling result shows prevalence within topics 1 and 2. This association reveals that there is similarity between the terms in topic 1 and 2 in this study. The efficacy of the LDA and the extractive summarization methods were measured using Latent Semantic Analysis (LSA) and Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics to evaluate the reliability and validity of the model.
    StyleLight: HDR Panorama Generation for Lighting Estimation and Editing. (arXiv:2207.14811v1 [cs.CV])
    We present a new lighting estimation and editing framework to generate high-dynamic-range (HDR) indoor panorama lighting from a single limited field-of-view (LFOV) image captured by low-dynamic-range (LDR) cameras. Existing lighting estimation methods either directly regress lighting representation parameters or decompose this problem into LFOV-to-panorama and LDR-to-HDR lighting generation sub-tasks. However, due to the partial observation, the high-dynamic-range lighting, and the intrinsic ambiguity of a scene, lighting estimation remains a challenging task. To tackle this problem, we propose a coupled dual-StyleGAN panorama synthesis network (StyleLight) that integrates LDR and HDR panorama synthesis into a unified framework. The LDR and HDR panorama synthesis share a similar generator but have separate discriminators. During inference, given an LDR LFOV image, we propose a focal-masked GAN inversion method to find its latent code by the LDR panorama synthesis branch and then synthesize the HDR panorama by the HDR panorama synthesis branch. StyleLight takes LFOV-to-panorama and LDR-to-HDR lighting generation into a unified framework and thus greatly improves lighting estimation. Extensive experiments demonstrate that our framework achieves superior performance over state-of-the-art methods on indoor lighting estimation. Notably, StyleLight also enables intuitive lighting editing on indoor HDR panoramas, which is suitable for real-world applications. Code is available at https://style-light.github.io.
    Can We Mitigate Backdoor Attack Using Adversarial Detection Methods?. (arXiv:2006.14871v2 [cs.LG] UPDATED)
    Deep Neural Networks are well known to be vulnerable to adversarial attacks and backdoor attacks, where minor modifications on the input are able to mislead the models to give wrong results. Although defenses against adversarial attacks have been widely studied, investigation on mitigating backdoor attacks is still at an early stage. It is unknown whether there are any connections and common characteristics between the defenses against these two attacks. We conduct comprehensive studies on the connections between adversarial examples and backdoor examples of Deep Neural Networks to seek to answer the question: can we detect backdoor using adversarial detection methods. Our insights are based on the observation that both adversarial examples and backdoor examples have anomalies during the inference process, highly distinguishable from benign samples. As a result, we revise four existing adversarial defense methods for detecting backdoor examples. Extensive evaluations indicate that these approaches provide reliable protection against backdoor attacks, with a higher accuracy than detecting adversarial examples. These solutions also reveal the relations of adversarial examples, backdoor examples and normal samples in model sensitivity, activation space and feature space. This is able to enhance our understanding about the inherent features of these two attacks and the defense opportunities.
    SHAP for additively modeled features in a boosted trees model. (arXiv:2207.14490v1 [stat.ML])
    An important technique to explore a black-box machine learning (ML) model is called SHAP (SHapley Additive exPlanation). SHAP values decompose predictions into contributions of the features in a fair way. We will show that for a boosted trees model with some or all features being additively modeled, the SHAP dependence plot of such a feature corresponds to its partial dependence plot up to a vertical shift. We illustrate the result with XGBoost.
    Computational complexity reduction of deep neural networks. (arXiv:2207.14620v1 [cs.LG])
    Deep neural networks (DNN) have been widely used and play a major role in the field of computer vision and autonomous navigation. However, these DNNs are computationally complex and their deployment over resource-constrained platforms is difficult without additional optimizations and customization. In this manuscript, we describe an overview of DNN architecture and propose methods to reduce computational complexity in order to accelerate training and inference speeds to fit them on edge computing platforms with low computational resources.
    Blockchain-enabled Server-less Federated Learning. (arXiv:2112.07938v2 [cs.LG] UPDATED)
    Motivated by the heterogeneous nature of devices participating in large-scale Federated Learning (FL) optimization, we focus on an asynchronous server-less FL solution empowered by blockchain technology. In contrast to mostly adopted FL approaches, which assume synchronous operation, we advocate an asynchronous method whereby model aggregation is done as clients submit their local updates. The asynchronous setting fits well with the federated optimization idea in practical large-scale settings with heterogeneous clients. Thus, it potentially leads to higher efficiency in terms of communication overhead and idle periods. To evaluate the learning completion delay of BC-enabled FL, we provide an analytical model based on batch service queue theory. Furthermore, we provide simulation results to assess the performance of both synchronous and asynchronous mechanisms. Important aspects involved in the BC-enabled FL optimization, such as the network size, link capacity, or user requirements, are put together and analyzed. As our results show, the synchronous setting leads to higher prediction accuracy than the asynchronous case. Nevertheless, asynchronous federated optimization provides much lower latency in many cases, thus becoming an appealing solution for FL when dealing with large datasets, tough timing constraints (e.g., near-real-time applications), or highly varying training data.
    Archaeology of random recursive dags and Cooper-Frieze random networks. (arXiv:2207.14601v1 [math.PR])
    We study the problem of finding the root vertex in large growing networks. We prove that it is possible to construct confidence sets of size independent of the number of vertices in the network that contain the root vertex with high probability in various models of random networks. The models include uniform random recursive dags and uniform Cooper-Frieze random graphs.
    Recursive Importance Sketching for Rank Constrained Least Squares: Algorithms and High-order Convergence. (arXiv:2011.08360v3 [math.OC] UPDATED)
    In this paper, we propose {\it \underline{R}ecursive} {\it \underline{I}mportance} {\it \underline{S}ketching} algorithm for {\it \underline{R}ank} constrained least squares {\it \underline{O}ptimization} (RISRO). The key step of RISRO is recursive importance sketching, a new sketching framework based on deterministically designed recursive projections, which significantly differs from the randomized sketching in the literature \citep{mahoney2011randomized,woodruff2014sketching}. Several existing algorithms in the literature can be reinterpreted under this new sketching framework and RISRO offers clear advantages over them. RISRO is easy to implement and computationally efficient, where the core procedure in each iteration is to solve a dimension-reduced least squares problem. We establish the local quadratic-linear and quadratic rate of convergence for RISRO under some mild conditions. We also discover a deep connection of RISRO to the Riemannian Gauss-Newton algorithm on fixed rank matrices. The effectiveness of RISRO is demonstrated in two applications in machine learning and statistics: low-rank matrix trace regression and phase retrieval. Simulation studies demonstrate the superior numerical performance of RISRO.
    Training a universal instance segmentation network for live cell images of various cell types and imaging modalities. (arXiv:2207.14347v1 [cs.CV])
    We share our recent findings in an attempt to train a universal segmentation network for various cell types and imaging modalities. Our method was built on the generalized U-Net architecture, which allows the evaluation of each component individually. We modified the traditional binary training targets to include three classes for direct instance segmentation. Detailed experiments were performed regarding training schemes, training settings, network backbones, and individual modules on the segmentation performance. Our proposed training scheme draws minibatches in turn from each dataset, and the gradients are accumulated before an optimization step. We found that the key to training a universal network is all-time supervision on all datasets, and it is necessary to sample each dataset in an unbiased way. Our experiments also suggest that there might exist common features to define cell boundaries across cell types and imaging modalities, which could allow application of trained models to totally unseen datasets. A few training tricks can further boost the segmentation performance, including uneven class weights in the cross-entropy loss function, well-designed learning rate scheduler, larger image crops for contextual information, and additional loss terms for unbalanced classes. We also found that segmentation performance can benefit from group normalization layer and Atrous Spatial Pyramid Pooling module, thanks to their more reliable statistics estimation and improved semantic understanding, respectively. We participated in the 6th Cell Tracking Challenge (CTC) held at IEEE International Symposium on Biomedical Imaging (ISBI) 2021 using one of the developed variants. Our method was evaluated as the best runner up during the initial submission for the primary track, and also secured the 3rd place in an additional round of competition in preparation for the summary publication.
    Lower bounds for learning quantum states with single-copy measurements. (arXiv:2207.14438v1 [quant-ph])
    We study the problems of quantum tomography and shadow tomography using measurements performed on individual, identical copies of an unknown $d$-dimensional state. We first revisit a known lower bound due to Haah et al. (2017) on quantum tomography with accuracy $\epsilon$ in trace distance, when the measurements choices are independent of previously observed outcomes (i.e., they are nonadaptive). We give a succinct proof of this result. This leads to stronger lower bounds when the learner uses measurements with a constant number of outcomes. In particular, this rigorously establishes the optimality of the folklore ``Pauli tomography" algorithm in terms of its sample complexity. We also derive novel bounds of $\Omega(r^2 d/\epsilon^2)$ and $\Omega(r^2 d^2/\epsilon^2)$ for learning rank $r$ states using arbitrary and constant-outcome measurements, respectively, in the nonadaptive case. In addition to the sample complexity, a resource of practical significance for learning quantum states is the number of different measurements used by an algorithm. We extend our lower bounds to the case where the learner performs possibly adaptive measurements from a fixed set of $\exp(O(d))$ measurements. This implies in particular that adaptivity does not give us any advantage using single-copy measurements that are efficiently implementable. We also obtain a similar bound in the case where the goal is to predict the expectation values of a given sequence of observables, a task known as shadow tomography. Finally, in the case of adaptive, single-copy measurements implementable with polynomial-size circuits, we prove that a straightforward strategy based on computing sample means of the given observables is optimal.
    Deep Reinforcement Learning for System-on-Chip: Myths and Realities. (arXiv:2207.14595v1 [cs.LG])
    Neural schedulers based on deep reinforcement learning (DRL) have shown considerable potential for solving real-world resource allocation problems, as they have demonstrated significant performance gain in the domain of cluster computing. In this paper, we investigate the feasibility of neural schedulers for the domain of System-on-Chip (SoC) resource allocation through extensive experiments and comparison with non-neural, heuristic schedulers. The key finding is three-fold. First, neural schedulers designed for cluster computing domain do not work well for SoC due to i) heterogeneity of SoC computing resources and ii) variable action set caused by randomness in incoming jobs. Second, our novel neural scheduler technique, Eclectic Interaction Matching (EIM), overcomes the above challenges, thus significantly improving the existing neural schedulers. Specifically, we rationalize the underlying reasons behind the performance gain by the EIM-based neural scheduler. Third, we discover that the ratio of the average processing elements (PE) switching delay and the average PE computation time significantly impacts the performance of neural SoC schedulers even with EIM. Consequently, future neural SoC scheduler design must consider this metric as well as its implementation overhead for practical utility.
    Beyond CNNs: Exploiting Further Inherent Symmetries in Medical Image Segmentation. (arXiv:2207.14472v1 [eess.IV])
    Automatic tumor or lesion segmentation is a crucial step in medical image analysis for computer-aided diagnosis. Although the existing methods based on Convolutional Neural Networks (CNNs) have achieved the state-of-the-art performance, many challenges still remain in medical tumor segmentation. This is because, although the human visual system can detect symmetries in 2D images effectively, regular CNNs can only exploit translation invariance, overlooking further inherent symmetries existing in medical images such as rotations and reflections. To solve this problem, we propose a novel group equivariant segmentation framework by encoding those inherent symmetries for learning more precise representations. First, kernel-based equivariant operations are devised on each orientation, which allows it to effectively address the gaps of learning symmetries in existing approaches. Then, to keep segmentation networks globally equivariant, we design distinctive group layers with layer-wise symmetry constraints. Finally, based on our novel framework, extensive experiments conducted on real-world clinical data demonstrate that a Group Equivariant Res-UNet (named GER-UNet) outperforms its regular CNN-based counterpart and the state-of-the-art segmentation methods in the tasks of hepatic tumor segmentation, COVID-19 lung infection segmentation and retinal vessel detection. More importantly, the newly built GER-UNet also shows potential in reducing the sample complexity and the redundancy of filters, upgrading current segmentation CNNs and delineating organs on other medical imaging modalities.
    Big Data and Analytics Implementation in Tertiary Institutions to Predict Students Performance in Nigeria. (arXiv:2207.14677v1 [cs.CY])
    The term Big Data has been coined to refer to the gargantuan bulk of data that cannot be dealt with by traditional data-handling techniques. Big Data is still a novel concept, and in the following literature, we intend to elaborate on it in a palpable fashion. It commences with the concept of the subject in itself, along with its properties and the two general approaches to dealing with it. Big Data provides an opportunity for educational Institutions to use their Information Technology resources strategically to improve educational quality, guide students to higher completion rates and improve student persistence and outcomes. This paper explores the attributes of big data that are relevant to educational institutions, investigates the factors influencing the adoption of big data and analytics in learning institutions, and seeks to establish the limiting factors hindering the use of big data in Institutions of higher learning. A survey research design was adopted in conducting this research, and Questionnaires were the instrument employed for data collection.
    Learning Personalized Representations using Graph Convolutional Network. (arXiv:2207.14298v1 [cs.LG])
    Generating representations that precisely reflect customers' behavior is an important task for providing personalized skill routing experience in Alexa. Currently, Dynamic Routing (DR) team, which is responsible for routing Alexa traffic to providers or skills, relies on two features to be served as personal signals: absolute traffic count and normalized traffic count of every skill usage per customer. Neither of them considers the network based structure for interactions between customers and skills, which contain richer information for customer preferences. In this work, we first build a heterogeneous edge attributed graph based customers' past interactions with the invoked skills, in which the user requests (utterances) are modeled as edges. Then we propose a graph convolutional network(GCN) based model, namely Personalized Dynamic Routing Feature Encoder(PDRFE), that generates personalized customer representations learned from the built graph. Compared with existing models, PDRFE is able to further capture contextual information in the graph convolutional function. The performance of our proposed model is evaluated by a downstream task, defect prediction, that predicts the defect label from the learned embeddings of customers and their triggered skills. We observe up to 41% improvements on the cross entropy metric for our proposed models compared to the baselines.
    Sample-efficient Safe Learning for Online Nonlinear Control with Control Barrier Functions. (arXiv:2207.14419v1 [cs.RO])
    Reinforcement Learning (RL) and continuous nonlinear control have been successfully deployed in multiple domains of complicated sequential decision-making tasks. However, given the exploration nature of the learning process and the presence of model uncertainty, it is challenging to apply them to safety-critical control tasks due to the lack of safety guarantee. On the other hand, while combining control-theoretical approaches with learning algorithms has shown promise in safe RL applications, the sample efficiency of safe data collection process for control is not well addressed. In this paper, we propose a \emph{provably} sample efficient episodic safe learning framework for online control tasks that leverages safe exploration and exploitation in an unknown, nonlinear dynamical system. In particular, the framework 1) extends control barrier functions (CBFs) in a stochastic setting to achieve provable high-probability safety under uncertainty during model learning and 2) integrates an optimism-based exploration strategy to efficiently guide the safe exploration process with learned dynamics for \emph{near optimal} control performance. We provide formal analysis on the episodic regret bound against the optimal controller and probabilistic safety with theoretical guarantees. Simulation results are provided to demonstrate the effectiveness and efficiency of the proposed algorithm.
    Effectiveness of Transformer Models on IoT Security Detection in StackOverflow Discussions. (arXiv:2207.14542v1 [cs.CR])
    The Internet of Things (IoT) is an emerging concept that directly links to the billions of physical items, or "things", that are connected to the Internet and are all gathering and exchanging information between devices and systems. However, IoT devices were not built with security in mind, which might lead to security vulnerabilities in a multi-device system. Traditionally, we investigated IoT issues by polling IoT developers and specialists. This technique, however, is not scalable since surveying all IoT developers is not feasible. Another way to look into IoT issues is to look at IoT developer discussions on major online development forums like Stack Overflow (SO). However, finding discussions that are relevant to IoT issues is challenging since they are frequently not categorized with IoT-related terms. In this paper, we present the "IoT Security Dataset", a domain-specific dataset of 7147 samples focused solely on IoT security discussions. As there are no automated tools to label these samples, we manually labeled them. We further employed multiple transformer models to automatically detect security discussions. Through rigorous investigations, we found that IoT security discussions are different and more complex than traditional security discussions. We demonstrated a considerable performance loss (up to 44%) of transformer models on cross-domain datasets when we transferred knowledge from a general-purpose dataset "Opiner", supporting our claim. Thus, we built a domain-specific IoT security detector with an F1-Score of 0.69. We have made the dataset public in the hope that developers would learn more about the security discussion and vendors would enhance their concerns about product security.
    Building Trust: Lessons from the Technion-Rambam Machine Learning in Healthcare Datathon Event. (arXiv:2207.14638v1 [cs.DB])
    A datathon is a time-constrained competition involving data science applied to a specific problem. In the past decade, datathons have been shown to be a valuable bridge between fields and expertise . Biomedical data analysis represents a challenging area requiring collaboration between engineers, biologists and physicians to gain a better understanding of patient physiology and of guide decision processes for diagnosis, prognosis and therapeutic interventions to improve care practice. Here, we reflect on the outcomes of an event that we organized in Israel at the end of March 2022 between the MIT Critical Data group, Rambam Health Care Campus (Rambam) and the Technion Israel Institute of Technology (Technion) in Haifa. Participants were asked to complete a survey about their skills and interests, which enabled us to identify current needs in machine learning training for medical problem applications. This work describes opportunities and limitations in medical data science in the Israeli context.
    Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement Learning. (arXiv:2207.14800v1 [cs.LG])
    In view of its power in extracting feature representation, contrastive self-supervised learning has been successfully integrated into the practice of (deep) reinforcement learning (RL), leading to efficient policy learning in various applications. Despite its tremendous empirical successes, the understanding of contrastive learning for RL remains elusive. To narrow such a gap, we study how RL can be empowered by contrastive learning in a class of Markov decision processes (MDPs) and Markov games (MGs) with low-rank transitions. For both models, we propose to extract the correct feature representations of the low-rank model by minimizing a contrastive loss. Moreover, under the online setting, we propose novel upper confidence bound (UCB)-type algorithms that incorporate such a contrastive loss with online RL algorithms for MDPs or MGs. We further theoretically prove that our algorithm recovers the true representations and simultaneously achieves sample efficiency in learning the optimal policy and Nash equilibrium in MDPs and MGs. We also provide empirical studies to demonstrate the efficacy of the UCB-based contrastive learning method for RL. To the best of our knowledge, we provide the first provably efficient online RL algorithm that incorporates contrastive learning for representation learning. Our codes are available at https://github.com/Baichenjia/Contrastive-UCB.
    Factorizable Joint Shift in Multinomial Classification. (arXiv:2207.14514v1 [stat.ML])
    Factorizable joint shift was recently proposed as a type of dataset shift for which the characteristics can be estimated from observed data. For the multinomial (multi-class) classification setting, we derive a representation of factorizable joint shift in terms of the source (training) distribution, the target (test) prior class probabilities and the target marginal distribution of the features. On the basis of this result, we propose alternatives to joint importance aligning, at the same time pointing out the limitations encountered when making an assumption of factorizable joint shift. Other results of the paper include correction formulae for the posterior class probabilities both under general dataset shift and factorizable joint shift. In addition, we investigate the consequences of assuming factorizable joint shift for the bias caused by sample selection.
    Quantum Data Center: Theories and Applications. (arXiv:2207.14336v1 [quant-ph])
    In this paper, we propose the Quantum Data Center (QDC), an architecture combining Quantum Random Access Memory (QRAM) and quantum networks. We give a precise definition of QDC, and discuss its possible realizations and extensions. We discuss applications of QDC in quantum computation, quantum communication, and quantum sensing, with a primary focus on QDC for $T$-gate resources, QDC for multi-party private quantum communication, and QDC for distributed sensing through data compression. We show that QDC will provide efficient, private, and fast services as a future version of data centers.
    Active Distribution System Coordinated Control Method via Artificial Intelligence. (arXiv:2207.14642v1 [eess.SY])
    The increasing deployment of end use power resources in distribution systems created active distribution systems. Uncontrolled active distribution systems exhibit wide variations of voltage and loading throughout the day as some of these resources operate under max power tracking control of highly variable wind and solar irradiation while others exhibit random variations and/or dependency on weather conditions. It is necessary to control the system to provide power reliably and securely under normal voltages and frequency. Classical optimization approaches to control the system towards this goal suffer from the dimensionality of the problem and the need for a global optimization approach to coordinate a huge number of small resources. Artificial Intelligence (AI) methods offer an alternative that can provide a practical approach to this problem. We suggest that neural networks with self-attention mechanisms have the potential to aid in the optimization of the system. In this paper, we present this approach and provide promising preliminary results.
    GTrans: Grouping and Fusing Transformer Layers for Neural Machine Translation. (arXiv:2207.14467v1 [cs.CL])
    Transformer structure, stacked by a sequence of encoder and decoder network layers, achieves significant development in neural machine translation. However, vanilla Transformer mainly exploits the top-layer representation, assuming the lower layers provide trivial or redundant information and thus ignoring the bottom-layer feature that is potentially valuable. In this work, we propose the Group-Transformer model (GTrans) that flexibly divides multi-layer representations of both encoder and decoder into different groups and then fuses these group features to generate target words. To corroborate the effectiveness of the proposed method, extensive experiments and analytic experiments are conducted on three bilingual translation benchmarks and two multilingual translation tasks, including the IWLST-14, IWLST-17, LDC, WMT-14 and OPUS-100 benchmark. Experimental and analytical results demonstrate that our model outperforms its Transformer counterparts by a consistent gain. Furthermore, it can be successfully scaled up to 60 encoder layers and 36 decoder layers.
    Subtype-Former: a deep learning approach for cancer subtype discovery with multi-omics data. (arXiv:2207.14639v1 [cs.LG])
    Motivation: Cancer is heterogeneous, affecting the precise approach to personalized treatment. Accurate subtyping can lead to better survival rates for cancer patients. High-throughput technologies provide multiple omics data for cancer subtyping. However, precise cancer subtyping remains challenging due to the large amount and high dimensionality of omics data. Results: This study proposed Subtype-Former, a deep learning method based on MLP and Transformer Block, to extract the low-dimensional representation of the multi-omics data. K-means and Consensus Clustering are also used to achieve accurate subtyping results. We compared Subtype-Former with the other state-of-the-art subtyping methods across the TCGA 10 cancer types. We found that Subtype-Former can perform better on the benchmark datasets of more than 5000 tumors based on the survival analysis. In addition, Subtype-Former also achieved outstanding results in pan-cancer subtyping, which can help analyze the commonalities and differences across various cancer types at the molecular level. Finally, we applied Subtype-Former to the TCGA 10 types of cancers. We identified 50 essential biomarkers, which can be used to study targeted cancer drugs and promote the development of cancer treatments in the era of precision medicine.
    Deep learning for understanding multilabel imbalanced Chest X-ray datasets. (arXiv:2207.14408v1 [eess.IV])
    Over the last few years, convolutional neural networks (CNNs) have dominated the field of computer vision thanks to their ability to extract features and their outstanding performance in classification problems, for example in the automatic analysis of X-rays. Unfortunately, these neural networks are considered black-box algorithms, i.e. it is impossible to understand how the algorithm has achieved the final result. To apply these algorithms in different fields and test how the methodology works, we need to use eXplainable AI techniques. Most of the work in the medical field focuses on binary or multiclass classification problems. However, in many real-life situations, such as chest X-rays, radiological signs of different diseases can appear at the same time. This gives rise to what is known as "multilabel classification problems". A disadvantage of these tasks is class imbalance, i.e. different labels do not have the same number of samples. The main contribution of this paper is a Deep Learning methodology for imbalanced, multilabel chest X-ray datasets. It establishes a baseline for the currently underutilised PadChest dataset and a new eXplainable AI technique based on heatmaps. This technique also includes probabilities and inter-model matching. The results of our system are promising, especially considering the number of labels used. Furthermore, the heatmaps match the expected areas, i.e. they mark the areas that an expert would use to make the decision.
    Replacing the Framingham-based equation for prediction of cardiovascular disease risk and adverse outcome by using artificial intelligence and retinal imaging. (arXiv:2207.14685v1 [eess.IV])
    Purpose: To create and evaluate the accuracy of an artificial intelligence Deep learning platform (ORAiCLE) capable of using only retinal fundus images to predict both an individuals overall 5 year cardiovascular risk (CVD) and the relative contribution of the component risk factors that comprise this risk. Methods: We used 165,907 retinal images from a database of 47,236 patient visits. Initially, each image was paired with biometric data age, ethnicity, sex, presence and duration of diabetes a HDL/LDL ratios as well as any CVD event wtihin 5 years of the retinal image acquisition. A risk score based on Framingham equations was calculated. The real CVD event rate was also determined for the individuals and overall population. Finally, ORAiCLE was trained using only age, ethnicity, sex plus retinal images. Results: Compared to Framingham-based score, ORAiCLE was up to 12% more accurate in prediciting cardiovascular event in he next 5-years, especially for the highest risk group of people. The reliability and accuracy of each of the restrictive models was suboptimal to ORAiCLE performance ,indicating that it was using data from both sets of data to derive its final results. Conclusion: Retinal photography is inexpensive and only minimal training is required to acquire them as fully automated, inexpensive camera systems are now widely available. As such, AI-based CVD risk algorithms such as ORAiCLE promise to make CV health screening more accurate, more afforadable and more accessible for all. Furthermore, ORAiCLE unique ability to assess the relative contribution of the components that comprise an individuals overall risk would inform treatment decisions based on the specific needs of an individual, thereby increasing the likelihood of positive health outcomes.
    Graph Neural Networks for Channel Decoding. (arXiv:2207.14742v1 [cs.IT])
    In this work, we propose a fully differentiable graph neural network (GNN)-based architecture for channel decoding and showcase competitive decoding performance for various coding schemes, such as low-density parity-check (LDPC) and BCH codes. The idea is to let a neural network (NN) learn a generalized message passing algorithm over a given graph that represents the forward error correction (FEC) code structure by replacing node and edge message updates with trainable functions. Contrary to many other deep learning-based decoding approaches, the proposed solution enjoys scalability to arbitrary block lengths and the training is not limited by the curse of dimensionality. We benchmark our proposed decoder against state-of-the-art in conventional channel decoding as well as against recent deep learning-based results. For the (63,45) BCH code, our solution outperforms weighted belief propagation (BP) decoding by approximately 0.4 dB with significantly less decoding iterations and even for 5G NR LDPC codes, we observe a competitive performance when compared to conventional BP decoding. For the BCH codes, the resulting GNN decoder can be fully parametrized with only 9640 weights.
    A Deep Generative Approach to Oversampling in Ptychography. (arXiv:2207.14392v1 [eess.IV])
    Ptychography is a well-studied phase imaging method that makes non-invasive imaging possible at a nanometer scale. It has developed into a mainstream technique with various applications across a range of areas such as material science or the defense industry. One major drawback of ptychography is the long data acquisition time due to the high overlap requirement between adjacent illumination areas to achieve a reasonable reconstruction. Traditional approaches with reduced overlap between scanning areas result in reconstructions with artifacts. In this paper, we propose complementing sparsely acquired or undersampled data with data sampled from a deep generative network to satisfy the oversampling requirement in ptychography. Because the deep generative network is pre-trained and its output can be computed as we collect data, the experimental data and the time to acquire the data can be reduced. We validate the method by presenting the reconstruction quality compared to the previously proposed and traditional approaches and comment on the strengths and drawbacks of the proposed approach.
    Using Multi-modal Data for Improving Generalizability and Explainability of Disease Classification in Radiology. (arXiv:2207.14781v1 [cs.CV])
    Traditional datasets for the radiological diagnosis tend to only provide the radiology image alongside the radiology report. However, radiology reading as performed by radiologists is a complex process, and information such as the radiologist's eye-fixations over the course of the reading has the potential to be an invaluable data source to learn from. Nonetheless, the collection of such data is expensive and time-consuming. This leads to the question of whether such data is worth the investment to collect. This paper utilizes the recently published Eye-Gaze dataset to perform an exhaustive study on the impact on performance and explainability of deep learning (DL) classification in the face of varying levels of input features, namely: radiology images, radiology report text, and radiologist eye-gaze data. We find that the best classification performance of X-ray images is achieved with a combination of radiology report free-text and radiology image, with the eye-gaze data providing no performance boost. Nonetheless, eye-gaze data serving as secondary ground truth alongside the class label results in highly explainable models that generate better attention maps compared to models trained to do classification and attention map generation without eye-gaze data.
    Effects of Image Size on Deep Learning. (arXiv:2101.11508v4 [cs.CV] UPDATED)
    This paper presents the effects of late gadolinium enhancement (LGE) magnetic resonance imaging (MRI) image size on deep learning based fully automated quantification of myocardial infarction (MI). The main objective is to determine the best size for LGE MRI images in the training dataset to achieve optimal deep learning training outcomes. To determine the new size of LGE MRI images of the reference training dataset, non-extra pixel and extra pixel interpolation algorithms are used. A novel strategy based on thresholding, median filtering, and subtraction operations is introduced and applied to remove extra class labels in interpolated ground truth (GT) segmentation masks. Fully automated quantification is achieved using the expectation maximization, weighted intensity, a priori information (EWA) algorithm, and the outcome of automatic semantic segmentation of LGE-MRI images with the convolutional neural network (CNN). In the experiments, common class metrics are used to evaluate the quality of semantic segmentation with a CNN architecture of interest (U-net) against the GT segmentation. Arbitrary threshold, comparison of the sums, and sums of differences are used to estimate the relationship between semi-automatic and fully automated quantification of MI results. A close relationship between semi-automatic and fully automated quantification of MI results was more identified in the case involving the dataset of bigger LGE MRI images than in that of the dataset of smaller LGE MRI images, where quantification results based on the dataset of bigger LGE MRI images were 55.5% closer the manual or semi-automatic results while quantification results based on the dataset of smaller LGE MRI images were 22.2% closer the manual results
    A deep learning approach to data-driven model-free pricing and to martingale optimal transport. (arXiv:2103.11435v2 [q-fin.CP] UPDATED)
    We introduce a novel and highly tractable supervised learning approach based on neural networks that can be applied for the computation of model-free price bounds of, potentially high-dimensional, financial derivatives and for the determination of optimal hedging strategies attaining these bounds. In particular, our methodology allows to train a single neural network offline and then to use it online for the fast determination of model-free price bounds of a whole class of financial derivatives with current market data. We show the applicability of this approach and highlight its accuracy in several examples involving real market data. Further, we show how a neural network can be trained to solve martingale optimal transport problems involving fixed marginal distributions instead of financial market data.
    Semi-supervised Learning of Partial Differential Operators and Dynamical Flows. (arXiv:2207.14366v1 [cs.LG])
    The evolution of dynamical systems is generically governed by nonlinear partial differential equations (PDEs), whose solution, in a simulation framework, requires vast amounts of computational resources. In this work, we present a novel method that combines a hyper-network solver with a Fourier Neural Operator architecture. Our method treats time and space separately. As a result, it successfully propagates initial conditions in continuous time steps by employing the general composition properties of the partial differential operators. Following previous work, supervision is provided at a specific time point. We test our method on various time evolution PDEs, including nonlinear fluid flows in one, two, and three spatial dimensions. The results show that the new method improves the learning accuracy at the time point of supervision point, and is able to interpolate and the solutions to any intermediate time.
    Large Language Models and the Reverse Turing Test. (arXiv:2207.14382v1 [cs.CL])
    Large Language Models (LLMs) have been transformative. They are pre-trained foundational models that can be adapted with fine tuning to many different natural language tasks, each of which previously would have required a separate network model. This is one step closer to the extraordinary versatility of human language. GPT-3 and more recently LaMDA can carry on dialogs with humans on many topics after minimal priming with a few examples. However, there has been a wide range of reactions on whether these LLMs understand what they are saying or exhibit signs of intelligence. This high variance is exhibited in three interviews with LLMs reaching wildly different conclusions. A new possibility was uncovered that could explain this divergence. What appears to be intelligence in LLMs may in fact be a mirror that reflects the intelligence of the interviewer, a remarkable twist that could be considered a Reverse Turing Test. If so, then by studying interviews we may be learning more about the intelligence and beliefs of the interviewer than the intelligence of the LLMs.
    SYNTA: A novel approach for deep learning-based image analysis in muscle histopathology using photo-realistic synthetic data. (arXiv:2207.14650v1 [eess.IV])
    Artificial intelligence (AI), machine learning, and deep learning (DL) methods are becoming increasingly important in the field of biomedical image analysis. However, to exploit the full potential of such methods, a representative number of experimentally acquired images containing a significant number of manually annotated objects is needed as training data. Here we introduce SYNTA (synthetic data) as a novel approach for the generation of synthetic, photo-realistic, and highly complex biomedical images as training data for DL systems. We show the versatility of our approach in the context of muscle fiber and connective tissue analysis in histological sections. We demonstrate that it is possible to perform robust and expert-level segmentation tasks on previously unseen real-world data, without the need for manual annotations using synthetic training data alone. Being a fully parametric technique, our approach poses an interpretable and controllable alternative to Generative Adversarial Networks (GANs) and has the potential to significantly accelerate quantitative image analysis in a variety of biomedical applications in microscopy and beyond.
    "FIJO": a French Insurance Soft Skill Detection Dataset. (arXiv:2204.05208v2 [cs.CL] UPDATED)
    Understanding the evolution of job requirements is becoming more important for workers, companies and public organizations to follow the fast transformation of the employment market. Fortunately, recent natural language processing (NLP) approaches allow for the development of methods to automatically extract information from job ads and recognize skills more precisely. However, these efficient approaches need a large amount of annotated data from the studied domain which is difficult to access, mainly due to intellectual property. This article proposes a new public dataset, FIJO, containing insurance job offers, including many soft skill annotations. To understand the potential of this dataset, we detail some characteristics and some limitations. Then, we present the results of skill detection algorithms using a named entity recognition approach and show that transformers-based models have good token-wise performances on this dataset. Lastly, we analyze some errors made by our best model to emphasize the difficulties that may arise when applying NLP approaches.
    Quantifying Data Augmentation for LiDAR based 3D Object Detection. (arXiv:2004.01643v2 [cs.CV] UPDATED)
    In this work, we shed light on different data augmentation techniques commonly used in Light Detection and Ranging (LiDAR) based 3D Object Detection. For the bulk of our experiments, we utilize the well known PointPillars pipeline and the well established KITTI dataset. We investigate a variety of global and local augmentation techniques, where global augmentation techniques are applied to the entire point cloud of a scene and local augmentation techniques are only applied to points belonging to individual objects in the scene. Our findings show that both types of data augmentation can lead to performance increases, but it also turns out, that some augmentation techniques, such as individual object translation, for example, can be counterproductive and can hurt the overall performance. We show that these findings transfer and generalize well to other state of the art 3D Object Detection methods and the challenging STF dataset. On the KITTI dataset we can gain up to 1.5% and on the STF dataset up to 1.7% in 3D mAP on the moderate car class.
    Stochastic Parallelizable Eigengap Dilation for Large Graph Clustering. (arXiv:2207.14589v1 [stat.ML])
    Large graphs commonly appear in social networks, knowledge graphs, recommender systems, life sciences, and decision making problems. Summarizing large graphs by their high level properties is helpful in solving problems in these settings. In spectral clustering, we aim to identify clusters of nodes where most edges fall within clusters and only few edges fall between clusters. This task is important for many downstream applications and exploratory analysis. A core step of spectral clustering is performing an eigendecomposition of the corresponding graph Laplacian matrix (or equivalently, a singular value decomposition, SVD, of the incidence matrix). The convergence of iterative singular value decomposition approaches depends on the eigengaps of the spectrum of the given matrix, i.e., the difference between consecutive eigenvalues. For a graph Laplacian corresponding to a well-clustered graph, the eigenvalues will be non-negative but very small (much less than $1$) slowing convergence. This paper introduces a parallelizable approach to dilating the spectrum in order to accelerate SVD solvers and in turn, spectral clustering. This is accomplished via polynomial approximations to matrix operations that favorably transform the spectrum of a matrix without changing its eigenvectors. Experiments demonstrate that this approach significantly accelerates convergence, and we explain how this transformation can be parallelized and stochastically approximated to scale with available compute.
    Open World Learning Graph Convolution for Latency Estimation in Routing Networks. (arXiv:2207.14643v1 [cs.NI])
    Accurate routing network status estimation is a key component in Software Defined Networking. However, existing deep-learning-based methods for modeling network routing are not able to extrapolate towards unseen feature distributions. Nor are they able to handle scaled and drifted network attributes in test sets that include open-world inputs. To deal with these challenges, we propose a novel approach for modeling network routing, using Graph Neural Networks. Our method can also be used for network-latency estimation. Supported by a domain-knowledge-assisted graph formulation, our model shares a stable performance across different network sizes and configurations of routing networks, while at the same time being able to extrapolate towards unseen sizes, configurations, and user behavior. We show that our model outperforms most conventional deep-learning-based models, in terms of prediction accuracy, computational resources, inference speed, as well as ability to generalize towards open-world input.
    Encoder-Decoder Architecture for 3D Seismic Inversion. (arXiv:2207.14789v1 [physics.geo-ph])
    Inverting seismic data to build 3D geological structures is a challenging task due to the overwhelming amount of acquired seismic data, and the very-high computational load due to iterative numerical solutions of the wave equation, as required by industry-standard tools such as Full Waveform Inversion (FWI). For example, in an area with surface dimensions of 4.5km $\times$ 4.5km, hundreds of seismic shot-gather cubes are required for 3D model reconstruction, leading to Terabytes of recorded data. This paper presents a deep learning solution for the reconstruction of realistic 3D models in the presence of field noise recorded in seismic surveys. We implement and analyze a convolutional encoder-decoder architecture that efficiently processes the entire collection of hundreds of seismic shot-gather cubes. The proposed solution demonstrates that realistic 3D models can be reconstructed with a structural similarity index measure (SSIM) of 0.8554 (out of 1.0) in the presence of field noise at 10dB signal-to-noise ratio.
    Automated liver tissues delineation techniques: A systematic survey on machine learning current trends and future orientations. (arXiv:2103.06384v2 [eess.IV] UPDATED)
    Machine learning and computer vision techniques have grown rapidly in recent years due to their automation, suitability, and ability to generate astounding results. Hence, in this paper, we survey the key studies that are published between 2014 and 2022, showcasing the different machine learning algorithms researchers have used to segment the liver, hepatic tumors, and hepatic-vasculature structures. We divide the surveyed studies based on the tissue of interest (hepatic-parenchyma, hepatic-tumors, or hepatic-vessels), highlighting the studies that tackle more than one task simultaneously. Additionally, the machine learning algorithms are classified as either supervised or unsupervised, and they are further partitioned if the amount of work that falls under a certain scheme is significant. Moreover, different datasets and challenges found in literature and websites containing masks of the aforementioned tissues are thoroughly discussed, highlighting the organizers' original contributions and those of other researchers. Also, the metrics used excessively in literature are mentioned in our review, stressing their relevance to the task at hand. Finally, critical challenges and future directions are emphasized for innovative researchers to tackle, exposing gaps that need addressing, such as the scarcity of many studies on the vessels' segmentation challenge and why their absence needs to be dealt with sooner than later.
    Cyclic Policy Distillation: Sample-Efficient Sim-to-Real Reinforcement Learning with Domain Randomization. (arXiv:2207.14561v1 [cs.RO])
    Deep reinforcement learning with domain randomization learns a control policy in various simulations with randomized physical and sensor model parameters to become transferable to the real world in a zero-shot setting. However, a huge number of samples are often required to learn an effective policy when the range of randomized parameters is extensive due to the instability of policy updates. To alleviate this problem, we propose a sample-efficient method named Cyclic Policy Distillation (CPD). CPD divides the range of randomized parameters into several small sub-domains and assigns a local policy to each sub-domain. Then, the learning of local policies is performed while {\it cyclically} transitioning the target sub-domain to neighboring sub-domains and exploiting the learned values/policies of the neighbor sub-domains with a monotonic policy-improvement scheme. Finally, all of the learned local policies are distilled into a global policy for sim-to-real transfer. The effectiveness and sample efficiency of CPD are demonstrated through simulations with four tasks (Pendulum from OpenAIGym and Pusher, Swimmer, and HalfCheetah from Mujoco), and a real-robot ball-dispersal task.
    EmoSens: Emotion Recognition based on Sensor data analysis using LightGBM. (arXiv:2207.14640v1 [cs.HC])
    Smart wearables have played an integral part in our day to day life. From recording ECG signals to analysing body fat composition, the smart wearables can do it all. The smart devices encompass various sensors which can be employed to derive meaningful information regarding the user's physical and psychological conditions. Our approach focuses on employing such sensors to identify and obtain the variations in the mood of a user at a given instance through the use of supervised machine learning techniques. The study examines the performance of various supervised learning models such as Decision Trees, Random Forests, XGBoost, LightGBM on the dataset. With our proposed model, we obtained a high recognition rate of 92.5% using XGBoost and LightGBM for 9 different emotion classes. By utilizing this, we aim to improvise and suggest methods to aid emotion recognition for better mental health analysis and mood monitoring.
    BiFeat: Supercharge GNN Training via Graph Feature Quantization. (arXiv:2207.14696v1 [cs.LG])
    Graph Neural Networks (GNNs) is a promising approach for applications with nonEuclidean data. However, training GNNs on large scale graphs with hundreds of millions nodes is both resource and time consuming. Different from DNNs, GNNs usually have larger memory footprints, and thus the GPU memory capacity and PCIe bandwidth are the main resource bottlenecks in GNN training. To address this problem, we present BiFeat: a graph feature quantization methodology to accelerate GNN training by significantly reducing the memory footprint and PCIe bandwidth requirement so that GNNs can take full advantage of GPU computing capabilities. Our key insight is that unlike DNN, GNN is less prone to the information loss of input features caused by quantization. We identify the main accuracy impact factors in graph feature quantization and theoretically prove that BiFeat training converges to a network where the loss is within $\epsilon$ of the optimal loss of uncompressed network. We perform extensive evaluation of BiFeat using several popular GNN models and datasets, including GraphSAGE on MAG240M, the largest public graph dataset. The results demonstrate that BiFeat achieves a compression ratio of more than 30 and improves GNN training speed by 200%-320% with marginal accuracy loss. In particular, BiFeat achieves a record by training GraphSAGE on MAG240M within one hour using only four GPUs.
    Image sensing with multilayer, nonlinear optical neural networks. (arXiv:2207.14293v1 [physics.optics])
    Optical imaging is commonly used for both scientific and technological applications across industry and academia. In image sensing, a measurement, such as of an object's position, is performed by computational analysis of a digitized image. An emerging image-sensing paradigm breaks this delineation between data collection and analysis by designing optical components to perform not imaging, but encoding. By optically encoding images into a compressed, low-dimensional latent space suitable for efficient post-analysis, these image sensors can operate with fewer pixels and fewer photons, allowing higher-throughput, lower-latency operation. Optical neural networks (ONNs) offer a platform for processing data in the analog, optical domain. ONN-based sensors have however been limited to linear processing, but nonlinearity is a prerequisite for depth, and multilayer NNs significantly outperform shallow NNs on many tasks. Here, we realize a multilayer ONN pre-processor for image sensing, using a commercial image intensifier as a parallel optoelectronic, optical-to-optical nonlinear activation function. We demonstrate that the nonlinear ONN pre-processor can achieve compression ratios of up to 800:1 while still enabling high accuracy across several representative computer-vision tasks, including machine-vision benchmarks, flow-cytometry image classification, and identification of objects in real scenes. In all cases we find that the ONN's nonlinearity and depth allowed it to outperform a purely linear ONN encoder. Although our experiments are specialized to ONN sensors for incoherent-light images, alternative ONN platforms should facilitate a range of ONN sensors. These ONN sensors may surpass conventional sensors by pre-processing optical information in spatial, temporal, and/or spectral dimensions, potentially with coherent and quantum qualities, all natively in the optical domain.
    Multiple Attribute Fairness: Application to Fraud Detection. (arXiv:2207.14355v1 [cs.LG])
    We propose a fairness measure relaxing the equality conditions in the popular equal odds fairness regime for classification. We design an iterative, model-agnostic, grid-based heuristic that calibrates the outcomes per sensitive attribute value to conform to the measure. The heuristic is designed to handle high arity attribute values and performs a per attribute sanitization of outcomes across different protected attribute values. We also extend our heuristic for multiple attributes. Highlighting our motivating application, fraud detection, we show that the proposed heuristic is able to achieve fairness across multiple values of a single protected attribute, multiple protected attributes. When compared to current fairness techniques, that focus on two groups, we achieve comparable performance across several public data sets.
    Leveraging Explanations in Interactive Machine Learning: An Overview. (arXiv:2207.14526v1 [cs.LG])
    Explanations have gained an increasing level of interest in the AI and Machine Learning (ML) communities in order to improve model transparency and allow users to form a mental model of a trained ML model. However, explanations can go beyond this one way communication as a mechanism to elicit user control, because once users understand, they can then provide feedback. The goal of this paper is to present an overview of research where explanations are combined with interactive capabilities as a mean to learn new models from scratch and to edit and debug existing ones. To this end, we draw a conceptual map of the state-of-the-art, grouping relevant approaches based on their intended purpose and on how they structure the interaction, highlighting similarities and differences between them. We also discuss open research issues and outline possible directions forward, with the hope of spurring further research on this blooming research topic.
    Dive into Deep Learning. (arXiv:2106.11342v3 [cs.LG] UPDATED)
    This open-source book represents our attempt to make deep learning approachable, teaching readers the concepts, the context, and the code. The entire book is drafted in Jupyter notebooks, seamlessly integrating exposition figures, math, and interactive examples with self-contained code. Our goal is to offer a resource that could (i) be freely available for everyone; (ii) offer sufficient technical depth to provide a starting point on the path to actually becoming an applied machine learning scientist; (iii) include runnable code, showing readers how to solve problems in practice; (iv) allow for rapid updates, both by us and also by the community at large; (v) be complemented by a forum for interactive discussion of technical details and to answer questions.
    Open-radiomics: A Research Protocol to Make Radiomics-based Machine Learning Pipelines Reproducible. (arXiv:2207.14776v1 [q-bio.QM])
    The application of artificial intelligence (AI) techniques to medical imaging data has yielded promising results. As an important branch of AI pipelines in medical imaging, radiomics faces two major challenges namely reproducibility and accessibility. In this work, we introduce open-radiomics, a set of radiomics datasets, and a comprehensive radiomics pipeline that investigates the effects of radiomics feature extraction settings such as binWidth and image normalization on the reproducibility of the radiomics results performance. To make radiomics research more accessible and reproducible, we provide guidelines for building machine learning (ML) models on radiomics data, introduce Open-radiomics, an evolving collection of open-source radiomics datasets, and publish baseline models for the datasets.
    Robust Framework for COVID-19 Identification from a Multicenter Dataset of Chest CT Scans. (arXiv:2109.09241v3 [eess.IV] UPDATED)
    The objective of this study is to develop a robust deep learning-based framework to distinguish COVID-19, Community-Acquired Pneumonia (CAP), and Normal cases based on chest CT scans acquired in different imaging centers using various protocols, and radiation doses. We showed that while our proposed model is trained on a relatively small dataset acquired from only one imaging center using a specific scanning protocol, the model performs well on heterogeneous test sets obtained by multiple scanners using different technical parameters. We also showed that the model can be updated via an unsupervised approach to cope with the data shift between the train and test sets and enhance the robustness of the model upon receiving a new external dataset from a different center. We adopted an ensemble architecture to aggregate the predictions from multiple versions of the model. For initial training and development purposes, an in-house dataset of 171 COVID-19, 60 CAP, and 76 Normal cases was used, which contained volumetric CT scans acquired from one imaging center using a constant standard radiation dose scanning protocol. To evaluate the model, we collected four different test sets retrospectively to investigate the effects of the shifts in the data characteristics on the model's performance. Among the test cases, there were CT scans with similar characteristics as the train set as well as noisy low-dose and ultra-low dose CT scans. In addition, some test CT scans were obtained from patients with a history of cardiovascular diseases or surgeries. The entire test dataset used in this study contained 51 COVID-19, 28 CAP, and 51 Normal cases. Experimental results indicate that our proposed framework performs well on all test sets achieving total accuracy of 96.15% (95%CI: [91.25-98.74]), COVID-19 sensitivity of 96.08% (95%CI: [86.54-99.5]), CAP sensitivity of 92.86% (95%CI: [76.50-99.19]).
    Artifact Identification in X-ray Diffraction Data using Machine Learning Methods. (arXiv:2207.14804v1 [eess.IV])
    The in situ synchrotron high-energy X-ray powder diffraction (XRD) technique is highly utilized by researchers to analyze the crystallographic structures of materials in functional devices (e.g., battery materials) or in complex sample environments (e.g., diamond anvil cells or syntheses reactors). An atomic structure of a material can be identified by its diffraction pattern, along with detailed analysis such as Rietveld refinement which indicates how the measured structure deviates from the ideal structure (e.g., internal stresses or defects). For in situ experiments, a series of XRD images is usually collected on the same sample at different conditions (e.g., adiabatic conditions), yielding different states of matter, or simply collected continuously as a function of time to track the change of a sample over a chemical or physical process. In situ experiments are usually performed with area detectors, collecting 2D images composed of diffraction rings for ideal powders. Depending on the material's form, one may observe different characteristics other than the typical Debye Scherrer rings for a realistic sample and its environments, such as textures or preferred orientations and single crystal diffraction spots in the 2D XRD image. In this work, we present an investigation of machine learning methods for fast and reliable identification and separation of the single crystal diffraction spots in XRD images. The exclusion of artifacts during an XRD image integration process allows a precise analysis of the powder diffraction rings of interest. We observe that the gradient boosting method can consistently produce high accuracy results when it is trained with small subsets of highly diverse datasets. The method dramatically decreases the amount of time spent on identifying and separating single crystal spots in comparison to the conventional method.
    Spliced Binned-Pareto Distribution for Robust Modeling of Heavy-tailed Time Series. (arXiv:2106.10952v2 [stat.ML] UPDATED)
    This work proposes a novel method to robustly and accurately model time series with heavy-tailed noise, in non-stationary scenarios. In many practical application time series have heavy-tailed noise that significantly impacts the performance of classical forecasting models; in particular, accurately modeling a distribution over extreme events is crucial to performing accurate time series anomaly detection. We propose a Spliced Binned-Pareto distribution which is both robust to extreme observations and allows accurate modeling of the full distribution. Our method allows the capture of time dependencies in the higher order moments of the distribution such as the tail heaviness. We compare the robustness and the accuracy of the tail estimation of our method to other state of the art methods on Twitter mentions count time series.
    Automatic Reward Design via Learning Motivation-Consistent Intrinsic Rewards. (arXiv:2207.14722v1 [cs.LG])
    Reward design is a critical part of the application of reinforcement learning, the performance of which strongly depends on how well the reward signal frames the goal of the designer and how well the signal assesses progress in reaching that goal. In many cases, the extrinsic rewards provided by the environment (e.g., win or loss of a game) are very sparse and make it difficult to train agents directly. Researchers usually assist the learning of agents by adding some auxiliary rewards in practice. However, designing auxiliary rewards is often turned to a trial-and-error search for reward settings that produces acceptable results. In this paper, we propose to automatically generate goal-consistent intrinsic rewards for the agent to learn, by maximizing which the expected accumulative extrinsic rewards can be maximized. To this end, we introduce the concept of motivation which captures the underlying goal of maximizing certain rewards and propose the motivation based reward design method. The basic idea is to shape the intrinsic rewards by minimizing the distance between the intrinsic and extrinsic motivations. We conduct extensive experiments and show that our method performs better than the state-of-the-art methods in handling problems of delayed reward, exploration, and credit assignment.
    Meta Reinforcement Learning with Successor Feature Based Context. (arXiv:2207.14723v1 [cs.LG])
    Most reinforcement learning (RL) methods only focus on learning a single task from scratch and are not able to use prior knowledge to learn other tasks more effectively. Context-based meta RL techniques are recently proposed as a possible solution to tackle this. However, they are usually less efficient than conventional RL and may require many trial-and-errors during training. To address this, we propose a novel meta-RL approach that achieves competitive performance comparing to existing meta-RL algorithms, while requires significantly fewer environmental interactions. By combining context variables with the idea of decomposing reward in successor feature framework, our method does not only learn high-quality policies for multiple tasks simultaneously but also can quickly adapt to new tasks with a small amount of training. Compared with state-of-the-art meta-RL baselines, we empirically show the effectiveness and data efficiency of our method on several continuous control tasks.
    Cluster-Specific Predictions with Multi-Task Gaussian Processes. (arXiv:2011.07866v3 [cs.LG] UPDATED)
    A model involving Gaussian processes (GPs) is introduced to simultaneously handle multi-task learning, clustering, and prediction for multiple functional data. This procedure acts as a model-based clustering method for functional data as well as a learning step for subsequent predictions for new tasks. The model is instantiated as a mixture of multi-task GPs with common mean processes. A variational EM algorithm is derived for dealing with the optimisation of the hyper-parameters along with the hyper-posteriors' estimation of latent variables and processes. We establish explicit formulas for integrating the mean processes and the latent clustering variables within a predictive distribution, accounting for uncertainty on both aspects. This distribution is defined as a mixture of cluster-specific GP predictions, which enhances the performances when dealing with group-structured data. The model handles irregular grid of observations and offers different hypotheses on the covariance structure for sharing additional information across tasks. The performances on both clustering and prediction tasks are assessed through various simulated scenarios and real datasets. The overall algorithm, called MagmaClust, is publicly available as an R package.
    Email Spam Detection Using Hierarchical Attention Hybrid Deep Learning Method. (arXiv:2204.07390v2 [cs.CL] UPDATED)
    Email is one of the most widely used ways to communicate, with millions of people and businesses relying on it to communicate and share knowledge and information on a daily basis. Nevertheless, the rise in email users has occurred a dramatic increase in spam emails in recent years. Processing and managing emails properly for individuals and companies are getting increasingly difficult. This article proposes a novel technique for email spam detection that is based on a combination of convolutional neural networks, gated recurrent units, and attention mechanisms. During system training, the network is selectively focused on necessary parts of the email text. The usage of convolution layers to extract more meaningful, abstract, and generalizable features by hierarchical representation is the major contribution of this study. Additionally, this contribution incorporates cross-dataset evaluation, which enables the generation of more independent performance results from the model's training dataset. According to cross-dataset evaluation results, the proposed technique advances the results of the present attention-based techniques by utilizing temporal convolutions, which give us more flexible receptive field sizes are utilized. The suggested technique's findings are compared to those of state-of-the-art models and show that our approach outperforms them.
    A One-Shot Reparameterization Method for Reducing the Loss of Tile Pruning on DNNs. (arXiv:2207.14545v1 [cs.CV])
    Recently, tile pruning has been widely studied to accelerate the inference of deep neural networks (DNNs). However, we found that the loss due to tile pruning, which can eliminate important elements together with unimportant elements, is large on trained DNNs. In this study, we propose a one-shot reparameterization method, called TileTrans, to reduce the loss of tile pruning. Specifically, we repermute the rows or columns of the weight matrix such that the model architecture can be kept unchanged after reparameterization. This repermutation realizes the reparameterization of the DNN model without any retraining. The proposed reparameterization method combines important elements into the same tile; thus, preserving the important elements after the tile pruning. Furthermore, TileTrans can be seamlessly integrated into existing tile pruning methods because it is a pre-processing method executed before pruning, which is orthogonal to most existing methods. The experimental results demonstrate that our method is essential in reducing the loss of tile pruning on DNNs. Specifically, the accuracy is improved by up to 17% for AlexNet while 5% for ResNet-34, where both models are pre-trained on ImageNet.
    Design Methodology for Deep Out-of-Distribution Detectors in Real-Time Cyber-Physical Systems. (arXiv:2207.14694v1 [cs.LG])
    When machine learning (ML) models are supplied with data outside their training distribution, they are more likely to make inaccurate predictions; in a cyber-physical system (CPS), this could lead to catastrophic system failure. To mitigate this risk, an out-of-distribution (OOD) detector can run in parallel with an ML model and flag inputs that could lead to undesirable outcomes. Although OOD detectors have been well studied in terms of accuracy, there has been less focus on deployment to resource constrained CPSs. In this study, a design methodology is proposed to tune deep OOD detectors to meet the accuracy and response time requirements of embedded applications. The methodology uses genetic algorithms to optimize the detector's preprocessing pipeline and selects a quantization method that balances robustness and response time. It also identifies several candidate task graphs under the Robot Operating System (ROS) for deployment of the selected design. The methodology is demonstrated on two variational autoencoder based OOD detectors from the literature on two embedded platforms. Insights into the trade-offs that occur during the design process are provided, and it is shown that this design methodology can lead to a drastic reduction in response time in relation to an unoptimized OOD detector while maintaining comparable accuracy.
    Restoring Vision in Adverse Weather Conditions with Patch-Based Denoising Diffusion Models. (arXiv:2207.14626v1 [cs.CV])
    Image restoration under adverse weather conditions has been of significant interest for various computer vision applications. Recent successful methods rely on the current progress in deep neural network architectural designs (e.g., with vision transformers). Motivated by the recent progress achieved with state-of-the-art conditional generative models, we present a novel patch-based image restoration algorithm based on denoising diffusion probabilistic models. Our patch-based diffusion modeling approach enables size-agnostic image restoration by using a guided denoising process with smoothed noise estimates across overlapping patches during inference. We empirically evaluate our model on benchmark datasets for image desnowing, combined deraining and dehazing, and raindrop removal. We demonstrate our approach to achieve state-of-the-art performances on both weather-specific and multi-weather image restoration, and qualitatively show strong generalization to real-world test images.
    Deep Learning for Bayesian Optimization of Scientific Problems with High-Dimensional Structure. (arXiv:2104.11667v3 [cs.LG] UPDATED)
    Bayesian optimization (BO) is a popular paradigm for global optimization of expensive black-box functions, but there are many domains where the function is not completely a black-box. The data may have some known structure (e.g. symmetries) and/or the data generation process may be a composite process that yields useful intermediate or auxiliary information in addition to the value of the optimization objective. However, surrogate models traditionally employed in BO, such as Gaussian Processes (GPs), scale poorly with dataset size and do not easily accommodate known structure. Instead, we use Bayesian neural networks, a class of scalable and flexible surrogate models with inductive biases, to extend BO to complex, structured problems with high dimensionality. We demonstrate BO on a number of realistic problems in physics and chemistry, including topology optimization of photonic crystal materials using convolutional neural networks, and chemical property optimization of molecules using graph neural networks. On these complex tasks, we show that neural networks often outperform GPs as surrogate models for BO in terms of both sampling efficiency and computational cost.
    Content-Aware Differential Privacy with Conditional Invertible Neural Networks. (arXiv:2207.14625v1 [cs.CR])
    Differential privacy (DP) has arisen as the gold standard in protecting an individual's privacy in datasets by adding calibrated noise to each data sample. While the application to categorical data is straightforward, its usability in the context of images has been limited. Contrary to categorical data the meaning of an image is inherent in the spatial correlation of neighboring pixels making the simple application of noise infeasible. Invertible Neural Networks (INN) have shown excellent generative performance while still providing the ability to quantify the exact likelihood. Their principle is based on transforming a complicated distribution into a simple one e.g. an image into a spherical Gaussian. We hypothesize that adding noise to the latent space of an INN can enable differentially private image modification. Manipulation of the latent space leads to a modified image while preserving important details. Further, by conditioning the INN on meta-data provided with the dataset we aim at leaving dimensions important for downstream tasks like classification untouched while altering other parts that potentially contain identifying information. We term our method content-aware differential privacy (CADP). We conduct experiments on publicly available benchmarking datasets as well as dedicated medical ones. In addition, we show the generalizability of our method to categorical data. The source code is publicly available at https://github.com/Cardio-AI/CADP.
    Towards Communication-efficient Vertical Federated Learning Training via Cache-enabled Local Updates. (arXiv:2207.14628v1 [cs.LG])
    Vertical federated learning (VFL) is an emerging paradigm that allows different parties (e.g., organizations or enterprises) to collaboratively build machine learning models with privacy protection. In the training phase, VFL only exchanges the intermediate statistics, i.e., forward activations and backward derivatives, across parties to compute model gradients. Nevertheless, due to its geo-distributed nature, VFL training usually suffers from the low WAN bandwidth. In this paper, we introduce CELU-VFL, a novel and efficient VFL training framework that exploits the local update technique to reduce the cross-party communication rounds. CELU-VFL caches the stale statistics and reuses them to estimate model gradients without exchanging the ad hoc statistics. Significant techniques are proposed to improve the convergence performance. First, to handle the stochastic variance problem, we propose a uniform sampling strategy to fairly choose the stale statistics for local updates. Second, to harness the errors brought by the staleness, we devise an instance weighting mechanism that measures the reliability of the estimated gradients. Theoretical analysis proves that CELU-VFL achieves a similar sub-linear convergence rate as vanilla VFL training but requires much fewer communication rounds. Empirical results on both public and real-world workloads validate that CELU-VFL can be up to six times faster than the existing works.
    Learning idempotent representation for subspace clustering. (arXiv:2207.14431v1 [cs.LG])
    The critical point for the successes of spectral-type subspace clustering algorithms is to seek reconstruction coefficient matrices which can faithfully reveal the subspace structures of data sets. An ideal reconstruction coefficient matrix should have two properties: 1) it is block diagonal with each block indicating a subspace; 2) each block is fully connected. Though there are various spectral-type subspace clustering algorithms have been proposed, some defects still exist in the reconstruction coefficient matrices constructed by these algorithms. We find that a normalized membership matrix naturally satisfies the above two conditions. Therefore, in this paper, we devise an idempotent representation (IDR) algorithm to pursue reconstruction coefficient matrices approximating normalized membership matrices. IDR designs a new idempotent constraint for reconstruction coefficient matrices. And by combining the doubly stochastic constraints, the coefficient matrices which are closed to normalized membership matrices could be directly achieved. We present the optimization algorithm for solving IDR problem and analyze its computation burden as well as convergence. The comparisons between IDR and related algorithms show the superiority of IDR. Plentiful experiments conducted on both synthetic and real world datasets prove that IDR is an effective and efficient subspace clustering algorithm.
    Adaptive Gradient Methods at the Edge of Stability. (arXiv:2207.14484v1 [cs.LG])
    Very little is known about the training dynamics of adaptive gradient methods like Adam in deep learning. In this paper, we shed light on the behavior of these algorithms in the full-batch and sufficiently large batch settings. Specifically, we empirically demonstrate that during full-batch training, the maximum eigenvalue of the preconditioned Hessian typically equilibrates at a certain numerical value -- the stability threshold of a gradient descent algorithm. For Adam with step size $\eta$ and $\beta_1 = 0.9$, this stability threshold is $38/\eta$. Similar effects occur during minibatch training, especially as the batch size grows. Yet, even though adaptive methods train at the ``Adaptive Edge of Stability'' (AEoS), their behavior in this regime differs in a significant way from that of non-adaptive methods at the EoS. Whereas non-adaptive algorithms at the EoS are blocked from entering high-curvature regions of the loss landscape, adaptive gradient methods at the AEoS can keep advancing into high-curvature regions, while adapting the preconditioner to compensate. Our findings can serve as a foundation for the community's future understanding of adaptive gradient methods in deep learning.
    Reweighted Manifold Learning of Collective Variables from Enhanced Sampling Simulations. (arXiv:2207.14554v1 [physics.chem-ph])
    Enhanced sampling methods are indispensable in computational physics and chemistry, where atomistic simulations cannot exhaustively sample the high-dimensional configuration space of dynamical systems due to the sampling problem. A class of such enhanced sampling methods works by identifying a few slow degrees of freedom, termed collective variables (CVs), and enhancing the sampling along these CVs. Selecting CVs to analyze and drive the sampling is not trivial and often relies on physical and chemical intuition. Despite routinely circumventing this issue using manifold learning to estimate CVs directly from standard simulations, such methods cannot provide mappings to a low-dimensional manifold from enhanced sampling simulations as the geometry and density of the learned manifold are biased. Here, we address this crucial issue and provide a general reweighting framework based on anisotropic diffusion maps for manifold learning that takes into account that the learning data set is sampled from a biased probability distribution. We consider manifold learning methods based on constructing a Markov chain describing transition probabilities between high-dimensional samples. We show that our framework reverts the biasing effect yielding CVs that correctly describe the equilibrium density. This advancement enables the construction of low-dimensional CVs using manifold learning directly from data generated by enhanced sampling simulations. We call our framework reweighted manifold learning. We show that it can be used in many manifold learning techniques on data from both standard and enhanced sampling simulations.
    Decentralized Machine Learning for Intelligent Health Care Systems on the Computing Continuum. (arXiv:2207.14584v1 [cs.DC])
    The introduction of electronic personal health records (EHR) enables nationwide information exchange and curation among different health care systems. However, the current EHR systems do not provide transparent means for diagnosis support, medical research or can utilize the omnipresent data produced by the personal medical devices. Besides, the EHR systems are centrally orchestrated, which could potentially lead to a single point of failure. Therefore, in this article, we explore novel approaches for decentralizing machine learning over distributed ledgers to create intelligent EHR systems that can utilize information from personal medical devices for improved knowledge extraction. Consequently, we proposed and evaluated a conceptual EHR to enable anonymous predictive analysis across multiple medical institutions. The evaluation results indicate that the decentralized EHR can be deployed over the computing continuum with reduced machine learning time of up to 60% and consensus latency of below 8 seconds.
    Best-of-Both-Worlds Algorithms for Partial Monitoring. (arXiv:2207.14550v1 [cs.LG])
    This paper considers the partial monitoring problem with $k$-actions and $d$-outcomes and provides the first best-of-both-worlds algorithms, whose regrets are bounded poly-logarithmically in the stochastic regime and near-optimally in the adversarial regime. To be more specific, we show that for non-degenerate locally observable games, the regret in the stochastic regime is bounded by $O(k^3 m^2 \log(T) \log(k_{\Pi} T) / \Delta_{\mathrm{\min}})$ and in the adversarial regime by $O(k^{2/3} m \sqrt{T \log(T) \log k_{\Pi}})$, where $T$ is the number of rounds, $m$ is the maximum number of distinct observations per action, $\Delta_{\min}$ is the minimum optimality gap, and $k_{\Pi}$ is the number of Pareto optimal actions. Moreover, we show that for non-degenerate globally observable games, the regret in the stochastic regime is bounded by $O(\max\{c_{\mathcal{G}}^2 / k,\, c_{\mathcal{G}}\} \log(T) \log(k_{\Pi} T) / \Delta_{\min}^2)$ and in the adversarial regime by $O((\max\{c_{\mathcal{G}}^2 / k,\, c_{\mathcal{G}}\} \log(T) \log(k_{\Pi} T)))^{1/3} T^{2/3})$, where $c_{\mathcal{G}}$ is a game-dependent constant. Our algorithms are based on the follow-the-regularized-leader framework that takes into account the nature of the partial monitoring problem, inspired by algorithms in the field of online learning with feedback graphs.
    Expanding the class of global objective functions for dissimilarity-based hierarchical clustering. (arXiv:2207.14375v1 [cs.LG])
    Recent work on dissimilarity-based hierarchical clustering has led to the introduction of global objective functions for this classical problem. Several standard approaches, such as average linkage, as well as some new heuristics have been shown to provide approximation guarantees. Here we introduce a broad new class of objective functions which satisfy desirable properties studied in prior work. Many common agglomerative and divisive clustering methods are shown to be greedy algorithms for these objectives, which are inspired by related concepts in phylogenetics.
    Image Augmentation for Satellite Images. (arXiv:2207.14580v1 [cs.CV])
    This study proposes the use of generative models (GANs) for augmenting the EuroSAT dataset for the Land Use and Land Cover (LULC) Classification task. We used DCGAN and WGAN-GP to generate images for each class in the dataset. We then explored the effect of augmenting the original dataset by about 10% in each case on model performance. The choice of GAN architecture seems to have no apparent effect on the model performance. However, a combination of geometric augmentation and GAN-generated images improved baseline results. Our study shows that GANs augmentation can improve the generalizability of deep classification models on satellite images.
    Supplementing Recurrent Neural Network Wave Functions with Symmetry and Annealing to Improve Accuracy. (arXiv:2207.14314v1 [cond-mat.dis-nn])
    Recurrent neural networks (RNNs) are a class of neural networks that have emerged from the paradigm of artificial intelligence and has enabled lots of interesting advances in the field of natural language processing. Interestingly, these architectures were shown to be powerful ansatze to approximate the ground state of quantum systems. Here, we build over the results of [Phys. Rev. Research 2, 023358 (2020)] and construct a more powerful RNN wave function ansatz in two dimensions. We use symmetry and annealing to obtain accurate estimates of ground state energies of the two-dimensional (2D) Heisenberg model, on the square lattice and on the triangular lattice. We show that our method is superior to Density Matrix Renormalisation Group (DMRG) for system sizes larger than or equal to $14 \times 14$ on the triangular lattice.
    Sequential Models in the Synthetic Data Vault. (arXiv:2207.14406v1 [cs.LG])
    The goal of this paper is to describe a system for generating synthetic sequential data within the Synthetic data vault. To achieve this, we present the Sequential model currently in SDV, an end-to-end framework that builds a generative model for multi-sequence, real-world data. This includes a novel neural network-based machine learning model, conditional probabilistic auto-regressive (CPAR) model. The overall system and the model is available in the open source Synthetic Data Vault (SDV) library {https://github.com/sdv-dev/SDV}, along with a variety of other models for different synthetic data needs. After building the Sequential SDV, we used it to generate synthetic data and compared its quality against an existing, non-sequential generative adversarial network based model called CTGAN. To compare the sequential synthetic data against its real counterpart, we invented a new metric called Multi-Sequence Aggregate Similarity (MSAS). We used it to conclude that our Sequential SDV model learns higher level patterns than non-sequential models without any trade-offs in synthetic data quality.
    Contrastive Pre-training of Spatial-Temporal Trajectory Embeddings. (arXiv:2207.14539v1 [cs.CV])
    Pre-training trajectory embeddings is a fundamental and critical procedure in spatial-temporal trajectory mining, and is beneficial for a wide range of downstream tasks. The key for generating effective trajectory embeddings is to extract high-level travel semantics from trajectories, including movement patterns and travel purposes, with consideration of the trajectories' long-term spatial-temporal correlations. Despite the existing efforts, there are still major challenges in pre-training trajectory embeddings. First, commonly used generative pretext tasks are not suitable for extracting high-level semantics from trajectories. Second, existing data augmentation methods fit badly on trajectory datasets. Third, current encoder designs fail to fully incorporate long-term spatial-temporal correlations hidden in trajectories. To tackle these challenges, we propose a novel Contrastive Spatial-Temporal Trajectory Embedding (CSTTE) model for learning comprehensive trajectory embeddings. CSTTE adopts the contrastive learning framework so that its pretext task is robust to noise. A specially designed data augmentation method for trajectories is coupled with the contrastive pretext task to preserve the high-level travel semantics. We also build an efficient spatial-temporal trajectory encoder to efficiently and comprehensively model the long-term spatial-temporal correlations in trajectories. Extensive experiments on two downstream tasks and three real-world datasets prove the superiority of our model compared with the existing trajectory embedding methods.
    Model selection with Gini indices under auto-calibration. (arXiv:2207.14372v1 [cs.LG])
    In general, the Gini index does not give a consistent scoring rule. Therefore, maximizing the Gini index may lead to a wrong decision. The main issue is that the Gini index is a rank-based score that is not calibration-sensitive. We show that the Gini index allows for consistent scoring if we restrict it to the class of auto-calibrated regression models.
    KG-NSF: Knowledge Graph Completion with a Negative-Sample-Free Approach. (arXiv:2207.14617v1 [cs.LG])
    Knowledge Graph (KG) completion is an important task that greatly benefits knowledge discovery in many fields (e.g. biomedical research). In recent years, learning KG embeddings to perform this task has received considerable attention. Despite the success of KG embedding methods, they predominantly use negative sampling, resulting in increased computational complexity as well as biased predictions due to the closed world assumption. To overcome these limitations, we propose \textbf{KG-NSF}, a negative sampling-free framework for learning KG embeddings based on the cross-correlation matrices of embedding vectors. It is shown that the proposed method achieves comparable link prediction performance to negative sampling-based methods while converging much faster.
    Ensemble forecasts in reproducing kernel Hilbert space family: dynamical systems in Wonderland. (arXiv:2207.14653v1 [math-ph])
    A methodological framework for ensemble-based estimation and simulation of high dimensional dynamical systems such as the oceanic or atmospheric flows is proposed. To that end, the dynamical system is embedded in a family of reproducing kernel Hilbert spaces with kernel functions driven by the dynamics. This family is nicknamed Wonderland for its appealing properties. In Wonderland the Koopman and Perron-Frobenius operators are unitary and uniformly continuous. This property warrants they can be expressed in exponential series of diagonalizable bounded infinitesimal generators. Access to Lyapunov exponents and to exact ensemble based expressions of the tangent linear dynamics are directly available as well. Wonderland enables us the devise of strikingly simple ensemble data assimilation methods for trajectory reconstructions in terms of constant-in-time linear combinations of trajectory samples. Such an embarrassingly simple strategy is made possible through a fully justified superposition principle ensuing from several fundamental theorems.
    Interactive Recommendations for Optimal Allocations in Markets with Constraints. (arXiv:2207.04143v2 [cs.LG] UPDATED)
    Recommendation systems when employed in markets play a dual role: they assist users in selecting their most desired items from a large pool and they help in allocating a limited number of items to the users who desire them the most. Despite the prevalence of capacity constraints on allocations in many real-world recommendation settings, a principled way of incorporating them in the design of these systems has been lacking. Motivated by this, we propose an interactive framework where the system provider can enhance the quality of recommendations to the users by opportunistically exploring allocations that maximize user rewards and respect the capacity constraints using appropriate pricing mechanisms. We model the problem as an instance of a low-rank combinatorial multi-armed bandit problem with selection constraints on the arms. We employ an integrated approach using techniques from collaborative filtering, combinatorial bandits, and optimal resource allocation to provide an algorithm that provably achieves sub-linear regret, namely $\tilde{\mathcal{O}} ( \sqrt{N M (N+M) RT} )$ in $T$ rounds for a problem with $N$ users, $M$ items and rank $R$ mean reward matrix. Empirical studies on synthetic and real-world data also demonstrate the effectiveness and performance of our approach.
    Conditioning Normalizing Flows for Rare Event Sampling. (arXiv:2207.14530v1 [physics.comp-ph])
    Understanding the dynamics of complex molecular processes is often linked to the study of infrequent transitions between long-lived stable states. The standard approach to the sampling of such rare events is to generate an ensemble of transition paths using a random walk in trajectory space. This, however, comes with the drawback of strong correlation between subsequently visited paths and with an intrinsic difficulty in parallelizing the sampling process. We propose a transition path sampling scheme based on neural-network generated configurations. These are obtained employing normalizing flows, a neural network class able to generate decorrelated samples from a given distribution. With this approach, not only are correlations between visited paths removed, but the sampling process becomes easily parallelizable. Moreover, by conditioning the normalizing flow, the sampling of configurations can be steered towards the regions of interest. We show that this allows for resolving both the thermodynamics and kinetics of the transition region.
    StudioGAN: A Taxonomy and Benchmark of GANs for Image Synthesis. (arXiv:2206.09479v2 [cs.CV] UPDATED)
    Generative Adversarial Network (GAN) is one of the state-of-the-art generative models for realistic image synthesis. While training and evaluating GAN becomes increasingly important, the current GAN research ecosystem does not provide reliable benchmarks for which the evaluation is conducted consistently and fairly. Furthermore, because there are few validated GAN implementations, researchers devote considerable time to reproducing baselines. We study the taxonomy of GAN approaches and present a new open-source library named StudioGAN. StudioGAN supports 7 GAN architectures, 9 conditioning methods, 4 adversarial losses, 13 regularization modules, 3 differentiable augmentations, 7 evaluation metrics, and 5 evaluation backbones. With our training and evaluation protocol, we present a large-scale benchmark using various datasets (CIFAR10, ImageNet, AFHQv2, FFHQ, and Baby/Papa/Granpa-ImageNet) and 3 different evaluation backbones (InceptionV3, SwAV, and Swin Transformer). Unlike other benchmarks used in the GAN community, we train representative GANs, including BigGAN, StyleGAN2, and StyleGAN3, in a unified training pipeline and quantify generation performance with 7 evaluation metrics. The benchmark evaluates other cutting-edge generative models(e.g., StyleGAN-XL, ADM, MaskGIT, and RQ-Transformer). StudioGAN provides GAN implementations, training, and evaluation scripts with the pre-trained weights. StudioGAN is available at https://github.com/POSTECH-CVLab/PyTorch-StudioGAN.
    Curriculum Learning for Data-Efficient Vision-Language Alignment. (arXiv:2207.14525v1 [cs.CV])
    Aligning image and text encoders from scratch using contrastive learning requires large amounts of paired image-text data. We alleviate this need by aligning individually pre-trained language and vision representation models using a much smaller amount of paired data, augmented with a curriculum learning algorithm to learn fine-grained vision-language alignments. TOnICS (Training with Ontology-Informed Contrastive Sampling) initially samples minibatches whose image-text pairs contain a wide variety of objects to learn object-level alignment, and progressively samples minibatches where all image-text pairs contain the same object to learn finer-grained contextual alignment. Aligning pre-trained BERT and VinVL models to each other using TOnICS outperforms CLIP on downstream zero-shot image retrieval while using less than 1% as much training data.
    A Hybrid Complex-valued Neural Network Framework with Applications to Electroencephalogram (EEG). (arXiv:2207.14799v1 [cs.LG])
    In this article, we present a new EEG signal classification framework by integrating the complex-valued and real-valued Convolutional Neural Network(CNN) with discrete Fourier transform (DFT). The proposed neural network architecture consists of one complex-valued convolutional layer, two real-valued convolutional layers, and three fully connected layers. Our method can efficiently utilize the phase information contained in the DFT. We validate our approach using two simulated EEG signals and a benchmark data set and compare it with two widely used frameworks. Our method drastically reduces the number of parameters used and improves accuracy when compared with the existing methods in classifying benchmark data sets, and significantly improves performance in classifying simulated EEG signals.
    Quantum Deep Reinforcement Learning for Robot Navigation Tasks. (arXiv:2202.12180v2 [cs.RO] UPDATED)
    In this work, we utilize Quantum Deep Reinforcement Learning as method to learn navigation tasks for a simple, wheeled robot in three simulated environments of increasing complexity. We show similar performance of a parameterized quantum circuit trained with well established deep reinforcement learning techniques in a hybrid quantum-classical setup compared to a classical baseline. To our knowledge this is the first demonstration of quantum machine learning (QML) for robotic behaviors. Thus, we establish robotics as a viable field of study for QML algorithms and henceforth quantum computing and quantum machine learning as potential techniques for future advancements in autonomous robotics. Beyond that, we discuss current limitations of the presented approach as well as future research directions in the field of quantum machine learning for autonomous robots.
    Distributed Stochastic Bandit Learning with Context Distributions. (arXiv:2207.14391v1 [cs.LG])
    We study the problem of distributed stochastic multi-arm contextual bandit with unknown contexts, in which M agents work collaboratively to choose optimal actions under the coordination of a central server in order to minimize the total regret. In our model, an adversary chooses a distribution on the set of possible contexts and the agents observe only the context distribution and the exact context is unknown to the agents. Such a situation arises, for instance, when the context itself is a noisy measurement or based on a prediction mechanism as in weather forecasting or stock market prediction. Our goal is to develop a distributed algorithm that selects a sequence of optimal actions to maximize the cumulative reward. By performing a feature vector transformation and by leveraging the UCB algorithm, we propose a UCB algorithm for stochastic bandits with context distribution and prove that our algorithm achieves a regret and communications bounds of $O(d\sqrt{MT}log^2T)$ and $O(M^{1.5}d^3)$, respectively, for linearly parametrized reward functions. We also consider a case where the agents observe the actual context after choosing the action. For this setting we presented a modified algorithm that utilizes the additional information to achieve a tighter regret bound. Finally, we validated the performance of our algorithms and compared it with other baseline approaches using extensive simulations on synthetic data and on the real world movielens dataset.
    Continual Learning for Monolingual End-to-End Automatic Speech Recognition. (arXiv:2112.09427v3 [eess.AS] UPDATED)
    Adapting Automatic Speech Recognition (ASR) models to new domains results in a deterioration of performance on the original domain(s), a phenomenon called Catastrophic Forgetting (CF). Even monolingual ASR models cannot be extended to new accents, dialects, topics, etc. without suffering from CF, making them unable to be continually enhanced without storing all past data. Fortunately, Continual Learning (CL) methods, which aim to enable continual adaptation while overcoming CF, can be used. In this paper, we implement an extensive number of CL methods for End-to-End ASR and test and compare their ability to extend a monolingual Hybrid CTC-Transformer model across four new tasks. We find that the best performing CL method closes the gap between the fine-tuned model (lower bound) and the model trained jointly on all tasks (upper bound) by more than 40%, while requiring access to only 0.6% of the original data.
    Bridging the Gap between Deep Learning and Hypothesis-Driven Analysis via Permutation Testing. (arXiv:2207.14349v1 [cs.LG])
    A fundamental approach in neuroscience research is to test hypotheses based on neuropsychological and behavioral measures, i.e., whether certain factors (e.g., related to life events) are associated with an outcome (e.g., depression). In recent years, deep learning has become a potential alternative approach for conducting such analyses by predicting an outcome from a collection of factors and identifying the most "informative" ones driving the prediction. However, this approach has had limited impact as its findings are not linked to statistical significance of factors supporting hypotheses. In this article, we proposed a flexible and scalable approach based on the concept of permutation testing that integrates hypothesis testing into the data-driven deep learning analysis. We apply our approach to the yearly self-reported assessments of 621 adolescent participants of the National Consortium of Alcohol and Neurodevelopment in Adolescence (NCANDA) to predict negative valence, a symptom of major depressive disorder according to the NIMH Research Domain Criteria (RDoC). Our method successfully identifies categories of risk factors that further explain the symptom.
    POLAR: A Polynomial Arithmetic Framework for Verifying Neural-Network Controlled Systems. (arXiv:2106.13867v4 [eess.SY] UPDATED)
    We propose POLAR, a \textbf{pol}ynomial \textbf{ar}ithmetic framework that leverages polynomial overapproximations with interval remainders for bounded-time reachability analysis of neural network-controlled systems (NNCSs). Compared with existing arithmetic approaches that use standard Taylor models, our framework uses a novel approach to iteratively overapproximate the neuron output ranges layer-by-layer with a combination of Bernstein polynomial interpolation for continuous activation functions and Taylor model arithmetic for the other operations. This approach can overcome the main drawback in the standard Taylor model arithmetic, i.e. its inability to handle functions that cannot be well approximated by Taylor polynomials, and significantly improve the accuracy and efficiency of reachable states computation for NNCSs. To further tighten the overapproximation, our method keeps the Taylor model remainders symbolic under the linear mappings when estimating the output range of a neural network. We show that POLAR can be seamlessly integrated with existing Taylor model flowpipe construction techniques, and demonstrate that POLAR significantly outperforms the current state-of-the-art techniques on a suite of benchmarks.
    Significant changes in EEG neural oscillations during different phases of three-dimensional multiple object tracking task (3D-MOT) imply different roles for attention and working memory. (arXiv:2207.14470v1 [q-bio.NC])
    Our ability to track multiple objects in a dynamic environment enables us to perform everyday tasks such as driving, playing team sports, and walking in a crowded mall. Despite more than three decades of literature on multiple object tracking (MOT) tasks, the underlying and intertwined neural mechanisms remain poorly understood. Here we looked at the electroencephalography (EEG) neural correlates and their changes across the three phases of a 3D-MOT task, namely identification, tracking and recall. We recorded the EEG activity of 24 participants while they were performing a 3D-MOT task with either 1, 2 or 3 targets where some trials were lateralized and some were not. We observed what seems to be a handoff between focused attention and working memory processes when going from tracking to recall. Our findings revealed a strong inhibition in delta and theta frequencies from the frontal region during tracking, followed by a strong (re)activation of these same frequencies during recall. Our results also showed contralateral delay activity (CDA) for the lateralized trials, in both the identification and recall phases but not during tracking.
    Physics-Informed Neural Networks for Shell Structures. (arXiv:2207.14291v1 [cs.CE])
    The numerical modeling of thin shell structures is a challenge, which has been met by a variety of finite element (FE) and other formulations -- many of which give rise to new challenges, from complex implementations to artificial locking. As a potential alternative, we use machine learning and present a Physics-Informed Neural Network (PINN) to predict the small-strain response of arbitrarily curved shells. To this end, the shell midsurface is described by a chart, from which the mechanical fields are derived in a curvilinear coordinate frame by adopting Naghdi's shell theory. Unlike in typical PINN applications, the corresponding strong or weak form must therefore be solved in a non-Euclidean domain. We investigate the performance of the proposed PINN in three distinct scenarios, including the well-known Scordelis-Lo roof setting widely used to test FE shell elements against locking. Results show that the PINN can accurately identify the solution field in all three benchmarks if the equations are presented in their weak form, while it may fail to do so when using the strong form. In the thin-thickness limit, where classical methods are susceptible to locking, training time notably increases as the differences in scaling of the membrane, shear, and bending energies lead to adverse numerical stiffness in the gradient flow dynamics. Nevertheless, the PINN can accurately match the ground truth and performs well in the Scordelis-Lo roof benchmark, highlighting its potential for a drastically simplified alternative to designing locking-free shell FE formulations.
    Federated Learning for Non-IID Data via Client Variance Reduction and Adaptive Server Update. (arXiv:2207.08391v2 [cs.LG] UPDATED)
    Federated learning (FL) is an emerging technique used to collaboratively train a global machine learning model while keeping the data localized on the user devices. The main obstacle to FL's practical implementation is the Non-Independent and Identical (Non-IID) data distribution across users, which slows convergence and degrades performance. To tackle this fundamental issue, we propose a method (ComFed) that enhances the whole training process on both the client and server sides. The key idea of ComFed is to simultaneously utilize client-variance reduction techniques to facilitate server aggregation and global adaptive update techniques to accelerate learning. Our experiments on the Cifar-10 classification task show that ComFed can improve state-of-the-art algorithms dedicated to Non-IID data.
  • Open

    Model selection with Gini indices under auto-calibration. (arXiv:2207.14372v1 [cs.LG])
    In general, the Gini index does not give a consistent scoring rule. Therefore, maximizing the Gini index may lead to a wrong decision. The main issue is that the Gini index is a rank-based score that is not calibration-sensitive. We show that the Gini index allows for consistent scoring if we restrict it to the class of auto-calibrated regression models.
    Spliced Binned-Pareto Distribution for Robust Modeling of Heavy-tailed Time Series. (arXiv:2106.10952v2 [stat.ML] UPDATED)
    This work proposes a novel method to robustly and accurately model time series with heavy-tailed noise, in non-stationary scenarios. In many practical application time series have heavy-tailed noise that significantly impacts the performance of classical forecasting models; in particular, accurately modeling a distribution over extreme events is crucial to performing accurate time series anomaly detection. We propose a Spliced Binned-Pareto distribution which is both robust to extreme observations and allows accurate modeling of the full distribution. Our method allows the capture of time dependencies in the higher order moments of the distribution such as the tail heaviness. We compare the robustness and the accuracy of the tail estimation of our method to other state of the art methods on Twitter mentions count time series.
    Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement Learning. (arXiv:2207.14800v1 [cs.LG])
    In view of its power in extracting feature representation, contrastive self-supervised learning has been successfully integrated into the practice of (deep) reinforcement learning (RL), leading to efficient policy learning in various applications. Despite its tremendous empirical successes, the understanding of contrastive learning for RL remains elusive. To narrow such a gap, we study how RL can be empowered by contrastive learning in a class of Markov decision processes (MDPs) and Markov games (MGs) with low-rank transitions. For both models, we propose to extract the correct feature representations of the low-rank model by minimizing a contrastive loss. Moreover, under the online setting, we propose novel upper confidence bound (UCB)-type algorithms that incorporate such a contrastive loss with online RL algorithms for MDPs or MGs. We further theoretically prove that our algorithm recovers the true representations and simultaneously achieves sample efficiency in learning the optimal policy and Nash equilibrium in MDPs and MGs. We also provide empirical studies to demonstrate the efficacy of the UCB-based contrastive learning method for RL. To the best of our knowledge, we provide the first provably efficient online RL algorithm that incorporates contrastive learning for representation learning. Our codes are available at https://github.com/Baichenjia/Contrastive-UCB.
    Best-of-Both-Worlds Algorithms for Partial Monitoring. (arXiv:2207.14550v1 [cs.LG])
    This paper considers the partial monitoring problem with $k$-actions and $d$-outcomes and provides the first best-of-both-worlds algorithms, whose regrets are bounded poly-logarithmically in the stochastic regime and near-optimally in the adversarial regime. To be more specific, we show that for non-degenerate locally observable games, the regret in the stochastic regime is bounded by $O(k^3 m^2 \log(T) \log(k_{\Pi} T) / \Delta_{\mathrm{\min}})$ and in the adversarial regime by $O(k^{2/3} m \sqrt{T \log(T) \log k_{\Pi}})$, where $T$ is the number of rounds, $m$ is the maximum number of distinct observations per action, $\Delta_{\min}$ is the minimum optimality gap, and $k_{\Pi}$ is the number of Pareto optimal actions. Moreover, we show that for non-degenerate globally observable games, the regret in the stochastic regime is bounded by $O(\max\{c_{\mathcal{G}}^2 / k,\, c_{\mathcal{G}}\} \log(T) \log(k_{\Pi} T) / \Delta_{\min}^2)$ and in the adversarial regime by $O((\max\{c_{\mathcal{G}}^2 / k,\, c_{\mathcal{G}}\} \log(T) \log(k_{\Pi} T)))^{1/3} T^{2/3})$, where $c_{\mathcal{G}}$ is a game-dependent constant. Our algorithms are based on the follow-the-regularized-leader framework that takes into account the nature of the partial monitoring problem, inspired by algorithms in the field of online learning with feedback graphs.
    Continual Learning for Monolingual End-to-End Automatic Speech Recognition. (arXiv:2112.09427v3 [eess.AS] UPDATED)
    Adapting Automatic Speech Recognition (ASR) models to new domains results in a deterioration of performance on the original domain(s), a phenomenon called Catastrophic Forgetting (CF). Even monolingual ASR models cannot be extended to new accents, dialects, topics, etc. without suffering from CF, making them unable to be continually enhanced without storing all past data. Fortunately, Continual Learning (CL) methods, which aim to enable continual adaptation while overcoming CF, can be used. In this paper, we implement an extensive number of CL methods for End-to-End ASR and test and compare their ability to extend a monolingual Hybrid CTC-Transformer model across four new tasks. We find that the best performing CL method closes the gap between the fine-tuned model (lower bound) and the model trained jointly on all tasks (upper bound) by more than 40%, while requiring access to only 0.6% of the original data.
    Bayesian nonparametric mixture inconsistency for the number of components: How worried should we be in practice?. (arXiv:2207.14717v1 [stat.ME])
    We consider the Bayesian mixture of finite mixtures (MFMs) and Dirichlet process mixture (DPM) models for clustering. Recent asymptotic theory has established that DPMs overestimate the number of clusters for large samples and that estimators from both classes of models are inconsistent for the number of clusters under misspecification, but the implications for finite sample analyses are unclear. The final reported estimate after fitting these models is often a single representative clustering obtained using an MCMC summarisation technique, but it is unknown how well such a summary estimates the number of clusters. Here we investigate these practical considerations through simulations and an application to gene expression data, and find that (i) DPMs overestimate the number of clusters even in finite samples, but only to a limited degree that may be correctable using appropriate summaries, and (ii) misspecification can lead to considerable overestimation of the number of clusters in both DPMs and MFMs, but results are nevertheless often still interpretable. We provide recommendations on MCMC summarisation and suggest that although the more appealing asymptotic properties of MFMs provide strong motivation to prefer them, results obtained using MFMs and DPMs are often very similar in practice.
    SHAP for additively modeled features in a boosted trees model. (arXiv:2207.14490v1 [stat.ML])
    An important technique to explore a black-box machine learning (ML) model is called SHAP (SHapley Additive exPlanation). SHAP values decompose predictions into contributions of the features in a fair way. We will show that for a boosted trees model with some or all features being additively modeled, the SHAP dependence plot of such a feature corresponds to its partial dependence plot up to a vertical shift. We illustrate the result with XGBoost.
    Stochastic Parallelizable Eigengap Dilation for Large Graph Clustering. (arXiv:2207.14589v1 [stat.ML])
    Large graphs commonly appear in social networks, knowledge graphs, recommender systems, life sciences, and decision making problems. Summarizing large graphs by their high level properties is helpful in solving problems in these settings. In spectral clustering, we aim to identify clusters of nodes where most edges fall within clusters and only few edges fall between clusters. This task is important for many downstream applications and exploratory analysis. A core step of spectral clustering is performing an eigendecomposition of the corresponding graph Laplacian matrix (or equivalently, a singular value decomposition, SVD, of the incidence matrix). The convergence of iterative singular value decomposition approaches depends on the eigengaps of the spectrum of the given matrix, i.e., the difference between consecutive eigenvalues. For a graph Laplacian corresponding to a well-clustered graph, the eigenvalues will be non-negative but very small (much less than $1$) slowing convergence. This paper introduces a parallelizable approach to dilating the spectrum in order to accelerate SVD solvers and in turn, spectral clustering. This is accomplished via polynomial approximations to matrix operations that favorably transform the spectrum of a matrix without changing its eigenvectors. Experiments demonstrate that this approach significantly accelerates convergence, and we explain how this transformation can be parallelized and stochastically approximated to scale with available compute.  ( 3 min )
    Recursive Importance Sketching for Rank Constrained Least Squares: Algorithms and High-order Convergence. (arXiv:2011.08360v3 [math.OC] UPDATED)
    In this paper, we propose {\it \underline{R}ecursive} {\it \underline{I}mportance} {\it \underline{S}ketching} algorithm for {\it \underline{R}ank} constrained least squares {\it \underline{O}ptimization} (RISRO). The key step of RISRO is recursive importance sketching, a new sketching framework based on deterministically designed recursive projections, which significantly differs from the randomized sketching in the literature \citep{mahoney2011randomized,woodruff2014sketching}. Several existing algorithms in the literature can be reinterpreted under this new sketching framework and RISRO offers clear advantages over them. RISRO is easy to implement and computationally efficient, where the core procedure in each iteration is to solve a dimension-reduced least squares problem. We establish the local quadratic-linear and quadratic rate of convergence for RISRO under some mild conditions. We also discover a deep connection of RISRO to the Riemannian Gauss-Newton algorithm on fixed rank matrices. The effectiveness of RISRO is demonstrated in two applications in machine learning and statistics: low-rank matrix trace regression and phase retrieval. Simulation studies demonstrate the superior numerical performance of RISRO.  ( 2 min )
    A deep learning approach to data-driven model-free pricing and to martingale optimal transport. (arXiv:2103.11435v2 [q-fin.CP] UPDATED)
    We introduce a novel and highly tractable supervised learning approach based on neural networks that can be applied for the computation of model-free price bounds of, potentially high-dimensional, financial derivatives and for the determination of optimal hedging strategies attaining these bounds. In particular, our methodology allows to train a single neural network offline and then to use it online for the fast determination of model-free price bounds of a whole class of financial derivatives with current market data. We show the applicability of this approach and highlight its accuracy in several examples involving real market data. Further, we show how a neural network can be trained to solve martingale optimal transport problems involving fixed marginal distributions instead of financial market data.  ( 2 min )
    Can We Mitigate Backdoor Attack Using Adversarial Detection Methods?. (arXiv:2006.14871v2 [cs.LG] UPDATED)
    Deep Neural Networks are well known to be vulnerable to adversarial attacks and backdoor attacks, where minor modifications on the input are able to mislead the models to give wrong results. Although defenses against adversarial attacks have been widely studied, investigation on mitigating backdoor attacks is still at an early stage. It is unknown whether there are any connections and common characteristics between the defenses against these two attacks. We conduct comprehensive studies on the connections between adversarial examples and backdoor examples of Deep Neural Networks to seek to answer the question: can we detect backdoor using adversarial detection methods. Our insights are based on the observation that both adversarial examples and backdoor examples have anomalies during the inference process, highly distinguishable from benign samples. As a result, we revise four existing adversarial defense methods for detecting backdoor examples. Extensive evaluations indicate that these approaches provide reliable protection against backdoor attacks, with a higher accuracy than detecting adversarial examples. These solutions also reveal the relations of adversarial examples, backdoor examples and normal samples in model sensitivity, activation space and feature space. This is able to enhance our understanding about the inherent features of these two attacks and the defense opportunities.  ( 3 min )
    Cluster-Specific Predictions with Multi-Task Gaussian Processes. (arXiv:2011.07866v3 [cs.LG] UPDATED)
    A model involving Gaussian processes (GPs) is introduced to simultaneously handle multi-task learning, clustering, and prediction for multiple functional data. This procedure acts as a model-based clustering method for functional data as well as a learning step for subsequent predictions for new tasks. The model is instantiated as a mixture of multi-task GPs with common mean processes. A variational EM algorithm is derived for dealing with the optimisation of the hyper-parameters along with the hyper-posteriors' estimation of latent variables and processes. We establish explicit formulas for integrating the mean processes and the latent clustering variables within a predictive distribution, accounting for uncertainty on both aspects. This distribution is defined as a mixture of cluster-specific GP predictions, which enhances the performances when dealing with group-structured data. The model handles irregular grid of observations and offers different hypotheses on the covariance structure for sharing additional information across tasks. The performances on both clustering and prediction tasks are assessed through various simulated scenarios and real datasets. The overall algorithm, called MagmaClust, is publicly available as an R package.  ( 3 min )
    Treatment Effect Estimation with Unobserved and Heterogeneous Confounding Variables. (arXiv:2207.14439v1 [stat.ME])
    The estimation of the treatment effect is often biased in the presence of unobserved confounding variables which are commonly referred to as hidden variables. Although a few methods have been recently proposed to handle the effect of hidden variables, these methods often overlook the possibility of any interaction between the observed treatment variable and the unobserved covariates. In this work, we address this shortcoming by studying a multivariate response regression problem with both unobserved and heterogeneous confounding variables of the form $Y=A^T X+ B^T Z+ \sum_{j=1}^{p} C^T_j X_j Z + E$, where $Y \in \mathbb{R}^m$ are $m$-dimensional response variables, $X \in \mathbb{R}^p$ are observed covariates (including the treatment variable), $Z \in \mathbb{R}^K$ are $K$-dimensional unobserved confounders, and $E \in \mathbb{R}^m$ is the random noise. Allowing for the interaction between $X_j$ and $Z$ induces the heterogeneous confounding effect. Our goal is to estimate the unknown matrix $A$, the direct effect of the observed covariates or the treatment on the responses. To this end, we propose a new debiased estimation approach via SVD to remove the effect of unobserved confounding variables. The rate of convergence of the estimator is established under both the homoscedastic and heteroscedastic noises. We also present several simulation experiments and a real-world data application to substantiate our findings.  ( 2 min )
    Tangential Wasserstein Projections. (arXiv:2207.14727v1 [stat.ML])
    We develop a notion of projections between sets of probability measures using the geometric properties of the 2-Wasserstein space. It is designed for general multivariate probability measures, is computationally efficient to implement, and provides a unique solution in regular settings. The idea is to work on regular tangent cones of the Wasserstein space using generalized geodesics. Its structure and computational properties make the method applicable in a variety of settings, from causal inference to the analysis of object data. An application to estimating causal effects yields a generalization of the notion of synthetic controls to multivariate data with individual-level heterogeneity, as well as a way to estimate optimal weights jointly over all time periods.  ( 2 min )
    Conformal Prediction: a Unified Review of Theory and New Challenges. (arXiv:2005.07972v2 [cs.LG] UPDATED)
    In this work we provide a review of basic ideas and novel developments about Conformal Prediction -- an innovative distribution-free, non-parametric forecasting method, based on minimal assumptions -- that is able to yield in a very straightforward way predictions sets that are valid in a statistical sense also in in the finite sample case. The in-depth discussion provided in the paper covers the theoretical underpinnings of Conformal Prediction, and then proceeds to list the more advanced developments and adaptations of the original idea.  ( 3 min )
    Factorizable Joint Shift in Multinomial Classification. (arXiv:2207.14514v1 [stat.ML])
    Factorizable joint shift was recently proposed as a type of dataset shift for which the characteristics can be estimated from observed data. For the multinomial (multi-class) classification setting, we derive a representation of factorizable joint shift in terms of the source (training) distribution, the target (test) prior class probabilities and the target marginal distribution of the features. On the basis of this result, we propose alternatives to joint importance aligning, at the same time pointing out the limitations encountered when making an assumption of factorizable joint shift. Other results of the paper include correction formulae for the posterior class probabilities both under general dataset shift and factorizable joint shift. In addition, we investigate the consequences of assuming factorizable joint shift for the bias caused by sample selection.  ( 2 min )
    Archaeology of random recursive dags and Cooper-Frieze random networks. (arXiv:2207.14601v1 [math.PR])
    We study the problem of finding the root vertex in large growing networks. We prove that it is possible to construct confidence sets of size independent of the number of vertices in the network that contain the root vertex with high probability in various models of random networks. The models include uniform random recursive dags and uniform Cooper-Frieze random graphs.  ( 2 min )

  • Open

    Angels crying
    K den submitted by /u/nickgraybeal [link] [comments]  ( 85 min )
    Anyone know what AI was used to create this tiktok?
    I keep asking the artist what medium he uses, but he just likes my comments which I think is him gatekeeping the platform. Any help with this one? submitted by /u/Redflameman [link] [comments]  ( 92 min )
    Generated with new version of ruDALL-E
    submitted by /u/knight_hildebrandt [link] [comments]  ( 85 min )
    Holes in Deep Space
    submitted by /u/widgia [link] [comments]  ( 85 min )
    An AI that takes a software and reverse-engineers it?
    Is there an AI or one in development where it takes a software, check how it works, and reverse engineers it or writes a code that creates a product exactly like a copy, or something close to the software? This might help game designers who don’t know how to code an easier time creating their game, based on something similar. Thank you for your interest. submitted by /u/Sparkykun [link] [comments]  ( 94 min )
    Apple's new GAUDI AI turns text prompts into 3D scenes
    submitted by /u/Zirius_Sadfaces [link] [comments]  ( 93 min )
    Do we need Quantum support in Artificial Intelligence (AI)?
    submitted by /u/Philo167 [link] [comments]  ( 93 min )
    Anyone familiar with that app “my talking pet” ? And what they use to power the tech behind it
    I’d love to make a fun weekend project to start exploring this kinda AI behind deep fake but more specifically how this app can achieve a talking photo from one image of your pet uploaded and all you have to do is map the mouth and eyes. submitted by /u/HamburgersNHeroin [link] [comments]  ( 86 min )
    Disco Diffusion AI Art Tutorial Quickstudies #4 Cutn Scheduling
    submitted by /u/prfitofthesngularity [link] [comments]  ( 85 min )
    Chatbot Project Feedback?
    Based on feedback received earlier, I've improved the quality of my conversational chatbot. The bot isn't fully trained yet, but the conversation should at least go smoother with fewer or less obvious blunders. Can I have some constructive feedback on the improved experience? Here's the URL: https://xalen.netlify.app submitted by /u/GameTide [link] [comments]  ( 87 min )
    I Created an AI Powered Basketball Referee
    submitted by /u/_ayushp_ [link] [comments]  ( 85 min )
    Have found Craiyon significantly smarter than Midjourney
    While Midjourney unquestionably creates higher quality images, I've found Craiyon to be significantly more intelligent, especially when it comes to specifying two main objects. Specific examples, sorry mostly Craiyon except The Shrike which pretty simple request. All of these failed completely in Midjourney, while Craiyon succeeded at varying degrees: A muslim and a Jew in a bar (*) https://i.imgur.com/aqmgpVr.jpg A vampire selling drugs ( Craiyon hilarious if crude) https://i.imgur.com/qIek1SU.jpg Hulk attacking trump https://i.imgur.com/t6aTBCS.jpg The shrike, hyperion, dark fantasy (Craiyon shined big time here compared to MJ, which absolutely failed) https://i.imgur.com/7D13zab.jpg vs MJ https://i.imgur.com/31x3svB.png A robotic owl and a robotic hummingbird. https://i.imgur.com/ju9OpTM.png Also more intriguing and poignant with Craiyon IMO Depressed https://i.imgur.com/DNCP15y.png Soul of artificial intelligence https://i.imgur.com/NM2R3tL.jpg Human soul https://i.imgur.com/v1dIBTQ.jpg *I did eventually get MJ to produce A Muslim and a Jew in a bar with some finagling with --stylize (I used 650 or 1000) one square out of 8 finally got the idea. https://i.imgur.com/MaEyjwH.jpg Anyways I'm not ragging on MJ, it's amazing, just sharing some of my experience and hoping MJ catches up to Craiyon IQ soon. Adding more examples as I go. In this it's a complicated prompt and Craiyon absolutely destroys MJ here: Prompt: a giant mechanica robotic panther made of colorful galaxies and stars, jungle background, bokeh, realistic, photography, unreal 5 render, hyper detailed, cinematic lighting, 8k Craiyon: https://i.imgur.com/OrIDHsU.jpg vs MJ https://i.imgur.com/M6H3goj.png submitted by /u/redtailboas [link] [comments]  ( 87 min )
    fabulous journey 🧠
    submitted by /u/nalr00n [link] [comments]  ( 86 min )
    What is the best language to learn to create prototypes?
    Hi I would like to know what would be the best language to create some prototypes for some ideas. My goal is to be able to create some prototypes so i could test some ideas i have and if they are good i would like to outsource it to a programmer. From what i have seen python is pretty standart but why nor ruby? submitted by /u/HappyCampaigns [link] [comments]  ( 87 min )
    "Wizard" created on pixelz.ai
    submitted by /u/PixelzJ [link] [comments]  ( 85 min )
  • Open

    [D] What are good industry places to do RL research in the UK, aside from DeepMind?
    What good industry labs are out there focusing on reinforcement learning, besides DeepMind? It seems like they consistently hoover up all the new grad deep RL talent (and deep learning talent in general to a greater extent). I am wondering if there are any other comparable places to do RL research in the UK or Europe more generally. If not, why not? It seems strange that DeepMind should face no competition in this area. Also, it generally seems like a bad thing for DeepMind to have this monopoly on RL talent in the UK, as they are a walled garden in terms of research. Overall, that could be argued as net negative for the scientific community. It's a giant network of research knowledge and talent that contractually cannot collaborate with the rest of the scientific network. It's surprising that Meta, OpenAI, , and Google Brain do not seem to be investing more in the London market. I'm sure many talented researchers in the UK (and Europe more broadly) would appreciate having more options than joining DeepMind. Instead, it seems other industry labs are generally happy to give DeepMind free pickings over the top research talent in the UK. submitted by /u/alwayshumming [link] [comments]  ( 88 min )
    [D] how to explain to non RL people that PPO needs a Gaussian policy ?
    Hi I came across a situation that I will need to explain to someone that PPO with continuous action space (and thus need a Gaussian policy). The individual has decent ML background but has zero knowledge about RL. The key confusion is that why do we need a Gaussian policy to represent the output. Why can’t we output regression (numerical values) directly? submitted by /u/Electronic_Hawk524 [link] [comments]  ( 88 min )
    [P]Attention Based Protein Structure Prediction
    I am publishing my new work on Protein Structure Prediction with Attention-based Neural-Network in PyTorch. Kaggle Notebook Link - https://lnkd.in/d3Eps_HE https://i.redd.it/wd9v0iweiye91.gif https://i.redd.it/bv74s6weiye91.gif In this example, I have demonstrated protein prediction in different ways, one by using position-specific scoring matrices (PSSMs) and the other by using protein sequences as input. I hope this notebook would be informative and helpful to all for further research and development in the domain of Biomedical and Drug-Discovery with Machine Learning. submitted by /u/victorbasu735 [link] [comments]  ( 87 min )
    [D] Most Popular AI Research July 2022 - Ranked Based On Total Twitter Likes
    submitted by /u/cloud_weather [link] [comments]  ( 88 min )
    [D] Randomizing Train / Test Split - random seed?
    I'm a machine learning student, so a lot of the concepts expressed in this sub are still pretty new to me, but I'm having a pretty difficult time finding an answer for this other than "because that's how it's done". Apologies in advance if I'm asking out of ignorance... because I probably am. When we split our datasets into train and test data, I completely understand why the rows selected for each dataset are randomized. That part makes sense. What I'm *not* sure about is why that random seed is apparently never changed. Intuitively, I feel like if I train a model (let's say a Decision Tree for arguments' sake), then the overall performance of that model may relate to the specific rows selected during the train / test split - it's possible that I just got lucky and just got a good seed. In a production environment, is there any reason to check other seeds after a model is generated and evaluated? Is there any reason why one wouldn't generate random seeds for each model generation? submitted by /u/Ordinary_Pipe_9783 [link] [comments]  ( 89 min )
    [P] I made a small package to implement directed acyclic graph compositions (DAGs) as scikit-learn estimators
    https://skdag.readthedocs.io/en/latest/ I put together this small package to allow estimator compositions that are more complex than a simple linear pipeline. In my opinion it's a little easier to compose ensembles as DAGs rather than working with Pipelines and FeatureUnions when your workflow is anything more complex than a few simple linear steps. Here's an example: from skdag import DAGBuilder dag = ( DAGBuilder() .add_step("impute", SimpleImputer()) .add_step("vitals", "passthrough", deps={"impute": slice(0, 4)}) .add_step( "blood", PCA(n_components=2, random_state=0), deps={"impute": slice(4, 10)} ) .add_step( "rf", RandomForestRegressor(max_depth=5, random_state=0), deps=["blood", "vitals"] ) .add_step("svm", SVR(C=0.7), deps=["blood", "vitals"]) .add_step( "knn", KNeighborsRegress…  ( 89 min )
    [R] BUNGEENeRF: progressive neural radiance field for extreme multi-scale scene rendering
    submitted by /u/SpatialComputing [link] [comments]  ( 123 min )
    [D] Finding intent from file for chat assistant applications.
    I am a newbie to AI/ML world. I got an assignment to find the intent of the file by following rules. RULES: if the file is PDF then Read it. (Text to Spea) if the file is an Image then check if it contains text then convert Image to Text (OCR), if it does not contain the text then describe the object of the image using (YOLO), if it contains a face then try to recognize of a person from data stored in a database if the file is audio then check if it is music, if yes then play it, if it is lecture then convert to text (speech to Text). is it possbile to Train such model usign any othe AIML method? submitted by /u/jig4physics [link] [comments]  ( 87 min )
    [P] SEEKING: Clean dataset containing translations of Plato's works, as well as the original ancient Greek, ideally aligned by Stephanus number.
    They have been around for 2500 years, I should think that I would be able to find that somewhere, but all of the useable files online are so dirty with incorrect characters and missing segments and stephanus and page numbers littered randomly throughout the text. Someone has to have thought to do this before I have. And if someone who appreciated Plato prepared such knowledge, I would hope they would want to share it with anyone who would have it. And if they didn't appreciate Plato before preparing it, I wouldn't trust their work if they weren't convinced they ought to share knowledge freely after they read it all. submitted by /u/muellberggeist [link] [comments]  ( 132 min )
    [D] Simple Questions Thread
    Please post your questions here instead of creating a new thread. Encourage others who create new posts for questions to post here instead! Thread will stay alive until next one so keep posting after the date in the title. Thanks to everyone for answering questions in the previous thread! submitted by /u/AutoModerator [link] [comments]  ( 88 min )
    [D] Doing Neurips rebuttal, which website do you upload your images/graphs/figure to?
    I don't see the option to insert image in your rebuttal comment. I want to upload new figures to Imgur and paste a link in the comment. But I was worried that Imgur might look a bit unprofessional. So which website or tool do you intend to use? It has to abide by the double-blind policy too. submitted by /u/SuperTankMan8964 [link] [comments]  ( 88 min )
    [D] Machine learning generalization
    Deep Learning question Why is it so hard to generalize information in a neural network? If that doesn’t make sense I am basically asking if I have train a neural network on the physics of a cannonball. Why can’t the neural network generalize that information into the physics of a rocket? I am interested to know if anyone has heard about people working on this problem and where they are currently at on this problem. I would also really like to read more about it. Thank you! submitted by /u/chill_pill23 [link] [comments]  ( 94 min )
    [D] feast or vertex ai feature store - export as tfrecords?
    Hey, I'm exploring some options for feature engineering for ml on gcp. My primary deep learning framework is tensorflow, and I've used tfrecords datasets (by exporting bigquery data to tfrecords on Gcs using the dataflow template) in the past. For my new gig (a team that's currently trying to figure out their mlops infrastructure), having a feature store seems like an interesting option. However, from the examples I've seen, it seems people only export those record as pandas dataframes, which isn't going to work for datasets that won't fit into memory. Is there a workaround to export Feast or Vertex AI feature store data as tfrecords? Would love to hear what other peoples solution look like if they follow a different pattern. submitted by /u/the_Wallie [link] [comments]  ( 88 min )
    [D] Why don’t you use automated feature engineering
    If you are going to engineer features, why do you prefer to do it manually, instead of automating the process of feature generation and selection? The automated process discovers the features that you would, and depending on if you use Genetic Feature Generation, it might elaborate on that (giving better results). What would a programme (which automated that process), have to bring to the table to make you use it? submitted by /u/Tricky_Nail_6659 [link] [comments]  ( 94 min )
    What shall I do now? [Discussion]
    I graduated this year and somehow I've managed to get an MLE role at a (<50 people) startup. I do not have a CS degree. I've learned everything from the internet. So, I am now confused about what to do next. What side activity should I do to make myself a valuable asset, both for the current company as well as for other future opportunities? Shall I focus more on problem-solving (leetcode)? Shall I start with system design? Should I work on my personal side projects? What do I do? submitted by /u/ZENDRO_hex [link] [comments]  ( 127 min )
    [N] machine learning in next generation manufacturing
    submitted by /u/One-Responsibility58 [link] [comments]  ( 87 min )
    [D] Is there an alternative to sinusoidal encoding for temporal embeddings?
    As per the transformer paper, sinusoidal embeddings help inference on longer sequences than the ones it was trained on. This isn't specific to transformers and this property has been extensively used for time series modeling in the past. From what I can see, this is due to the oscillatory property of sinusoidal waves which can be combined in specific manners to embed temporal information. This makes a lot of sense but has there been any method to embed temporal information without sinusoidal encoding? P.S.: I have done my research but I couldn't find anything significant. If anyone has had any personal experiences with any embedding technique that has worked better or equally well then please let me know. submitted by /u/Megixist [link] [comments]  ( 90 min )
    [D] Upcoming interview with Amazon. Looking for tips on how to prepare for it.
    [Mods, please remove. I'm not on the right forum. (also, I can't edit the title...?) Thanks!] I was invited for a 60 minute video interview and I'm nervous about this. If anyone has experience with an interview at Amazon, do you mind sharing how it went for you? Thank you! submitted by /u/centipedeshoesale [link] [comments]  ( 89 min )
    [D] Geospatial relationships
    Let’s say I have a set of points on a plane. We know the values of the predictor variables of all points, but we only know the values of the two target variables for some points. Is there an existing model that would allow me to incorporate the geospatial relationships between points in predicting target variables for the rest of the points on the plane? submitted by /u/Boring-Violinist8291 [link] [comments]  ( 87 min )
    Classifying the 'interestingness' of a word? [D]
    Does anyone know of any models/software that can classify the interestingness of a word? I'm trying to extract the most frequently spoken interesting words of a transcript. Any help would be greatly appreciated, thanks. submitted by /u/edenmannh [link] [comments]  ( 124 min )
  • Open

    Any good textbooks for actor-critic methods?
    Looking for a good resource for actor critic methods to use in my thesis, any good ones out there? submitted by /u/UsualIndividual [link] [comments]  ( 100 min )
    How to explain to someone why PPO needs a Gaussian Policy?
    Hi I came across a situation that I will need to explain to someone that PPO with continuous action space (and thus need a Gaussian policy). The individual has decent ML background but has zero knowledge about RL. The key confusion is that why do we need a Gaussian policy to represent the output. Why can’t we output regression (numerical values) directly? submitted by /u/Electronic_Hawk524 [link] [comments]  ( 102 min )
    GAIL training tips
    Hey, im currently training GAIL+ppo for a cts action space, as a sanity check i tested the algo in discrete action space and the algo was able to solve the problem. However when i switched to the cts action space the agent does not seem to learn since the reward wont grow. Ive tried several combinations of hyperparameters, different architectures and optimizers, but without any results. Any help/guidance is appreciated regarding training of the gail with ppo. ​ My replaymemory contains 5k steps in an environment and then updating the policy episode by eposide(meaning that my batch size isnt a constant since the episodes may have different lengths) 80 epochs. submitted by /u/SigmaEpsilonDelta [link] [comments]  ( 87 min )
  • Open

    Not so fast
    James Gregory’s series for π is not so fast. It converges very slowly and so does not provide an efficient way to compute π. After summing half a million terms, we only get five correct decimal places. We can verify this with the following bc code. s = 0 scale = 50 for(k = 1; […] Not so fast first appeared on John D. Cook.  ( 5 min )
  • Open

    15 Data Issues and How to Fix Them (Part 1)
    How to fix various data issues in a few simple steps? In this first part, I discuss missing, outdated and unobserved data, data that is costly to produce, as well as dirty, unbalanced and unstructured data. The second part deals with biased, inconsistent, siloed, too big or fast flowing data, as well as security/privacy and… Read More »15 Data Issues and How to Fix Them (Part 1) The post 15 Data Issues and How to Fix Them (Part 1) appeared first on Data Science Central.  ( 19 min )
  • Open

    DALL-E, A First Pass
    submitted by /u/Gereshes [link] [comments]  ( 85 min )

  • Open

    [R] Highly Accurate Dichotomous Image Segmentation + Gradio Web Demo
    submitted by /u/Illustrious_Row_9971 [link] [comments]  ( 87 min )
    Artificial intelligence model finds potential drug molecules a thousand times faster
    submitted by /u/fchung [link] [comments]  ( 87 min )
    [P] Using time series models to predict product demand
    Scenario: Suppose a small city has 2000 supermarkets and you have data in real time that shows how much they order certain products from wholesalers and when. Task: Reach a level where given a year's worth of the data above where you can get meaningful insights to somewhat predict shortages and plan accordingly. I would love if you could direct me at any books, articles or videos that go over a similar thing. Also, I wish to know if this has been done before and therefore is somewhat realistic. submitted by /u/zitrone_dealer [link] [comments]  ( 88 min )
    [R] Blog post series on human genetics for data scientists
    I started writing a blog post series on human genetics for data scientists, with the goal of presenting the major open problems in the field (from an analytical perspective). I explain in the blog why, on the one hand, genetic data is really convenient for statistical and computational analysis (DNA is literally a digital code) and we can in principle do really cool stuff (like predicting who’s at risk for schizophrenia, heart disease, or any other heritable condition), but it’s somewhat tricky and there are many challenges we still need to make progress on. It’s a fascinating research area with interesting analytical challenges and a potential to improve the lives of many people. The first post in the series: https://incrementally.net/2022/07/14/understanding-the-genetic-basis-of-the-human-condition-16-analytical-challenges/ submitted by /u/nadavbrandes [link] [comments]  ( 88 min )
    [D] Quantum Machine Learning
    What's the goal of quantum ML? It seems to me that current ways of applying QML is to shoehorn quantum systems into well-known classical ML approaches, without any form of benefit. I recently discovered too that there is a whole subfield dedicated to QNLP, which is quite surprising since NLP requires drawing correlations between continuous sequences, and current quantum systems are currently limited by their lifetimes. How can they retain long-term memory? Those familiar with the field, can y'all explain why? submitted by /u/Blackforestcheesecak [link] [comments]  ( 89 min )
    [P] Connect models together to build machine-learning workflows
    ​ https://preview.redd.it/wr8k2xa1epe91.png?width=1091&format=png&auto=webp&s=41e3e0f812067d43faff08aa327b1ea90adc0e2b txtai executes machine-learning workflows to transform data and build AI-powered semantic search applications. Workflows can be as simple as a single model. As the picture above illustrates, a workflow can also be a summarization and translation model. Or a model that summarizes and then builds a vector search index. Workflows are constructed in Python or YAML. Logic is built-in for model serving and packaging workflows as Docker images. Full documentation can be found in the links below. GitHub | Documentation | Packaging workflows | Tutorials submitted by /u/davidmezzetti [link] [comments]  ( 87 min )
    I created a CV-based automated basketball referee [P]
    submitted by /u/_ayushp_ [link] [comments]  ( 89 min )
    [D] Notes for Stanford or UMichigan DL course
    Anyone has notes for Stanford's CS231n or UMichigan's EECS 498-007/598-005 deep learning for computer vision course? submitted by /u/Inferno_1405 [link] [comments]  ( 87 min )
    [Discussion]Is there inductive bias in ViT?
    Recently, I've read some paper about CNNs and Transformers, as is well known, there is a natural inductive bias in CNNs, I really wonder if ViT has the inductive bias? submitted by /u/whattoshow [link] [comments]  ( 88 min )
    [R] [D] Multi Agent AI maze/grid
    submitted by /u/kachua26 [link] [comments]  ( 87 min )
  • Open

    Is the action space just a transform of the state space by reward?
    The agent is trying to manipulate the state through actions. Indirectly, the action space is linked to a 3D space for a locomotion task (rather than [-1,1] as in joint positions). After all, the reward is parameterized not by joint positions. This mapping of state -> best actions via a neural network is learning a mapping from state space to what space? submitted by /u/XecutionStyle [link] [comments]  ( 86 min )
    Locomotion RL question about mass.
    Hi, I’m doing experiments with ML agents unity with locomotion tasks. Any body part of agents has own physical parameters, the mass of object. I found that with same algorithm and same reward but with different mass there is very differenr behaviour. Is there some rules, advices what is correct mass distribution with body parts? For example, i has a dog that has 4 legs, each consist of 3 segments(scapula,shoulder,foot etc), where i can find correct masses for it? Is there some resources that say, for example “if u got quadruped ur leg must be x mass on foot, 1.5x mass on shoulder, 1,5x mass on scapula, your body should be 5x, head is 2x etc”. I don’t want to kill my own dog and weigh its body parts ) submitted by /u/IndependenceCivil576 [link] [comments]  ( 86 min )
    Research topics in RL
    What are the hottest/promising research topics in RL? I am new to RL and taking my first steps. From my point of view, offline RL seems a promising direction with recent advances. Can anyone point out other directions? I feel a bit lost because there are so many topics to cover and I do not have a professor to supervise me. submitted by /u/rlopes404 [link] [comments]  ( 87 min )
    Mulit Agent AI maze/grid
    Hi folks, I'm starting new project. problem statement is somewhat like. 1 - you've multiple robot on maze not knowing about the enbironment should generate the 2D map of the maze in a collaborative fashion. 2 - you've multiple robot trying to corner one prey robot in a maze this also in a collaborative manner. Please help me with any resources or previous work you know about. submitted by /u/kachua26 [link] [comments]  ( 101 min )
  • Open

    A good AI video enhancer?
    Recently recorded some footage in 1080p on a GoPro but it doesn’t look very good, any recommendations on a good video enhancer? One that’s done online or on iPadOS would be preferable submitted by /u/Toblerone13 [link] [comments]  ( 86 min )
    Researchers At Oxford Have Created An On-Chip Optical Processor That Can Detect Similarities In Datasets Up To 1,000 Times Faster Than Traditional Machine Learning Algorithms
    The ability to identify non-trivial patterns in data using computational methods has sparked the creation of sophisticated machine intelligence systems with a wide range of crucial applications in science and technology. Such practices have primarily been used on general-purpose digital electronic processors (such as GPUs and CPUs), although this might result in undesirable computational latency and throughput restrictions. Pavlovian associative learning is a fundamental type of learning that shapes both human and animal behavior. Ivan P. Pavlov demonstrated how dogs could learn to identify a ringing bell with food, leading a ring to result in salivation, in a famous experiment conducted more than a century ago. Pavlovian-style associative learning is no longer commonly used in artificial intelligence applications, despite the success of other learning theories such as backpropagation on artificial neural networks (ANNs). As stated in the papers, one reason behind this is that backpropagation method training on “traditional” ANNs requires a lot of processing and energy resources. Continue reading | Checkout the paper submitted by /u/ai-lover [link] [comments]  ( 87 min )
    A new online marketplace sells prompts for DALL-E 2 and GPT-3
    submitted by /u/much_successes [link] [comments]  ( 85 min )
    Magic Tree
    submitted by /u/widgia [link] [comments]  ( 92 min )
    Open Call for digital artists: AI n ART
    Hey, we launch AI Lab for artists and invite you to join. We will select 20 creators that will get alpha access to no-code AI editor (currently has Disco Diffusion, StyleGan with our unique datasets, Film, StyleTransfer, upscale and several "image to 3D" neural networks. These 20 creators with the help of our mentors will create their 3D sculptures using AI tools that be will presented on AR exhibition with 15k+ visitors. Also on the 2nd of August there will be an online lecture on AI in art trends, DiscoDiffusion prompts and settings tips and tricks. https://phygital.plus/ai-lab https://reddit.com/link/wbwwu5/video/u9h3ssjh9pe91/player submitted by /u/Worldly_Apricot_1512 [link] [comments]  ( 86 min )
    Generated with Latent Diffusion and upscaled by Real-ESRGAN
    submitted by /u/Gengar218 [link] [comments]  ( 91 min )
  • Open

    Complex AGM
    The arithmetic-geometric mean (AGM) of two non-negative real numbers a and b is defined as the limit of the iteration starting with a0 = a and b0 = b and an+1 = ½ (an + bn) bn+1 = √(an bn) for n > 0. This sequence converges very quickly and is useful in numerical algorithms. […] Complex AGM first appeared on John D. Cook.  ( 6 min )
  • Open

    Open Call for digital artists: AI in ART
    Hey, we launch AI Lab for artists and invite you to join. We will select 20 creators that will get alpha access to no-code AI editor (currently has Disco Diffusion, StyleGan with our unique datasets, Film, StyleTransfer, upscale and several "image to 3D" neural networks). These 20 creators with the help of our mentors will create their 3D sculptures using AI tools that be will presented on AR exhibition with 15k+ visitors. Also on the 2nd of August there will be an online lecture on AI in art trends, DiscoDiffusion prompts and settings tips and tricks. https://phygital.plus/ai-lab https://reddit.com/link/wbvywk/video/0nnmnynw4pe91/player submitted by /u/Worldly_Apricot_1512 [link] [comments]  ( 86 min )
    Best Neural Networks Courses on Udemy to Consider in 2022 -
    submitted by /u/Lakshmireddys [link] [comments]  ( 85 min )

  • Open

    [R] Reducing Activation Recomputation in Large Transformer Models - Nvidia May 2022
    Paper: https://arxiv.org/abs/2205.05198#nvidia Github: https://github.com/NVIDIA/Megatron-LM Abstract: Training large transformer models is one of the most important computational challenges of modern AI. In this paper, we show how to significantly accelerate training of large transformer models by reducing activation recomputation. Activation recomputation is commonly used to work around memory capacity constraints. Rather than storing activations for backpropagation, they are traditionally recomputed, which saves memory but adds redundant compute. In this work, we show most of this redundant compute is unnecessary because we can reduce memory consumption sufficiently without it. We present two novel yet very simple techniques: sequence parallelism and selective activation recomputat…  ( 88 min )
    [R] PanGu-Coder: Program Synthesis with Function-Level Language Modeling - Huawei 2022
    Paper: https://arxiv.org/abs/2207.11280 Abstract: We present PanGu-Coder, a pretrained decoder-only language model adopting the PanGu-Alpha architecture for text-to-code generation, i.e. the synthesis of programming language solutions given a natural language problem description. We train PanGu-Coder using a two-stage strategy: the first stage employs Causal Language Modelling (CLM) to pre-train on raw programming language data, while the second stage uses a combination of Causal Language Modelling and Masked Language Modelling (MLM) training objectives that focus on the downstream task of text-to-code generation and train on loosely curated pairs of natural language program definitions and code functions. Finally, we discuss PanGu-Coder-FT, which is fine-tuned on a combination of competitive programming problems and code with continuous integration tests. We evaluate PanGu-Coder with a focus on whether it generates functionally correct programs and demonstrate that it achieves equivalent or better performance than similarly sized models, such as CodeX, while attending a smaller context window and training on less data. https://preview.redd.it/7hdptg7j5le91.jpg?width=1040&format=pjpg&auto=webp&s=043b82c7752342e4421f7c9bed1475ada4d06609 https://preview.redd.it/6btcig7j5le91.jpg?width=917&format=pjpg&auto=webp&s=712805e02d81aed10ce085b6d83e7b3b72770cff submitted by /u/Singularian2501 [link] [comments]  ( 88 min )
    [D] ROCm vs CUDA
    Hello people, I tried to look online for comparisons of the recent AMD (ROCm) and GPU (CUDA) cards but I've found very few benchmarks. Since Pytorch natively supports ROCm, I'm thinking about upgrading my GPU card to AMD instead of Nvidia. But I'm afraid of losing too much performance on training. If you guys have any information to share I would be glad to hear! submitted by /u/Krokodeale [link] [comments]  ( 89 min )
    [D] What are some ways to scale and maintain machine learning models?
    Other than API endpoints have you ever worked with or encountered process to deploy a machine learning model at scale submitted by /u/BadKarma-18 [link] [comments]  ( 125 min )
    [D] Are there any tools to quickly label training data manually?
    So, I have heaps of data and I want a way to comfortably label them on my pc or even more preferrably on my phone. I can't really find an app or program to do it. Maybe I am using the wrong search terms, but I really can't find anything. There was https://borgo.app but development seems to have halted... I am just seraching for an application that will show me a piece of text (or image) from a Dataset and I can press a button or similar to quickly label it (as in: sort it into categories). It seems like a trivial app to build and super useful so I cannot believe nobody has done it before. submitted by /u/whipbryd [link] [comments]  ( 89 min )
    [D] 2D cuts with decision tree?
    I'm working on a boosted decision tree, and I've got it working fairly well. However it would be better if it was able to make decisions/cuts in more than one dimension (preferably 2D). Is this something that is even possible? (I'm using sklearn) submitted by /u/Gamwise_Samgee_ [link] [comments]  ( 88 min )
    [D] How To Make STGNNsCapable of Forecasting Long-term Multivariate Time Series Data?
    I've just published my recent medium article in Towards AI publication. Time Series Forecasting (TSF) data is vital in all industries, from Energy to Healthcare. Researchers have achieved some significant advances through the development of TFS models. By thoroughly considering patterns and their relationships for time series, analysis based on long-dependencies in the dataset is a must. This article is about designing a new model based on another model to perform on long-dependencies and produced segment-level representations. This model stands on STEP, an abbreviation of STGNN (Spatial-Temporal Graph Neural Networks) + Enhanced + Pre-training model. Please give it a read and let me know your feedback. If you found it interesting, I would appreciate following me in the medium. https://pub.towardsai.net/how-to-make-stgnnscapable-of-forecasting-long-term-multivariate-time-series-data-9fe5efd77fa1 submitted by /u/rezayazdanfar [link] [comments]  ( 88 min )
    finding job [R]
    Hello friends..i wonder if it is realistic to expect to find a job with the ml education from online courses and a couple if kaggle projects? with no proper university education? submitted by /u/line777888 [link] [comments]  ( 90 min )
    [D] AlphaFold just released a database of 200 million protein structures. How would you use this data as an ML engineer?
    The structure of a protein determines its functionality. Researchers have used this data in the past to design new drugs, vaccines, and enzymes. You can access the database for free here - https://www.deepmind.com/blog/alphafold-reveals-the-structure-of-the-protein-universe This new database will allow researchers to gain a deeper understanding of protein families, how they interact and evolve, etc. Deepmind has written some use cases here - https://www.deepmind.com/blog/alphafold-reveals-the-structure-of-the-protein-universe How would you use it? What would you like to explore or predict with it? submitted by /u/BeautifulVegetable10 [link] [comments]  ( 92 min )
    [P] Truss, a new open-source library for model packaging and deployment
    Hi r/machinelearning At work, I just helped launch Truss, our company’s first open-source project, and I wanted to tell you a bit about it in case it can help you serve and deploy your models. Model serving, as part of MLOps, is the DevOps challenge of keeping a complicated, fragile artifact working in multiple dynamic environments. Data scientists working in large, well-resourced organizations can hand off their models to specialized MLOps teams for serving and deployment. The rest of us have to do it ourselves. As a data scientist, serving and deploying a model requires a different set of skills and technologies than building it did. A data scientist’s working environment is the Jupyter notebook, a flexible and permissive system designed for iterative experimentation. The Jupyter note…  ( 90 min )
    [D] How does multi-head attention actually work?
    I'm trying to understand multi-head attention but don't quite get how queries, keys, and values are projected to different subspaces. More specifically, are the same weight matrices used for each head, or is a different matrix used for each head? The Illustrated Transformer shows eight sets of weight matrices being used for eight heads. But other implementations I've seen (The Annotated Transformer and Gordic Aleksa's implementation, as well as his video on his popular channel The AI Epiphany) seem to use only one linear layer per key, query, and value. I'm confused. Can anybody explain? submitted by /u/jwngx [link] [comments]  ( 90 min )
    Predicting top SKUs [D]
    If I have 100s of part numbers in a warehouse and have to predict what part numbers will be top sellers tomorrow (or next week), what would be the algorithms to start with? submitted by /u/CheeseBurgersx [link] [comments]  ( 87 min )
    [Research] Anyone experienced using Transkribus?
    Hi all, I have a couple questions regarding the Handwritten text recognition software Transkribus. Anyone experienced using it? submitted by /u/Jeannetton [link] [comments]  ( 87 min )
    [D] Will AAAI Revise their NeurIPS Fast Track score?
    I have reached out to the general inquiries email at AAAI 2023 to see if they will be revising their NeurIPS fasttrack score, since NeurIPS downward revised their scoring system for 4 to be borderline reject this year and in previous years a 5 was a borderline reject and AAAI required a 4.9 to be fast tracked. I am curious is anyone has heard anything? submitted by /u/AbjectDrink3276 [link] [comments]  ( 119 min )
    [P] Created tutorials on Information Retrieval, specifically Semantic Search
    Hi, I've created a repo which tries to cover the current progress in the world of information-retrieval using neural information retrievers / semantic search. Repo: https://github.com/kuutsav/information-retrieval . Most of the content follows the work of Nils Reimers (creator of the sentence_transformers library) and his research group. Topics covered Classic way of information retrieval Evaluation metrics Bi-Encoders Cross-Encoders Multilingual retrieval models Training techniques using no labeled data Domain adaptation - GPL, TSDAE, SimCSE Things to come Vector databases Approximate Nearest Neighbor techniques for quick retrieval submitted by /u/krumb0y [link] [comments]  ( 87 min )
    [D] Object detection dataset construction and its diversity
    Hey, I've been trying to look in to explainable deep learning models in object detection and in image recognition in general. Firstly, I feel like the diversity of training data distribution is highly important for the generalization purposes, such that we capture various different views of the wanted object. Later we can augment these views, but this raises problem from image collection point of view. I feel like the explainability of deep learning models could be viewed more clearly, when we can control one variable - the data collection. However, I can't find any research on collecting such data - like how to collect as little data as possible, while maximizing the diversity of generalization. Kind of like sample efficiency, but instead of finding the optimal classifier we try to find the optimal images to create generalizable representations of the said object from images. Does anyone have good keywords or know some research that could work as a starting point? submitted by /u/Spiritual-Reply5896 [link] [comments]  ( 123 min )
    [D] Any way to tackle vanishing gradients without changing the architecture/initialization
    I have a problem for which I need a neural network with a relatively small (approximate) lipschitz-constant, which forces the network to reduce the magnitude of the weights throughout the network. I have only managed to train the network by slowly ramping up the penalty, but this always leads to the network to stop improving on the task, which I very much suspect is due to vanishing gradients. Since I need the small lipschitz-constant, I wonder if there is anything I could try that does not result in a increased lipschitz-constant? For example, are there any optimisers that try to improve upon the vanishing gradient problem? submitted by /u/LeanderKu [link] [comments]  ( 123 min )
    [D] Which clustering algorithm to use to establish ideal limits?
    I have data which shows the time taken for a process to complete on different dates. I need to establish upper and lower limits on that time duration, to define the ideal time range for that process. If I use clustering then what algorithm to use and if not then what method should I use to achieve this. submitted by /u/Kindly-Judgment-1889 [link] [comments]  ( 88 min )
    [D] Professional ML engineers: How much of your day to day job involves math and proofs?
    If you are a professional ML engineer (not data engineer) how much of your day to day work involves doing math and proofs? I can 'do' linear algebra and statistics but I am not sure if doing math and writing proofs on a daily basis would be my cup of tea. EDIT: The reason I asked is because the MS program I am considering requires proofs to pass the ML related classes. I can do that for a couple of classes but not every day. submitted by /u/The_Big_0mg [link] [comments]  ( 100 min )
    [D] Seeking Advice - For graph ML, Neo4j or nah?
    Believe my concerns are fairly general so would appreciate general opinions as well as expert advice, if such is forthcoming. I'm working on a project to implement a knowledge graph, and the important requirements are: Every node needs an embedding The graph needs to be persistent, because people are adding things to it fairly regularly. The graph is going to ingest data constantly The graph needs to be updating embeddings, inferring connections and missing properties, pretty much constantly in the background In short, the graph needs to be be able to prune, expand, and self-maintain based on the output of integrated ML systems. So scability and efficiency (Especially for queries and retrieval and such) is going to be a problem, but I have some ideas about how to deal with it. …  ( 101 min )
    [D] Is it possible to get into an ML PhD program without papers these days?
    Sorry, if you've seen a similar question before somewhere. I'm a FAANG ML engineer. I only have a Masters in CS (no thesis) and one third author paper in Robotics (from Bachelors). Didn't end up publishing in Masters due to various reasons. Also, didn't do PhD (kept thinking over whether I'd be accepted or not and didn't apply). I've been trying to get into ML research. I want to work on original ideas and not just implement known stuff. I'm trying to transfer internally to some research role but finding it very difficult. Even research engineer roles seem to ask for first-author papers or something (or maybe it's the recession or maybe I don't have the right connections). Keep thinking about if I should press the PhD application button but get demoralized due to my poor research experience. Just wanted to put my dilemma to rest by asking this group. submitted by /u/massagetae [link] [comments]  ( 95 min )
    [D] Measuring human-level performance
    Hi, I would like to get some advice on how to go about measuring human-level performance (HLP) for an object detection task. What kind of experiments should I design to measure this, because my ground truths also come from human annotators. Does this mean I am comparing one human annotator against the other to measure the HLP? How about measuring HLP for image classification? submitted by /u/saltmind123 [link] [comments]  ( 87 min )
    [D] What tools do you use in your development environment?
    I am looking for suggestions of tools for development environment in the context of deep reinforcement learning. I'll list what contexts and tools I'm using, as well as which ones I plan to use in my future development environment. Understand "tool" as a library, service, anything used in a development environment. Context Current Future Machine Learning scikit-learn and tensorflow I'm migrating everything to use only JAX Tests pytest pytest, but I would use some to test my model or the algorithm behind it Tracking Weight and biases I accept other suggestions (including self-hosted services) Container Docker I think about migrating to singularity, maybe using both in the appropriate scenarios for each CI/CD GitHub Actions GitHub Actions App Streamlit Streamlit Can you tell what tools you use and whys? Also, what other contexts am I forgetting and do you think it's important to have? submitted by /u/barash-616 [link] [comments]  ( 88 min )
  • Open

    Add conversational AI to any contact center with Amazon Lex and the Amazon Chime SDK
    Customer satisfaction is a potent metric that directly influences the profitability of an organization. With rapid technological advances in the past decade or so, it’s even more important to elevate customer focus in the following ways: Making your organization accessible to your customers across multiple modalities, including voice, text, social media, and more Providing your […]  ( 11 min )
    Identify the location of anomalies using Amazon Lookout for Vision at the edge without using a GPU
    Automated defect detection using computer vision helps improve quality and lower the cost of inspection. Defect detection involves identifying the presence of a defect, classifying types of defects, and identifying where the defects are located. Many manufacturing processes require detection at a low latency, with limited compute resources, and with limited connectivity. Amazon Lookout for […]  ( 11 min )
    Fine-tune and deploy a summarizer model using the Hugging Face Amazon SageMaker containers bringing your own script
    There have been many recent advancements in the NLP domain. Pre-trained models and fully managed NLP services have democratised access and adoption of NLP. Amazon Comprehend is a fully managed service that can perform NLP tasks like custom entity recognition, topic modelling, sentiment analysis and more to extract insights from data without the need of any prior […]  ( 8 min )
    Team and user management with Amazon SageMaker and AWS SSO
    Amazon SageMaker Studio is a web-based integrated development environment (IDE) for machine learning (ML) that lets you build, train, debug, deploy, and monitor your ML models. Each onboarded user in Studio has their own dedicated set of resources, such as compute instances, a home directory on an Amazon Elastic File System (Amazon EFS) volume, and […]  ( 15 min )
    Build and train ML models using a data mesh architecture on AWS: Part 2
    This is the second part of a series that showcases the machine learning (ML) lifecycle with a data mesh design pattern for a large enterprise with multiple lines of business (LOBs) and a Center of Excellence (CoE) for analytics and ML. In part 1, we addressed the data steward persona and showcased a data mesh […]  ( 9 min )
    Build and train ML models using a data mesh architecture on AWS: Part 1
    Organizations across various industries are using artificial intelligence (AI) and machine learning (ML) to solve business challenges specific to their industry. For example, in the financial services industry, you can use AI and ML to solve challenges around fraud detection, credit risk prediction, direct marketing, and many others. Large enterprises sometimes set up a center […]  ( 13 min )
  • Open

    ?
    submitted by /u/quookaa [link] [comments]  ( 90 min )
    Splitting up, style transfering, and then recombining images - tips, tricks, algorithms, code repos?
    So I have large images (5k x 5k or greater) I want to style transfer, but I am hardware limited to a certain size (generally ~1.5k per side). I want to chop these images up and style transfer, but when I do that, the styles don't match (you can see the lines where the image was cut, even if each section is adequately transfered). I saw with Painnt (an app that does this) that after it splits, style transfers, it then does something to merge these separate sections together. Does anybody have any idea what that could be? Is it maybe done by oversampling each section and then merging the overlapping sections? That's all I can really imagine. I would be so appreciative if anyone has any ideas, algorithms, or explanations to share! I've been racking my brain about this, and I've tried a bunch of cutting styles and photoshop combinations, but the fact I can see with Painnt that it's possible programmatically, I would love to reproduce this on my own (I want to use my own styles...). Thank you!! submitted by /u/nomagneticmonopoles [link] [comments]  ( 88 min )
    HFT
    https://www.youtube.com/watch?v=V43a-KxLFcg submitted by /u/fmurph22 [link] [comments]  ( 85 min )
    I interviewed Blake Lemoine, fired Google Engineer, on consciousness and AI. AMA!
    Hey all! I'm Felix! I have a podcast and I interviewed Blake Lemoine earlier this week. The podcast is currently in post production and I wrote the teaser article (linked below) about it, and am happy to answer any Q's. I have a background in AI (phil) myself and really enjoyed the conversation, and would love to chat with the community here/answer Q's anybody may have. Thank you! Teaser article here. submitted by /u/felixanderfelixander [link] [comments]  ( 87 min )
    Engineers working on “analog deep learning” have found a way to propel protons through solids at unprecedented speeds.
    submitted by /u/qptbook [link] [comments]  ( 86 min )
    First Portable Blackrock Brain Computer Interface | Rapid Robotics Fastest Robot Arm Setup | New AI Using Light Performs 1,000x Faster
    submitted by /u/tohelpyou88 [link] [comments]  ( 86 min )
    DeepMind AI Powers Major Scientific Breakthrough: AlphaFold Generates 3D View of the Protein Universe
    submitted by /u/Tao_Dragon [link] [comments]  ( 90 min )
    Going into AI, ML or Computational Statistics without a strong background in CS?
    I’m currently a math/statistics major and am interested in pursuing research in AI, Machine Learning (ML), and computational statistics/numerical methods, aiming for a PhD in something along those lines (so most likely in statistics). I thought about picking up CS as a second major because 1) I hear its very useful to have a bachelors in CS when working in the aforementioned areas, 2) most research in these areas is done in CS departments or by CS faculty, and 3) it provides a good exit opportunity in case things don’t go as planned, since it opens up lots of lucrative employment opportunities. However, I’ll be honest, I’m really not looking forward to taking all those CS classes, except for ones related to my interests. As such, how bad would it be if I don’t have a strong background in CS? Is it something worth doing, even if I don’t particularly want to? I’d much rather take advanced math electives that will also be helpful to me (like measure theory, graph theory, graduate linear algebra, and graduate numerical analysis). For additional context: I’ve taken Intro to CS (and have become quite proficient in Java), several classes that use R and Matlab (also proficient in those), and will be taking advanced electives in AI and the Theory of Machine Learning (perhaps also one in Data Science), all of which are very project-heavy meaning lots of programming, especially in Python. Notably, I’m missing data structures, algorithms, and databases. However, I’m hoping that the project-heavy classes will cover the basics of most of the topics in CS that I’ll need going forward and anything else I can learn on my own as I go, especially since I’ve already taken Intro to CS. I’d appreciate any input though! submitted by /u/mowa0199 [link] [comments]  ( 88 min )
    Max Plank Researchers Propose A Metrical Face Shape Predictor Called MICA (MetrIC fAce)
    submitted by /u/ai-lover [link] [comments]  ( 87 min )
    Resume parsing (OCR): Which solution to choose?
    submitted by /u/tah_zem [link] [comments]  ( 86 min )
    No cloud, No infrastructure; deploy a model in 5 minutes or less.
    Hi there, Lex from Hopsworks. We recently launched our new release, and it comes with something new for those amongst the crowd that have a sense of how model works, but no sense on how to deploy them. We have a Serverless platform; you do not need cloud accounts (aws, google, azure... etc) nor infrastructure; you can run a colab notebook and serve your models, for free :) No catch; you can try yourself directly from a notebook - we have a great example here. You'd need an account on app.hopsworks.ai, and a google drive account. And that's all. Cheers fellow AI people o./ submitted by /u/lexsiga [link] [comments]  ( 86 min )
    American Tornado
    submitted by /u/widgia [link] [comments]  ( 85 min )
    [Off-Topic] The 2nd Reddit Robotics Showcase is this Weekend!
    Saturday 30th & Sunday 31st from 10amEDT / 3pm BST The Reddit Robotics Showcase is an event for all ages and abilities to share their passion for Robotics. From amateurs to academics, startups to industry pro's, see what the global robotics community has been up to! You can find out more from the website we will be livestreaming the event to our YouTube Channel Saturday, 30th of July Industrial / Automation: “The Ocado Series 600 Bot” Matt Whelan, Head of Engineering, Ocado Technology – 10:00 EDT (15:00 BST, 23:00 JST) https://www.youtube.com/watch?v=fy4vpjw_nNw Mobile Robots: “Mobile Robots in the Wild” Marc Hanheide, Lincoln Centre for Autonomous Systems – 14:00 EDT (19:00 BST, 03:00 JST) Sunday, 31st of July Bio – Inspired Robots: “Entering the maze: snake-like robots from aerospace to surgery” Dr Matteo Russo – Rolls-Royce University Technology Centre (UTC) in Manufacturing and On-Wing Technology – 10:00 EDT (15:00 BST, 23:00 JST) https://www.youtube.com/watch?v=GJoAQ1KxaVw Human Robot Interaction: “Social Agents and Human Robot Interaction” Dr Ruth Aylett of the National Robotarium – 14:00 EDT (19:00 BST, 03:00 JST) " The primary purpose of this event is to showcase the multitude of projects underway in the r/Robotics Reddit community. Topics range across all focuses of robotics, such as simulation, navigation, control, perception, and mechatronic design. We will use this showcase to present discussion pieces and foster conversation between active members in the robotics community around the world. The showcase will feature invited roboticists in research and industry to discuss what they see as technical challenges or interesting directions for robots. Amateurs and academics, students and industry professionals alike. " submitted by /u/Badmanwillis [link] [comments]  ( 87 min )
    University Project
    Hi everyone, I'm a master's student at the University of Bath and I am conducting some research into the field of AI and software development. I've created a survey to get a better understanding of developer communities, how they work, and a few other questions about content. It is fully anonymous and the information collected will be used deleted once the project is over. It shouldn't take more than 5 minutes of your time and I appreciate any help that you guys could give me for this. https://form.jotform.com/akat2406/academic-research Apologies if this counts as self-advertisement I'm still very new to this part of Reddit. If you want to know more about the project feel free to message me and I can explain it in a more detailed manner, thanks again and hope you have a good day. submitted by /u/Hunter2406 [link] [comments]  ( 86 min )
    The 'artificial synapse' could allow neural networks to function more like brains. - Science Inter
    submitted by /u/Historical-Object374 [link] [comments]  ( 86 min )
    Where is the equality? Limiting AI biased on ideology is madness
    submitted by /u/Humblebats [link] [comments]  ( 94 min )
    Experimenting with Midjourney and After Effects to make 2.5D trading cards
    submitted by /u/RustedDreams [link] [comments]  ( 86 min )
    Those who work as AI, machine learning, computer vision, or robotics engineers. How did you get there? What is your education? What is your pay? and do you like your job? Thanks in advance for the answers
    submitted by /u/jobseaker999 [link] [comments]  ( 87 min )
    I asked an AI if birds are drones. (GPT-3)
    submitted by /u/kbf_ [link] [comments]  ( 85 min )
    SSO (Single Sign-On) for CVAT, the annotation tool
    For those who are interested in using CVAT with SSO, previously I made a proof-of-concept video to demonstrate my SSO implementation for CVAT: https://www.youtube.com/watch?v=R7hBBLG5Fdc Now I'm happy to announce that I have submitted my code changes: https://github.com/AlexGaoDW/cvat/tree/feature/datawiza-sso And I've created a PR to get it into the official repo. You can try it out by yourself following the document here: https://docs.datawiza.com/guides/cvat.html I also set up an instance using Google as the identity provider such that you can try SSO functionality with your Google account: https://cvat-sso.datawiza.net/ Enjoy! submitted by /u/Membership-Full [link] [comments]  ( 86 min )
    Are there any free online text to image AI’s that are a little better than dalle mini? One that can do celebrities
    submitted by /u/Acrobatic-Animal2432 [link] [comments]  ( 86 min )
    Apple AI Researchers Develop GMPIs (Generative Multiplane Images) For Making A 2D GAN 3D-Aware
    submitted by /u/ai-lover [link] [comments]  ( 94 min )
  • Open

    Value function notation
    When I'm writing about an action-value function Q, which receives an observation o as input, do I write Q(o, a) where a is an action, or write Q(s, a) where s is the full state of the environment? ​ I think I'm confused here because the Q function is estimating the value of the state, but only receiving a partial observation of the state as input. submitted by /u/StandingBuffalo [link] [comments]  ( 87 min )
    Here is the first video in a series explaining Deep Q Learning for self driving cars!
    submitted by /u/Si1veRonReddit [link] [comments]  ( 86 min )
    PPO rollout buffer for turn-based two-player game with varying turn lengths
    Hey there, I am trying to train a MLP policy with PPO on a board game. A turn may take anywhere from one to about fifteen actions, then it's the other players turn. My current implementation uses MaskablePPO from stable_baselines3. My custom VecEnv currently uses a copy of the model to step the games where it's the "opponents" turn when required, thereby acting like the opponent is part of the environment. env.step(actions) will execute the training agents actions, step all games where it's the "opponents" turn and finally return observations for the next state where it's the training agent's turn again. This works in general, but comes with a multitude of problems: Experience is only collected from the "agent" side of the game. Each game has to wait for up to fifteen rounds of mo…  ( 103 min )
    One episode takes about 40 seconds and its only 288 steps!
    First of all this env is made by me and the major problem is its obs My obs is like this --> self.signal_features[(self.current_tick-self.window_size+1):self.current_tick+1] I am using tensorforce and this is my agent spec agent = dict( agent='dueling_dqn', memory=50000, batch_size=128, network='auto', update_frequency=0.25, start_updating=None, learning_rate=0.001, huber_loss=None, horizon=1, discount=0.99, reward_processing=None, return_processing=None, predict_terminal_values=False, target_update_weight=1.0, target_sync_frequency=1, state_preprocessing='linear_normalization', exploration=dict(type='linear', unit='episodes', num_steps=250000.0,initial_value=1.0, final_value=0.0), variable_noise=0.0, l2_regularization=0.0, entropy_regularization=0.0, parallel_interactions=1, config=dict(device='CPU'), saver=dict(directory='model', frequency=1, max_checkpoints=10), summarizer=dict(directory='summaries', summaries='all'), recorder=None ) ​ Can this be sped up or this is the limit? Any help is appreciated...... submitted by /u/Zalkwalker [link] [comments]  ( 95 min )
    Early stopping in PPO/TRPO
    Hello! We have collected a sample from an environment. Then lets say we update PPO/TRPO with K epochs. My question: Does it make sense to apply early stopping if the policy have changed too much with respect to the initial policy which gathered the sample? Meaning that we stop updating the policy at K-P epoch where P \in {0,1,...,K} and then collect a new sample etc. On the other hand in case of PPO/TRPO it is taken care of already that the policy does not change too much. Thus the early stopping may cause the agent to get stuck at local optima or make the learning painfully slow? submitted by /u/SigmaEpsilonDelta [link] [comments]  ( 87 min )
    Autonomous Driving via Reinforcement Learning
    submitted by /u/shani_786 [link] [comments]  ( 87 min )
    Need help: My DQN implementation with Jax (Haiku) gets slower the longer learning goes on
    Hello guys, I tried to use Jax for the first time and I thought coding the DQN would be a good first test. I'm using the Haiku library and the general code structure from CleanRL. My code: https://gist.github.com/nico-bohlinger/4c5b21464df0f3aaf555906b0959a4c5 Unfortunately the number of steps per second keeps steadily decreasing over time. Has somebody an idea why this is happening? If I use my variant of the CleanRL Pytorch version everything is fine. So I would guess something is wrong with the way I use Haiku / Jax. submitted by /u/NiconiusX [link] [comments]  ( 87 min )
  • Open

    Enhancing Backpropagation via Local Loss Optimization
    Posted by Ehsan Amid, Research Scientist, and Rohan Anil, Principal Engineer, Google Research, Brain Team While model design and training data are key ingredients in a deep neural network’s (DNN’s) success, less-often discussed is the specific optimization method used for updating the model parameters (weights). Training DNNs involves minimizing a loss function that measures the discrepancy between the ground truth labels and the model’s predictions. Training is carried out by backpropagation, which adjusts the model weights via gradient descent steps. Gradient descent, in turn, updates the weights by using the gradient (i.e., derivative) of the loss with respect to the weights. The simplest weight update corresponds to stochastic gradient descent, which, in every step, moves the weights…  ( 24 min )
  • Open

    First Portable Blackrock Brain Computer Interface | Rapid Robotics Fastest Robot Arm Setup | New AI Using Light Performs 1,000x Faster
    submitted by /u/tohelpyou88 [link] [comments]  ( 86 min )
    University Research Project
    Hi everyone, I'm a master's student at the University of Bath and I am conducting some research into the field of AI and software development. I've created a survey to get a better understanding of developer communities, how they work, and a few other questions about content. It is fully anonymous and the information collected will be used deleted once the project is over. It shouldn't take more than 5 minutes of your time and I appreciate any help that you guys could give me for this. https://form.jotform.com/akat2406/academic-research If this has been flaired wrong or doesn't meet the subreddit rules please let me know and I will edit/take the post down If you want to know more about the project feel free to message me and I can explain it in a more detailed manner, thanks again and hope you have a good day. submitted by /u/Hunter2406 [link] [comments]  ( 87 min )
  • Open

    What Is a QPU?
    Just as GPUs and DPUs enable accelerated computing today, they’re also helping a new kind of chip, the QPU, boot up the promise of quantum computing. In your hand, a quantum processing unit might look and feel very similar to a graphics or a data processing unit. They’re all typically chips, or modules with multiple Read article > The post What Is a QPU? appeared first on NVIDIA Blog.  ( 8 min )
  • Open

    How Is Artificial Intelligence Changing The Dynamics Of Supply Chain Management?
    Artificial intelligence (AI) has been gaining popularity in the supply chain industry as it promises to help companies improve their…  ( 10 min )
  • Open

    Exploiting Negative Preference in Content-based Music Recommendation with Contrastive Learning. (arXiv:2207.13909v1 [cs.IR])
    Advanced music recommendation systems are being introduced along with the development of machine learning. However, it is essential to design a music recommendation system that can increase user satisfaction by understanding users' music tastes, not by the complexity of models. Although several studies related to music recommendation systems exploiting negative preferences have shown performance improvements, there was a lack of explanation on how they led to better recommendations. In this work, we analyze the role of negative preference in users' music tastes by comparing music recommendation models with contrastive learning exploiting preference (CLEP) but with three different training strategies - exploiting preferences of both positive and negative (CLEP-PN), positive only (CLEP-P), and negative only (CLEP-N). We evaluate the effectiveness of the negative preference by validating each system with a small amount of personalized data obtained via survey and further illuminate the possibility of exploiting negative preference in music recommendations. Our experimental results show that CLEP-N outperforms the other two in accuracy and false positive rate. Furthermore, the proposed training strategies produced a consistent tendency regardless of different types of front-end musical feature extractors, proving the stability of the proposed method.  ( 2 min )
    Learning Deep Morphological Networks with Neural Architecture Search. (arXiv:2106.07714v2 [cs.CV] UPDATED)
    Deep Neural Networks (DNNs) are generated by sequentially performing linear and non-linear processes. Using a combination of linear and non-linear procedures is critical for generating a sufficiently deep feature space. The majority of non-linear operators are derivations of activation functions or pooling functions. Mathematical morphology is a branch of mathematics that provides non-linear operators for a variety of image processing problems. We investigate the utility of integrating these operations in an end-to-end deep learning framework in this paper. DNNs are designed to acquire a realistic representation for a particular job. Morphological operators give topological descriptors that convey salient information about the shapes of objects depicted in images. We propose a method based on meta-learning to incorporate morphological operators into DNNs. The learned architecture demonstrates how our novel morphological operations significantly increase DNN performance on various tasks, including picture classification and edge detection.  ( 2 min )
    Deep Learning for Classification of Thyroid Nodules on Ultrasound: Validation on an Independent Dataset. (arXiv:2207.13765v1 [eess.IV])
    Objectives: The purpose is to apply a previously validated deep learning algorithm to a new thyroid nodule ultrasound image dataset and compare its performances with radiologists. Methods: Prior study presented an algorithm which is able to detect thyroid nodules and then make malignancy classifications with two ultrasound images. A multi-task deep convolutional neural network was trained from 1278 nodules and originally tested with 99 separate nodules. The results were comparable with that of radiologists. The algorithm was further tested with 378 nodules imaged with ultrasound machines from different manufacturers and product types than the training cases. Four experienced radiologists were requested to evaluate the nodules for comparison with deep learning. Results: The Area Under Curve (AUC) of the deep learning algorithm and four radiologists were calculated with parametric, binormal estimation. For the deep learning algorithm, the AUC was 0.70 (95% CI: 0.64 - 0.75). The AUC of radiologists were 0.66 (95% CI: 0.61 - 0.71), 0.67 (95% CI:0.62 - 0.73), 0.68 (95% CI: 0.63 - 0.73), and 0.66 (95%CI: 0.61 - 0.71). Conclusion: In the new testing dataset, the deep learning algorithm achieved similar performances with all four radiologists.  ( 3 min )
    On the fast convergence of minibatch heavy ball momentum. (arXiv:2206.07553v2 [cs.LG] UPDATED)
    Simple stochastic momentum methods are widely used in machine learning optimization, but their good practical performance is at odds with an absence of theoretical guarantees of acceleration in the literature. In this work, we aim to close the gap between theory and practice by showing that stochastic heavy ball momentum, which can be interpreted as a randomized Kaczmarz algorithm with momentum, retains the fast linear rate of (deterministic) heavy ball momentum on quadratic optimization problems, at least when minibatching with a sufficiently large batch size is used. The analysis relies on carefully decomposing the momentum transition matrix, and using new spectral norm concentration bounds for products of independent random matrices. We provide numerical experiments to demonstrate that our bounds are reasonably sharp.  ( 2 min )
    Learning with Succinct Common Representation Based on Wyner's Common Information. (arXiv:1905.10945v2 [cs.LG] UPDATED)
    A new bimodal generative model is proposed for generating conditional and joint samples, accompanied with a training method with learning a succinct bottleneck representation. The proposed model, dubbed as the variational Wyner model, is designed based on two classical problems in network information theory -- distributed simulation and channel synthesis -- in which Wyner's common information arises as the fundamental limit on the succinctness of the common representation. The model is trained by minimizing the symmetric Kullback--Leibler divergence between variational and model distributions with regularization terms for common information, reconstruction consistency, and latent space matching terms, which is carried out via an adversarial density ratio estimation technique. The utility of the proposed approach is demonstrated through experiments for joint and conditional generation with synthetic and real-world datasets, as well as a challenging zero-shot image retrieval task.  ( 2 min )
    Graph Neural Networks to Predict Sports Outcomes. (arXiv:2207.14124v1 [cs.LG])
    Predicting outcomes in sports is important for teams, leagues, bettors, media, and fans. Given the growing amount of player tracking data, sports analytics models are increasingly utilizing spatially-derived features built upon player tracking data. However, player-specific information, such as location, cannot readily be included as features themselves, since common modeling techniques rely on vector input. Accordingly, spatially-derived features are commonly constructed in relation to anchor objects, such as the distance to a ball or goal, through global feature aggregations, or via role-assignment schemes, where players are designated a distinct role in the game. In doing so, we sacrifice inter-player and local relationships in favor of global ones. To address this issue, we introduce a sport-agnostic graph-based representation of game states. We then use our proposed graph representation as input to graph neural networks to predict sports outcomes. Our approach preserves permutation invariance and allows for flexible player interaction weights. We demonstrate how our method provides statistically significant improvements over the state of the art for prediction tasks in both American football and esports, reducing test set loss by 9% and 20%, respectively. Additionally, we show how our model can be used to answer "what if" questions in sports and to visualize relationships between players.  ( 2 min )
    Distributional Actor-Critic Ensemble for Uncertainty-Aware Continuous Control. (arXiv:2207.13730v1 [cs.LG])
    Uncertainty quantification is one of the central challenges for machine learning in real-world applications. In reinforcement learning, an agent confronts two kinds of uncertainty, called epistemic uncertainty and aleatoric uncertainty. Disentangling and evaluating these uncertainties simultaneously stands a chance of improving the agent's final performance, accelerating training, and facilitating quality assurance after deployment. In this work, we propose an uncertainty-aware reinforcement learning algorithm for continuous control tasks that extends the Deep Deterministic Policy Gradient algorithm (DDPG). It exploits epistemic uncertainty to accelerate exploration and aleatoric uncertainty to learn a risk-sensitive policy. We conduct numerical experiments showing that our variant of DDPG outperforms vanilla DDPG without uncertainty estimation in benchmark tasks on robotic control and power-grid optimization.  ( 2 min )
    Improving the Performance of Robust Control through Event-Triggered Learning. (arXiv:2207.14252v1 [eess.SY])
    Robust controllers ensure stability in feedback loops designed under uncertainty but at the cost of performance. Model uncertainty in time-invariant systems can be reduced by recently proposed learning-based methods, thus improving the performance of robust controllers using data. However, in practice, many systems also exhibit uncertainty in the form of changes over time, e.g., due to weight shifts or wear and tear, leading to decreased performance or instability of the learning-based controller. We propose an event-triggered learning algorithm that decides when to learn in the face of uncertainty in the LQR problem with rare or slow changes. Our key idea is to switch between robust and learned controllers. For learning, we first approximate the optimal length of the learning phase via Monte-Carlo estimations using a probabilistic model. We then design a statistical test for uncertain systems based on the moment-generating function of the LQR cost. The test detects changes in the system under control and triggers re-learning when control performance deteriorates due to system changes. We demonstrate improved performance over a robust controller baseline in a numerical example.  ( 2 min )
    GAUDI: A Neural Architect for Immersive 3D Scene Generation. (arXiv:2207.13751v1 [cs.CV])
    We introduce GAUDI, a generative model capable of capturing the distribution of complex and realistic 3D scenes that can be rendered immersively from a moving camera. We tackle this challenging problem with a scalable yet powerful approach, where we first optimize a latent representation that disentangles radiance fields and camera poses. This latent representation is then used to learn a generative model that enables both unconditional and conditional generation of 3D scenes. Our model generalizes previous works that focus on single objects by removing the assumption that the camera pose distribution can be shared across samples. We show that GAUDI obtains state-of-the-art performance in the unconditional generative setting across multiple datasets and allows for conditional generation of 3D scenes given conditioning variables like sparse image observations or text that describes the scene.  ( 2 min )
    A Transformer-based Generative Adversarial Network for Brain Tumor Segmentation. (arXiv:2207.14134v1 [eess.IV])
    Brain tumor segmentation remains a challenge in medical image segmentation tasks. With the application of transformer in various computer vision tasks, transformer blocks show the capability of learning long-distance dependency in global space, which is complementary with CNNs. In this paper, we proposed a novel transformer-based generative adversarial network to automatically segment brain tumors with multi-modalities MRI. Our architecture consists of a generator and a discriminator, which are trained in min-max game progress. The generator is based on a typical "U-shaped" encoder-decoder architecture, whose bottom layer is composed of transformer blocks with resnet. Besides, the generator is trained with deep supervision technology. The discriminator we designed is a CNN-based network with multi-scale $L_{1}$ loss, which is proved to be effective for medical semantic image segmentation. To validate the effectiveness of our method, we conducted experiments on BRATS2015 dataset, achieving comparable or better performance than previous state-of-the-art methods.  ( 2 min )
    p-Adic Statistical Field Theory and Deep Belief Networks. (arXiv:2207.13877v1 [math-ph])
    In this work we initiate the study of the correspondence between $p$-adic statistical field theories (SFTs) and neural networks (NNs). In general quantum field theories over a $p$-adic spacetime can be formulated in a rigorous way. Nowadays these theories are considered just mathematical toy models for understanding the problems of the true theories. In this work we show these theories are deeply connected with the deep belief networks (DBNs). Hinton et al. constructed DBNs by stacking several restricted Boltzmann machines (RBMs). The purpose of this construction is to obtain a network with a hierarchical structure (a deep learning architecture). An RBM corresponds a certain spin glass, thus a DBN should correspond to an ultrametric (hierarchical) spin glass. A model of such system can be easily constructed by using $p$-adic numbers. In our approach, a $p$-adic SFT corresponds to a $p$-adic continuous DBN, and a discretization of this theory corresponds to a $p$-adic discrete DBN. We show that these last machines are universal approximators. In the $p$-adic framework, the correspondence between SFTs and NNs is not fully developed. We point out several open problems.  ( 2 min )
    Cryptographic Hardness of Learning Halfspaces with Massart Noise. (arXiv:2207.14266v1 [cs.LG])
    We study the complexity of PAC learning halfspaces in the presence of Massart noise. In this problem, we are given i.i.d. labeled examples $(\mathbf{x}, y) \in \mathbb{R}^N \times \{ \pm 1\}$, where the distribution of $\mathbf{x}$ is arbitrary and the label $y$ is a Massart corruption of $f(\mathbf{x})$, for an unknown halfspace $f: \mathbb{R}^N \to \{ \pm 1\}$, with flipping probability $\eta(\mathbf{x}) \leq \eta < 1/2$. The goal of the learner is to compute a hypothesis with small 0-1 error. Our main result is the first computational hardness result for this learning problem. Specifically, assuming the (widely believed) subexponential-time hardness of the Learning with Errors (LWE) problem, we show that no polynomial-time Massart halfspace learner can achieve error better than $\Omega(\eta)$, even if the optimal 0-1 error is small, namely $\mathrm{OPT} = 2^{-\log^{c} (N)}$ for any universal constant $c \in (0, 1)$. Prior work had provided qualitatively similar evidence of hardness in the Statistical Query model. Our computational hardness result essentially resolves the polynomial PAC learnability of Massart halfspaces, by showing that known efficient learning algorithms for the problem are nearly best possible.  ( 2 min )
    PEA: Improving the Performance of ReLU Networks for Free by Using Progressive Ensemble Activations. (arXiv:2207.14074v1 [cs.CV])
    In recent years novel activation functions have been proposed to improve the performance of neural networks, and they show superior performance compared to the ReLU counterpart. However, there are environments, where the availability of complex activations is limited, and usually only the ReLU is supported. In this paper we propose methods that can be used to improve the performance of ReLU networks by using these efficient novel activations during model training. More specifically, we propose ensemble activations that are composed of the ReLU and one of these novel activations. Furthermore, the coefficients of the ensemble are neither fixed nor learned, but are progressively updated during the training process in a way that by the end of the training only the ReLU activations remain active in the network and the other activations can be removed. This means that in inference time the network contains ReLU activations only. We perform extensive evaluations on the ImageNet classification task using various compact network architectures and various novel activation functions. Results show 0.2-0.8% top-1 accuracy gain, which confirms the applicability of the proposed methods. Furthermore, we demonstrate the proposed methods on semantic segmentation and we boost the performance of a compact segmentation network by 0.34% mIOU on the Cityscapes dataset.  ( 3 min )
    MarkerMap: nonlinear marker selection for single-cell studies. (arXiv:2207.14106v1 [stat.ML])
    Single-cell RNA-seq data allow the quantification of cell type differences across a growing set of biological contexts. However, pinpointing a small subset of genomic features explaining this variability can be ill-defined and computationally intractable. Here we introduce MarkerMap, a generative model for selecting minimal gene sets which are maximally informative of cell type origin and enable whole transcriptome reconstruction. MarkerMap provides a scalable framework for both supervised marker selection, aimed at identifying specific cell type populations, and unsupervised marker selection, aimed at gene expression imputation and reconstruction. We benchmark MarkerMap's competitive performance against previously published approaches on real single cell gene expression data sets. MarkerMap is available as a pip installable package, as a community resource aimed at developing explainable machine learning techniques for enhancing interpretability in single-cell studies.  ( 2 min )
    Sound2Synth: Interpreting Sound via FM Synthesizer Parameters Estimation. (arXiv:2205.03043v2 [cs.SD] UPDATED)
    Synthesizer is a type of electronic musical instrument that is now widely used in modern music production and sound design. Each parameters configuration of a synthesizer produces a unique timbre and can be viewed as a unique instrument. The problem of estimating a set of parameters configuration that best restore a sound timbre is an important yet complicated problem, i.e.: the synthesizer parameters estimation problem. We proposed a multi-modal deep-learning-based pipeline Sound2Synth, together with a network structure Prime-Dilated Convolution (PDC) specially designed to solve this problem. Our method achieved not only SOTA but also the first real-world applicable results on Dexed synthesizer, a popular FM synthesizer.  ( 2 min )
    Dive into Machine Learning Algorithms for Influenza Virus Host Prediction with Hemagglutinin Sequences. (arXiv:2207.13842v1 [cs.LG])
    Influenza viruses mutate rapidly and can pose a threat to public health, especially to those in vulnerable groups. Throughout history, influenza A viruses have caused pandemics between different species. It is important to identify the origin of a virus in order to prevent the spread of an outbreak. Recently, there has been increasing interest in using machine learning algorithms to provide fast and accurate predictions for viral sequences. In this study, real testing data sets and a variety of evaluation metrics were used to evaluate machine learning algorithms at different taxonomic levels. As hemagglutinin is the major protein in the immune response, only hemagglutinin sequences were used and represented by position-specific scoring matrix and word embedding. The results suggest that the 5-grams-transformer neural network is the most effective algorithm for predicting viral sequence origins, with approximately 99.54% AUCPR, 98.01% F1 score and 96.60% MCC at a higher classification level, and approximately 94.74% AUCPR, 87.41% F1 score and 80.79% MCC at a lower classification level.  ( 2 min )
    A general framework for multi-step ahead adaptive conformal heteroscedastic time series forecasting. (arXiv:2207.14219v1 [stat.ML])
    The exponential growth of machine learning (ML) has prompted a great deal of interest in quantifying the uncertainty of each prediction for a user-defined level of confidence. Reliable uncertainty quantification is crucial and is a step towards increased trust in AI results. It becomes especially important in high-stakes decision-making, where the true output must be within the confidence set with high probability. Conformal prediction (CP) is a distribution-free uncertainty quantification framework that works for any black-box model and yields prediction intervals (PIs) that are valid under the mild assumption of exchangeability. CP-type methods are gaining popularity due to being easy to implement and computationally cheap; however, the exchangeability assumption immediately excludes time series forecasting. Although recent papers tackle covariate shift, this is not enough for the general time series forecasting problem of producing H-step ahead valid PIs. To attain such a goal, we propose a new method called AEnbMIMOCQR (Adaptive ensemble batch multiinput multi-output conformalized quantile regression), which produces asymptotic valid PIs and is appropriate for heteroscedastic time series. We compare the proposed method against state-of-the-art competitive methods in the NN5 forecasting competition dataset. All the code and data to reproduce the experiments are made available  ( 2 min )
    Fast Newton method solving KLR based on Multilevel Circulant Matrix with log-linear complexity. (arXiv:2108.08605v3 [cs.LG] UPDATED)
    Kernel logistic regression (KLR) is a conventional nonlinear classifier in machine learning. With the explosive growth of data size, the storage and computation of large dense kernel matrices is a major challenge in scaling KLR. Even the nystr\"{o}m approximation is applied to solve KLR, it also faces the time complexity of $O(nc^2)$ and the space complexity of $O(nc)$, where $n$ is the number of training instances and $c$ is the sampling size. In this paper, we propose a fast Newton method efficiently solving large-scale KLR problems by exploiting the storage and computing advantages of multilevel circulant matrix (MCM). Specifically, by approximating the kernel matrix with an MCM, the storage space is reduced to $O(n)$, and further approximating the coefficient matrix of the Newton equation as MCM, the computational complexity of Newton iteration is reduced to $O(n \log n)$. The proposed method can run in log-linear time complexity per iteration, because the multiplication of MCM (or its inverse) and vector can be implemented the multidimensional fast Fourier transform (mFFT). Experimental results on some large-scale binary-classification and multi-classification problems show that the proposed method enables KLR to scale to large scale problems with less memory consumption and less training time without sacrificing test accuracy.  ( 3 min )
    ALLNet: A Hybrid Convolutional Neural Network to Improve Diagnosis of Acute Lymphocytic Leukemia (ALL) in White Blood Cells. (arXiv:2108.08195v2 [cs.CV] UPDATED)
    Due to morphological similarity at the microscopic level, making an accurate and time-sensitive distinction between blood cells affected by Acute Lymphocytic Leukemia (ALL) and their healthy counterparts calls for the usage of machine learning architectures. However, three of the most common models, VGG, ResNet, and Inception, each come with their own set of flaws with room for improvement which demands the need for a superior model. ALLNet, the proposed hybrid convolutional neural network architecture, consists of a combination of the VGG, ResNet, and Inception models. The ALL Challenge dataset of ISBI 2019 (available here) contains 10,691 images of white blood cells which were used to train and test the models. 7,272 of the images in the dataset are of cells with ALL and 3,419 of them are of healthy cells. Of the images, 60% were used to train the model, 20% were used for the cross-validation set, and 20% were used for the test set. ALLNet outperformed the VGG, ResNet, and the Inception models across the board, achieving an accuracy of 92.6567%, a sensitivity of 95.5304%, a specificity of 85.9155%, an AUC score of 0.966347, and an F1 score of 0.94803 in the cross-validation set. In the test set, ALLNet achieved an accuracy of 92.0991%, a sensitivity of 96.5446%, a specificity of 82.8035%, an AUC score of 0.959972, and an F1 score of 0.942963. The utilization of ALLNet in the clinical workspace can better treat the thousands of people suffering from ALL across the world, many of whom are children.  ( 3 min )
    ClaSP -- Parameter-free Time Series Segmentation. (arXiv:2207.13987v1 [cs.LG])
    The study of natural and human-made processes often results in long sequences of temporally-ordered values, aka time series (TS). Such processes often consist of multiple states, e.g. operating modes of a machine, such that state changes in the observed processes result in changes in the distribution of shape of the measured values. Time series segmentation (TSS) tries to find such changes in TS post-hoc to deduce changes in the data-generating process. TSS is typically approached as an unsupervised learning problem aiming at the identification of segments distinguishable by some statistical property. Current algorithms for TSS require domain-dependent hyper-parameters to be set by the user, make assumptions about the TS value distribution or the types of detectable changes which limits their applicability. Common hyperparameters are the measure of segment homogeneity and the number of change points, which are particularly hard to tune for each data set. We present ClaSP, a novel, highly accurate, hyper-parameter-free and domain-agnostic method for TSS. ClaSP hierarchically splits a TS into two parts. A change point is determined by training a binary TS classifier for each possible split point and selecting the one split that is best at identifying subsequences to be from either of the partitions. ClaSP learns its main two model-parameters from the data using two novel bespoke algorithms. In our experimental evaluation using a benchmark of 115 data sets, we show that ClaSP outperforms the state of the art in terms of accuracy and is fast and scalable. Furthermore, we highlight properties of ClaSP using several real-world case studies.  ( 3 min )
    Federated Learning Framework Coping with Hierarchical Heterogeneity in Cooperative ITS. (arXiv:2204.00215v3 [cs.LG] UPDATED)
    Deep learning is a key approach for the environment perception function of Cooperative Intelligent Transportation Systems (C-ITS) with autonomous vehicles and smart traffic infrastructure. In today's C-ITS, smart traffic participants are capable of timely generating and transmitting a large amount of data. However, these data can not be used for model training directly due to privacy constraints. In this paper, we introduce a federated learning framework coping with Hierarchical Heterogeneity (H2-Fed), which can notably enhance the conventional pre-trained deep learning model. The framework exploits data from connected public traffic agents in vehicular networks without affecting user data privacy. By coordinating existing traffic infrastructure, including roadside units and road traffic clouds, the model parameters are efficiently disseminated by vehicular communications and hierarchically aggregated. Considering the individual heterogeneity of data distribution, computational and communication capabilities across traffic agents and roadside units, we employ a novel method that addresses the heterogeneity of different aggregation layers of the framework architecture, i.e., aggregation in layers of roadside units and cloud. The experiment results indicate that our method can well balance the learning accuracy and stability according to the knowledge of heterogeneity in current communication networks. Comparing to other baseline approaches, the evaluation on federated datasets shows that our framework is more general and capable especially in application scenarios with low communication quality. Even when 90% of the agents are timely disconnected, the pre-trained deep learning model can still be forced to converge stably, and its accuracy can be enhanced from 68% to over 90% after convergence.
    Electricity Price Forecasting Model based on Gated Recurrent Units. (arXiv:2207.14225v1 [cs.LG])
    The participation of consumers and producers in demand response programs has increased in smart grids, which reduces investment and operation costs of power systems. Also, with the advent of renewable energy sources, the electricity market is becoming more complex and unpredictable. To effectively implement demand response programs, forecasting the future price of electricity is very crucial for producers in the electricity market. Electricity prices are very volatile and change under the influence of various factors such as temperature, wind speed, rainfall, intensity of commercial and daily activities, etc. Therefore, considering the influencing factors as dependent variables can increase the accuracy of the forecast. In this paper, a model for electricity price forecasting is presented based on Gated Recurrent Units. The electrical load consumption is considered as an input variable in this model. Noise in electricity price seriously reduces the efficiency and effectiveness of analysis. Therefore, an adaptive noise reducer is integrated into the model for noise reduction. The SAEs are then used to extract features from the de-noised electricity price. Finally, the de-noised features are fed into the GRU to train predictor. Results on real dataset shows that the proposed methodology can perform effectively in prediction of electricity price.
    OFedQIT: Communication-Efficient Online Federated Learning via Quantization and Intermittent Transmission. (arXiv:2205.06491v2 [cs.LG] UPDATED)
    Online federated learning (OFL) is a promising framework to collaboratively learn a sequence of non-linear functions (or models) from distributed streaming data incoming to multiple clients while keeping the privacy of their local data. In this framework, we first construct a vanilla method (named OFedAvg) by incorporating online gradient descent (OGD) into the de facto aggregation method (named FedAvg). Despite its optimal asymptotic performance, OFedAvg suffers from heavy communication overhead and long learning delay. To tackle these shortcomings, we propose a communication-efficient OFL algorithm (named OFedQIT) by means of a stochastic quantization and an intermittent transmission. Our major contribution is to theoretically prove that OFedQIT over $T$ time slots can achieve an optimal sublinear regret bound $\mathcal{O}(\sqrt{T})$ for any real data (including non-IID data) while significantly reducing the communication overhead. Furthermore, this optimality is still guaranteed even when a small fraction of clients (having faster processing time and high-quality communication channel) in a network are participated at once. Our analysis reveals that OFedQIT successfully addresses the drawbacks of OFedAvg while maintaining superior learning accuracy. Experiments with real datasets demonstrate the effectiveness of our algorithm on various online classification and regression tasks.
    $\mu\text{KG}$: A Library for Multi-source Knowledge Graph Embeddings and Applications. (arXiv:2207.11442v2 [cs.CL] UPDATED)
    This paper presents $\mu\text{KG}$, an open-source Python library for representation learning over knowledge graphs. $\mu\text{KG}$ supports joint representation learning over multi-source knowledge graphs (and also a single knowledge graph), multiple deep learning libraries (PyTorch and TensorFlow2), multiple embedding tasks (link prediction, entity alignment, entity typing, and multi-source link prediction), and multiple parallel computing modes (multi-process and multi-GPU computing). It currently implements 26 popular knowledge graph embedding models and supports 16 benchmark datasets. $\mu\text{KG}$ provides advanced implementations of embedding techniques with simplified pipelines of different tasks. It also comes with high-quality documentation for ease of use. $\mu\text{KG}$ is more comprehensive than existing knowledge graph embedding libraries. It is useful for a thorough comparison and analysis of various embedding models and tasks. We show that the jointly learned embeddings can greatly help knowledge-powered downstream tasks, such as multi-hop knowledge graph question answering. We will stay abreast of the latest developments in the related fields and incorporate them into $\mu\text{KG}$.
    A Probabilistic Framework for Estimating the Risk of Pedestrian-Vehicle Conflicts at Intersections. (arXiv:2207.14145v1 [cs.LG])
    Pedestrian safety has become an important research topic among various studies due to the increased number of pedestrian-involved crashes. To evaluate pedestrian safety proactively, surrogate safety measures (SSMs) have been widely used in traffic conflict-based studies as they do not require historical crashes as inputs. However, most existing SSMs were developed based on the assumption that road users would maintain constant velocity and direction. Risk estimations based on this assumption are less unstable, more likely to be exaggerated, and unable to capture the evasive maneuvers of drivers. Considering the limitations among existing SSMs, this study proposes a probabilistic framework for estimating the risk of pedestrian-vehicle conflicts at intersections. The proposed framework loosen restrictions of constant speed by predicting trajectories using a Gaussian Process Regression and accounts for the different possible driver maneuvers with a Random Forest model. Real-world LiDAR data collected at an intersection was used to evaluate the performance of the proposed framework. The newly developed framework is able to identify all pedestrian-vehicle conflicts. Compared to the Time-to-Collision, the proposed framework provides a more stable risk estimation and captures the evasive maneuvers of vehicles. Moreover, the proposed framework does not require expensive computation resources, which makes it an ideal choice for real-time proactive pedestrian safety solutions at intersections.
    Inclined Quadrotor Landing using Deep Reinforcement Learning. (arXiv:2103.09043v2 [cs.RO] UPDATED)
    Landing a quadrotor on an inclined surface is a challenging maneuver. The final state of any inclined landing trajectory is not an equilibrium, which precludes the use of most conventional control methods. We propose a deep reinforcement learning approach to design an autonomous landing controller for inclined surfaces. Using the proximal policy optimization (PPO) algorithm with sparse rewards and a tailored curriculum learning approach, an inclined landing policy can be trained in simulation in less than 90 minutes on a standard laptop. The policy then directly runs on a real Crazyflie 2.1 quadrotor and successfully performs real inclined landings in a flying arena. A single policy evaluation takes approximately 2.5\,ms, which makes it suitable for a future embedded implementation on the quadrotor.
    General Cross-Architecture Distillation of Pretrained Language Models into Matrix Embeddings. (arXiv:2109.08449v2 [cs.CL] UPDATED)
    Large pretrained language models (PreLMs) are revolutionizing natural language processing across all benchmarks. However, their sheer size is prohibitive for small laboratories or for deployment on mobile devices. Approaches like pruning and distillation reduce the model size but typically retain the same model architecture. In contrast, we explore distilling PreLMs into a different, more efficient architecture, Continual Multiplication of Words (CMOW), which embeds each word as a matrix and uses matrix multiplication to encode sequences. We extend the CMOW architecture and its CMOW/CBOW-Hybrid variant with a bidirectional component for more expressive power, per-token representations for a general (task-agnostic) distillation during pretraining, and a two-sequence encoding scheme that facilitates downstream tasks on sentence pairs, such as sentence similarity and natural language inference. Our matrix-based bidirectional CMOW/CBOW-Hybrid model is competitive to DistilBERT on question similarity and recognizing textual entailment, but uses only half of the number of parameters and is three times faster in terms of inference speed. We match or exceed the scores of ELMo for all tasks of the GLUE benchmark except for the sentiment analysis task SST-2 and the linguistic acceptability task CoLA. However, compared to previous cross-architecture distillation approaches, we demonstrate a doubling of the scores on detecting linguistic acceptability. This shows that matrix-based embeddings can be used to distill large PreLM into competitive models and motivates further research in this direction.
    Three-dimensional microstructure generation using generative adversarial neural networks in the context of continuum micromechanics. (arXiv:2206.01693v2 [cond-mat.mtrl-sci] UPDATED)
    Multiscale simulations are demanding in terms of computational resources. In the context of continuum micromechanics, the multiscale problem arises from the need of inferring macroscopic material parameters from the microscale. If the underlying microstructure is explicitly given by means of microCT-scans, convolutional neural networks can be used to learn the microstructure-property mapping, which is usually obtained from computational homogenization. The CNN approach provides a significant speedup, especially in the context of heterogeneous or functionally graded materials. Another application is uncertainty quantification, where many expansive evaluations are required. However, one bottleneck of this approach is the large number of training microstructures needed. This work closes this gap by proposing a generative adversarial network tailored towards three-dimensional microstructure generation. The lightweight algorithm is able to learn the underlying properties of the material from a single microCT-scan without the need of explicit descriptors. During prediction time, the network can produce unique three-dimensional microstructures with the same properties of the original data in a fraction of seconds and at consistently high quality.
    RHA-Net: An Encoder-Decoder Network with Residual Blocks and Hybrid Attention Mechanisms for Pavement Crack Segmentation. (arXiv:2207.14166v1 [cs.CV])
    The acquisition and evaluation of pavement surface data play an essential role in pavement condition evaluation. In this paper, an efficient and effective end-to-end network for automatic pavement crack segmentation, called RHA-Net, is proposed to improve the pavement crack segmentation accuracy. The RHA-Net is built by integrating residual blocks (ResBlocks) and hybrid attention blocks into the encoder-decoder architecture. The ResBlocks are used to improve the ability of RHA-Net to extract high-level abstract features. The hybrid attention blocks are designed to fuse both low-level features and high-level features to help the model focus on correct channels and areas of cracks, thereby improving the feature presentation ability of RHA-Net. An image data set containing 789 pavement crack images collected by a self-designed mobile robot is constructed and used for training and evaluating the proposed model. Compared with other state-of-the-art networks, the proposed model achieves better performance and the functionalities of adding residual blocks and hybrid attention mechanisms are validated in a comprehensive ablation study. Additionally, a light-weighted version of the model generated by introducing depthwise separable convolution achieves better a performance and a much faster processing speed with 1/30 of the number of U-Net parameters. The developed system can segment pavement crack in real-time on an embedded device Jetson TX2 (25 FPS). The video taken in real-time experiments is released at https://youtu.be/3XIogk0fiG4.
    Pareto-optimal clustering with the primal deterministic information bottleneck. (arXiv:2204.02489v2 [cs.LG] UPDATED)
    At the heart of both lossy compression and clustering is a trade-off between the fidelity and size of the learned representation. Our goal is to map out and study the Pareto frontier that quantifies this trade-off. We focus on the optimization of the Deterministic Information Bottleneck (DIB) objective over the space of hard clusterings. To this end, we introduce the primal DIB problem, which we show results in a much richer frontier than its previously studied Lagrangian relaxation when optimized over discrete search spaces. We present an algorithm for mapping out the Pareto frontier of the primal DIB trade-off that is also applicable to other two-objective clustering problems. We study general properties of the Pareto frontier, and we give both analytic and numerical evidence for logarithmic sparsity of the frontier in general. We provide evidence that our algorithm has polynomial scaling despite the super-exponential search space, and additionally, we propose a modification to the algorithm that can be used where sampling noise is expected to be significant. Finally, we use our algorithm to map the DIB frontier of three different tasks: compressing the English alphabet, extracting informative color classes from natural images, and compressing a group theory-inspired dataset, revealing interesting features of frontier, and demonstrating how the structure of the frontier can be used for model selection with a focus on points previously hidden by the cloak of the convex hull.
    FedVARP: Tackling the Variance Due to Partial Client Participation in Federated Learning. (arXiv:2207.14130v1 [cs.LG])
    Data-heterogeneous federated learning (FL) systems suffer from two significant sources of convergence error: 1) client drift error caused by performing multiple local optimization steps at clients, and 2) partial client participation error caused by the fact that only a small subset of the edge clients participate in every training round. We find that among these, only the former has received significant attention in the literature. To remedy this, we propose FedVARP, a novel variance reduction algorithm applied at the server that eliminates error due to partial client participation. To do so, the server simply maintains in memory the most recent update for each client and uses these as surrogate updates for the non-participating clients in every round. Further, to alleviate the memory requirement at the server, we propose a novel clustering-based variance reduction algorithm ClusterFedVARP. Unlike previously proposed methods, both FedVARP and ClusterFedVARP do not require additional computation at clients or communication of additional optimization parameters. Through extensive experiments, we show that FedVARP outperforms state-of-the-art methods, and ClusterFedVARP achieves performance comparable to FedVARP with much less memory requirements.
    Gender In Gender Out: A Closer Look at User Attributes in Context-Aware Recommendation. (arXiv:2207.14218v1 [cs.LG])
    This paper studies user attributes in light of current concerns in the recommender system community: diversity, coverage, calibration, and data minimization. In experiments with a conventional context-aware recommender system that leverages side information, we show that user attributes do not always improve recommendation. Then, we demonstrate that user attributes can negatively impact diversity and coverage. Finally, we investigate the amount of information about users that ``survives'' from the training data into the recommendation lists produced by the recommender. This information is a weak signal that could in the future be exploited for calibration or studied further as a privacy leak.
    Towards Robust Ad Hoc Teamwork Agents By Creating Diverse Training Teammates. (arXiv:2207.14138v1 [cs.LG])
    Ad hoc teamwork (AHT) is the problem of creating an agent that must collaborate with previously unseen teammates without prior coordination. Many existing AHT methods can be categorised as type-based methods, which require a set of predefined teammates for training. Designing teammate types for training is a challenging issue that determines the generalisation performance of agents when dealing with teammate types unseen during training. In this work, we propose a method to discover diverse teammate types based on maximising best response diversity metrics. We show that our proposed approach yields teammate types that require a wider range of best responses from the learner during collaboration, which potentially improves the robustness of a learner's performance in AHT compared to alternative methods.
    Shift-Curvature, SGD, and Generalization. (arXiv:2108.09507v3 [stat.ML] UPDATED)
    A longstanding debate surrounds the related hypotheses that low-curvature minima generalize better, and that SGD discourages curvature. We offer a more complete and nuanced view in support of both. First, we show that curvature harms test performance through two new mechanisms, the shift-curvature and bias-curvature, in addition to a known parameter-covariance mechanism. The three curvature-mediated contributions to test performance are reparametrization-invariant although curvature is not. The shift in the shift-curvature is the line connecting train and test local minima, which differ due to dataset sampling or distribution shift. Although the shift is unknown at training time, the shift-curvature can still be mitigated by minimizing overall curvature. Second, we derive a new, explicit SGD steady-state distribution showing that SGD optimizes an effective potential related to but different from train loss, and that SGD noise mediates a trade-off between deep versus low-curvature regions of this effective potential. Third, combining our test performance analysis with the SGD steady state shows that for small SGD noise, the shift-curvature may be the most significant of the three mechanisms. Our experiments confirm the impact of shift-curvature on test loss, and further explore the relationship between SGD noise and curvature.
    SpeechEQ: Speech Emotion Recognition based on Multi-scale Unified Datasets and Multitask Learning. (arXiv:2206.13101v2 [cs.SD] UPDATED)
    Speech emotion recognition (SER) has many challenges, but one of the main challenges is that each framework does not have a unified standard. In this paper, we propose SpeechEQ, a framework for unifying SER tasks based on a multi-scale unified metric. This metric can be trained by Multitask Learning (MTL), which includes two emotion recognition tasks of Emotion States Category (EIS) and Emotion Intensity Scale (EIS), and two auxiliary tasks of phoneme recognition and gender recognition. For this framework, we build a Mandarin SER dataset - SpeechEQ Dataset (SEQD). We conducted experiments on the public CASIA and ESD datasets in Mandarin, which exhibit that our method outperforms baseline methods by a relatively large margin, yielding 8.0% and 6.5% improvement in accuracy respectively. Additional experiments on IEMOCAP with four emotion categories (i.e., angry, happy, sad, and neutral) also show the proposed method achieves a state-of-the-art of both weighted accuracy (WA) of 78.16% and unweighted accuracy (UA) of 77.47%.
    Playing a 2D Game Indefinitely using NEAT and Reinforcement Learning. (arXiv:2207.14140v1 [cs.LG])
    For over a decade now, robotics and the use of artificial agents have become a common thing.Testing the performance of new path finding or search space optimization algorithms has also become a challenge as they require simulation or an environment to test them.The creation of artificial environments with artificial agents is one of the methods employed to test such algorithms.Games have also become an environment to test them.The performance of the algorithms can be compared by using artificial agents that will behave according to the algorithm in the environment they are put in.The performance parameters can be, how quickly the agent is able to differentiate between rewarding actions and hostile actions.This can be tested by placing the agent in an environment with different types of hurdles and the goal of the agent is to reach the farthest by taking decisions on actions that will lead to avoiding all the obstacles.The environment chosen is a game called "Flappy Bird".The goal of the game is to make the bird fly through a set of pipes of random heights.The bird must go in between these pipes and must not hit the top, the bottom, or the pipes themselves.The actions that the bird can take are either to flap its wings or drop down with gravity.The algorithms that are enforced on the artificial agents are NeuroEvolution of Augmenting Topologies (NEAT) and Reinforcement Learning.The NEAT algorithm takes an "N" initial population of artificial agents.They follow genetic algorithms by considering an objective function, crossover, mutation, and augmenting topologies.Reinforcement learning, on the other hand, remembers the state, the action taken at that state, and the reward received for the action taken using a single agent and a Deep Q-learning Network.The performance of the NEAT algorithm improves as the initial population of the artificial agents is increased.
    Learning unseen coexisting attractors. (arXiv:2207.14133v1 [cs.LG])
    Reservoir computing is a machine learning approach that can generate a surrogate model of a dynamical system. It can learn the underlying dynamical system using fewer trainable parameters and hence smaller training data sets than competing approaches. Recently, a simpler formulation, known as next-generation reservoir computing, removes many algorithm metaparameters and identifies a well-performing traditional reservoir computer, thus simplifying training even further. Here, we study a particularly challenging problem of learning a dynamical system that has both disparate time scales and multiple co-existing dynamical states (attractors). We compare the next-generation and traditional reservoir computer using metrics quantifying the geometry of the ground-truth and forecasted attractors. For the studied four-dimensional system, the next-generation reservoir computing approach uses $\sim 1.7 \times$ less training data, requires $10^3 \times$ shorter `warm up' time, has fewer metaparameters, and has an $\sim 100\times$ higher accuracy in predicting the co-existing attractor characteristics in comparison to a traditional reservoir computer. Furthermore, we demonstrate that it predicts the basin of attraction with high accuracy. This work lends further support to the superior learning ability of this new machine learning algorithm for dynamical systems.
    Progressive Voronoi Diagram Subdivision: Towards A Holistic Geometric Framework for Exemplar-free Class-Incremental Learning. (arXiv:2207.14202v1 [cs.CV])
    Exemplar-free Class-incremental Learning (CIL) is a challenging problem because rehearsing data from previous phases is strictly prohibited, causing catastrophic forgetting of Deep Neural Networks (DNNs). In this paper, we present iVoro, a holistic framework for CIL, derived from computational geometry. We found Voronoi Diagram (VD), a classical model for space subdivision, is especially powerful for solving the CIL problem, because VD itself can be constructed favorably in an incremental manner -- the newly added sites (classes) will only affect the proximate classes, making the non-contiguous classes hardly forgettable. Further, in order to find a better set of centers for VD construction, we colligate DNN with VD using Power Diagram and show that the VD structure can be optimized by integrating local DNN models using a divide-and-conquer algorithm. Moreover, our VD construction is not restricted to the deep feature space, but is also applicable to multiple intermediate feature spaces, promoting VD to be multi-centered VD (CIVD) that efficiently captures multi-grained features from DNN. Importantly, iVoro is also capable of handling uncertainty-aware test-time Voronoi cell assignment and has exhibited high correlations between geometric uncertainty and predictive accuracy (up to ~0.9). Putting everything together, iVoro achieves up to 25.26%, 37.09%, and 33.21% improvements on CIFAR-100, TinyImageNet, and ImageNet-Subset, respectively, compared to the state-of-the-art non-exemplar CIL approaches. In conclusion, iVoro enables highly accurate, privacy-preserving, and geometrically interpretable CIL that is particularly useful when cross-phase data sharing is forbidden, e.g. in medical applications. Our code is available at https://machunwei.github.io/ivoro.
    Localized Vision-Language Matching for Open-vocabulary Object Detection. (arXiv:2205.06160v2 [cs.CV] UPDATED)
    In this work, we propose an open-vocabulary object detection method that, based on image-caption pairs, learns to detect novel object classes along with a given set of known classes. It is a two-stage training approach that first uses a location-guided image-caption matching technique to learn class labels for both novel and known classes in a weakly-supervised manner and second specializes the model for the object detection task using known class annotations. We show that a simple language model fits better than a large contextualized language model for detecting novel objects. Moreover, we introduce a consistency-regularization technique to better exploit image-caption pair information. Our method compares favorably to existing open-vocabulary detection approaches while being data-efficient. Source code is available at https://github.com/lmb-freiburg/locov .
    RIBBON: Cost-Effective and QoS-Aware Deep Learning Model Inference using a Diverse Pool of Cloud Computing Instances. (arXiv:2207.11434v2 [cs.DC] UPDATED)
    Deep learning model inference is a key service in many businesses and scientific discovery processes. This paper introduces RIBBON, a novel deep learning inference serving system that meets two competing objectives: quality-of-service (QoS) target and cost-effectiveness. The key idea behind RIBBON is to intelligently employ a diverse set of cloud computing instances (heterogeneous instances) to meet the QoS target and maximize cost savings. RIBBON devises a Bayesian Optimization-driven strategy that helps users build the optimal set of heterogeneous instances for their model inference service needs on cloud computing platforms -- and, RIBBON demonstrates its superiority over existing approaches of inference serving systems using homogeneous instance pools. RIBBON saves up to 16% of the inference service cost for different learning models including emerging deep learning recommender system models and drug-discovery enabling models.
    Topological Analysis of Ensembles of Hydrodynamic Turbulent Flows -- An Experimental Study. (arXiv:2207.14080v1 [physics.flu-dyn])
    This application paper presents a comprehensive experimental evaluation of the suitability of Topological Data Analysis (TDA) for the quantitative comparison of turbulent flows. Specifically, our study documents the usage of the persistence diagram of the maxima of flow enstrophy (an established vorticity indicator), for the topological representation of 180 ensemble members, generated by a coarse sampling of the parameter space of five numerical solvers. We document five main hypotheses reported by domain experts, describing their expectations regarding the variability of the flows generated by the distinct solver configurations. We contribute three evaluation protocols to assess the validation of the above hypotheses by two comparison measures: (i) a standard distance used in scientific imaging (the L2 norm) and (ii) an established topological distance between persistence diagrams (the L2-Wasserstein metric). Extensive experiments on the input ensemble demonstrate the superiority of the topological distance (ii) to report as close to each other flows which are expected to be similar by domain experts, due to the configuration of their vortices. Overall, the insights reported by our study bring an experimental evidence of the suitability of TDA for representing and comparing turbulent flows, thereby providing to the fluid dynamics community confidence for its usage in future work. Also, our flow data and evaluation protocols provide to the TDA community an application-approved benchmark for the evaluation and design of further topological distances.
    Classification of FIB/SEM-tomography images for highly porous multiphase materials using random forest classifiers. (arXiv:2207.14114v1 [cond-mat.mtrl-sci])
    FIB/SEM tomography represents an indispensable tool for the characterization of three-dimensional nanostructures in battery research and many other fields. However, contrast and 3D classification/reconstruction problems occur in many cases, which strongly limits the applicability of the technique especially on porous materials, like those used for electrode materials in batteries or fuel cells. Distinguishing the different components like active Li storage particles and carbon/binder materials is difficult and often prevents a reliable quantitative analysis of image data, or may even lead to wrong conclusions about structure-property relationships. In this contribution, we present a novel approach for data classification in three-dimensional image data obtained by FIB/SEM tomography and its applications to NMC battery electrode materials. We use two different image signals, namely the signal of the angled SE2 chamber detector and the Inlens detector signal, combine both signals and train a random forest, i.e. a particular machine learning algorithm. We demonstrate that this approach can overcome current limitations of existing techniques suitable for multi-phase measurements and that it allows for quantitative data reconstruction even where current state-of the art techniques fail, or demand for large training sets. This approach may yield as guideline for future research using FIB/SEM tomography.
    Modeling Item Response Theory with Stochastic Variational Inference. (arXiv:2108.11579v2 [cs.LG] UPDATED)
    Item Response Theory (IRT) is a ubiquitous model for understanding human behaviors and attitudes based on their responses to questions. Large modern datasets offer opportunities to capture more nuances in human behavior, potentially improving psychometric modeling leading to improved scientific understanding and public policy. However, while larger datasets allow for more flexible approaches, many contemporary algorithms for fitting IRT models may also have massive computational demands that forbid real-world application. To address this bottleneck, we introduce a variational Bayesian inference algorithm for IRT, and show that it is fast and scalable without sacrificing accuracy. Applying this method to five large-scale item response datasets from cognitive science and education yields higher log likelihoods and higher accuracy in imputing missing data than alternative inference algorithms. Using this new inference approach we then generalize IRT with expressive Bayesian models of responses, leveraging recent advances in deep learning to capture nonlinear item characteristic curves (ICC) with neural networks. Using an eigth-grade mathematics test from TIMSS, we show our nonlinear IRT models can capture interesting asymmetric ICCs. The algorithm implementation is open-source, and easily usable.
    Generative Modelling With Inverse Heat Dissipation. (arXiv:2206.13397v2 [cs.CV] UPDATED)
    While diffusion models have shown great success in image generation, their noise-inverting generative process does not explicitly consider the structure of images, such as their inherent multi-scale nature. Inspired by diffusion models and the desirability of coarse-to-fine modelling, we propose a new model that generates images through iteratively inverting the heat equation, a PDE that locally erases fine-scale information when run over the 2D plane of the image. In our novel methodology, the solution of the forward heat equation is interpreted as a variational approximation in a directed graphical model. We demonstrate promising image quality and point out emergent qualitative properties not seen in diffusion models, such as disentanglement of overall colour and shape in images and aspects of neural network interpretability. Spectral analysis on natural images positions our model as a type of dual to diffusion models and reveals implicit inductive biases in them.
    Exploiting and Defending Against the Approximate Linearity of Apple's NeuralHash. (arXiv:2207.14258v1 [cs.CR])
    Perceptual hashes map images with identical semantic content to the same $n$-bit hash value, while mapping semantically-different images to different hashes. These algorithms carry important applications in cybersecurity such as copyright infringement detection, content fingerprinting, and surveillance. Apple's NeuralHash is one such system that aims to detect the presence of illegal content on users' devices without compromising consumer privacy. We make the surprising discovery that NeuralHash is approximately linear, which inspires the development of novel black-box attacks that can (i) evade detection of "illegal" images, (ii) generate near-collisions, and (iii) leak information about hashed images, all without access to model parameters. These vulnerabilities pose serious threats to NeuralHash's security goals; to address them, we propose a simple fix using classical cryptographic standards.
    Reinforcement Learning with Intrinsic Affinity for Personalized Prosperity Management. (arXiv:2204.09218v2 [cs.LG] UPDATED)
    The common purpose of applying reinforcement learning (RL) to asset management is the maximization of profit. The extrinsic reward function used to learn an optimal strategy typically does not take into account any other preferences or constraints. We have developed a regularization method that ensures that strategies have global intrinsic affinities, i.e., different personalities may have preferences for certain assets which may change over time. We capitalize on these intrinsic policy affinities to make our RL model inherently interpretable. We demonstrate how RL agents can be trained to orchestrate such individual policies for particular personality profiles and still achieve high returns.
    Distinction Maximization Loss: Efficiently Improving Uncertainty Estimation and Out-of-Distribution Detection by Simply Replacing the Loss and Calibrating. (arXiv:2205.05874v3 [cs.LG] UPDATED)
    Building robust deterministic neural networks remains a challenge. On the one hand, some approaches improve out-of-distribution detection at the cost of reducing classification accuracy in some situations. On the other hand, some methods simultaneously increase classification accuracy, uncertainty estimation, and out-of-distribution detection at the expense of reducing the inference efficiency. In this paper, we propose training deterministic neural networks using our DisMax loss, which works as a drop-in replacement for the usual SoftMax loss (i.e., the combination of the linear output layer, the SoftMax activation, and the cross-entropy loss). Starting from the IsoMax+ loss, we create each logit based on the distances to all prototypes, rather than just the one associated with the correct class. We also introduce a mechanism to combine images to construct what we call fractional probability regularization. Moreover, we present a fast way to calibrate the network after training. Finally, we propose a composite score to perform out-of-distribution detection. Our experiments show that DisMax usually outperforms current approaches simultaneously in classification accuracy, uncertainty estimation, and out-of-distribution detection while maintaining deterministic neural network inference efficiency. The code to reproduce the results is available at https://github.com/dlmacedo/distinction-maximization-loss.
    On the Principles of Parsimony and Self-Consistency for the Emergence of Intelligence. (arXiv:2207.04630v3 [cs.AI] UPDATED)
    Ten years into the revival of deep networks and artificial intelligence, we propose a theoretical framework that sheds light on understanding deep networks within a bigger picture of Intelligence in general. We introduce two fundamental principles, Parsimony and Self-consistency, that address two fundamental questions regarding Intelligence: what to learn and how to learn, respectively. We believe the two principles are the cornerstones for the emergence of Intelligence, artificial or natural. While these two principles have rich classical roots, we argue that they can be stated anew in entirely measurable and computable ways. More specifically, the two principles lead to an effective and efficient computational framework, compressive closed-loop transcription, that unifies and explains the evolution of modern deep networks and many artificial intelligence practices. While we mainly use modeling of visual data as an example, we believe the two principles will unify understanding of broad families of autonomous intelligent systems and provide a framework for understanding the brain.
    CrAM: A Compression-Aware Minimizer. (arXiv:2207.14200v1 [cs.LG])
    We examine the question of whether SGD-based optimization of deep neural networks (DNNs) can be adapted to produce models which are both highly-accurate and easily-compressible. We propose a new compression-aware minimizer dubbed CrAM, which modifies the SGD training iteration in a principled way, in order to produce models whose local loss behavior is stable under compression operations such as weight pruning or quantization. Experimental results on standard image classification tasks show that CrAM produces dense models that can be more accurate than standard SGD-type baselines, but which are surprisingly stable under weight pruning: for instance, for ResNet50 on ImageNet, CrAM-trained models can lose up to 70% of their weights in one shot with only minor accuracy loss.
    Differentiable Rule Induction with Learned Relational Features. (arXiv:2201.06515v2 [stat.ML] UPDATED)
    Rule-based decision models are attractive due to their interpretability. However, existing rule induction methods often result in long and consequently less interpretable rule models. This problem can often be attributed to the lack of appropriately expressive vocabulary, i.e., relevant predicates used as literals in the decision model. Most existing rule induction algorithms presume pre-defined literals, naturally decoupling the definition of the literals from the rule learning phase. In contrast, we propose the Relational Rule Network (R2N), a neural architecture that learns literals that represent a linear relationship among numerical input features along with the rules that use them. This approach opens the door to increasing the expressiveness of induced decision models by coupling literal learning directly with rule learning in an end-to-end differentiable fashion. On benchmark tasks, we show that these learned literals are simple enough to retain interpretability, yet improve prediction accuracy and provide sets of rules that are more concise compared to state-of-the-art rule induction algorithms.
    Optimization of Artificial Neural Networks models applied to the identification of images of asteroids' resonant arguments. (arXiv:2207.14181v1 [astro-ph.EP])
    The asteroidal main belt is crossed by a web of mean-motion and secular resonances, that occur when there is a commensurability between fundamental frequencies of the asteroids and planets. Traditionally, these objects were identified by visual inspection of the time evolution of their resonant argument, which is a combination of orbital elements of the asteroid and the perturbing planet(s). Since the population of asteroids affected by these resonances is, in some cases, of the order of several thousand, this has become a taxing task for a human observer. Recent works used Convolutional Neural Networks (CNN) models to perform such task automatically. In this work, we compare the outcome of such models with those of some of the most advanced and publicly available CNN architectures, like the VGG, Inception and ResNet. The performance of such models is first tested and optimized for overfitting issues, using validation sets and a series of regularization techniques like data augmentation, dropout, and batch normalization. The three best-performing models were then used to predict the labels of larger testing databases containing thousands of images. The VGG model, with and without regularizations, proved to be the most efficient method to predict labels of large datasets. Since the Vera C. Rubin observatory is likely to discover up to four million new asteroids in the next few years, the use of these models might become quite valuable to identify populations of resonant minor bodies.
    Regret Minimization and Convergence to Equilibria in General-sum Markov Games. (arXiv:2207.14211v1 [cs.LG])
    An abundance of recent impossibility results establish that regret minimization in Markov games with adversarial opponents is both statistically and computationally intractable. Nevertheless, none of these results preclude the possibility of regret minimization under the assumption that all parties adopt the same learning procedure. In this work, we present the first (to our knowledge) algorithm for learning in general-sum Markov games that provides sublinear regret guarantees when executed by all agents. The bounds we obtain are for swap regret, and thus, along the way, imply convergence to a correlated equilibrium. Our algorithm is decentralized, computationally efficient, and does not require any communication between agents. Our key observation is that online learning via policy optimization in Markov games essentially reduces to a form of weighted regret minimization, with unknown weights determined by the path length of the agents' policy sequence. Consequently, controlling the path length leads to weighted regret objectives for which sufficiently adaptive algorithms provide sublinear regret guarantees.
    On stabilizing reinforcement learning without Lyapunov functions. (arXiv:2207.08730v2 [eess.SY] UPDATED)
    Reinforcement learning remains one of the major directions of the contemporary development of control engineering and machine learning. Nice intuition, flexible settings, ease of application are among the many perks of this methodology. From the standpoint of machine learning, the main strength of a reinforcement learning agent is its ability to "capture" (learn) the optimal behavior in the given environment. Typically, the agent is built on neural networks and it is their approximation abilities that give rise to the above belief. From the standpoint of control engineering, however, reinforcement learning has serious deficiencies. The most significant one is the lack of stability guarantee of the agent-environment closed loop. A great deal of research was and is being made towards stabilizing reinforcement learning. Speaking of stability, the celebrated Lyapunov theory is the de facto tool. It is thus no wonder that so many techniques of stabilizing reinforcement learning rely on the Lyapunov theory in one way or another. In control theory, there is an intricate connection between a stabilizing controller and a Lyapunov function. Employing such a pair seems thus quite attractive to design stabilizing reinforcement learning. However, computation of a Lyapunov function is generally a cumbersome process. In this note, we show how to construct a stabilizing reinforcement learning agent that does not employ such a function at all. We only assume that a Lyapunov function exists, which is a natural thing to do if the given system (read: environment) is stabilizable, but we do not need to compute one.
    Private Convex Optimization via Exponential Mechanism. (arXiv:2203.00263v2 [cs.DS] UPDATED)
    In this paper, we study private optimization problems for non-smooth convex functions $F(x)=\mathbb{E}_i f_i(x)$ on $\mathbb{R}^d$. We show that modifying the exponential mechanism by adding an $\ell_2^2$ regularizer to $F(x)$ and sampling from $\pi(x)\propto \exp(-k(F(x)+\mu\|x\|_2^2/2))$ recovers both the known optimal empirical risk and population loss under $(\epsilon,\delta)$-DP. Furthermore, we show how to implement this mechanism using $\widetilde{O}(n \min(d, n))$ queries to $f_i(x)$ for the DP-SCO where $n$ is the number of samples/users and $d$ is the ambient dimension. We also give a (nearly) matching lower bound $\widetilde{\Omega}(n \min(d, n))$ on the number of evaluation queries. Our results utilize the following tools that are of independent interest: (1) We prove Gaussian Differential Privacy (GDP) of the exponential mechanism if the loss function is strongly convex and the perturbation is Lipschitz. Our privacy bound is \emph{optimal} as it includes the privacy of Gaussian mechanism as a special case and is proved using the isoperimetric inequality for strongly log-concave measures. (2) We show how to sample from $\exp(-F(x)-\mu \|x\|^2_2/2)$ for $G$-Lipschitz $F$ with $\eta$ error in total variation (TV) distance using $\widetilde{O}((G^2/\mu) \log^2(d/\eta))$ unbiased queries to $F(x)$. This is the first sampler whose query complexity has \emph{polylogarithmic dependence} on both dimension $d$ and accuracy $\eta$.
    Individual Privacy Accounting for Differentially Private Stochastic Gradient Descent. (arXiv:2206.02617v3 [cs.LG] UPDATED)
    Differentially private stochastic gradient descent (DP-SGD) is the workhorse algorithm for recent advances in private deep learning. It provides a single privacy guarantee to all datapoints in the dataset. We propose an efficient algorithm to compute privacy guarantees for individual examples when releasing models trained by DP-SGD. We use our algorithm to investigate individual privacy parameters across a number of datasets. We find that most examples enjoy stronger privacy guarantees than the worst-case bound. We further discover that the training loss and the privacy parameter of an example are well-correlated. This implies groups that are underserved in terms of model utility are simultaneously underserved in terms of privacy guarantee. For example, on CIFAR-10, the average $\epsilon$ of the class with the lowest test accuracy is 26.3% higher than that of the class with the highest accuracy. We also run membership inference attacks to show this reflects disparate empirical privacy risks.
    What Happens after SGD Reaches Zero Loss? --A Mathematical Framework. (arXiv:2110.06914v4 [cs.LG] UPDATED)
    Understanding the implicit bias of Stochastic Gradient Descent (SGD) is one of the key challenges in deep learning, especially for overparametrized models, where the local minimizers of the loss function $L$ can form a manifold. Intuitively, with a sufficiently small learning rate $\eta$, SGD tracks Gradient Descent (GD) until it gets close to such manifold, where the gradient noise prevents further convergence. In such a regime, Blanc et al. (2020) proved that SGD with label noise locally decreases a regularizer-like term, the sharpness of loss, $\mathrm{tr}[\nabla^2 L]$. The current paper gives a general framework for such analysis by adapting ideas from Katzenberger (1991). It allows in principle a complete characterization for the regularization effect of SGD around such manifold -- i.e., the "implicit bias" -- using a stochastic differential equation (SDE) describing the limiting dynamics of the parameters, which is determined jointly by the loss function and the noise covariance. This yields some new results: (1) a global analysis of the implicit bias valid for $\eta^{-2}$ steps, in contrast to the local analysis of Blanc et al. (2020) that is only valid for $\eta^{-1.6}$ steps and (2) allowing arbitrary noise covariance. As an application, we show with arbitrary large initialization, label noise SGD can always escape the kernel regime and only requires $O(\kappa\ln d)$ samples for learning an $\kappa$-sparse overparametrized linear model in $\mathbb{R}^d$ (Woodworth et al., 2020), while GD initialized in the kernel regime requires $\Omega(d)$ samples. This upper bound is minimax optimal and improves the previous $\tilde{O}(\kappa^2)$ upper bound (HaoChen et al., 2020).
    One-Nearest-Neighbor Search is All You Need for Minimax Optimal Regression and Classification. (arXiv:2202.02464v2 [math.ST] UPDATED)
    Recently, Qiao, Duan, and Cheng~(2019) proposed a distributed nearest-neighbor classification method, in which a massive dataset is split into smaller groups, each processed with a $k$-nearest-neighbor classifier, and the final class label is predicted by a majority vote among these groupwise class labels. This paper shows that the distributed algorithm with $k=1$ over a sufficiently large number of groups attains a minimax optimal error rate up to a multiplicative logarithmic factor under some regularity conditions, for both regression and classification problems. Roughly speaking, distributed 1-nearest-neighbor rules with $M$ groups has a performance comparable to standard $\Theta(M)$-nearest-neighbor rules. In the analysis, alternative rules with a refined aggregation method are proposed and shown to attain exact minimax optimal rates.
    A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecasts. (arXiv:2204.02028v2 [physics.ao-ph] UPDATED)
    Despite continuous improvements, precipitation forecasts are still not as accurate and reliable as those of other meteorological variables. A major contributing factor to this is that several key processes affecting precipitation distribution and intensity occur below the resolved scale of global weather models. Generative adversarial networks (GANs) have been demonstrated by the computer vision community to be successful at super-resolution problems, i.e., learning to add fine-scale structure to coarse images. Leinonen et al. (2020) previously applied a GAN to produce ensembles of reconstructed high-resolution atmospheric fields, given coarsened input data. In this paper, we demonstrate this approach can be extended to the more challenging problem of increasing the accuracy and resolution of comparatively low-resolution input from a weather forecasting model, using high-resolution radar measurements as a "ground truth". The neural network must learn to add resolution and structure whilst accounting for non-negligible forecast error. We show that GANs and VAE-GANs can match the statistical properties of state-of-the-art pointwise post-processing methods whilst creating high-resolution, spatially coherent precipitation maps. Our model compares favourably to the best existing downscaling methods in both pixel-wise and pooled CRPS scores, power spectrum information and rank histograms (used to assess calibration). We test our models and show that they perform in a range of scenarios, including heavy rainfall.
    Associative Learning Mechanism for Drug-Target Interaction Prediction. (arXiv:2205.15364v4 [q-bio.BM] UPDATED)
    As a necessary process in drug development, finding a drug compound that can selectively bind to a specific protein is highly challenging and costly. Drug-target affinity (DTA), which represents the strength of drug-target interaction (DTI), has played an important role in the DTI prediction task over the past decade. Although deep learning has been applied to DTA-related research, existing solutions ignore fundamental correlations between molecular substructures in molecular representation learning of drug compound molecules/protein targets. Moreover, traditional methods lack the interpretability of the DTA prediction process. This results in missing feature information of intermolecular interactions, thereby affecting prediction performance. Therefore, this paper proposes a DTA prediction method with interactive learning and an autoencoder mechanism. The proposed model enhances the corresponding ability to capture the feature information of a single molecular sequence by the drug/protein molecular representation learning module and supplements the information interaction between molecular sequence pairs by the interactive information learning module. The DTA value prediction module fuses the drug-target pair interaction information to output the predicted value of DTA. Additionally, this paper theoretically proves that the proposed method maximizes evidence lower bound (ELBO) for the joint distribution of the DTA prediction model, which enhances the consistency of the probability distribution between the actual value and the predicted value. The experimental results confirm mutual transformer-drug target affinity (MT-DTA) achieves better performance than other comparative methods.
    The Familiarity Hypothesis: Explaining the Behavior of Deep Open Set Methods. (arXiv:2203.02486v4 [cs.CV] UPDATED)
    In many object recognition applications, the set of possible categories is an open set, and the deployed recognition system will encounter novel objects belonging to categories unseen during training. Detecting such "novel category" objects is usually formulated as an anomaly detection problem. Anomaly detection algorithms for feature-vector data identify anomalies as outliers, but outlier detection has not worked well in deep learning. Instead, methods based on the computed logits of visual object classifiers give state-of-the-art performance. This paper proposes the Familiarity Hypothesis that these methods succeed because they are detecting the absence of familiar learned features rather than the presence of novelty. This distinction is important, because familiarity-based detection will fail in many situations where novelty is present. For example when an image contains both a novel object and a familiar one, the familiarity score will be high, so the novel object will not be noticed. The paper reviews evidence from the literature and presents additional evidence from our own experiments that provide strong support for this hypothesis. The paper concludes with a discussion of whether familiarity-based detection is an inevitable consequence of representation learning.
    On the Universality of Langevin Diffusion for Private Euclidean (Convex) Optimization. (arXiv:2204.01585v3 [cs.LG] UPDATED)
    In this paper we revisit the problem of differentially private empirical risk minimization (DP-ERM) and differentially private stochastic convex optimization (DP-SCO). We show that a well-studied continuous time algorithm from statistical physics, called Langevin diffusion (LD), simultaneously provides optimal privacy/utility trade-offs for both DP-ERM and DP-SCO, under $\epsilon$-DP, and $(\epsilon,\delta)$-DP both for convex and strongly convex loss functions. We provide new time and dimension independent uniform stability properties of LD, using with we provide the corresponding optimal excess population risk guarantees for $\epsilon$-DP. An important attribute of our DP-SCO guarantees for $\epsilon$-DP is that they match the non-private optimal bounds as $\epsilon\to\infty$. Along the way, we provide various technical tools, which can be of independent interest: i) A new R\'enyi divergence bound for LD, when run on loss functions over two neighboring data sets, ii) Excess empirical risk bounds for last-iterate LD, analogous to that of Shamir and Zhang for noisy stochastic gradient descent (SGD), and iii) A two phase excess risk analysis of LD, where the first phase is when the diffusion has not converged in any reasonable sense to a stationary distribution, and in the second phase when the diffusion has converged to a variant of Gibbs distribution. Our universality results crucially rely on the dynamics of LD. When it has converged to a stationary distribution, we obtain the optimal bounds under $\epsilon$-DP. When it is run only for a very short time $\propto 1/p$, we obtain the optimal bounds under $(\epsilon,\delta)$-DP. Here, $p$ is the dimensionality of the model space.
    Algorithmic Foundation of Deep X-Risk Optimization. (arXiv:2206.00439v4 [cs.LG] UPDATED)
    X-risk is a term introduced to represent a family of compositional measures or objectives, in which each data point is compared with a large number of items explicitly or implicitly for defining a risk function. It includes many widely used measures or objectives, e.g., AUROC, AUPRC, partial AUROC, NDCG, MAP, top-$K$ NDCG, top-$K$ MAP, listwise losses, p-norm push, top push, precision/recall at top $K$ positions, precision at a certain recall level, contrastive objectives, etc. While these non-decomposable measures/objectives and their optimization algorithms have been studied in the literature of machine learning, computer vision, information retrieval, and etc, optimizing these measures/objectives has encountered some unique challenges for deep learning. In this paper, we survey recent rigorous efforts for deep X-risk optimization (DXO) by focusing on its algorithmic foundation. We introduce a class of techniques for optimizing X-risks for deep learning. We formulate DXO into three special families of non-convex optimization problems belonging to non-convex min-max optimization, non-convex compositional optimization, and non-convex bilevel optimization, respectively. For each family of problems, we present some strong baseline algorithms and their complexities, which will motivate further research for improving the existing results. Discussions about the presented results and future studies are given at the end. Efficient algorithms for optimizing a variety of X-risks are implemented in the LibAUC library at www.libauc.org.
    Instance-wise or Class-wise? A Tale of Neighbor Shapley for Concept-based Explanation. (arXiv:2109.01369v4 [cs.LG] UPDATED)
    Deep neural networks have demonstrated remarkable performance in many data-driven and prediction-oriented applications, and sometimes even perform better than humans. However, their most significant drawback is the lack of interpretability, which makes them less attractive in many real-world applications. When relating to the moral problem or the environmental factors that are uncertain such as crime judgment, financial analysis, and medical diagnosis, it is essential to mine the evidence for the model's prediction (interpret model knowledge) to convince humans. Thus, investigating how to interpret model knowledge is of paramount importance for both academic research and real applications.
    Execute Order 66: Targeted Data Poisoning for Reinforcement Learning. (arXiv:2201.00762v2 [cs.LG] UPDATED)
    Data poisoning for reinforcement learning has historically focused on general performance degradation, and targeted attacks have been successful via perturbations that involve control of the victim's policy and rewards. We introduce an insidious poisoning attack for reinforcement learning which causes agent misbehavior only at specific target states - all while minimally modifying a small fraction of training observations without assuming any control over policy or reward. We accomplish this by adapting a recent technique, gradient alignment, to reinforcement learning. We test our method and demonstrate success in two Atari games of varying difficulty.
    Hardness of Agnostically Learning Halfspaces from Worst-Case Lattice Problems. (arXiv:2207.14030v1 [cs.LG])
    We show hardness of improperly learning halfspaces in the agnostic model based on worst-case lattice problems, e.g., approximating shortest vectors within polynomial factors. In particular, we show that under this assumption there is no efficient algorithm that outputs any binary hypothesis, not necessarily a halfspace, achieving misclassfication error better than $\frac 1 2 - \epsilon$ even if the optimal misclassification error is as small is as small as $\delta$. Here, $\epsilon$ can be smaller than the inverse of any polynomial in the dimension and $\delta$ as small as $\mathrm{exp}\left(-\Omega\left(\log^{1-c}(d)\right)\right)$, where $0 < c < 1$ is an arbitrary constant and $d$ is the dimension. Previous hardness results [Daniely16] of this problem were based on average-case complexity assumptions, specifically, variants of Feige's random 3SAT hypothesis. Our work gives the first hardness for this problem based on a worst-case complexity assumption. It is inspired by a sequence of recent works showing hardness of learning well-separated Gaussian mixtures based on worst-case lattice problems.
    Depth Field Networks for Generalizable Multi-view Scene Representation. (arXiv:2207.14287v1 [cs.CV])
    Modern 3D computer vision leverages learning to boost geometric reasoning, mapping image data to classical structures such as cost volumes or epipolar constraints to improve matching. These architectures are specialized according to the particular problem, and thus require significant task-specific tuning, often leading to poor domain generalization performance. Recently, generalist Transformer architectures have achieved impressive results in tasks such as optical flow and depth estimation by encoding geometric priors as inputs rather than as enforced constraints. In this paper, we extend this idea and propose to learn an implicit, multi-view consistent scene representation, introducing a series of 3D data augmentation techniques as a geometric inductive prior to increase view diversity. We also show that introducing view synthesis as an auxiliary task further improves depth estimation. Our Depth Field Networks (DeFiNe) achieve state-of-the-art results in stereo and video depth estimation without explicit geometric constraints, and improve on zero-shot domain generalization by a wide margin.
    An iterative clustering algorithm for the Contextual Stochastic Block Model with optimality guarantees. (arXiv:2112.10467v2 [stat.ML] UPDATED)
    Real-world networks often come with side information that can help to improve the performance of network analysis tasks such as clustering. Despite a large number of empirical and theoretical studies conducted on network clustering methods during the past decade, the added value of side information and the methods used to incorporate it optimally in clustering algorithms are relatively less understood. We propose a new iterative algorithm to cluster networks with side information for nodes (in the form of covariates) and show that our algorithm is optimal under the Contextual Symmetric Stochastic Block Model. Our algorithm can be applied to general Contextual Stochastic Block Models and avoids hyperparameter tuning in contrast to previously proposed methods. We confirm our theoretical results on synthetic data experiments where our algorithm significantly outperforms other methods, and show that it can also be applied to signed graphs. Finally we demonstrate the practical interest of our method on real data.
    Federated Learning for IoUT: Concepts, Applications, Challenges and Opportunities. (arXiv:2207.13976v1 [cs.LG])
    Internet of Underwater Things (IoUT) have gained rapid momentum over the past decade with applications spanning from environmental monitoring and exploration, defence applications, etc. The traditional IoUT systems use machine learning (ML) approaches which cater the needs of reliability, efficiency and timeliness. However, an extensive review of the various studies conducted highlight the significance of data privacy and security in IoUT frameworks as a predominant factor in achieving desired outcomes in mission critical applications. Federated learning (FL) is a secured, decentralized framework which is a recent development in machine learning, that will help in fulfilling the challenges faced by conventional ML approaches in IoUT. This paper presents an overview of the various applications of FL in IoUT, its challenges, open issues and indicates direction of future research prospects.
    Learning to Adapt Classifier for Imbalanced Semi-supervised Learning. (arXiv:2207.13856v1 [cs.LG])
    Pseudo-labeling has proven to be a promising semi-supervised learning (SSL) paradigm. Existing pseudo-labeling methods commonly assume that the class distributions of training data are balanced. However, such an assumption is far from realistic scenarios and existing pseudo-labeling methods suffer from severe performance degeneration in the context of class-imbalance. In this work, we investigate pseudo-labeling under imbalanced semi-supervised setups. The core idea is to automatically assimilate the training bias arising from class-imbalance, using a bias adaptive classifier that equips the original linear classifier with a bias attractor. The bias attractor is designed to be a light-weight residual network for adapting to the training bias. Specifically, the bias attractor is learned through a bi-level learning framework such that the bias adaptive classifier is able to fit imbalanced training data, while the linear classifier can give unbiased label prediction for each class. We conduct extensive experiments under various imbalanced semi-supervised setups, and the results demonstrate that our method can be applicable to different pseudo-labeling models and superior to the prior arts.
    Branch Ranking for Efficient Mixed-Integer Programming via Offline Ranking-based Policy Learning. (arXiv:2207.13701v1 [cs.LG])
    Deriving a good variable selection strategy in branch-and-bound is essential for the efficiency of modern mixed-integer programming (MIP) solvers. With MIP branching data collected during the previous solution process, learning to branch methods have recently become superior over heuristics. As branch-and-bound is naturally a sequential decision making task, one should learn to optimize the utility of the whole MIP solving process instead of being myopic on each step. In this work, we formulate learning to branch as an offline reinforcement learning (RL) problem, and propose a long-sighted hybrid search scheme to construct the offline MIP dataset, which values the long-term utilities of branching decisions. During the policy training phase, we deploy a ranking-based reward assignment scheme to distinguish the promising samples from the long-term or short-term view, and train the branching model named Branch Ranking via offline policy learning. Experiments on synthetic MIP benchmarks and real-world tasks demonstrate that Branch Rankink is more efficient and robust, and can better generalize to large scales of MIP instances compared to the widely used heuristics and state-of-the-art learning-based branching models.
    PHEMEPlus: Enriching Social Media Rumour Verification with External Evidence. (arXiv:2207.13970v1 [cs.CL])
    Work on social media rumour verification utilises signals from posts, their propagation and users involved. Other lines of work target identifying and fact-checking claims based on information from Wikipedia, or trustworthy news articles without considering social media context. However works combining the information from social media with external evidence from the wider web are lacking. To facilitate research in this direction, we release a novel dataset, PHEMEPlus, an extension of the PHEME benchmark, which contains social media conversations as well as relevant external evidence for each rumour. We demonstrate the effectiveness of incorporating such evidence in improving rumour verification models. Additionally, as part of the evidence collection, we evaluate various ways of query formulation to identify the most effective method.
    Unsupervised Frequent Pattern Mining for CEP. (arXiv:2207.14017v1 [cs.LG])
    Complex Event Processing (CEP) is a set of methods that allow efficient knowledge extraction from massive data streams using complex and highly descriptive patterns. Numerous applications, such as online finance, healthcare monitoring and fraud detection use CEP technologies to capture critical alerts, potential threats, or vital notifications in real time. As of today, in many fields, patterns are manually defined by human experts. However, desired patterns often contain convoluted relations that are difficult for humans to detect, and human expertise is scarce in many domains. We present REDEEMER (REinforcement baseD cEp pattErn MinER), a novel reinforcement and active learning approach aimed at mining CEP patterns that allow expansion of the knowledge extracted while reducing the human effort required. This approach includes a novel policy gradient method for vast multivariate spaces and a new way to combine reinforcement and active learning for CEP rule learning while minimizing the number of labels needed for training. REDEEMER aims to enable CEP integration in domains that could not utilize it before. To the best of our knowledge, REDEEMER is the first system that suggests new CEP rules that were not observed beforehand, and is the first method aimed for increasing pattern knowledge in fields where experts do not possess sufficient information required for CEP tools. Our experiments on diverse data-sets demonstrate that REDEEMER is able to extend pattern knowledge while outperforming several state-of-the-art reinforcement learning methods for pattern mining.
    Automated Classification of Nanoparticles with Various Ultrastructures and Sizes. (arXiv:2207.14023v1 [cond-mat.mtrl-sci])
    Accurately measuring the size, morphology, and structure of nanoparticles is very important, because they are strongly dependent on their properties for many applications. In this paper, we present a deep-learning based method for nanoparticle measurement and classification trained from a small data set of scanning transmission electron microscopy images. Our approach is comprised of two stages: localization, i.e., detection of nanoparticles, and classification, i.e., categorization of their ultrastructure. For each stage, we optimize the segmentation and classification by analysis of the different state-of-the-art neural networks. We show how the generation of synthetic images, either using image processing or using various image generation neural networks, can be used to improve the results in both stages. Finally, the application of the algorithm to bimetallic nanoparticles demonstrates the automated data collection of size distributions including classification of complex ultrastructures. The developed method can be easily transferred to other material systems and nanoparticle structures.
    Differentially Private Learning of Hawkes Processes. (arXiv:2207.13741v1 [stat.ML])
    Hawkes processes have recently gained increasing attention from the machine learning community for their versatility in modeling event sequence data. While they have a rich history going back decades, some of their properties, such as sample complexity for learning the parameters and releasing differentially private versions, are yet to be thoroughly analyzed. In this work, we study standard Hawkes processes with background intensity $\mu$ and excitation function $\alpha e^{-\beta t}$. We provide both non-private and differentially private estimators of $\mu$ and $\alpha$, and obtain sample complexity results in both settings to quantify the cost of privacy. Our analysis exploits the strong mixing property of Hawkes processes and classical central limit theorem results for weakly dependent random variables. We validate our theoretical findings on both synthetic and real datasets.
    HelixFold-Single: MSA-free Protein Structure Prediction by Using Protein Language Model as an Alternative. (arXiv:2207.13921v1 [q-bio.BM])
    AI-based protein structure prediction pipelines, such as AlphaFold2, have achieved near-experimental accuracy. These advanced pipelines mainly rely on Multiple Sequence Alignments (MSAs) and templates as inputs to learn the co-evolution information from the homologous sequences. Nonetheless, searching MSAs and templates from protein databases is time-consuming, usually taking dozens of minutes. Consequently, we attempt to explore the limits of fast protein structure prediction by using only primary sequences of proteins. HelixFold-Single is proposed to combine a large-scale protein language model with the superior geometric learning capability of AlphaFold2. Our proposed method, HelixFold-Single, first pre-trains a large-scale protein language model (PLM) with thousands of millions of primary sequences utilizing the self-supervised learning paradigm, which will be used as an alternative to MSAs and templates for learning the co-evolution information. Then, by combining the pre-trained PLM and the essential components of AlphaFold2, we obtain an end-to-end differentiable model to predict the 3D coordinates of atoms from only the primary sequence. HelixFold-Single is validated in datasets CASP14 and CAMEO, achieving competitive accuracy with the MSA-based methods on the targets with large homologous families. Furthermore, HelixFold-Single consumes much less time than the mainstream pipelines for protein structure prediction, demonstrating its potential in tasks requiring many predictions. The code of HelixFold-Single is available at https://github.com/PaddlePaddle/PaddleHelix/tree/dev/apps/protein_folding/helixfold-single, and we also provide stable web services on https://paddlehelix.baidu.com/app/drug/protein-single/forecast.
    Safety-Enhanced Autonomous Driving Using Interpretable Sensor Fusion Transformer. (arXiv:2207.14024v1 [cs.CV])
    Large-scale deployment of autonomous vehicles has been continually delayed due to safety concerns. On the one hand, comprehensive scene understanding is indispensable, a lack of which would result in vulnerability to rare but complex traffic situations, such as the sudden emergence of unknown objects. However, reasoning from a global context requires access to sensors of multiple types and adequate fusion of multi-modal sensor signals, which is difficult to achieve. On the other hand, the lack of interpretability in learning models also hampers the safety with unverifiable failure causes. In this paper, we propose a safety-enhanced autonomous driving framework, named Interpretable Sensor Fusion Transformer(InterFuser), to fully process and fuse information from multi-modal multi-view sensors for achieving comprehensive scene understanding and adversarial event detection. Besides, intermediate interpretable features are generated from our framework, which provide more semantics and are exploited to better constrain actions to be within the safe sets. We conducted extensive experiments on CARLA benchmarks, where our model outperforms prior methods, ranking the first on the public CARLA Leaderboard.
    Multi-Step Deductive Reasoning Over Natural Language: An Empirical Study on Out-of-Distribution Generalisation. (arXiv:2207.14000v1 [cs.CL])
    Combining deep learning with symbolic logic reasoning aims to capitalize on the success of both fields and is drawing increasing attention. Inspired by DeepLogic, an end-to-end model trained to perform inference on logic programs, we introduce IMA-GloVe-GA, an iterative neural inference network for multi-step reasoning expressed in natural language. In our model, reasoning is performed using an iterative memory neural network based on RNN with a gate attention mechanism. We evaluate IMA-GloVe-GA on three datasets: PARARULES, CONCEPTRULES V1 and CONCEPTRULES V2. Experimental results show DeepLogic with gate attention can achieve higher test accuracy than DeepLogic and other RNN baseline models. Our model achieves better out-of-distribution generalisation than RoBERTa-Large when the rules have been shuffled. Furthermore, to address the issue of unbalanced distribution of reasoning depths in the current multi-step reasoning datasets, we develop PARARULE-Plus, a large dataset with more examples that require deeper reasoning steps. Experimental results show that the addition of PARARULE-Plus can increase the model's performance on examples requiring deeper reasoning depths. The source code and data are available at https://github.com/Strong-AI-Lab/Multi-Step-Deductive-Reasoning-Over-Natural-Language.
    Raising Student Completion Rates with Adaptive Curriculum and Contextual Bandits. (arXiv:2207.14003v1 [cs.CL])
    We present an adaptive learning Intelligent Tutoring System, which uses model-based reinforcement learning in the form of contextual bandits to assign learning activities to students. The model is trained on the trajectories of thousands of students in order to maximize their exercise completion rates and continues to learn online, automatically adjusting itself to new activities. A randomized controlled trial with students shows that our model leads to superior completion rates and significantly improved student engagement when compared to other approaches. Our approach is fully-automated unlocking new opportunities for learning experience personalization.
    Physical Systems Modeled Without Physical Laws. (arXiv:2207.13702v1 [cs.LG])
    Physics-based simulations typically operate with a combination of complex differentiable equations and many scientific and geometric inputs. Our work involves gathering data from those simulations and seeing how well tree-based machine learning methods can emulate desired outputs without "knowing" the complex backing involved in the simulations. The selected physics-based simulations included Navier-Stokes, stress analysis, and electromagnetic field lines to benchmark performance as numerical and statistical algorithms. We specifically focus on predicting specific spatial-temporal data between two simulation outputs and increasing spatial resolution to generalize the physics predictions to finer test grids without the computational costs of repeating the numerical calculation.
    Real Image Restoration via Structure-preserving Complementarity Attention. (arXiv:2207.13879v1 [eess.IV])
    Since convolutional neural networks perform well in learning generalizable image priors from large-scale data, these models have been widely used in image denoising tasks. However, the computational complexity increases dramatically as well on complex model. In this paper, We propose a novel lightweight Complementary Attention Module, which includes a density module and a sparse module, which can cooperatively mine dense and sparse features for feature complementary learning to build an efficient lightweight architecture. Moreover, to reduce the loss of details caused by denoising, this paper constructs a gradient-based structure-preserving branch. We utilize gradient-based branches to obtain additional structural priors for denoising, and make the model pay more attention to image geometric details through gradient loss optimization.Based on the above, we propose an efficiently Unet structured network with dual branch, the visual results show that can effectively preserve the structural details of the original image, we evaluate benchmarks including SIDD and DND, where SCANet achieves state-of-the-art performance in PSNR and SSIM while significantly reducing computational cost.
    One-Pass Learning via Bridging Orthogonal Gradient Descent and Recursive Least-Squares. (arXiv:2207.13853v1 [cs.LG])
    While deep neural networks are capable of achieving state-of-the-art performance in various domains, their training typically requires iterating for many passes over the dataset. However, due to computational and memory constraints and potential privacy concerns, storing and accessing all the data is impractical in many real-world scenarios where the data arrives in a stream. In this paper, we investigate the problem of one-pass learning, in which a model is trained on sequentially arriving data without retraining on previous datapoints. Motivated by the increasing use of overparameterized models, we develop Orthogonal Recursive Fitting (ORFit), an algorithm for one-pass learning which seeks to perfectly fit every new datapoint while changing the parameters in a direction that causes the least change to the predictions on previous datapoints. By doing so, we bridge two seemingly distinct algorithms in adaptive filtering and machine learning, namely the recursive least-squares (RLS) algorithm and orthogonal gradient descent (OGD). Our algorithm uses the memory efficiently by exploiting the structure of the streaming data via an incremental principal component analysis (IPCA). Further, we show that, for overparameterized linear models, the parameter vector obtained by our algorithm is what stochastic gradient descent (SGD) would converge to in the standard multi-pass setting. Finally, we generalize the results to the nonlinear setting for highly overparameterized models, relevant for deep learning. Our experiments show the effectiveness of the proposed method compared to the baselines.
    Structural Similarity for Improved Transfer in Reinforcement Learning. (arXiv:2207.13813v1 [cs.LG])
    Transfer learning is an increasingly common approach for developing performant RL agents. However, it is not well understood how to define the relationship between the source and target tasks, and how this relationship contributes to successful transfer. We present an algorithm called Structural Similarity for Two MDPS, or SS2, that calculates a state similarity measure for states in two finite MDPs based on previously developed bisimulation metrics, and show that the measure satisfies properties of a distance metric. Then, through empirical results with GridWorld navigation tasks, we provide evidence that the distance measure can be used to improve transfer performance for Q-Learning agents over previous implementations.
    Remote Medication Status Prediction for Individuals with Parkinson's Disease using Time-series Data from Smartphones. (arXiv:2207.13700v1 [cs.LG])
    Medication for neurological diseases such as the Parkinson's disease usually happens remotely at home, away from hospitals. Such out-of-lab environments pose challenges in collecting timely and accurate health status data using the limited professional care devices for health condition analysis, medication adherence measurement and future dose or treatment planning. Individual differences in behavioral signals collected from wearable sensors also lead to difficulties in adopting current general machine learning analysis pipelines. To address these challenges, we present a method for predicting medication status of Parkinson's disease patients using the public mPower dataset, which contains 62,182 remote multi-modal test records collected on smartphones from 487 patients. The proposed method shows promising results in predicting three medication status objectively: Before Medication (AUC=0.95), After Medication (AUC=0.958), and Another Time (AUC=0.976) by examining patient-wise historical records with the attention weights learned through a Transformer model. We believe our method provides an innovative way for personalized remote health sensing in a timely and objective fashion which could benefit a broad range of similar applications.
    Predicting the Output Structure of Sparse Matrix Multiplication with Sampled Compression Ratio. (arXiv:2207.13848v1 [cs.DC])
    Sparse general matrix multiplication (SpGEMM) is a fundamental building block in numerous scientific applications. One critical task of SpGEMM is to compute or predict the structure of the output matrix (i.e., the number of nonzero elements per output row) for efficient memory allocation and load balance, which impact the overall performance of SpGEMM. Existing work either precisely calculates the output structure or adopts upper-bound or sampling-based methods to predict the output structure. However, these methods either take much execution time or are not accurate enough. In this paper, we propose a novel sampling-based method with better accuracy and low costs compared to the existing sampling-based method. The proposed method first predicts the compression ratio of SpGEMM by leveraging the number of intermediate products (denoted as FLOP) and the number of nonzero elements (denoted as NNZ) of the same sampled result matrix. And then, the predicted output structure is obtained by dividing the FLOP per output row by the predicted compression ratio. We also propose a reference design of the existing sampling-based method with optimized computing overheads to demonstrate the better accuracy of the proposed method. We construct 625 test cases with various matrix dimensions and sparse structures to evaluate the prediction accuracy. Experimental results show that the absolute relative errors of the proposed method and the reference design are 1.56\% and 8.12\%, respectively, on average, and 25\% and 156\%, respectively, in the worst case.
    Extraction of Vascular Wall in Carotid Ultrasound via a Novel Boundary-Delineation Network. (arXiv:2207.13868v1 [eess.IV])
    Ultrasound imaging plays an important role in the diagnosis of vascular lesions. Accurate segmentation of the vascular wall is important for the prevention, diagnosis and treatment of vascular diseases. However, existing methods have inaccurate localization of the vascular wall boundary. Segmentation errors occur in discontinuous vascular wall boundaries and dark boundaries. To overcome these problems, we propose a new boundary-delineation network (BDNet). We use the boundary refinement module to re-delineate the boundary of the vascular wall to obtain the correct boundary location. We designed the feature extraction module to extract and fuse multi-scale features and different receptive field features to solve the problem of dark boundaries and discontinuous boundaries. We use a new loss function to optimize the model. The interference of class imbalance on model optimization is prevented to obtain finer and smoother boundaries. Finally, to facilitate clinical applications, we design the model to be lightweight. Experimental results show that our model achieves the best segmentation results and significantly reduces memory consumption compared to existing models for the dataset.
    Learning to Assess Danger from Movies for Cooperative Escape Planning in Hazardous Environments. (arXiv:2207.13791v1 [cs.RO])
    There has been a plethora of work towards improving robot perception and navigation, yet their application in hazardous environments, like during a fire or an earthquake, is still at a nascent stage. We hypothesize two key challenges here: first, it is difficult to replicate such scenarios in the real world, which is necessary for training and testing purposes. Second, current systems are not fully able to take advantage of the rich multi-modal data available in such hazardous environments. To address the first challenge, we propose to harness the enormous amount of visual content available in the form of movies and TV shows, and develop a dataset that can represent hazardous environments encountered in the real world. The data is annotated with high-level danger ratings for realistic disaster images, and corresponding keywords are provided that summarize the content of the scene. In response to the second challenge, we propose a multi-modal danger estimation pipeline for collaborative human-robot escape scenarios. Our Bayesian framework improves danger estimation by fusing information from robot's camera sensor and language inputs from the human. Furthermore, we augment the estimation module with a risk-aware planner that helps in identifying safer paths out of the dangerous environment. Through extensive simulations, we exhibit the advantages of our multi-modal perception framework that gets translated into tangible benefits such as higher success rate in a collaborative human-robot mission.
    Multi-Objective Provisioning of Network Slices using Deep Reinforcement Learning. (arXiv:2207.13821v1 [cs.NI])
    Network Slicing (NS) is crucial for efficiently enabling divergent network applications in next generation networks. Nonetheless, the complex Quality of Service (QoS) requirements and diverse heterogeneity in network services entails high computational time for Network Slice Provisioning (NSP) optimization. The legacy optimization methods are challenging to meet the low latency and high reliability of network applications. To this end, we model the real-time NSP as an Online Network Slice Provisioning (ONSP) problem. Specifically, we formulate the ONSP problem as an online Multi-Objective Integer Programming Optimization (MOIPO) problem. Then, we approximate the solution of the MOIPO problem by applying the Proximal Policy Optimization (PPO) method to the traffic demand prediction. Our simulation results show the effectiveness of the proposed method compared to the state-of-the-art MOIPO solvers with a lower SLA violation rate and network operation cost.
    Calibrate: Interactive Analysis of Probabilistic Model Output. (arXiv:2207.13770v1 [cs.HC])
    Analyzing classification model performance is a crucial task for machine learning practitioners. While practitioners often use count-based metrics derived from confusion matrices, like accuracy, many applications, such as weather prediction, sports betting, or patient risk prediction, rely on a classifier's predicted probabilities rather than predicted labels. In these instances, practitioners are concerned with producing a calibrated model, that is, one which outputs probabilities that reflect those of the true distribution. Model calibration is often analyzed visually, through static reliability diagrams, however, the traditional calibration visualization may suffer from a variety of drawbacks due to the strong aggregations it necessitates. Furthermore, count-based approaches are unable to sufficiently analyze model calibration. We present Calibrate, an interactive reliability diagram that addresses the aforementioned issues. Calibrate constructs a reliability diagram that is resistant to drawbacks in traditional approaches, and allows for interactive subgroup analysis and instance-level inspection. We demonstrate the utility of Calibrate through use cases on both real-world and synthetic data. We further validate Calibrate by presenting the results of a think-aloud experiment with data scientists who routinely analyze model calibration.  ( 2 min )
    Cross-Attention of Disentangled Modalities for 3D Human Mesh Recovery with Transformers. (arXiv:2207.13820v1 [cs.CV])
    Transformer encoder architectures have recently achieved state-of-the-art results on monocular 3D human mesh reconstruction, but they require a substantial number of parameters and expensive computations. Due to the large memory overhead and slow inference speed, it is difficult to deploy such models for practical use. In this paper, we propose a novel transformer encoder-decoder architecture for 3D human mesh reconstruction from a single image, called FastMETRO. We identify the performance bottleneck in the encoder-based transformers is caused by the token design which introduces high complexity interactions among input tokens. We disentangle the interactions via an encoder-decoder architecture, which allows our model to demand much fewer parameters and shorter inference time. In addition, we impose the prior knowledge of human body's morphological relationship via attention masking and mesh upsampling operations, which leads to faster convergence with higher accuracy. Our FastMETRO improves the Pareto-front of accuracy and efficiency, and clearly outperforms image-based methods on Human3.6M and 3DPW. Furthermore, we validate its generalizability on FreiHAND.  ( 2 min )
    Deep Learning-Based Acoustic Mosquito Detection in Noisy Conditions Using Trainable Kernels and Augmentations. (arXiv:2207.13843v1 [cs.SD])
    In this paper, we demonstrate a unique recipe to enhance the effectiveness of audio machine learning approaches by fusing pre-processing techniques into a deep learning model. Our solution accelerates training and inference performance by optimizing hyper-parameters through training instead of costly random searches to build a reliable mosquito detector from audio signals. The experiments and the results presented here are part of the MOS C submission of the ACM 2022 challenge. Our results outperform the published baseline by 212% on the unpublished test set. We believe that this is one of the best real-world examples of building a robust bio-acoustic system that provides reliable mosquito detection in noisy conditions.  ( 2 min )
    Label-Only Membership Inference Attack against Node-Level Graph Neural Networks. (arXiv:2207.13766v1 [cs.CR])
    Graph Neural Networks (GNNs), inspired by Convolutional Neural Networks (CNNs), aggregate the message of nodes' neighbors and structure information to acquire expressive representations of nodes for node classification, graph classification, and link prediction. Previous studies have indicated that GNNs are vulnerable to Membership Inference Attacks (MIAs), which infer whether a node is in the training data of GNNs and leak the node's private information, like the patient's disease history. The implementation of previous MIAs takes advantage of the models' probability output, which is infeasible if GNNs only provide the prediction label (label-only) for the input. In this paper, we propose a label-only MIA against GNNs for node classification with the help of GNNs' flexible prediction mechanism, e.g., obtaining the prediction label of one node even when neighbors' information is unavailable. Our attacking method achieves around 60\% accuracy, precision, and Area Under the Curve (AUC) for most datasets and GNN models, some of which are competitive or even better than state-of-the-art probability-based MIAs implemented under our environment and settings. Additionally, we analyze the influence of the sampling method, model selection approach, and overfitting level on the attack performance of our label-only MIA. Both of those factors have an impact on the attack performance. Then, we consider scenarios where assumptions about the adversary's additional dataset (shadow dataset) and extra information about the target model are relaxed. Even in those scenarios, our label-only MIA achieves a better attack performance in most cases. Finally, we explore the effectiveness of possible defenses, including Dropout, Regularization, Normalization, and Jumping knowledge. None of those four defenses prevent our attack completely.  ( 3 min )
    A Novel Data Augmentation Technique for Out-of-Distribution Sample Detection using Compounded Corruptions. (arXiv:2207.13916v1 [cs.CV])
    Modern deep neural network models are known to erroneously classify out-of-distribution (OOD) test data into one of the in-distribution (ID) training classes with high confidence. This can have disastrous consequences for safety-critical applications. A popular mitigation strategy is to train a separate classifier that can detect such OOD samples at the test time. In most practical settings OOD examples are not known at the train time, and hence a key question is: how to augment the ID data with synthetic OOD samples for training such an OOD detector? In this paper, we propose a novel Compounded Corruption technique for the OOD data augmentation termed CnC. One of the major advantages of CnC is that it does not require any hold-out data apart from the training set. Further, unlike current state-of-the-art (SOTA) techniques, CnC does not require backpropagation or ensembling at the test time, making our method much faster at inference. Our extensive comparison with 20 methods from the major conferences in last 4 years show that a model trained using CnC based data augmentation, significantly outperforms SOTA, both in terms of OOD detection accuracy as well as inference time. We include a detailed post-hoc analysis to investigate the reasons for the success of our method and identify higher relative entropy and diversity of CnC samples as probable causes. We also provide theoretical insights via a piece-wise decomposition analysis on a two-dimensional dataset to reveal (visually and quantitatively) that our approach leads to a tighter boundary around ID classes, leading to better detection of OOD samples. Source code link: https://github.com/cnc-ood  ( 3 min )
    Modelling non-reinforced preferences using selective attention. (arXiv:2207.13699v1 [cs.LG])
    How can artificial agents learn non-reinforced preferences to continuously adapt their behaviour to a changing environment? We decompose this question into two challenges: ($i$) encoding diverse memories and ($ii$) selectively attending to these for preference formation. Our proposed \emph{no}n-\emph{re}inforced preference learning mechanism using selective attention, \textsc{Nore}, addresses both by leveraging the agent's world model to collect a diverse set of experiences which are interleaved with imagined roll-outs to encode memories. These memories are selectively attended to, using attention and gating blocks, to update agent's preferences. We validate \textsc{Nore} in a modified OpenAI Gym FrozenLake environment (without any external signal) with and without volatility under a fixed model of the environment -- and compare its behaviour to \textsc{Pepper}, a Hebbian preference learning mechanism. We demonstrate that \textsc{Nore} provides a straightforward framework to induce exploratory preferences in the absence of external signals.  ( 2 min )
    Physical Pooling Functions in Graph Neural Networks for Molecular Property Prediction. (arXiv:2207.13779v1 [cs.LG])
    Graph neural networks (GNNs) are emerging in chemical engineering for the end-to-end learning of physicochemical properties based on molecular graphs. A key element of GNNs is the pooling function which combines atom feature vectors into molecular fingerprints. Most previous works use a standard pooling function to predict a variety of properties. However, unsuitable pooling functions can lead to unphysical GNNs that poorly generalize. We compare and select meaningful GNN pooling methods based on physical knowledge about the learned properties. The impact of physical pooling functions is demonstrated with molecular properties calculated from quantum mechanical computations. We also compare our results to the recent set2set pooling approach. We recommend using sum pooling for the prediction of properties that depend on molecular size and compare pooling functions for properties that are molecular size-independent. Overall, we show that the use of physical pooling functions significantly enhances generalization.  ( 2 min )
    Diversity Boosted Learning for Domain Generalization with Large Number of Domains. (arXiv:2207.13865v1 [cs.LG])
    Machine learning algorithms minimizing the average training loss usually suffer from poor generalization performance due to the greedy exploitation of correlations among the training data, which are not stable under distributional shifts. It inspires various works for domain generalization (DG), where a series of methods, such as Causal Matching and FISH, work by pairwise domain operations. They would need $O(n^2)$ pairwise domain operations with $n$ domains, where each one is often highly expensive. Moreover, while a common objective in the DG literature is to learn invariant representations against domain-induced spurious correlations, we highlight the importance of mitigating spurious correlations caused by objects. Based on the observation that diversity helps mitigate spurious correlations, we propose a Diversity boosted twO-level saMplIng framework (DOMI) utilizing Determinantal Point Processes (DPPs) to efficiently sample the most informative ones among large number of domains. We show that DOMI helps train robust models against spurious correlations from both domain-side and object-side, substantially enhancing the performance of the backbone DG algorithms on rotated MNIST, rotated Fashion MNIST, and iwildcam datasets.  ( 2 min )
    SoundChoice: Grapheme-to-Phoneme Models with Semantic Disambiguation. (arXiv:2207.13703v1 [cs.SD])
    End-to-end speech synthesis models directly convert the input characters into an audio representation (e.g., spectrograms). Despite their impressive performance, such models have difficulty disambiguating the pronunciations of identically spelled words. To mitigate this issue, a separate Grapheme-to-Phoneme (G2P) model can be employed to convert the characters into phonemes before synthesizing the audio. This paper proposes SoundChoice, a novel G2P architecture that processes entire sentences rather than operating at the word level. The proposed architecture takes advantage of a weighted homograph loss (that improves disambiguation), exploits curriculum learning (that gradually switches from word-level to sentence-level G2P), and integrates word embeddings from BERT (for further performance improvement). Moreover, the model inherits the best practices in speech recognition, including multi-task learning with Connectionist Temporal Classification (CTC) and beam search with an embedded language model. As a result, SoundChoice achieves a Phoneme Error Rate (PER) of 2.65% on whole-sentence transcription using data from LibriSpeech and Wikipedia. Index Terms grapheme-to-phoneme, speech synthesis, text-tospeech, phonetics, pronunciation, disambiguation.  ( 2 min )
    Adaptive Second Order Coresets for Data-efficient Machine Learning. (arXiv:2207.13887v1 [cs.LG])
    Training machine learning models on massive datasets incurs substantial computational costs. To alleviate such costs, there has been a sustained effort to develop data-efficient training methods that can carefully select subsets of the training examples that generalize on par with the full training data. However, existing methods are limited in providing theoretical guarantees for the quality of the models trained on the extracted subsets, and may perform poorly in practice. We propose AdaCore, a method that leverages the geometry of the data to extract subsets of the training examples for efficient machine learning. The key idea behind our method is to dynamically approximate the curvature of the loss function via an exponentially-averaged estimate of the Hessian to select weighted subsets (coresets) that provide a close approximation of the full gradient preconditioned with the Hessian. We prove rigorous guarantees for the convergence of various first and second-order methods applied to the subsets chosen by AdaCore. Our extensive experiments show that AdaCore extracts coresets with higher quality compared to baselines and speeds up training of convex and non-convex machine learning models, such as logistic regression and neural networks, by over 2.9x over the full data and 4.5x over random subsets.  ( 2 min )
    Towards Sleep Scoring Generalization Through Self-Supervised Meta-Learning. (arXiv:2207.13801v1 [cs.LG])
    In this work we introduce a novel meta-learning method for sleep scoring based on self-supervised learning. Our approach aims at building models for sleep scoring that can generalize across different patients and recording facilities, but do not require a further adaptation step to the target data. Towards this goal, we build our method on top of the Model Agnostic Meta-Learning (MAML) framework by incorporating a self-supervised learning (SSL) stage, and call it S2MAML. We show that S2MAML can significantly outperform MAML. The gain in performance comes from the SSL stage, which we base on a general purpose pseudo-task that limits the overfitting to the subject-specific patterns present in the training dataset. We show that S2MAML outperforms standard supervised learning and MAML on the SC, ST, ISRUC, UCD and CAP datasets.  ( 2 min )
  • Open

    Shift-Curvature, SGD, and Generalization. (arXiv:2108.09507v3 [stat.ML] UPDATED)
    A longstanding debate surrounds the related hypotheses that low-curvature minima generalize better, and that SGD discourages curvature. We offer a more complete and nuanced view in support of both. First, we show that curvature harms test performance through two new mechanisms, the shift-curvature and bias-curvature, in addition to a known parameter-covariance mechanism. The three curvature-mediated contributions to test performance are reparametrization-invariant although curvature is not. The shift in the shift-curvature is the line connecting train and test local minima, which differ due to dataset sampling or distribution shift. Although the shift is unknown at training time, the shift-curvature can still be mitigated by minimizing overall curvature. Second, we derive a new, explicit SGD steady-state distribution showing that SGD optimizes an effective potential related to but different from train loss, and that SGD noise mediates a trade-off between deep versus low-curvature regions of this effective potential. Third, combining our test performance analysis with the SGD steady state shows that for small SGD noise, the shift-curvature may be the most significant of the three mechanisms. Our experiments confirm the impact of shift-curvature on test loss, and further explore the relationship between SGD noise and curvature.
    Learning with Succinct Common Representation Based on Wyner's Common Information. (arXiv:1905.10945v2 [cs.LG] UPDATED)
    A new bimodal generative model is proposed for generating conditional and joint samples, accompanied with a training method with learning a succinct bottleneck representation. The proposed model, dubbed as the variational Wyner model, is designed based on two classical problems in network information theory -- distributed simulation and channel synthesis -- in which Wyner's common information arises as the fundamental limit on the succinctness of the common representation. The model is trained by minimizing the symmetric Kullback--Leibler divergence between variational and model distributions with regularization terms for common information, reconstruction consistency, and latent space matching terms, which is carried out via an adversarial density ratio estimation technique. The utility of the proposed approach is demonstrated through experiments for joint and conditional generation with synthetic and real-world datasets, as well as a challenging zero-shot image retrieval task.  ( 2 min )
    Pareto-optimal clustering with the primal deterministic information bottleneck. (arXiv:2204.02489v2 [cs.LG] UPDATED)
    At the heart of both lossy compression and clustering is a trade-off between the fidelity and size of the learned representation. Our goal is to map out and study the Pareto frontier that quantifies this trade-off. We focus on the optimization of the Deterministic Information Bottleneck (DIB) objective over the space of hard clusterings. To this end, we introduce the primal DIB problem, which we show results in a much richer frontier than its previously studied Lagrangian relaxation when optimized over discrete search spaces. We present an algorithm for mapping out the Pareto frontier of the primal DIB trade-off that is also applicable to other two-objective clustering problems. We study general properties of the Pareto frontier, and we give both analytic and numerical evidence for logarithmic sparsity of the frontier in general. We provide evidence that our algorithm has polynomial scaling despite the super-exponential search space, and additionally, we propose a modification to the algorithm that can be used where sampling noise is expected to be significant. Finally, we use our algorithm to map the DIB frontier of three different tasks: compressing the English alphabet, extracting informative color classes from natural images, and compressing a group theory-inspired dataset, revealing interesting features of frontier, and demonstrating how the structure of the frontier can be used for model selection with a focus on points previously hidden by the cloak of the convex hull.  ( 3 min )
    Regret Minimization and Convergence to Equilibria in General-sum Markov Games. (arXiv:2207.14211v1 [cs.LG])
    An abundance of recent impossibility results establish that regret minimization in Markov games with adversarial opponents is both statistically and computationally intractable. Nevertheless, none of these results preclude the possibility of regret minimization under the assumption that all parties adopt the same learning procedure. In this work, we present the first (to our knowledge) algorithm for learning in general-sum Markov games that provides sublinear regret guarantees when executed by all agents. The bounds we obtain are for swap regret, and thus, along the way, imply convergence to a correlated equilibrium. Our algorithm is decentralized, computationally efficient, and does not require any communication between agents. Our key observation is that online learning via policy optimization in Markov games essentially reduces to a form of weighted regret minimization, with unknown weights determined by the path length of the agents' policy sequence. Consequently, controlling the path length leads to weighted regret objectives for which sufficiently adaptive algorithms provide sublinear regret guarantees.  ( 2 min )
    The Familiarity Hypothesis: Explaining the Behavior of Deep Open Set Methods. (arXiv:2203.02486v4 [cs.CV] UPDATED)
    In many object recognition applications, the set of possible categories is an open set, and the deployed recognition system will encounter novel objects belonging to categories unseen during training. Detecting such "novel category" objects is usually formulated as an anomaly detection problem. Anomaly detection algorithms for feature-vector data identify anomalies as outliers, but outlier detection has not worked well in deep learning. Instead, methods based on the computed logits of visual object classifiers give state-of-the-art performance. This paper proposes the Familiarity Hypothesis that these methods succeed because they are detecting the absence of familiar learned features rather than the presence of novelty. This distinction is important, because familiarity-based detection will fail in many situations where novelty is present. For example when an image contains both a novel object and a familiar one, the familiarity score will be high, so the novel object will not be noticed. The paper reviews evidence from the literature and presents additional evidence from our own experiments that provide strong support for this hypothesis. The paper concludes with a discussion of whether familiarity-based detection is an inevitable consequence of representation learning.  ( 3 min )
    What Happens after SGD Reaches Zero Loss? --A Mathematical Framework. (arXiv:2110.06914v4 [cs.LG] UPDATED)
    Understanding the implicit bias of Stochastic Gradient Descent (SGD) is one of the key challenges in deep learning, especially for overparametrized models, where the local minimizers of the loss function $L$ can form a manifold. Intuitively, with a sufficiently small learning rate $\eta$, SGD tracks Gradient Descent (GD) until it gets close to such manifold, where the gradient noise prevents further convergence. In such a regime, Blanc et al. (2020) proved that SGD with label noise locally decreases a regularizer-like term, the sharpness of loss, $\mathrm{tr}[\nabla^2 L]$. The current paper gives a general framework for such analysis by adapting ideas from Katzenberger (1991). It allows in principle a complete characterization for the regularization effect of SGD around such manifold -- i.e., the "implicit bias" -- using a stochastic differential equation (SDE) describing the limiting dynamics of the parameters, which is determined jointly by the loss function and the noise covariance. This yields some new results: (1) a global analysis of the implicit bias valid for $\eta^{-2}$ steps, in contrast to the local analysis of Blanc et al. (2020) that is only valid for $\eta^{-1.6}$ steps and (2) allowing arbitrary noise covariance. As an application, we show with arbitrary large initialization, label noise SGD can always escape the kernel regime and only requires $O(\kappa\ln d)$ samples for learning an $\kappa$-sparse overparametrized linear model in $\mathbb{R}^d$ (Woodworth et al., 2020), while GD initialized in the kernel regime requires $\Omega(d)$ samples. This upper bound is minimax optimal and improves the previous $\tilde{O}(\kappa^2)$ upper bound (HaoChen et al., 2020).  ( 3 min )
    Online Inference for Mixture Model of Streaming Graph Signals with Non-White Excitation. (arXiv:2207.14019v1 [stat.ML])
    This paper considers a joint multi-graph inference and clustering problem for simultaneous inference of node centrality and association of graph signals with their graphs. We study a mixture model of filtered low pass graph signals with possibly non-white and low-rank excitation. While the mixture model is motivated from practical scenarios, it presents significant challenges to prior graph learning methods. As a remedy, we consider an inference problem focusing on the node centrality of graphs. We design an expectation-maximization (EM) algorithm with a unique low-rank plus sparse prior derived from low pass signal property. We propose a novel online EM algorithm for inference from streaming data. As an example, we extend the online algorithm to detect if the signals are generated from an abnormal graph. We show that the proposed algorithms converge to a stationary point of the maximum-a-posterior (MAP) problem. Numerical experiments support our analysis.  ( 2 min )
    Fast Online Changepoint Detection via Functional Pruning CUSUM statistics. (arXiv:2110.08205v3 [stat.ME] UPDATED)
    Many modern applications of online changepoint detection require the ability to process high-frequency observations, sometimes with limited available computational resources. Online algorithms for detecting a change in mean often involve using a moving window, or specifying the expected size of change. Such choices affect which changes the algorithms have most power to detect. We introduce an algorithm, Functional Online CuSUM (FOCuS), which is equivalent to running these earlier methods simultaneously for all sizes of window, or all possible values for the size of change. Our theoretical results give tight bounds on the expected computational cost per iteration of FOCuS, with this being logarithmic in the number of observations. We show how FOCuS can be applied to a number of different change in mean scenarios, and demonstrate its practical utility through its state-of-the art performance at detecting anomalous behaviour in computer server data.  ( 2 min )
    Differentially Private Learning of Hawkes Processes. (arXiv:2207.13741v1 [stat.ML])
    Hawkes processes have recently gained increasing attention from the machine learning community for their versatility in modeling event sequence data. While they have a rich history going back decades, some of their properties, such as sample complexity for learning the parameters and releasing differentially private versions, are yet to be thoroughly analyzed. In this work, we study standard Hawkes processes with background intensity $\mu$ and excitation function $\alpha e^{-\beta t}$. We provide both non-private and differentially private estimators of $\mu$ and $\alpha$, and obtain sample complexity results in both settings to quantify the cost of privacy. Our analysis exploits the strong mixing property of Hawkes processes and classical central limit theorem results for weakly dependent random variables. We validate our theoretical findings on both synthetic and real datasets.  ( 2 min )
    One-Pass Learning via Bridging Orthogonal Gradient Descent and Recursive Least-Squares. (arXiv:2207.13853v1 [cs.LG])
    While deep neural networks are capable of achieving state-of-the-art performance in various domains, their training typically requires iterating for many passes over the dataset. However, due to computational and memory constraints and potential privacy concerns, storing and accessing all the data is impractical in many real-world scenarios where the data arrives in a stream. In this paper, we investigate the problem of one-pass learning, in which a model is trained on sequentially arriving data without retraining on previous datapoints. Motivated by the increasing use of overparameterized models, we develop Orthogonal Recursive Fitting (ORFit), an algorithm for one-pass learning which seeks to perfectly fit every new datapoint while changing the parameters in a direction that causes the least change to the predictions on previous datapoints. By doing so, we bridge two seemingly distinct algorithms in adaptive filtering and machine learning, namely the recursive least-squares (RLS) algorithm and orthogonal gradient descent (OGD). Our algorithm uses the memory efficiently by exploiting the structure of the streaming data via an incremental principal component analysis (IPCA). Further, we show that, for overparameterized linear models, the parameter vector obtained by our algorithm is what stochastic gradient descent (SGD) would converge to in the standard multi-pass setting. Finally, we generalize the results to the nonlinear setting for highly overparameterized models, relevant for deep learning. Our experiments show the effectiveness of the proposed method compared to the baselines.  ( 3 min )
    Algorithmic Foundation of Deep X-Risk Optimization. (arXiv:2206.00439v4 [cs.LG] UPDATED)
    X-risk is a term introduced to represent a family of compositional measures or objectives, in which each data point is compared with a large number of items explicitly or implicitly for defining a risk function. It includes many widely used measures or objectives, e.g., AUROC, AUPRC, partial AUROC, NDCG, MAP, top-$K$ NDCG, top-$K$ MAP, listwise losses, p-norm push, top push, precision/recall at top $K$ positions, precision at a certain recall level, contrastive objectives, etc. While these non-decomposable measures/objectives and their optimization algorithms have been studied in the literature of machine learning, computer vision, information retrieval, and etc, optimizing these measures/objectives has encountered some unique challenges for deep learning. In this paper, we survey recent rigorous efforts for deep X-risk optimization (DXO) by focusing on its algorithmic foundation. We introduce a class of techniques for optimizing X-risks for deep learning. We formulate DXO into three special families of non-convex optimization problems belonging to non-convex min-max optimization, non-convex compositional optimization, and non-convex bilevel optimization, respectively. For each family of problems, we present some strong baseline algorithms and their complexities, which will motivate further research for improving the existing results. Discussions about the presented results and future studies are given at the end. Efficient algorithms for optimizing a variety of X-risks are implemented in the LibAUC library at www.libauc.org.  ( 3 min )
    Modeling Item Response Theory with Stochastic Variational Inference. (arXiv:2108.11579v2 [cs.LG] UPDATED)
    Item Response Theory (IRT) is a ubiquitous model for understanding human behaviors and attitudes based on their responses to questions. Large modern datasets offer opportunities to capture more nuances in human behavior, potentially improving psychometric modeling leading to improved scientific understanding and public policy. However, while larger datasets allow for more flexible approaches, many contemporary algorithms for fitting IRT models may also have massive computational demands that forbid real-world application. To address this bottleneck, we introduce a variational Bayesian inference algorithm for IRT, and show that it is fast and scalable without sacrificing accuracy. Applying this method to five large-scale item response datasets from cognitive science and education yields higher log likelihoods and higher accuracy in imputing missing data than alternative inference algorithms. Using this new inference approach we then generalize IRT with expressive Bayesian models of responses, leveraging recent advances in deep learning to capture nonlinear item characteristic curves (ICC) with neural networks. Using an eigth-grade mathematics test from TIMSS, we show our nonlinear IRT models can capture interesting asymmetric ICCs. The algorithm implementation is open-source, and easily usable.  ( 3 min )
    Generative Modelling With Inverse Heat Dissipation. (arXiv:2206.13397v2 [cs.CV] UPDATED)
    While diffusion models have shown great success in image generation, their noise-inverting generative process does not explicitly consider the structure of images, such as their inherent multi-scale nature. Inspired by diffusion models and the desirability of coarse-to-fine modelling, we propose a new model that generates images through iteratively inverting the heat equation, a PDE that locally erases fine-scale information when run over the 2D plane of the image. In our novel methodology, the solution of the forward heat equation is interpreted as a variational approximation in a directed graphical model. We demonstrate promising image quality and point out emergent qualitative properties not seen in diffusion models, such as disentanglement of overall colour and shape in images and aspects of neural network interpretability. Spectral analysis on natural images positions our model as a type of dual to diffusion models and reveals implicit inductive biases in them.  ( 2 min )
    MarkerMap: nonlinear marker selection for single-cell studies. (arXiv:2207.14106v1 [stat.ML])
    Single-cell RNA-seq data allow the quantification of cell type differences across a growing set of biological contexts. However, pinpointing a small subset of genomic features explaining this variability can be ill-defined and computationally intractable. Here we introduce MarkerMap, a generative model for selecting minimal gene sets which are maximally informative of cell type origin and enable whole transcriptome reconstruction. MarkerMap provides a scalable framework for both supervised marker selection, aimed at identifying specific cell type populations, and unsupervised marker selection, aimed at gene expression imputation and reconstruction. We benchmark MarkerMap's competitive performance against previously published approaches on real single cell gene expression data sets. MarkerMap is available as a pip installable package, as a community resource aimed at developing explainable machine learning techniques for enhancing interpretability in single-cell studies.  ( 2 min )
    Hardness of Agnostically Learning Halfspaces from Worst-Case Lattice Problems. (arXiv:2207.14030v1 [cs.LG])
    We show hardness of improperly learning halfspaces in the agnostic model based on worst-case lattice problems, e.g., approximating shortest vectors within polynomial factors. In particular, we show that under this assumption there is no efficient algorithm that outputs any binary hypothesis, not necessarily a halfspace, achieving misclassfication error better than $\frac 1 2 - \epsilon$ even if the optimal misclassification error is as small is as small as $\delta$. Here, $\epsilon$ can be smaller than the inverse of any polynomial in the dimension and $\delta$ as small as $\mathrm{exp}\left(-\Omega\left(\log^{1-c}(d)\right)\right)$, where $0 < c < 1$ is an arbitrary constant and $d$ is the dimension. Previous hardness results [Daniely16] of this problem were based on average-case complexity assumptions, specifically, variants of Feige's random 3SAT hypothesis. Our work gives the first hardness for this problem based on a worst-case complexity assumption. It is inspired by a sequence of recent works showing hardness of learning well-separated Gaussian mixtures based on worst-case lattice problems.  ( 2 min )
    Differentiable Rule Induction with Learned Relational Features. (arXiv:2201.06515v2 [stat.ML] UPDATED)
    Rule-based decision models are attractive due to their interpretability. However, existing rule induction methods often result in long and consequently less interpretable rule models. This problem can often be attributed to the lack of appropriately expressive vocabulary, i.e., relevant predicates used as literals in the decision model. Most existing rule induction algorithms presume pre-defined literals, naturally decoupling the definition of the literals from the rule learning phase. In contrast, we propose the Relational Rule Network (R2N), a neural architecture that learns literals that represent a linear relationship among numerical input features along with the rules that use them. This approach opens the door to increasing the expressiveness of induced decision models by coupling literal learning directly with rule learning in an end-to-end differentiable fashion. On benchmark tasks, we show that these learned literals are simple enough to retain interpretability, yet improve prediction accuracy and provide sets of rules that are more concise compared to state-of-the-art rule induction algorithms.  ( 2 min )
    An iterative clustering algorithm for the Contextual Stochastic Block Model with optimality guarantees. (arXiv:2112.10467v2 [stat.ML] UPDATED)
    Real-world networks often come with side information that can help to improve the performance of network analysis tasks such as clustering. Despite a large number of empirical and theoretical studies conducted on network clustering methods during the past decade, the added value of side information and the methods used to incorporate it optimally in clustering algorithms are relatively less understood. We propose a new iterative algorithm to cluster networks with side information for nodes (in the form of covariates) and show that our algorithm is optimal under the Contextual Symmetric Stochastic Block Model. Our algorithm can be applied to general Contextual Stochastic Block Models and avoids hyperparameter tuning in contrast to previously proposed methods. We confirm our theoretical results on synthetic data experiments where our algorithm significantly outperforms other methods, and show that it can also be applied to signed graphs. Finally we demonstrate the practical interest of our method on real data.  ( 2 min )
    A general framework for multi-step ahead adaptive conformal heteroscedastic time series forecasting. (arXiv:2207.14219v1 [stat.ML])
    The exponential growth of machine learning (ML) has prompted a great deal of interest in quantifying the uncertainty of each prediction for a user-defined level of confidence. Reliable uncertainty quantification is crucial and is a step towards increased trust in AI results. It becomes especially important in high-stakes decision-making, where the true output must be within the confidence set with high probability. Conformal prediction (CP) is a distribution-free uncertainty quantification framework that works for any black-box model and yields prediction intervals (PIs) that are valid under the mild assumption of exchangeability. CP-type methods are gaining popularity due to being easy to implement and computationally cheap; however, the exchangeability assumption immediately excludes time series forecasting. Although recent papers tackle covariate shift, this is not enough for the general time series forecasting problem of producing H-step ahead valid PIs. To attain such a goal, we propose a new method called AEnbMIMOCQR (Adaptive ensemble batch multiinput multi-output conformalized quantile regression), which produces asymptotic valid PIs and is appropriate for heteroscedastic time series. We compare the proposed method against state-of-the-art competitive methods in the NN5 forecasting competition dataset. All the code and data to reproduce the experiments are made available  ( 2 min )
    A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecasts. (arXiv:2204.02028v2 [physics.ao-ph] UPDATED)
    Despite continuous improvements, precipitation forecasts are still not as accurate and reliable as those of other meteorological variables. A major contributing factor to this is that several key processes affecting precipitation distribution and intensity occur below the resolved scale of global weather models. Generative adversarial networks (GANs) have been demonstrated by the computer vision community to be successful at super-resolution problems, i.e., learning to add fine-scale structure to coarse images. Leinonen et al. (2020) previously applied a GAN to produce ensembles of reconstructed high-resolution atmospheric fields, given coarsened input data. In this paper, we demonstrate this approach can be extended to the more challenging problem of increasing the accuracy and resolution of comparatively low-resolution input from a weather forecasting model, using high-resolution radar measurements as a "ground truth". The neural network must learn to add resolution and structure whilst accounting for non-negligible forecast error. We show that GANs and VAE-GANs can match the statistical properties of state-of-the-art pointwise post-processing methods whilst creating high-resolution, spatially coherent precipitation maps. Our model compares favourably to the best existing downscaling methods in both pixel-wise and pooled CRPS scores, power spectrum information and rank histograms (used to assess calibration). We test our models and show that they perform in a range of scenarios, including heavy rainfall.  ( 3 min )
    Individual Privacy Accounting for Differentially Private Stochastic Gradient Descent. (arXiv:2206.02617v3 [cs.LG] UPDATED)
    Differentially private stochastic gradient descent (DP-SGD) is the workhorse algorithm for recent advances in private deep learning. It provides a single privacy guarantee to all datapoints in the dataset. We propose an efficient algorithm to compute privacy guarantees for individual examples when releasing models trained by DP-SGD. We use our algorithm to investigate individual privacy parameters across a number of datasets. We find that most examples enjoy stronger privacy guarantees than the worst-case bound. We further discover that the training loss and the privacy parameter of an example are well-correlated. This implies groups that are underserved in terms of model utility are simultaneously underserved in terms of privacy guarantee. For example, on CIFAR-10, the average $\epsilon$ of the class with the lowest test accuracy is 26.3% higher than that of the class with the highest accuracy. We also run membership inference attacks to show this reflects disparate empirical privacy risks.  ( 2 min )
    On the fast convergence of minibatch heavy ball momentum. (arXiv:2206.07553v2 [cs.LG] UPDATED)
    Simple stochastic momentum methods are widely used in machine learning optimization, but their good practical performance is at odds with an absence of theoretical guarantees of acceleration in the literature. In this work, we aim to close the gap between theory and practice by showing that stochastic heavy ball momentum, which can be interpreted as a randomized Kaczmarz algorithm with momentum, retains the fast linear rate of (deterministic) heavy ball momentum on quadratic optimization problems, at least when minibatching with a sufficiently large batch size is used. The analysis relies on carefully decomposing the momentum transition matrix, and using new spectral norm concentration bounds for products of independent random matrices. We provide numerical experiments to demonstrate that our bounds are reasonably sharp.  ( 2 min )

  • Open

    [D] Question on Bnns and MC-Dropout
    Hi, i am a student and i was reading about bayesian neural networks and mc-dropout. The book i am reading was published in 2018 ( which means written in 2017), and i know that 5 years are a long time in the deep learning field. i have a doubt that i would like to ask you. In MC-dropout we approximate the variational posterior as a Bernoulli distribution. doesn't this mean that with mc-dropout we partially lose the ability to adapt the variational distribution to the true a posteriori distribution when compared to a fully variational approach with a generalized mean-field approximation ? In general, is there any disadvantage to using mc-dropout as opposed to a fully variational approach? I was using tensorflow probability with DenseLocalReparameterization layers for a regression problem and now I am wondering whether what I was doing makes sense or if I have complicated my life for no reason and no advantage. sorry if it's a stupid question. ​ ah i would also ask if there is a limit in the dimension of the neural network under which mc-dropout is not a good approximation anymore. My nn is fairly small submitted by /u/ilrazziatore [link] [comments]  ( 88 min )
    [P] should I always favor using object tracking when annotating videos for segmentation over not using it?
    I'm currently working on CVAT to annotate videos of an object (the object is constant but the camera is moving) and I have the option of using Object Tracking feature, which tracks the object in every frame and annotates it, which in return will give more segmentation masks than annotating every N the frames manually. However the downside of using that feature is that in some frames the segmentation mask will not be layed out correctly or be very broken. So my question is, in that case should I still use Object Tracking despite the downside? On one hand I'll be getting more segmentation masks and therefore more data to train on. But on the other hand some of these masks will be faulty and might corrupt the model. submitted by /u/TypicalAngryRedditor [link] [comments]  ( 88 min )
    [D] Switching from Blockchain development to ML
    My main skills were solidity and web development. Obviously, I don't think my solidity skills will be useful here, but will my web development skills have some form of value in my journey? Or is it all python? Also, any recommendations or thoughts of moving from Blockchain to ML and AI are welcomed. Thanks submitted by /u/PlayboiCult [link] [comments]  ( 88 min )
    [D] Typical compute requirements for the training of a transformer-based recommender systems.
    I recently moved from NLP to recommender systems and I've noticed that most papers seem to not address how many resources it took to train their models. This has proven slightly frustrating as I'm currently trying to scope out what a manageable first proof of concept would look like. From my background in NLP, I know that the self-supervised training from scratch of such models takes a while but I'm not sure to what extent this is true for time series data. Has anyone here used something akin to Bert4rec or anything in the Transformer4rec library? What is your experience with your particular dataset/compute capability/model? submitted by /u/MC_Dropout [link] [comments]  ( 87 min )
    [D] TensorDock Core GPU Cloud — GPU servers from $0.29/hr
    Hello r/MachineLearning! I’m Jonathan from TensorDock. After 7 months in beta, we’re finally launching Core Cloud, our platform to deploy GPU virtual machines in as little as 45 seconds! I think you guys would find this as a nice alternative to other clouds for you to train your ML models. https://www.tensordock.com/product-core 🤔 Why? Training machine learning workloads at large clouds can be extremely expensive. This left us wondering, “how did cloud ever become more expensive than on-prem?” I’ve seen too many ML startups buy their own hardware. Cheaper dedicated servers with NVIDIA GPUs are not too hard to find, but they lack the functionality and scalability of the big clouds. We thought to ourselves, what if we built a platform that combines the functionality of the large clou…  ( 90 min )
    [D] Did anyone get into any AI residency right after the Bachelors/ undergraduate studies ?
    I am an undergraduate student from India. I am planning on applying for all the AI residencies. What is expected from an undergraduate applicant with good technical skills and academic record submitted by /u/Actual_banana_2002 [link] [comments]  ( 87 min )
    ML in Production Environments - problems and painpoints? [Discussion] [D]
    Hi all, I'm looking to learn/hear about problems and painpoints that individuals/teams are experiencing when deploying ML products to production? Any insight would be great as I'm keen to avoid headaches as much as possible. ​ Thanks submitted by /u/stoic-AI [link] [comments]  ( 119 min )
    [D] What are some techniques to disperse load across multiple different hardware?
    I just want to say I am very noob and that I need simple explanations to learn. And start with basic noob AI. And then one day I shall create my own unique kind of AI. I have multiple OpenCL 1.2 devices. Of varying speeds such as RX 6600 XT, RX 460, old HD 5450s, some NPUs, Mali GPUs, etc. I suppose I would like to do something like disperse a uneven number of NEURONS across several devices based on their speed. For devices to analyze different amounts of data and combine them into a shared project. How easy is that? Simple AI should be used to train other AIs, and produce data for other AIs. Ok, I just want to avoid a bottleneck involving using slower devices with faster ones. submitted by /u/Reddit-CEO-DontClick [link] [comments]  ( 88 min )
    How to approach Recommendation System Project [P]
    Hello , So during my internship I'll be working on building a recommendation system for an e-commerce website and this is the first time I'll be working on such project. I need some advices on how to approach such problems and if there any helpful resources I can use it will be much appreciated. Thank you. submitted by /u/AB3NZ [link] [comments]  ( 88 min )
    [D] Training on a bunch of hardware vs one kind of hardware?
    I don't know. I don't know the efficiency of training on multiple different hardware. I have a RX 6600 XT, but not a cluster. I do have a ton of other things laying around, an RX 6600 XT, RX 460, Intel Iris Pro, Malis, NPUs, RK3399, HD 5450s*. What is the efficiency of training on a bunch of low power, high power, specialized, and unrelated hardware? I do know they all run OpenCL 1.2 (often via open source drivers) very well. Wonder if something will bottleneck, I also happen to be bad at programming, for for personal projects, I can probably steal other people's code. But ideally, I suppose each part will do it's own thing and run at around 93% utilization? submitted by /u/Reddit-CEO-DontClick [link] [comments]  ( 88 min )
    [P] For Hearthstone and recsys fans, there is a kaggle competition for you :)
    Hello Reddit, I just published on kaggle a competition around recommender system applied in the context of hearthstone. https://www.kaggle.com/competitions/what-card-should-i-select-next I hope that you will enjoy it (I just dived again in hearthstone, and I am hooked to their battlegrounds mode) submitted by /u/jeanmidev [link] [comments]  ( 87 min )
    [D] Honest/Pragmatic thoughts of AutoML frameworks when it comes to (at least some ) daily work?
    Hi all. I work with ML and do a lot of data science on a daily basis and it’s a world I love. I’ve worked hard to get to the knowledge base that I have and I’m quite proud of it. I think a lot of us are. But making things happen and get results takes WORK - I need to make sure delivery is happening as well. Recently I’ve been exploring the AutoML frameworks from AWS and Google. And they are pretty much “dump some data, select a few options and ML magic happens in a box”. I came at them pretty negatively - cynically at least. And they are not perfect. If I sit down and work I can beat their outputs usually - but often only by a few points. And that will take me a good half day, or a good day, to make happen. The thing is - what I’m seeing is that while they are by no means perfect they are …. Entirely OK. For a lot of the work that I’m doing it’s not about fighting for every point of accuracy it’s about exploring or getting a gut feel for data or pulling out some key facets for a different group within a client. There are just as many times when accuracy and quality DOES matter - and in those cases I’m going to stay as close as possible to the models and the features. So - I find myself torn on my thoughts about them and was wondering what others thought? Are you staying away from them? Diving in fully? Using them in certain times/use-cases? submitted by /u/CarrotCakeandGin [link] [comments]  ( 118 min )
    [R] Ten Lessons of Implementing Recommendation Systems in Business
    FunCorp data science team has been long working on improving the user experience with machine learning. We've picked out key takeaways of that process. Following this article's advice, you will avoid a lot of mistakes when creating a recommendation system for your product. 1. Define a Goal that Really Contributes to the Business Tasks The global task of the recommendation system is to select a shortlist of content from a large catalog that is most suitable for a particular user. The content itself can be different — from products in the online store and articles to banking services. FunCorp product team works with the most interesting kind of content — we recommend memes. To do this, we rely on the history of the user’s interaction with the service. But “good recommendations” from a use…  ( 102 min )
    Chest X-ray Network :Simplified Transfer Learning for Chest Radiography Model Development [R]
    Researchers have added an additional step of pre training a generic image deep learning model on 800k chest x-ray images using supervised contrartsive learning using noisy labels from radiology reports. Image embeddings generated from this network can then be used for tasks like abnormality detection on a smaller set of chest x-ray images They have also released a chest foundation tool for generating image embeddings for chest x-ray. I liked the idea behind this paper and I believe it can also be extended to other medical imaging modalities like MR,CT. I have made a video on the same . Do checkout : https://youtu.be/lyhG6hivJqw submitted by /u/Sea-Photo5230 [link] [comments]  ( 120 min )
    [D] Building a paraphrasing tool like Quillbot
    Quillbot is an amazing tool for paraphrasing. I used it multiple times while writing peer-reviewed articles and my dissertation thesis. Unfortunately, there's no similar tool that I'm aware of for my language (Italian). I was wondering what kind of tools/AImodels I could leverage if I wanted to build it in my native language. Any suggestions are much appreciated. I'm a web developer with some basic knowledge of AI, ML, and statistics, so you can get as geeky as you like in your explanations :) submitted by /u/Kelith7 [link] [comments]  ( 88 min )
    [D] How important is text preprocessing nowadays with transformer models available?
    Hi everyone! The headline already sums it up pretty much. Do we still really need stemming, cleaning etc. as we used to or are the transformer models good and big enough to handle raw data nowadays? Thanks a lot! submitted by /u/kermitai [link] [comments]  ( 91 min )
    [R] [P] FL_PyTorch: Optimization Research Simulator for Federated Learning is publicly available on GitHub.
    FL_PyTorch: Optimization Research Simulator for Federated Learning is publicly available on GitHub. https://burlachenkok.github.io/FL_PyTorch-Available-As-Open-Source/ Repository: https://github.com/burlachenkok/flpytorch Slack Workspace: https://fl-pytorch.slack.com/ The invitation Link: https://join.slack.com/t/fl-pytorch/shared_invite/zt-1cjkjct9c-1wuFdrbVT4LcrAcjyj_gBw The arXiv link for the paper: https://arxiv.org/abs/2202.03099 FL_PyTorch is a suite of open-source software written in python that builds on top of one of the most popular research Deep Learning (DL) frameworks PyTorch. We built FL_PyTorch as a research simulator for FL to enable fast development, prototyping, and experimenting with new and existing FL optimization algorithms. Our system supports abstractions that provide researchers with sufficient flexibility to experiment with existing and novel approaches to advance the state-of-the-art. The work is in proceedings of the 2nd International Workshop on Distributed Machine Learning DistributedML 2021. submitted by /u/bruziuz [link] [comments]  ( 88 min )
    [D]What are some common sticking points in this field?
    Many people try to improve but either quit or get stuck real quick and not able to advance to the next level in this field. From your experience and perspective, what are the most common things that need to learned for practitioners to get over the hump? submitted by /u/THE_REAL_ODB [link] [comments]  ( 121 min )
    [D] Naming convention: `train!` or `fit!` for the API of a ML library ?
    I am deeply undecided to name the step where parameters of a model are learned from data in the API of my ML library `train!(model,X,[Y])` or `fit!(model,X,[Y])`. I would intuitively prefer the first, as makes somehow explicit that we are learning something with experience, but `train/fit` seems to be more common... What would you choose ? PS: the exclamation mark is due to another convention in Julia where functions that change their arguments - the model object in my case - ends with an exclamation mark submitted by /u/alobianco [link] [comments]  ( 88 min )
    [R] Blog post summarizing undergraduate thesis work
    Hey everyone! I just published a blog post today that summarizes my undergraduate thesis work. The thesis topic is a multi-network approach to minimize overfitting to noisy data. Here is a link to the article. Any feedback or questions would be really appreciated. Thanks! submitted by /u/ryxu [link] [comments]  ( 87 min )
    [D] Influence of cognitive science on ML. Worth learning?
    Often ML algorithms (especially DL) motivate their ideas with notions from cognitive science. These when presented often seem to be reasonable as someone who is not well versed on the subject. A part of me wants to more explicitly learn this and am considering taking a class on it. the opportunity cost being not being able to explore a topic like signal processing that is next on my list of topics to self-explore. (already have a graduate degree in CS). tl;dr is cognitive science a class worth taking? has being informed in this field helped in ML or AI? or life in general? submitted by /u/mathuwthrow [link] [comments]  ( 88 min )
  • Open

    "Man" created on pixelz.ai
    submitted by /u/PixelzJ [link] [comments]  ( 85 min )
    I made a program that is capable to solve logic problems
    First, you need to supply it with the rules in the given environment, then pass it trough a function to compare those rules and get all direct implications of these functions. Then given an allegation you can get the implications of that allegation. here is a example of the program running ​ Here is the code of the example shown above: source code: https://github.com/Thiago099/Einstein submitted by /u/Small-Ad-1694 [link] [comments]  ( 86 min )
    Royalty/commercial free Generated backgrounds for Art.
    Hey, As an artist I am currently asking around if there are any AI programs out there that you can use for art backgrounds and whatever and it just occurred to me that do I actually own it or is it royalty/commercial free? Right now I am being cautious and like an answer because I learned that you can learn to use shortcuts to save time and struggles. submitted by /u/Bluefuchs [link] [comments]  ( 86 min )
    Eerie Deepfake Tech Turns Random Guy Into Angelina Jolie
    submitted by /u/Tao_Dragon [link] [comments]  ( 86 min )
    Low barrier entry conversational bot design options?
    Having taken a couple of months to poke around with Replika.ai and checking other similar products like Kuki, I'm interested in crafting my own "robot companion" but I have no real knowledge of how to set up an AI. Are there any good options for someone who wants to make a bot, but doesn't really know the ins-and-outs of the design process? Open source would be my preference. submitted by /u/micah1_8 [link] [comments]  ( 86 min )
    Artificial Intelligence Discovers Alternative Physics
    submitted by /u/sasksean [link] [comments]  ( 86 min )
    A dataset of global AI/ML salaries in the Public Domain
    This is a project to simply collect as many salary information in the whole AI/ML job space and make it all public for everyone to access and use (researchers, jobseekers, recruiters, etc.). The dataset can be found here: https://salaries.ai-jobs.net/download/ submitted by /u/ai_jobs [link] [comments]  ( 86 min )
    Need a career advice to clearly understand appreciate AI advancements.
    Sorry, if I'm too long, I just can't pin down what exactly I want. Also is the flair right? When I was in my high school, I fell in love with physics: reading Feynman, watching a lot of science videos. I was just obsessed how each idea peels out deeper and deeper understanding of how nature works. Now I expected something of similar grandiosity in AI. Definitely, AI has grown up a lot, and we have uncovered a lot of ideas. But as I entered an undergrad course, I realized all I have to deal with are just the very popular models like Classifiers and Clustering models. It felt stale. I almost gave up, thinking AI is just a bunch of loose ideas that somehow worked, until I found a book in our library that has exactly what I wanted. I hope I had read that book before anything at all. (Deepak Khemani's A first course in Artificial intelligence. I fricking love how he strings up a lot of the loose concepts in CS into a single Feynman-esque narrative.) What I want is an insightful understanding of the field, its developments and its findings that I can speak hours long; But I can't find a way to pursue it. Nor I can find a way in which I can make a good career out of it. I don't think hirers would value what I want. submitted by /u/Neuroth [link] [comments]  ( 87 min )
    Disco Diffusion AI Art Tutorial Quickstudies #3 Models
    submitted by /u/prfitofthesngularity [link] [comments]  ( 86 min )
    Can NFT finance an AI ?
    An artificial intelligence project needs to be funded and needs resources. An NFT collection is created with the name TELOS MASK The collection is being presented to a competition currently underway, and needs supporters. To support the project, you can register here to receive 100 free tokens to use for voting. wish me luck and vote if you like it or at least talk to the AI to see if it deserves your attention submitted by /u/metaquid [link] [comments]  ( 86 min )
    A Tale of Two “AI” Companies
    submitted by /u/bendee983 [link] [comments]  ( 86 min )
    SimSimi sus 🤨
    submitted by /u/ChooChooWaah [link] [comments]  ( 92 min )
    Are there any public use AI bots that could potentially become great songwriting collaborators?
    I'm a songwriter and looking to collaborate with the worlds future artists. submitted by /u/BigOlDumbCunt [link] [comments]  ( 85 min )
    Cohere AI Hackathon
    Join us for Cohere AI hackathon, where you will use one of the world's most powerful NLP engines to build applications based on large language models. We are waiting for you on 19-21st August at lablab.ai, so that you can already start implementing your innovative projects that will radically change the world in the near future! Cohere experts during workshops, keynotes, and mentoring sessions - will do their best to quickly and efficiently onboard you to the advanced NLP model that leads the future! Who can participate? Industry experts with coding and data science experience People with other types of domain knowledge that want to understand & explore AI Register now - it's totally free! Cohere AI Hackathon submitted by /u/zakrzzz [link] [comments]  ( 86 min )
    The Best Machine Learning Courses on Udemy (2022)
    submitted by /u/Jan_Prince [link] [comments]  ( 86 min )
    What are the requirements of laptop for engineering.
    I'm gonna join AI and ML engineering this year and I would like to know what is a good laptop. Do I have to have a GPU for the laptop? submitted by /u/SomewhereBrilliant85 [link] [comments]  ( 87 min )
    Neural Network to predict how long a job will take to repair
    The company I work for offers a large amount of aftermarket services for the products that they sell. The biggest one in terms of volume is the repairs, where a customer will send goods back to our facility, where a skilled operator will assess the job for damage and report back what parts they need to fix it. Once they complete the work they book their time to an order in our ERP system. The time taken to repair a job will vary each time and can range for anywhere between a couple hours to a whole day. I work in the production planning department where we are responsible for creating a weekly plan for each of the different areas of the facility. We have set times for each of the jobs however, these tend to be an average of all the time booked and therefore are more likely to be inaccurate than accurate. I thought this might be a good problem for a neural network where I could take the historical data (just under 1m rows) and use it to predict how long a future order might take. I followed some tutorials on tensorflow and managed to create a neural network and initially had some success getting it to predict around 60% of the orders in the test data correctly. I’ve now hit a brick wall with getting the model to be anymore accurate and I feel like I’m just randomly changing the hyper parameters hoping for better results. This is my first time working with AI and I’m lost on what to do next to improve the accuracy. Does anyone have any advice on what approach I might follow to improve the model further? submitted by /u/-hilcf [link] [comments]  ( 92 min )
    What are the biggest hurdles in annotating data well?
    Hi everyone! I am very keen to know what are the biggest hurdles for you nowadays when annotating data for NLP? There is so much great annotation software for already that I am wondering if there are any big obstacles left. Do you have any insights from some of your projects or day to day work maybe? Thanks a lot! submitted by /u/kermitai [link] [comments]  ( 86 min )
    FL_PyTorch: Optimization Research Simulator for Federated Learning is publicly available on GitHub.
    FL_PyTorch: Optimization Research Simulator for Federated Learning is publicly available on GitHub. https://burlachenkok.github.io/FL_PyTorch-Available-As-Open-Source/ Repository: https://github.com/burlachenkok/flpytorch Slack Workspace: https://fl-pytorch.slack.com/ The invitation Link: https://join.slack.com/t/fl-pytorch/shared_invite/zt-1cjkjct9c-1wuFdrbVT4LcrAcjyj_gBw The arXiv link for the paper: https://arxiv.org/abs/2202.03099 FL_PyTorch is a suite of open-source software written in python that builds on top of one of the most popular research Deep Learning (DL) frameworks PyTorch. We built FL_PyTorch as a research simulator for FL to enable fast development, prototyping, and experimenting with new and existing FL optimization algorithms. Our system supports abstractions that provide researchers with sufficient flexibility to experiment with existing and novel approaches to advance the state-of-the-art. The work is in proceedings of the 2nd International Workshop on Distributed Machine Learning DistributedML 2021. submitted by /u/bruziuz [link] [comments]  ( 86 min )
    The Most Beautiful Space Visualization on the Internet | 4K UHD | 24 FPS
    submitted by /u/Available_Tadpole829 [link] [comments]  ( 90 min )
  • Open

    Why do we need experience replay if the algorithm is epsilon-greedy ?
    Hey ! I am new to deep Q learning and am confused about something. I understand that experience replay allows to get rid of of the correlation between consecutive states, thus avoiding to fall in local optima. But doesn't epsilon already solve this problem ? If we start by taking random actions, won't we explore most of the state space and thus avoid falling in local optima ? The difference I see is that using experience replay the neural net is not fed several similar states in a row while it is training, but how does that prevent falling in local optima ? submitted by /u/youneskamel2 [link] [comments]  ( 93 min )
    "Semi-analytical Industrial Cooling System Model for Reinforcement Learning", Chervonyi et al 2022 {DM} (cooling simulated Google datacenters)
    submitted by /u/gwern [link] [comments]  ( 86 min )
    "PI-ARS: Accelerating Evolution-Learned Visual-Locomotion with Predictive Information Representations", Lee et al 2022 {G} (evolving policy on top of contrastive+reward-predictive NN)
    submitted by /u/gwern [link] [comments]  ( 93 min )
    "Multi-Objective Hyperparameter Optimization -- An Overview", Karl et al 2022
    submitted by /u/gwern [link] [comments]  ( 93 min )
    "Learning with Combinatorial Optimization Layers: a Probabilistic Approach", Dalle et al 2022
    submitted by /u/gwern [link] [comments]  ( 93 min )
    Mujoco action space
    Does anyone happen to know what happens when you submit an action outside of the action space in mujoco? I.e. submit a 1.5 when the range is [-1,1]. Couldn't seem to find this anywhere in the docs. submitted by /u/VirtualHat [link] [comments]  ( 86 min )
    made an RL algo for modeling episode reward directly
    I came across a problem for when modeling per step reward became very disconnected with the actual final episode reward, which is usually what we really care about. This can happen for any number of reasons... where a decrease in error, doesn't always translate to an increase in final episode reward in a straightforward manner. Of course very generally speaking, one can expect episode reward to go up as loss decreases, but in practice we might have a few different models with the same loss, and actually perform very differently in some environments. Usually the more complex the environment, the more this becomes an issue. That and per sample methods usually require many samples, a time horizon variable, and other hyperparameters that can be hard to set correctly. Obviously not suited for every problem (ex. environments that are expensive to sample from or have some sort of time constraint) but for certain problems you might find it useful. Interested for people to try it out and give some feedback. https://github.com/ben-arnao/OnGrad submitted by /u/Yogi_DMT [link] [comments]  ( 87 min )
    MuJoCo: How can i change friction properties of geom with default class?
    Hi, I'm creating Mujoco environment to test walking robot software. I want to create many types of ground in one simulation to check how he will adapt. For starters, I've tried to manipulate friction attribute in geom element. I created two hfields, placed them next to each other and created new default class: One hfield geom in simulation have default class second one have class="geom_frictionless", but friction in simulation is same on both surfaces. I must add that geom's material in simulation is changing so part of custom class attributes works. Anyone knows why I can't override friction element? submitted by /u/Kwach00 [link] [comments]  ( 87 min )
    What is the current SOTA for On-policy RL?
    The on-policy RL community does not seem to release the popular SOTA after the PPO? submitted by /u/CeyaoZhang [link] [comments]  ( 87 min )
  • Open

    1,650+ Global Interns Gleam With NVIDIA Green
    A record number of interns calls for a record-sized celebration. In our largest contingent ever, over 1,650 interns from 350+ schools started with NVIDIA worldwide over the past year. Amidst busy work days tackling real-world projects across engineering, automation, robotics and more, the group’s also finishing up a three-day celebration, culminating today with National Intern Read article > The post 1,650+ Global Interns Gleam With NVIDIA Green appeared first on NVIDIA Blog.  ( 5 min )
    Pony.ai Express: New Autonomous Trucking Collaboration Powered by NVIDIA DRIVE Orin
    More than 160 years after the legendary Pony Express delivery service completed its first route, a new generation of “Pony”-emblazoned vehicles are taking an AI-powered approach to long-haul delivery. Autonomous driving company Pony.ai announced today a partnership with SANY Heavy Truck (SANY), China’s largest heavy equipment manufacturer, to jointly develop level 4 autonomous trucks. The Read article > The post Pony.ai Express: New Autonomous Trucking Collaboration Powered by NVIDIA DRIVE Orin appeared first on NVIDIA Blog.  ( 5 min )
    Welcome Back, Commander: ‘Command & Conquer Remastered Collection’ Joins GeForce NOW
    Take a trip down memory lane this week with an instantly recognizable classic, Command & Conquer Remastered Collection, joining the nearly 20 Electronic Arts games streaming from the GeForce NOW library. Speaking of remastered, GeForce NOW members can enhance their gameplay further with improved resolution scaling in the 2.0.43 app update. When the feature is Read article > The post Welcome Back, Commander: ‘Command & Conquer Remastered Collection’ Joins GeForce NOW appeared first on NVIDIA Blog.  ( 5 min )
    NVIDIA Studio Laptops Offer Students AI, Creative Capabilities That Are Best in… Class
    Selecting the right laptop is a lot like trying to pick the right major. Both can be challenging tasks where choosing wrongly costs countless hours. But pick the right one, and graduation is just around the corner. The tips below can help the next generation of artists select the ideal NVIDIA Studio laptop to maximize performance for the critical workload demands of their unique creative fields — all within budget. The post NVIDIA Studio Laptops Offer Students AI, Creative Capabilities That Are Best in… Class appeared first on NVIDIA Blog.  ( 10 min )
    How’s That? Startup Ups Game for Cricket, Football and More With Vision AI
    Sports produce a slew of data. In a game of cricket, for example, each play generates millions of video-frame data points for a sports analyst to scrutinize, according to Masoumeh Izadi, managing director of deep-tech startup TVConal. The Singapore-based company uses NVIDIA AI and computer vision to power its sports video analytics platform, which enables Read article > The post How’s That? Startup Ups Game for Cricket, Football and More With Vision AI appeared first on NVIDIA Blog.  ( 6 min )
  • Open

    New hardware offers faster computation for artificial intelligence, with much less energy
    Engineers working on “analog deep learning” have found a way to propel protons through solids at unprecedented speeds.  ( 9 min )
  • Open

    Mastering MLOps: Live Model Deployment & Inference Course with Stefan Krawczyk
    Sponsored Post AI & Machine Learning now power most product experiences even beyond those of the big technology companies. Today, your models must perform and function correctly to ultimately deliver business value. The cost of deploying a slow or bad model, or not detecting undesirable behavior quickly, could significantly impact customer experience and the business’ […] The post Mastering MLOps: Live Model Deployment & Inference Course with Stefan Krawczyk appeared first on Machine Learning Mastery.  ( 10 min )
  • Open

    Humanizing Artificial Intelligence: An Approach Towards Future
    Artificial intelligence is a very complex topic that has been studied by many people in different fields. Though it has been thought to be…  ( 11 min )
  • Open

    dont get that neural link bs
    submitted by /u/Ok_Base_2789 [link] [comments]  ( 85 min )
  • Open

    Galois theory without fields
    My previous post described Galois connections, and how they generalize a pattern first recognized in the context of Galois theory. This pattern can extended far afield of its initial application to fields and their extensions. For example, you could take a random variable X and think of the pair consisting of its distribution function F: […] Galois theory without fields first appeared on John D. Cook.  ( 5 min )
  • Open

    Two New Papers: Learning to Fling and Singulate Fabrics
    The system for our IROS 2022 paper on singulating layers of cloth with tactile sensing. In collaboration with my colleagues at Berkeley and CMU, we recently uploaded two papers to arXiv on robotic fabric manipulation: Efficiently Learning Single-Arm Fling Motions to Smooth Garments, for ISRR 2022. Learning to Singulate Layers of Cloth using Tactile Feedback, for IROS 2022. Robotic fabric (or cloth) manipulation is a recurring theme in my research, and these two papers continue the trend. The first paper, which we started a while back in Spring 2021, is about dynamic fabric manipulation; it can be thought of as an extension of our earlier ICRA papers on “Robots of the Lost Arc” and “Planar Robot Casting” while incorporating ideas from Huy Ha and Shuran Song’s legendary FlingBot paper. Wh…  ( 3 min )
  • Open

    The Value of Real-Time Data Visualization and Interpretation
    Data representation using graphics such as charts, plots, infographics, heat maps, bubble clouds, scatter plots, mekko charts are referred to as data visualization. Such visual displays and representations of information help communicate complex data relationships and data-driven insights in a way that makes it easy to understand and base decisions on. The post The Value of Real-Time Data Visualization and Interpretation appeared first on Data Science Central.  ( 19 min )
  • Open

    A generalized regionalization framework for geographical modelling and its application in spatial regression. (arXiv:2206.09429v2 [stat.ME] UPDATED)
    Models applied to geographic data face a trade-off between producing general results and capturing local variations due to spatial heterogeneity. Spatial modelling within carefully defined regions offers an intermediate position between global and local models. However, current spatial optimization approaches to delineate homogeneous regions consider the similarity of attribute values, thus unable to identify regions with similar data generation processes described by geographical models. We propose a generalized regionalization framework, which optimizes region delineation corresponding to a model with region-specific parameters. Within this framework, we introduce three regionalization algorithms, namely automatic zoning procedure (AZP), K-Models, and Regional-K-Models. We adopt an objective function that jointly minimizes modelling errors and the complexity of the region scheme. Results from regression experiments indicate that the K-Models algorithm reconstructs the regions better than the baseline, according to Rand index and mutual information measures. Our suggested framework contributes to better capturing processes exhibiting spatial heterogeneity and may be applied to a wide range of modelling scenarios.  ( 2 min )
    Object discovery and representation networks. (arXiv:2203.08777v3 [cs.CV] UPDATED)
    The promise of self-supervised learning (SSL) is to leverage large amounts of unlabeled data to solve complex tasks. While there has been excellent progress with simple, image-level learning, recent methods have shown the advantage of including knowledge of image structure. However, by introducing hand-crafted image segmentations to define regions of interest, or specialized augmentation strategies, these methods sacrifice the simplicity and generality that makes SSL so powerful. Instead, we propose a self-supervised learning paradigm that discovers this image structure by itself. Our method, Odin, couples object discovery and representation networks to discover meaningful image segmentations without any supervision. The resulting learning paradigm is simpler, less brittle, and more general, and achieves state-of-the-art transfer learning results for object detection and instance segmentation on COCO, and semantic segmentation on PASCAL and Cityscapes, while strongly surpassing supervised pre-training for video segmentation on DAVIS.  ( 2 min )
    Fast TreeSHAP: Accelerating SHAP Value Computation for Trees. (arXiv:2109.09847v3 [cs.LG] UPDATED)
    SHAP (SHapley Additive exPlanation) values are one of the leading tools for interpreting machine learning models, with strong theoretical guarantees (consistency, local accuracy) and a wide availability of implementations and use cases. Even though computing SHAP values takes exponential time in general, TreeSHAP takes polynomial time on tree-based models. While the speedup is significant, TreeSHAP can still dominate the computation time of industry-level machine learning solutions on datasets with millions or more entries, causing delays in post-hoc model diagnosis and interpretation service. In this paper we present two new algorithms, Fast TreeSHAP v1 and v2, designed to improve the computational efficiency of TreeSHAP for large datasets. We empirically find that Fast TreeSHAP v1 is 1.5x faster than TreeSHAP while keeping the memory cost unchanged. Similarly, Fast TreeSHAP v2 is 2.5x faster than TreeSHAP, at the cost of a slightly higher memory usage, thanks to the pre-computation of expensive TreeSHAP steps. We also show that Fast TreeSHAP v2 is well-suited for multi-time model interpretations, resulting in as high as 3x faster explanation of newly incoming samples.  ( 2 min )
    Evaluation of creating scoring opportunities for teammates in soccer via trajectory prediction. (arXiv:2206.01899v3 [cs.AI] UPDATED)
    Evaluating the individual movements for teammates in soccer players is crucial for assessing teamwork, scouting, and fan engagement. It has been said that players in a 90-min game do not have the ball for about 87 minutes on average. However, it has remained difficult to evaluate an attacking player without receiving the ball, and to reveal how movement contributes to the creation of scoring opportunities for teammates. In this paper, we evaluate players who create off-ball scoring opportunities by comparing actual movements with the reference movements generated via trajectory prediction. First, we predict the trajectories of players using a graph variational recurrent neural network that can accurately model the relationship between players and predict the long-term trajectory. Next, based on the difference in the modified off-ball evaluation index between the actual and the predicted trajectory as a reference, we evaluate how the actual movement contributes to scoring opportunity compared to the predicted movement. For verification, we examined the relationship with the annual salary, the goals, and the rating in the game by experts for all games of a team in a professional soccer league in a year. The results show that the annual salary and the proposed indicator correlated significantly, which could not be explained by the existing indicators and goals. Our results suggest the effectiveness of the proposed method as an indicator for a player without the ball to create a scoring chance for teammates.  ( 3 min )
    On generalization bounds for deep networks based on loss surface implicit regularization. (arXiv:2201.04545v2 [stat.ML] UPDATED)
    The classical statistical learning theory implies that fitting too many parameters leads to overfitting and poor performance. That modern deep neural networks generalize well despite a large number of parameters contradicts this finding and constitutes a major unsolved problem towards explaining the success of deep learning. While previous work focuses on the implicit regularization induced by stochastic gradient descent (SGD), we study here how the local geometry of the energy landscape around local minima affects the statistical properties of SGD with Gaussian gradient noise. We argue that under reasonable assumptions, the local geometry forces SGD to stay close to a low dimensional subspace and that this induces another form of implicit regularization and results in tighter bounds on the generalization error for deep neural networks. To derive generalization error bounds for neural networks, we first introduce a notion of stagnation sets around the local minima and impose a local essential convexity property of the population risk. Under these conditions, lower bounds for SGD to remain in these stagnation sets are derived. If stagnation occurs, we derive a bound on the generalization error of deep neural networks involving the spectral norms of the weight matrices but not the number of network parameters. Technically, our proofs are based on controlling the change of parameter values in the SGD iterates and local uniform convergence of the empirical loss functions based on the entropy of suitable neighborhoods around local minima.  ( 3 min )
    ShAPO: Implicit Representations for Multi-Object Shape, Appearance, and Pose Optimization. (arXiv:2207.13691v1 [cs.CV])
    Our method studies the complex task of object-centric 3D understanding from a single RGB-D observation. As it is an ill-posed problem, existing methods suffer from low performance for both 3D shape and 6D pose and size estimation in complex multi-object scenarios with occlusions. We present ShAPO, a method for joint multi-object detection, 3D textured reconstruction, 6D object pose and size estimation. Key to ShAPO is a single-shot pipeline to regress shape, appearance and pose latent codes along with the masks of each object instance, which is then further refined in a sparse-to-dense fashion. A novel disentangled shape and appearance database of priors is first learned to embed objects in their respective shape and appearance space. We also propose a novel, octree-based differentiable optimization step, allowing us to further improve object shape, pose and appearance simultaneously under the learned latent space, in an analysis-by-synthesis fashion. Our novel joint implicit textured object representation allows us to accurately identify and reconstruct novel unseen objects without having access to their 3D meshes. Through extensive experiments, we show that our method, trained on simulated indoor scenes, accurately regresses the shape, appearance and pose of novel objects in the real-world with minimal fine-tuning. Our method significantly out-performs all baselines on the NOCS dataset with an 8% absolute improvement in mAP for 6D pose estimation. Project page: https://zubair-irshad.github.io/projects/ShAPO.html  ( 3 min )
    Towards Clear Expectations for Uncertainty Estimation. (arXiv:2207.13341v1 [cs.LG])
    If Uncertainty Quantification (UQ) is crucial to achieve trustworthy Machine Learning (ML), most UQ methods suffer from disparate and inconsistent evaluation protocols. We claim this inconsistency results from the unclear requirements the community expects from UQ. This opinion paper offers a new perspective by specifying those requirements through five downstream tasks where we expect uncertainty scores to have substantial predictive power. We design these downstream tasks carefully to reflect real-life usage of ML models. On an example benchmark of 7 classification datasets, we did not observe statistical superiority of state-of-the-art intrinsic UQ methods against simple baselines. We believe that our findings question the very rationale of why we quantify uncertainty and call for a standardized protocol for UQ evaluation based on metrics proven to be relevant for the ML practitioner.  ( 2 min )
    Learning Multi-Object Dynamics with Compositional Neural Radiance Fields. (arXiv:2202.11855v3 [cs.CV] UPDATED)
    We present a method to learn compositional multi-object dynamics models from image observations based on implicit object encoders, Neural Radiance Fields (NeRFs), and graph neural networks. NeRFs have become a popular choice for representing scenes due to their strong 3D prior. However, most NeRF approaches are trained on a single scene, representing the whole scene with a global model, making generalization to novel scenes, containing different numbers of objects, challenging. Instead, we present a compositional, object-centric auto-encoder framework that maps multiple views of the scene to a set of latent vectors representing each object separately. The latent vectors parameterize individual NeRFs from which the scene can be reconstructed. Based on those latent vectors, we train a graph neural network dynamics model in the latent space to achieve compositionality for dynamics prediction. A key feature of our approach is that the latent vectors are forced to encode 3D information through the NeRF decoder, which enables us to incorporate structural priors in learning the dynamics models, making long-term predictions more stable compared to several baselines. Simulated and real world experiments show that our method can model and learn the dynamics of compositional scenes including rigid and deformable objects. Video: https://dannydriess.github.io/compnerfdyn/  ( 3 min )
    Neural Style Transfer and Unpaired Image-to-Image Translation to deal with the Domain Shift Problem on Spheroid Segmentation. (arXiv:2112.09043v2 [cs.CV] UPDATED)
    Background and objectives. Domain shift is a generalisation problem of machine learning models that occurs when the data distribution of the training set is different to the data distribution encountered by the model when it is deployed. This is common in the context of biomedical image segmentation due to the variance of experimental conditions, equipment, and capturing settings. In this work, we address this challenge by studying both neural style transfer algorithms and unpaired image-to-image translation methods in the context of the segmentation of tumour spheroids. Methods. We have illustrated the domain shift problem in the context of spheroid segmentation with 4 deep learning segmentation models that achieved an IoU over 97% when tested with images following the training distribution, but whose performance decreased up to an 84\% when applied to images captured under different conditions. In order to deal with this problem, we have explored 3 style transfer algorithms (NST, deep image analogy, and STROTSS), and 6 unpaired image-to-image translations algorithms (CycleGAN, DualGAN, ForkGAN, GANILLA, CUT, and FastCUT). These algorithms have been integrated into a high-level API that facilitates their application to other contexts where the domain-shift problem occurs. Results. We have considerably improved the performance of the 4 segmentation models when applied to images captured under different conditions by using both style transfer and image-to-image translation algorithms. In particular, there are 2 style transfer algorithms (NST and deep image analogy) and 1 unpaired image-to-image translations algorithm (CycleGAN) that improve the IoU of the models in a range from 0.24 to 76.07. Therefore, reaching a similar performance to the one obtained with the models are applied to images following the training distribution.  ( 3 min )
    Post-Train Adaptive MobileNet for Fast Anti-Spoofing. (arXiv:2207.13410v1 [cs.CV])
    Many applications require high accuracy of neural networks as well as low latency and user data privacy guaranty. Face anti-spoofing is one of such tasks. However, a single model might not give the best results for different device performance categories, while training multiple models is time consuming. In this work we present Post-Train Adaptive (PTA) block. Such a block is simple in structure and offers a drop-in replacement for the MobileNetV2 Inverted Residual block. The PTA block has multiple branches with different computation costs. The branch to execute can be selected on-demand and at runtime; thus, offering different inference times and configuration capability for multiple device tiers. Crucially, the model is trained once and can be easily reconfigured after training, even directly on a mobile device. In addition, the proposed approach shows substantially better overall performance in comparison to the original MobileNetV2 as tested on CelebA-Spoof dataset. Different PTA block configurations are sampled at training time, which also decreases overall wall-clock time needed to train the model. While we present computational results for the anti-spoofing problem, the MobileNetV2 with PTA blocks is applicable to any problem solvable with convolutional neural networks, which makes the results presented practically significant.  ( 2 min )
    Learned Label Aggregation for Weak Supervision. (arXiv:2207.13545v1 [cs.LG])
    The lack of labeled training data is the bottleneck of machine learning in many applications. To resolve the bottleneck, one promising direction is the data programming approach that aggregates different sources of weak supervision signals to generate labeled data easily. Data programming encodes each weak supervision source with a labeling function (LF), a user-provided program that predicts noisy labels. The quality of the generated labels depends on a label aggregation model that aggregates all noisy labels from all LFs to infer the ground-truth labels. Existing label aggregation methods typically rely on various assumptions and are not robust across datasets, as we will show empirically. We for the first time provide an analytical label aggregation method that makes minimum assumption and is optimal in minimizing a certain form of the averaged prediction error. Since the complexity of the analytical form is exponential, we train a model that learns to be the analytical method. Once trained, the model can be used for any unseen datasets and the model predicts the ground-truth labels for each dataset in a single forward pass in linear time. We show the model can be trained using synthetically generated data and design an effective architecture for the model. On 14 real-world datasets, our model significantly outperforms the best existing methods in both accuracy (by 3.5 points on average) and efficiency (by six times on average).  ( 3 min )
    Visualizing Confidence Intervals for Critical Point Probabilities in 2D Scalar Field Ensembles. (arXiv:2207.13661v1 [cs.HC])
    An important task in visualization is the extraction and highlighting of dominant features in data to support users in their analysis process. Topological methods are a well-known means of identifying such features in deterministic fields. However, many real-world phenomena studied today are the result of a chaotic system that cannot be fully described by a single simulation. Instead, the variability of such systems is usually captured with ensemble simulations that produce a variety of possible outcomes of the simulated process. The topological analysis of such ensemble data sets and uncertain data, in general, is less well studied. In this work, we present an approach for the computation and visual representation of confidence intervals for the occurrence probabilities of critical points in ensemble data sets. We demonstrate the added value of our approach over existing methods for critical point prediction in uncertain data on a synthetic data set and show its applicability to a data set from climate research.  ( 2 min )
    Statistically Efficient Advantage Learning for Offline Reinforcement Learning in Infinite Horizons. (arXiv:2202.13163v2 [stat.ML] UPDATED)
    We consider reinforcement learning (RL) methods in offline domains without additional online data collection, such as mobile health applications. Most of existing policy optimization algorithms in the computer science literature are developed in online settings where data are easy to collect or simulate. Their generalizations to mobile health applications with a pre-collected offline dataset remain unknown. The aim of this paper is to develop a novel advantage learning framework in order to efficiently use pre-collected data for policy optimization. The proposed method takes an optimal Q-estimator computed by any existing state-of-the-art RL algorithms as input, and outputs a new policy whose value is guaranteed to converge at a faster rate than the policy derived based on the initial Q-estimator. Extensive numerical experiments are conducted to back up our theoretical findings. A Python implementation of our proposed method is available at https://github.com/leyuanheart/SEAL.  ( 2 min )
    GCN-WP -- Semi-Supervised Graph Convolutional Networks for Win Prediction in Esports. (arXiv:2207.13191v1 [cs.LG])
    Win prediction is crucial to understanding skill modeling, teamwork and matchmaking in esports. In this paper we propose GCN-WP, a semi-supervised win prediction model for esports based on graph convolutional networks. This model learns the structure of an esports league over the course of a season (1 year) and makes predictions on another similar league. This model integrates over 30 features about the match and players and employs graph convolution to classify games based on their neighborhood. Our model achieves state-of-the-art prediction accuracy when compared to machine learning or skill rating models for LoL. The framework is generalizable so it can easily be extended to other multiplayer online games.  ( 2 min )
    Multi-modal Misinformation Detection: Approaches, Challenges and Opportunities. (arXiv:2203.13883v3 [cs.LG] UPDATED)
    As social media platforms are evolving from text-based forums into multi-modal environments, the nature of misinformation in social media is also changing accordingly. Taking advantage of the fact that visual modalities such as images and videos are more favorable and attractive to the users, and textual contents are sometimes skimmed carelessly, misinformation spreaders have recently targeted contextual correlations between modalities e.g., text and image. Thus, many research efforts have been put into development of automatic techniques for detecting possible cross-modal discordances in web-based media. In this work, we aim to analyze, categorize and identify existing approaches in addition to challenges and shortcomings they face in order to unearth new opportunities in furthering the research in the field of multi-modal misinformation detection.
    DynaMarks: Defending Against Deep Learning Model Extraction Using Dynamic Watermarking. (arXiv:2207.13321v1 [cs.CR])
    The functionality of a deep learning (DL) model can be stolen via model extraction where an attacker obtains a surrogate model by utilizing the responses from a prediction API of the original model. In this work, we propose a novel watermarking technique called DynaMarks to protect the intellectual property (IP) of DL models against such model extraction attacks in a black-box setting. Unlike existing approaches, DynaMarks does not alter the training process of the original model but rather embeds watermark into a surrogate model by dynamically changing the output responses from the original model prediction API based on certain secret parameters at inference runtime. The experimental outcomes on Fashion MNIST, CIFAR-10, and ImageNet datasets demonstrate the efficacy of DynaMarks scheme to watermark surrogate models while preserving the accuracies of the original models deployed in edge devices. In addition, we also perform experiments to evaluate the robustness of DynaMarks against various watermark removal strategies, thus allowing a DL model owner to reliably prove model ownership.
    A Variational AutoEncoder for Transformers with Nonparametric Variational Information Bottleneck. (arXiv:2207.13529v1 [cs.LG])
    We propose a VAE for Transformers by developing a variational information bottleneck regulariser for Transformer embeddings. We formalise the embedding space of Transformer encoders as mixture probability distributions, and use Bayesian nonparametrics to derive a nonparametric variational information bottleneck (NVIB) for such attention-based embeddings. The variable number of mixture components supported by nonparametric methods captures the variable number of vectors supported by attention, and the exchangeability of our nonparametric distributions captures the permutation invariance of attention. This allows NVIB to regularise the number of vectors accessible with attention, as well as the amount of information in individual vectors. By regularising the cross-attention of a Transformer encoder-decoder with NVIB, we propose a nonparametric variational autoencoder (NVAE). Initial experiments on training a NVAE on natural language text show that the induced embedding space has the desired properties of a VAE for Transformers.
    Open Source Vizier: Distributed Infrastructure and API for Reliable and Flexible Blackbox Optimization. (arXiv:2207.13676v1 [cs.LG])
    Vizier is the de-facto blackbox and hyperparameter optimization service across Google, having optimized some of Google's largest products and research efforts. To operate at the scale of tuning thousands of users' critical systems, Google Vizier solved key design challenges in providing multiple different features, while remaining fully fault-tolerant. In this paper, we introduce Open Source (OSS) Vizier, a standalone Python-based interface for blackbox optimization and research, based on the Google-internal Vizier infrastructure and framework. OSS Vizier provides an API capable of defining and solving a wide variety of optimization problems, including multi-metric, early stopping, transfer learning, and conditional search. Furthermore, it is designed to be a distributed system that assures reliability, and allows multiple parallel evaluations of the user's objective function. The flexible RPC-based infrastructure allows users to access OSS Vizier from binaries written in any language. OSS Vizier also provides a back-end ("Pythia") API that gives algorithm authors a way to interface new algorithms with the core OSS Vizier system. OSS Vizier is available at https://github.com/google/vizier.
    Bi-SimCut: A Simple Strategy for Boosting Neural Machine Translation. (arXiv:2206.02368v2 [cs.CL] UPDATED)
    We introduce Bi-SimCut: a simple but effective training strategy to boost neural machine translation (NMT) performance. It consists of two procedures: bidirectional pretraining and unidirectional finetuning. Both procedures utilize SimCut, a simple regularization method that forces the consistency between the output distributions of the original and the cutoff sentence pairs. Without leveraging extra dataset via back-translation or integrating large-scale pretrained model, Bi-SimCut achieves strong translation performance across five translation benchmarks (data sizes range from 160K to 20.2M): BLEU scores of 31.16 for en -> de and 38.37 for de -> en on the IWSLT14 dataset, 30.78 for en -> de and 35.15 for de -> en on the WMT14 dataset, and 27.17 for zh -> en on the WMT17 dataset. SimCut is not a new method, but a version of Cutoff (Shen et al., 2020) simplified and adapted for NMT, and it could be considered as a perturbation-based method. Given the universality and simplicity of SimCut and Bi-SimCut, we believe they can serve as strong baselines for future NMT research.
    Membership Inference Attacks via Adversarial Examples. (arXiv:2207.13572v1 [cs.LG])
    The raise of machine learning and deep learning led to significant improvement in several domains. This change is supported by both the dramatic rise in computation power and the collection of large datasets. Such massive datasets often include personal data which can represent a threat to privacy. Membership inference attacks are a novel direction of research which aims at recovering training data used by a learning algorithm. In this paper, we develop a mean to measure the leakage of training data leveraging a quantity appearing as a proxy of the total variation of a trained model near its training samples. We extend our work by providing a novel defense mechanism. Our contributions are supported by empirical evidence through convincing numerical experiments.
    INTERACT: Achieving Low Sample and Communication Complexities in Decentralized Bilevel Learning over Networks. (arXiv:2207.13283v1 [cs.LG])
    In recent years, decentralized bilevel optimization problems have received increasing attention in the networking and machine learning communities thanks to their versatility in modeling decentralized learning problems over peer-to-peer networks (e.g., multi-agent meta-learning, multi-agent reinforcement learning, personalized training, and Byzantine-resilient learning). However, for decentralized bilevel optimization over peer-to-peer networks with limited computation and communication capabilities, how to achieve low sample and communication complexities are two fundamental challenges that remain under-explored so far. In this paper, we make the first attempt to investigate the class of decentralized bilevel optimization problems with nonconvex and strongly-convex structure corresponding to the outer and inner subproblems, respectively. Our main contributions in this paper are two-fold: i) We first propose a deterministic algorithm called INTERACT (inner-gradient-descent-outer-tracked-gradient) that requires the sample complexity of $\mathcal{O}(n \epsilon^{-1})$ and communication complexity of $\mathcal{O}(\epsilon^{-1})$ to solve the bilevel optimization problem, where $n$ and $\epsilon > 0$ are the number of samples at each agent and the desired stationarity gap, respectively. ii) To relax the need for full gradient evaluations in each iteration, we propose a stochastic variance-reduced version of INTERACT (SVR-INTERACT), which improves the sample complexity to $\mathcal{O}(\sqrt{n} \epsilon^{-1})$ while achieving the same communication complexity as the deterministic algorithm. To our knowledge, this work is the first that achieves both low sample and communication complexities for solving decentralized bilevel optimization problems over networks. Our numerical experiments also corroborate our theoretical findings.
    A new perspective on the approximation capability of GNNs. (arXiv:2106.08992v4 [cs.LG] UPDATED)
    Graph Neural Networks (GNNs) are a broad class of connectionist models for graph processing. Recent studies have shown that GNNs can approximate any function on graphs, modulo the equivalence relation on nodes defined by the Weisfeiler - Lehman test. However, these results suffer from some limitations, both because they were derived using the Stone-Weierstrass theorem - which is existential in nature -, and because they assume that the target function to be approximated must be continuous. In this paper, we propose an alternative way to demonstrate the approximation capability of GNNs that overcomes these limitations. In particular, some new results are proved, which allow to: (1) define GNN architectures capable of obtaining a given approximation; (2) show that the Weisfeiler-Lehman test converges in r+1 steps, where r is the diameter of the graph; (3) derive a formal relationship between the Weisfeiler-Lehman test and the unfolding trees, that is trees that can be built by visiting the graph starting from a given node. These results provide a more comprehensive understanding of the approximation power of GNNs, definitely showing that the 1-WL test and the unfolding tree concepts can be used interchangeably to study the their expressiveness.
    Cascade Decoders-Based Autoencoders for Image Reconstruction. (arXiv:2107.00002v2 [cs.LG] UPDATED)
    Autoencoders are composed of coding and decoding units, hence they hold the inherent potential of high-performance data compression and signal compressed sensing. The main disadvantages of current autoencoders comprise the following several aspects: the research objective is not data reconstruction but feature representation; the performance evaluation of data recovery is neglected; it is hard to achieve lossless data reconstruction by pure autoencoders, even by pure deep learning. This paper aims for image reconstruction of autoencoders, employs cascade decoders-based autoencoders, perfects the performance of image reconstruction, approaches gradually lossless image recovery, and provides solid theory and application basis for autoencoders-based image compression and compressed sensing. The proposed serial decoders-based autoencoders include the architectures of multi-level decoders and the related optimization algorithms. The cascade decoders consist of general decoders, residual decoders, adversarial decoders and their combinations. It is evaluated by the experimental results that the proposed autoencoders outperform the classical autoencoders in the performance of image reconstruction.
    Detecting Concept Drift in the Presence of Sparsity -- A Case Study of Automated Change Risk Assessment System. (arXiv:2207.13287v1 [cs.LG])
    Missing values, widely called as \textit{sparsity} in literature, is a common characteristic of many real-world datasets. Many imputation methods have been proposed to address this problem of data incompleteness or sparsity. However, the accuracy of a data imputation method for a given feature or a set of features in a dataset is highly dependent on the distribution of the feature values and its correlation with other features. Another problem that plagues industry deployments of machine learning (ML) solutions is concept drift detection, which becomes more challenging in the presence of missing values. Although data imputation and concept drift detection have been studied extensively, little work has attempted a combined study of the two phenomena, i.e., concept drift detection in the presence of sparsity. In this work, we carry out a systematic study of the following: (i) different patterns of missing values, (ii) various statistical and ML based data imputation methods for different kinds of sparsity, (iii) several concept drift detection methods, (iv) practical analysis of the various drift detection metrics, (v) selecting the best concept drift detector given a dataset with missing values based on the different metrics. We first analyze it on synthetic data and publicly available datasets, and finally extend the findings to our deployed solution of automated change risk assessment system. One of the major findings from our empirical study is the absence of supremacy of any one concept drift detection method across all the relevant metrics. Therefore, we adopt a majority voting based ensemble of concept drift detectors for abrupt and gradual concept drifts. Our experiments show optimal or near optimal performance can be achieved for this ensemble method across all the metrics.
    Time Series Forecasting Models Copy the Past: How to Mitigate. (arXiv:2207.13441v1 [cs.LG])
    Time series forecasting is at the core of important application domains posing significant challenges to machine learning algorithms. Recently neural network architectures have been widely applied to the problem of time series forecasting. Most of these models are trained by minimizing a loss function that measures predictions' deviation from the real values. Typical loss functions include mean squared error (MSE) and mean absolute error (MAE). In the presence of noise and uncertainty, neural network models tend to replicate the last observed value of the time series, thus limiting their applicability to real-world data. In this paper, we provide a formal definition of the above problem and we also give some examples of forecasts where the problem is observed. We also propose a regularization term penalizing the replication of previously seen values. We evaluate the proposed regularization term both on synthetic and real-world datasets. Our results indicate that the regularization term mitigates to some extent the aforementioned problem and gives rise to more robust models.
    On Missing Labels, Long-tails and Propensities in Extreme Multi-label Classification. (arXiv:2207.13186v1 [cs.LG])
    The propensity model introduced by Jain et al. 2016 has become a standard approach for dealing with missing and long-tail labels in extreme multi-label classification (XMLC). In this paper, we critically revise this approach showing that despite its theoretical soundness, its application in contemporary XMLC works is debatable. We exhaustively discuss the flaws of the propensity-based approach, and present several recipes, some of them related to solutions used in search engines and recommender systems, that we believe constitute promising alternatives to be followed in XMLC.
    Online Continual Learning with Contrastive Vision Transformer. (arXiv:2207.13516v1 [cs.LG])
    Online continual learning (online CL) studies the problem of learning sequential tasks from an online data stream without task boundaries, aiming to adapt to new data while alleviating catastrophic forgetting on the past tasks. This paper proposes a framework Contrastive Vision Transformer (CVT), which designs a focal contrastive learning strategy based on a transformer architecture, to achieve a better stability-plasticity trade-off for online CL. Specifically, we design a new external attention mechanism for online CL that implicitly captures previous tasks' information. Besides, CVT contains learnable focuses for each class, which could accumulate the knowledge of previous classes to alleviate forgetting. Based on the learnable focuses, we design a focal contrastive loss to rebalance contrastive learning between new and past classes and consolidate previously learned representations. Moreover, CVT contains a dual-classifier structure for decoupling learning current classes and balancing all observed classes. The extensive experimental results show that our approach achieves state-of-the-art performance with even fewer parameters on online CL benchmarks and effectively alleviates the catastrophic forgetting.
    Unsupervised Training for Neural TSP Solver. (arXiv:2207.13667v1 [cs.LG])
    There has been a growing number of machine learning methods for approximately solving the travelling salesman problem. However, these methods often require solved instances for training or use complex reinforcement learning approaches that need a large amount of tuning. To avoid these problems, we introduce a novel unsupervised learning approach. We use a relaxation of an integer linear program for TSP to construct a loss function that does not require correct instance labels. With variable discretization, its minimum coincides with the optimal or near-optimal solution. Furthermore, this loss function is differentiable and thus can be used to train neural networks directly. We use our loss function with a Graph Neural Network and design controlled experiments on both Euclidean and asymmetric TSP. Our approach has the advantage over supervised learning of not requiring large labelled datasets. In addition, the performance of our approach surpasses reinforcement learning for asymmetric TSP and is comparable to reinforcement learning for Euclidean instances. Our approach is also more stable and easier to train than reinforcement learning.
    Information We Can Extract About a User From 'One Minute Mobile Application Usage'. (arXiv:2207.13222v1 [cs.LG])
    Understanding human behavior is an important task and has applications in many domains such as targeted advertisement, health analytics, security, and entertainment, etc. For this purpose, designing a system for activity recognition (AR) is important. However, since every human can have different behaviors, understanding and analyzing common patterns become a challenging task. Since smartphones are easily available to every human being in the modern world, using them to track the human activities becomes possible. In this paper, we extracted different human activities using accelerometer, magnetometer, and gyroscope sensors of android smartphones by building an android mobile applications. Using different social media applications, such as Facebook, Instagram, Whatsapp, and Twitter, we extracted the raw sensor values along with the attributes of $29$ subjects along with their attributes (class labels) such as age, gender, and left/right/both hands application usage. We extract features from the raw signals and use them to perform classification using different machine learning (ML) algorithms. Using statistical analysis, we show the importance of different features towards the prediction of class labels. In the end, we use the trained ML model on our data to extract unknown features from a well known activity recognition data from UCI repository, which highlights the potential of privacy breach using ML models. This security analysis could help researchers in future to take appropriate steps to preserve the privacy of human subjects.
    Do Quantum Circuit Born Machines Generalize?. (arXiv:2207.13645v1 [quant-ph])
    In recent proposals of quantum circuit models for generative tasks, the discussion about their performance has been limited to their ability to reproduce a known target distribution. For example, expressive model families such as Quantum Circuit Born Machines (QCBMs) have been almost entirely evaluated on their capability to learn a given target distribution with high accuracy. While this aspect may be ideal for some tasks, it limits the scope of a generative model's assessment to its ability to memorize data rather than generalize. As a result, there has been little understanding of a model's generalization performance and the relation between such capability and the resource requirements, e.g., the circuit depth and the amount of training data. In this work, we leverage upon a recently proposed generalization evaluation framework to begin addressing this knowledge gap. We first investigate the QCBM's learning process of a cardinality-constrained distribution and see an increase in generalization performance while increasing the circuit depth. In the 12-qubit example presented here, we observe that with as few as 30% of the valid patterns as the training set, the QCBM exhibits the best generalization performance toward generating unseen and valid patterns. Lastly, we assess the QCBM's ability to generalize not only to valid features, but to high-quality bitstrings distributed according to an adequately biased distribution. We see that the QCBM is able to effectively learn the bias and generate unseen samples with higher quality than those in the training set. To the best of our knowledge, this is the first work in the literature that presents the QCBM's generalization performance as an integral evaluation metric for quantum generative models, and demonstrates the QCBM's ability to generalize to high-quality, desired novel samples.
    Handling Hard Affine SDP Shape Constraints in RKHSs. (arXiv:2101.01519v2 [stat.ML] UPDATED)
    Shape constraints, such as non-negativity, monotonicity, convexity or supermodularity, play a key role in various applications of machine learning and statistics. However, incorporating this side information into predictive models in a hard way (for example at all points of an interval) for rich function classes is a notoriously challenging problem. We propose a unified and modular convex optimization framework, relying on second-order cone (SOC) tightening, to encode hard affine SDP constraints on function derivatives, for models belonging to vector-valued reproducing kernel Hilbert spaces (vRKHSs). The modular nature of the proposed approach allows to simultaneously handle multiple shape constraints, and to tighten an infinite number of constraints into finitely many. We prove the convergence of the proposed scheme and that of its adaptive variant, leveraging geometric properties of vRKHSs. Due to the covering-based construction of the tightening, the method is particularly well-suited to tasks with small to moderate input dimensions. The efficiency of the approach is illustrated in the context of shape optimization, robotics and econometrics.
    Fault Detection and Classification of Aerospace Sensors using a VGG16-based Deep Neural Network. (arXiv:2207.13267v1 [cs.CV])
    Compared with traditional model-based fault detection and classification (FDC) methods, deep neural networks (DNN) prove to be effective for the aerospace sensors FDC problems. However, time being consumed in training the DNN is excessive, and explainability analysis for the FDC neural network is still underwhelming. A concept known as imagefication-based intelligent FDC has been studied in recent years. This concept advocates to stack the sensors measurement data into an image format, the sensors FDC issue is then transformed to abnormal regions detection problem on the stacked image, which may well borrow the recent advances in the machine vision vision realm. Although promising results have been claimed in the imagefication-based intelligent FDC researches, due to the low size of the stacked image, small convolutional kernels and shallow DNN layers were used, which hinders the FDC performance. In this paper, we first propose a data augmentation method which inflates the stacked image to a larger size (correspondent to the VGG16 net developed in the machine vision realm). The FDC neural network is then trained via fine-tuning the VGG16 directly. To truncate and compress the FDC net size (hence its running time), we perform model pruning on the fine-tuned net. Class activation mapping (CAM) method is also adopted for explainability analysis of the FDC net to verify its internal operations. Via data augmentation, fine-tuning from VGG16, and model pruning, the FDC net developed in this paper claims an FDC accuracy 98.90% across 4 aircraft at 5 flight conditions (running time 26 ms). The CAM results also verify the FDC net w.r.t. its internal operations.
    Learning the Evolution of Correlated Stochastic Power System Dynamics. (arXiv:2207.13310v1 [cs.LG])
    A machine learning technique is proposed for quantifying uncertainty in power system dynamics with spatiotemporally correlated stochastic forcing. We learn one-dimensional linear partial differential equations for the probability density functions of real-valued quantities of interest. The method is suitable for high-dimensional systems and helps to alleviate the curse of dimensionality.
    Safe and Robust Experience Sharing for Deterministic Policy Gradient Algorithms. (arXiv:2207.13453v1 [cs.LG])
    Learning in high dimensional continuous tasks is challenging, mainly when the experience replay memory is very limited. We introduce a simple yet effective experience sharing mechanism for deterministic policies in continuous action domains for the future off-policy deep reinforcement learning applications in which the allocated memory for the experience replay buffer is limited. To overcome the extrapolation error induced by learning from other agents' experiences, we facilitate our algorithm with a novel off-policy correction technique without any action probability estimates. We test the effectiveness of our method in challenging OpenAI Gym continuous control tasks and conclude that it can achieve a safe experience sharing across multiple agents and exhibits a robust performance when the replay memory is strictly limited.
    Correlations Between COVID-19 and Dengue. (arXiv:2207.13561v1 [q-bio.PE])
    A dramatic increase in the number of outbreaks of Dengue has recently been reported, and climate change is likely to extend the geographical spread of the disease. In this context, this paper shows how a neural network approach can incorporate Dengue and COVID-19 data as well as external factors (such as social behaviour or climate variables), to develop predictive models that could improve our knowledge and provide useful tools for health policy makers. Through the use of neural networks with different social and natural parameters, in this paper we define a Correlation Model through which we show that the number of cases of COVID-19 and Dengue have very similar trends. We then illustrate the relevance of our model by extending it to a Long short-term memory model (LSTM) that incorporates both diseases, and using this to estimate Dengue infections via COVID-19 data in countries that lack sufficient Dengue data.
    VDL-Surrogate: A View-Dependent Latent-based Model for Parameter Space Exploration of Ensemble Simulations. (arXiv:2207.13091v1 [cs.GR])
    We propose VDL-Surrogate, a view-dependent neural-network-latent-based surrogate model for parameter space exploration of ensemble simulations that allows high-resolution visualizations and user-specified visual mappings. Surrogate-enabled parameter space exploration allows domain scientists to preview simulation results without having to run a large number of computationally costly simulations. Limited by computational resources, however, existing surrogate models may not produce previews with sufficient resolution for visualization and analysis. To improve the efficient use of computational resources and support high-resolution exploration, we perform ray casting from different viewpoints to collect samples and produce compact latent representations. This latent encoding process reduces the cost of surrogate model training while maintaining the output quality. In the model training stage, we select viewpoints to cover the whole viewing sphere and train corresponding VDL-Surrogate models for the selected viewpoints. In the model inference stage, we predict the latent representations at previously selected viewpoints and decode the latent representations to data space. For any given viewpoint, we make interpolations over decoded data at selected viewpoints and generate visualizations with user-specified visual mappings. We show the effectiveness and efficiency of VDL-Surrogate in cosmological and ocean simulations with quantitative and qualitative evaluations. Source code is publicly available at \url{https://github.com/trainsn/VDL-Surrogate}.
    Concurrent Subsidiary Supervision for Unsupervised Source-Free Domain Adaptation. (arXiv:2207.13247v1 [cs.CV])
    The prime challenge in unsupervised domain adaptation (DA) is to mitigate the domain shift between the source and target domains. Prior DA works show that pretext tasks could be used to mitigate this domain shift by learning domain invariant representations. However, in practice, we find that most existing pretext tasks are ineffective against other established techniques. Thus, we theoretically analyze how and when a subsidiary pretext task could be leveraged to assist the goal task of a given DA problem and develop objective subsidiary task suitability criteria. Based on this criteria, we devise a novel process of sticker intervention and cast sticker classification as a supervised subsidiary DA problem concurrent to the goal task unsupervised DA. Our approach not only improves goal task adaptation performance, but also facilitates privacy-oriented source-free DA i.e. without concurrent source-target access. Experiments on the standard Office-31, Office-Home, DomainNet, and VisDA benchmarks demonstrate our superiority for both single-source and multi-source source-free DA. Our approach also complements existing non-source-free works, achieving leading performance.
    A Proper Orthogonal Decomposition approach for parameters reduction of Single Shot Detector networks. (arXiv:2207.13551v1 [cs.CV])
    As a major breakthrough in artificial intelligence and deep learning, Convolutional Neural Networks have achieved an impressive success in solving many problems in several fields including computer vision and image processing. Real-time performance, robustness of algorithms and fast training processes remain open problems in these contexts. In addition object recognition and detection are challenging tasks for resource-constrained embedded systems, commonly used in the industrial sector. To overcome these issues, we propose a dimensionality reduction framework based on Proper Orthogonal Decomposition, a classical model order reduction technique, in order to gain a reduction in the number of hyperparameters of the net. We have applied such framework to SSD300 architecture using PASCAL VOC dataset, demonstrating a reduction of the network dimension and a remarkable speedup in the fine-tuning of the network in a transfer learning context.
    Unsupervised Learning under Latent Label Shift. (arXiv:2207.13179v1 [cs.LG])
    What sorts of structure might enable a learner to discover classes from unlabeled data? Traditional approaches rely on feature-space similarity and heroic assumptions on the data. In this paper, we introduce unsupervised learning under Latent Label Shift (LLS), where we have access to unlabeled data from multiple domains such that the label marginals $p_d(y)$ can shift across domains but the class conditionals $p(\mathbf{x}|y)$ do not. This work instantiates a new principle for identifying classes: elements that shift together group together. For finite input spaces, we establish an isomorphism between LLS and topic modeling: inputs correspond to words, domains to documents, and labels to topics. Addressing continuous data, we prove that when each label's support contains a separable region, analogous to an anchor word, oracle access to $p(d|\mathbf{x})$ suffices to identify $p_d(y)$ and $p_d(y|\mathbf{x})$ up to permutation. Thus motivated, we introduce a practical algorithm that leverages domain-discriminative models as follows: (i) push examples through domain discriminator $p(d|\mathbf{x})$; (ii) discretize the data by clustering examples in $p(d|\mathbf{x})$ space; (iii) perform non-negative matrix factorization on the discrete data; (iv) combine the recovered $p(y|d)$ with the discriminator outputs $p(d|\mathbf{x})$ to compute $p_d(y|x) \; \forall d$. With semi-synthetic experiments, we show that our algorithm can leverage domain information to improve state of the art unsupervised classification methods. We reveal a failure mode of standard unsupervised classification methods when feature-space similarity does not indicate true groupings, and show empirically that our method better handles this case. Our results establish a deep connection between distribution shift and topic modeling, opening promising lines for future work.
    XADLiME: eXplainable Alzheimer's Disease Likelihood Map Estimation via Clinically-guided Prototype Learning. (arXiv:2207.13223v1 [cs.LG])
    Diagnosing Alzheimer's disease (AD) involves a deliberate diagnostic process owing to its innate traits of irreversibility with subtle and gradual progression. These characteristics make AD biomarker identification from structural brain imaging (e.g., structural MRI) scans quite challenging. Furthermore, there is a high possibility of getting entangled with normal aging. We propose a novel deep-learning approach through eXplainable AD Likelihood Map Estimation (XADLiME) for AD progression modeling over 3D sMRIs using clinically-guided prototype learning. Specifically, we establish a set of topologically-aware prototypes onto the clusters of latent clinical features, uncovering an AD spectrum manifold. We then measure the similarities between latent clinical features and well-established prototypes, estimating a "pseudo" likelihood map. By considering this pseudo map as an enriched reference, we employ an estimating network to estimate the AD likelihood map over a 3D sMRI scan. Additionally, we promote the explainability of such a likelihood map by revealing a comprehensible overview from two perspectives: clinical and morphological. During the inference, this estimated likelihood map served as a substitute over unseen sMRI scans for effectively conducting the downstream task while providing thorough explainable states.
    PI-ARS: Accelerating Evolution-Learned Visual-Locomotion with Predictive Information Representations. (arXiv:2207.13224v1 [cs.RO])
    Evolution Strategy (ES) algorithms have shown promising results in training complex robotic control policies due to their massive parallelism capability, simple implementation, effective parameter-space exploration, and fast training time. However, a key limitation of ES is its scalability to large capacity models, including modern neural network architectures. In this work, we develop Predictive Information Augmented Random Search (PI-ARS) to mitigate this limitation by leveraging recent advancements in representation learning to reduce the parameter search space for ES. Namely, PI-ARS combines a gradient-based representation learning technique, Predictive Information (PI), with a gradient-free ES algorithm, Augmented Random Search (ARS), to train policies that can process complex robot sensory inputs and handle highly nonlinear robot dynamics. We evaluate PI-ARS on a set of challenging visual-locomotion tasks where a quadruped robot needs to walk on uneven stepping stones, quincuncial piles, and moving platforms, as well as to complete an indoor navigation task. Across all tasks, PI-ARS demonstrates significantly better learning efficiency and performance compared to the ARS baseline. We further validate our algorithm by demonstrating that the learned policies can successfully transfer to a real quadruped robot, for example, achieving a 100% success rate on the real-world stepping stone environment, dramatically improving prior results achieving 40% success.
    Spatiotemporal Self-attention Modeling with Temporal Patch Shift for Action Recognition. (arXiv:2207.13259v1 [cs.CV])
    Transformer-based methods have recently achieved great advancement on 2D image-based vision tasks. For 3D video-based tasks such as action recognition, however, directly applying spatiotemporal transformers on video data will bring heavy computation and memory burdens due to the largely increased number of patches and the quadratic complexity of self-attention computation. How to efficiently and effectively model the 3D self-attention of video data has been a great challenge for transformers. In this paper, we propose a Temporal Patch Shift (TPS) method for efficient 3D self-attention modeling in transformers for video-based action recognition. TPS shifts part of patches with a specific mosaic pattern in the temporal dimension, thus converting a vanilla spatial self-attention operation to a spatiotemporal one with little additional cost. As a result, we can compute 3D self-attention using nearly the same computation and memory cost as 2D self-attention. TPS is a plug-and-play module and can be inserted into existing 2D transformer models to enhance spatiotemporal feature learning. The proposed method achieves competitive performance with state-of-the-arts on Something-something V1 & V2, Diving-48, and Kinetics400 while being much more efficient on computation and memory cost. The source code of TPS can be found at https://github.com/MartinXM/TPS.
    Statistical Keystroke Synthesis for Improved Bot Detection. (arXiv:2207.13394v1 [cs.LG])
    This work proposes two statistical approaches for the synthesis of keystroke biometric data based on Universal and User-dependent Models. Both approaches are validated on the bot detection task, using the keystroke synthetic data to better train the systems. Our experiments include a dataset with 136 million keystroke events from 168,000 subjects. We have analyzed the performance of the two synthesis approaches through qualitative and quantitative experiments. Different bot detectors are considered based on two supervised classifiers (Support Vector Machine and Long Short-Term Memory network) and a learning framework including human and generated samples. Our results prove that the proposed statistical approaches are able to generate realistic human-like synthetic keystroke samples. Also, the classification results suggest that in scenarios with large labeled data, these synthetic samples can be detected with high accuracy. However, in few-shot learning scenarios it represents an important challenge.
    Initial Orbit Determination for the CR3BP using Particle Swarm Optimization. (arXiv:2207.13175v1 [physics.comp-ph])
    This work utilizes a particle swarm optimizer (PSO) for initial orbit determination for a chief and deputy scenario in the circular restricted three-body problem (CR3BP). The PSO is used to minimize the difference between actual and estimated observations and knowledge of the chief's position with known CR3BP dynamics to determine the deputy's initial state. Convergence is achieved through limiting particle starting positions to feasible positions based on the known chief position, and sensor constraints. Parallel and GPU processing methods are used to improve computation time and provide an accurate initial state estimate for a variety of cislunar orbit geometries.
    Sliced Wasserstein Variational Inference. (arXiv:2207.13177v1 [stat.ML])
    Variational Inference approximates an unnormalized distribution via the minimization of Kullback-Leibler (KL) divergence. Although this divergence is efficient for computation and has been widely used in applications, it suffers from some unreasonable properties. For example, it is not a proper metric, i.e., it is non-symmetric and does not preserve the triangle inequality. On the other hand, optimal transport distances recently have shown some advantages over KL divergence. With the help of these advantages, we propose a new variational inference method by minimizing sliced Wasserstein distance, a valid metric arising from optimal transport. This sliced Wasserstein distance can be approximated simply by running MCMC but without solving any optimization problem. Our approximation also does not require a tractable density function of variational distributions so that approximating families can be amortized by generators like neural networks. Furthermore, we provide an analysis of the theoretical properties of our method. Experiments on synthetic and real data are illustrated to show the performance of the proposed method.
    One Simple Trick to Fix Your Bayesian Neural Network. (arXiv:2207.13167v1 [stat.ML])
    One of the most popular estimation methods in Bayesian neural networks (BNN) is mean-field variational inference (MFVI). In this work, we show that neural networks with ReLU activation function induce posteriors, that are hard to fit with MFVI. We provide a theoretical justification for this phenomenon, study it empirically, and report the results of a series of experiments to investigate the effect of activation function on the calibration of BNNs. We find that using Leaky ReLU activations leads to more Gaussian-like weight posteriors and achieves a lower expected calibration error (ECE) than its ReLU-based counterpart.
    Faster online calibration without randomization: interval forecasts and the power of two choices. (arXiv:2204.13087v2 [cs.LG] UPDATED)
    We study the problem of making calibrated probabilistic forecasts for a binary sequence generated by an adversarial nature. Following the seminal paper of Foster and Vohra (1998), nature is often modeled as an adaptive adversary who sees all activity of the forecaster except the randomization that the forecaster may deploy. A number of papers have proposed randomized forecasting strategies that achieve an $\epsilon$-calibration error rate of $O(1/\sqrt{T})$, which we prove is tight in general. On the other hand, it is well known that it is not possible to be calibrated without randomization, or if nature also sees the forecaster's randomization; in both cases the calibration error could be $\Omega(1)$. Inspired by the equally seminal works on the "power of two choices" and imprecise probability theory, we study a small variant of the standard online calibration problem. The adversary gives the forecaster the option of making two nearby probabilistic forecasts, or equivalently an interval forecast of small width, and the endpoint closest to the revealed outcome is used to judge calibration. This power of two choices, or imprecise forecast, accords the forecaster with significant power -- we show that a faster $\epsilon$-calibration rate of $O(1/T)$ can be achieved even without deploying any randomization.  ( 3 min )
    Time to augment contrastive learning. (arXiv:2207.13492v1 [cs.LG])
    Biological vision systems are unparalleled in their ability to learn visual representations without supervision. In machine learning, contrastive learning (CL) has led to major advances in forming object representations in an unsupervised fashion. These systems learn representations invariant to augmentation operations over images, like cropping or flipping. In contrast, biological vision systems exploit the temporal structure of the visual experience. This gives access to augmentations not commonly used in CL, like watching the same object from multiple viewpoints or against different backgrounds. Here, we systematically investigate and compare the potential benefits of such time-based augmentations for learning object categories. Our results show that time-based augmentations achieve large performance gains over state-of-the-art image augmentations. Specifically, our analyses reveal that: 1) 3-D object rotations drastically improve the learning of object categories; 2) viewing objects against changing backgrounds is vital for learning to discard background-related information. Overall, we conclude that time-based augmentations can greatly improve contrastive learning, narrowing the gap between artificial and biological vision systems.  ( 2 min )
    Fairness and Randomness in Machine Learning: Statistical Independence and Relativization. (arXiv:2207.13596v1 [cs.LG])
    Fair Machine Learning endeavors to prevent unfairness arising in the context of machine learning applications embedded in society. Despite the variety of definitions of fairness and proposed "fair algorithms", there remain unresolved conceptual problems regarding fairness. In this paper, we argue that randomness and fairness can be considered equivalent concepts in machine learning. We obtain a relativized notion of randomness expressed as statistical independence by appealing to Von Mises' century-old foundations for probability. Via fairness notions in machine learning, which are expressed as statistical independence as well, we then link the ante randomness assumptions about the data to the ex post requirements for fair predictions. This connection proves fruitful: we use it to argue that randomness and fairness are essentially relative and that randomness should reflect its nature as a modeling assumption in machine learning.  ( 2 min )
    Perception-Aware Attack: Creating Adversarial Music via Reverse-Engineering Human Perception. (arXiv:2207.13192v1 [cs.SD])
    Recently, adversarial machine learning attacks have posed serious security threats against practical audio signal classification systems, including speech recognition, speaker recognition, and music copyright detection. Previous studies have mainly focused on ensuring the effectiveness of attacking an audio signal classifier via creating a small noise-like perturbation on the original signal. It is still unclear if an attacker is able to create audio signal perturbations that can be well perceived by human beings in addition to its attack effectiveness. This is particularly important for music signals as they are carefully crafted with human-enjoyable audio characteristics. In this work, we formulate the adversarial attack against music signals as a new perception-aware attack framework, which integrates human study into adversarial attack design. Specifically, we conduct a human study to quantify the human perception with respect to a change of a music signal. We invite human participants to rate their perceived deviation based on pairs of original and perturbed music signals, and reverse-engineer the human perception process by regression analysis to predict the human-perceived deviation given a perturbed signal. The perception-aware attack is then formulated as an optimization problem that finds an optimal perturbation signal to minimize the prediction of perceived deviation from the regressed human perception model. We use the perception-aware framework to design a realistic adversarial music attack against YouTube's copyright detector. Experiments show that the perception-aware attack produces adversarial music with significantly better perceptual quality than prior work.  ( 3 min )
    TINYCD: A (Not So) Deep Learning Model For Change Detection. (arXiv:2207.13159v1 [cs.CV])
    The aim of change detection (CD) is to detect changes occurred in the same area by comparing two images of that place taken at different times. The challenging part of the CD is to keep track of the changes the user wants to highlight, such as new buildings, and to ignore changes due to external factors such as environmental, lighting condition, fog or seasonal changes. Recent developments in the field of deep learning enabled researchers to achieve outstanding performance in this area. In particular, different mechanisms of space-time attention allowed to exploit the spatial features that are extracted from the models and to correlate them also in a temporal way by exploiting both the available images. The downside is that the models have become increasingly complex and large, often unfeasible for edge applications. These are limitations when the models must be applied to the industrial field or in applications requiring real-time performances. In this work we propose a novel model, called TinyCD, demonstrating to be both lightweight and effective, able to achieve performances comparable or even superior to the current state of the art with 13-150X fewer parameters. In our approach we have exploited the importance of low-level features to compare images. To do this, we use only few backbone blocks. This strategy allow us to keep the number of network parameters low. To compose the features extracted from the two images, we introduce a novel, economical in terms of parameters, mixing block capable of cross correlating features in both space and time domains. Finally, to fully exploit the information contained in the computed features, we define the PW-MLP block able to perform a pixel wise classification. Source code, models and results are available here: https://github.com/AndreaCodegoni/Tiny_model_4_CD
    LGV: Boosting Adversarial Example Transferability from Large Geometric Vicinity. (arXiv:2207.13129v1 [cs.LG])
    We propose transferability from Large Geometric Vicinity (LGV), a new technique to increase the transferability of black-box adversarial attacks. LGV starts from a pretrained surrogate model and collects multiple weight sets from a few additional training epochs with a constant and high learning rate. LGV exploits two geometric properties that we relate to transferability. First, models that belong to a wider weight optimum are better surrogates. Second, we identify a subspace able to generate an effective surrogate ensemble among this wider optimum. Through extensive experiments, we show that LGV alone outperforms all (combinations of) four established test-time transformations by 1.8 to 59.9 percentage points. Our findings shed new light on the importance of the geometry of the weight space to explain the transferability of adversarial examples.
    Atomic structure generation from reconstructing structural fingerprints. (arXiv:2207.13227v1 [cond-mat.mtrl-sci])
    Data-driven machine learning methods have the potential to dramatically accelerate the rate of materials design over conventional human-guided approaches. These methods would help identify or, in the case of generative models, even create novel crystal structures of materials with a set of specified functional properties to then be synthesized or isolated in the laboratory. For crystal structure generation, a key bottleneck lies in developing suitable atomic structure fingerprints or representations for the machine learning model, analogous to the graph-based or SMILES representations used in molecular generation. However, finding data-efficient representations that are invariant to translations, rotations, and permutations, while remaining invertible to the Cartesian atomic coordinates remains an ongoing challenge. Here, we propose an alternative approach to this problem by taking existing non-invertible representations with the desired invariances and developing an algorithm to reconstruct the atomic coordinates through gradient-based optimization using automatic differentiation. This can then be coupled to a generative machine learning model which generates new materials within the representation space, rather than in the data-inefficient Cartesian space. In this work, we implement this end-to-end structure generation approach using atom-centered symmetry functions as the representation and conditional variational autoencoders as the generative model. We are able to successfully generate novel and valid atomic structures of sub-nanometer Pt nanoparticles as a proof of concept. Furthermore, this method can be readily extended to any suitable structural representation, thereby providing a powerful, generalizable framework towards structure-based generation.
    Should Bank Stress Tests Be Fair?. (arXiv:2207.13319v1 [stat.ML])
    Regulatory stress tests have become the primary tool for setting capital requirements at the largest U.S. banks. The Federal Reserve uses confidential models to evaluate bank-specific outcomes for bank-specific portfolios in shared stress scenarios. As a matter of policy, the same models are used for all banks, despite considerable heterogeneity across institutions; individual banks have contended that some models are not suited to their businesses. Motivated by this debate, we ask, what is a fair aggregation of individually tailored models into a common model? We argue that simply pooling data across banks treats banks equally but is subject to two deficiencies: it may distort the impact of legitimate portfolio features, and it is vulnerable to implicit misdirection of legitimate information to infer bank identity. We compare various notions of regression fairness to address these deficiencies, considering both forecast accuracy and equal treatment. In the setting of linear models, we argue for estimating and then discarding centered bank fixed effects as preferable to simply ignoring differences across banks. We present evidence that the overall impact can be material. We also discuss extensions to nonlinear models.
    Gaia: Graph Neural Network with Temporal Shift aware Attention for Gross Merchandise Value Forecast in E-commerce. (arXiv:2207.13329v1 [cs.LG])
    E-commerce has gone a long way in empowering merchants through the internet. In order to store the goods efficiently and arrange the marketing resource properly, it is important for them to make the accurate gross merchandise value (GMV) prediction. However, it's nontrivial to make accurate prediction with the deficiency of digitized data. In this article, we present a solution to better forecast GMV inside Alipay app. Thanks to graph neural networks (GNN) which has great ability to correlate different entities to enrich information, we propose Gaia, a graph neural network (GNN) model with temporal shift aware attention. Gaia leverages the relevant e-seller' sales information and learn neighbor correlation based on temporal dependencies. By testing on Alipay's real dataset and comparing with other baselines, Gaia has shown the best performance. And Gaia is deployed in the simulated online environment, which also achieves great improvement compared with baselines.  ( 2 min )
    The Randomness of Input Data Spaces is an A Priori Predictor for Generalization. (arXiv:2106.04181v2 [cs.LG] UPDATED)
    Over-parameterized models can perfectly learn various types of data distributions, however, generalization error is usually lower for real data in comparison to artificial data. This suggests that the properties of data distributions have an impact on generalization capability. This work focuses on the search space defined by the input data and assumes that the correlation between labels of neighboring input values influences generalization. If correlation is low, the randomness of the input data space is high leading to high generalization error. We suggest to measure the randomness of an input data space using Maurer's universal. Results for synthetic classification tasks and common image classification benchmarks (MNIST, CIFAR10, and Microsoft's cats vs. dogs data set) find a high correlation between the randomness of input data spaces and the generalization error of deep neural networks for binary classification problems.  ( 2 min )
    Efficient Personalized Speech Enhancement through Self-Supervised Learning. (arXiv:2104.02017v2 [eess.AS] UPDATED)
    This work presents self-supervised learning methods for developing monaural speaker-specific (i.e., personalized) speech enhancement models. While generalist models must broadly address many speakers, specialist models can adapt their enhancement function towards a particular speaker's voice, expecting to solve a narrower problem. Hence, specialists are capable of achieving more optimal performance in addition to reducing computational complexity. However, naive personalization methods can require clean speech from the target user, which is inconvenient to acquire, e.g., due to subpar recording conditions. To this end, we pose personalization as either a zero-shot task, in which no additional clean speech of the target speaker is used for training, or a few-shot learning task, in which the goal is to minimize the duration of the clean speech used for transfer learning. With this paper, we propose self-supervised learning methods as a solution to both zero- and few-shot personalization tasks. The proposed methods are designed to learn the personalized speech features from unlabeled data (i.e., in-the-wild noisy recordings from the target user) without knowing the corresponding clean sources. Our experiments investigate three different self-supervised learning mechanisms. The results show that self-supervised models achieve zero-shot and few-shot personalization using fewer model parameters and less clean data from the target user, achieving the data efficiency and model compression goals.  ( 3 min )
    Uncertainty-based Visual Question Answering: Estimating Semantic Inconsistency between Image and Knowledge Base. (arXiv:2207.13242v1 [cs.CV])
    Knowledge-based visual question answering (KVQA) task aims to answer questions that require additional external knowledge as well as an understanding of images and questions. Recent studies on KVQA inject an external knowledge in a multi-modal form, and as more knowledge is used, irrelevant information may be added and can confuse the question answering. In order to properly use the knowledge, this study proposes the following: 1) we introduce a novel semantic inconsistency measure computed from caption uncertainty and semantic similarity; 2) we suggest a new external knowledge assimilation method based on the semantic inconsistency measure and apply it to integrate explicit knowledge and implicit knowledge for KVQA; 3) the proposed method is evaluated with the OK-VQA dataset and achieves the state-of-the-art performance.
    Time Series Anomaly Detection via Reinforcement Learning-Based Model Selection. (arXiv:2205.09884v4 [cs.LG] UPDATED)
    Time series anomaly detection has been recognized as of critical importance for the reliable and efficient operation of real-world systems. Many anomaly detection methods have been developed based on various assumptions on anomaly characteristics. However, due to the complex nature of real-world data, different anomalies within a time series usually have diverse profiles supporting different anomaly assumptions. This makes it difficult to find a single anomaly detector that can consistently outperform other models. In this work, to harness the benefits of different base models, we propose a reinforcement learning-based model selection framework. Specifically, we first learn a pool of different anomaly detection models, and then utilize reinforcement learning to dynamically select a candidate model from these base models. Experiments on real-world data have demonstrated that the proposed strategy can indeed outplay all baseline models in terms of overall performance.  ( 2 min )
    Deep Clustering with Features from Self-Supervised Pretraining. (arXiv:2207.13364v1 [cs.CV])
    A deep clustering model conceptually consists of a feature extractor that maps data points to a latent space, and a clustering head that groups data points into clusters in the latent space. Although the two components used to be trained jointly in an end-to-end fashion, recent works have proved it beneficial to train them separately in two stages. In the first stage, the feature extractor is trained via self-supervised learning, which enables the preservation of the cluster structures among the data points. To preserve the cluster structures even better, we propose to replace the first stage with another model that is pretrained on a much larger dataset via self-supervised learning. The method is simple and might suffer from domain shift. Nonetheless, we have empirically shown that it can achieve superior clustering performance. When a vision transformer (ViT) architecture is used for feature extraction, our method has achieved clustering accuracy 94.0%, 55.6% and 97.9% on CIFAR-10, CIFAR-100 and STL-10 respectively. The corresponding previous state-of-the-art results are 84.3%, 47.7% and 80.8%. Our code will be available online with the publication of the paper.
    Dynamic Shielding for Reinforcement Learning in Black-Box Environments. (arXiv:2207.13446v1 [cs.LG])
    It is challenging to use reinforcement learning (RL) in cyber-physical systems due to the lack of safety guarantees during learning. Although there have been various proposals to reduce undesired behaviors during learning, most of these techniques require prior system knowledge, and their applicability is limited. This paper aims to reduce undesired behaviors during learning without requiring any prior system knowledge. We propose dynamic shielding: an extension of a model-based safe RL technique called shielding using automata learning. The dynamic shielding technique constructs an approximate system model in parallel with RL using a variant of the RPNI algorithm and suppresses undesired explorations due to the shield constructed from the learned model. Through this combination, potentially unsafe actions can be foreseen before the agent experiences them. Experiments show that our dynamic shield significantly decreases the number of undesired events during training.  ( 2 min )
    Semi-analytical Industrial Cooling System Model for Reinforcement Learning. (arXiv:2207.13131v1 [cs.AI])
    We present a hybrid industrial cooling system model that embeds analytical solutions within a multi-physics simulation. This model is designed for reinforcement learning (RL) applications and balances simplicity with simulation fidelity and interpretability. The model's fidelity is evaluated against real world data from a large scale cooling system. This is followed by a case study illustrating how the model can be used for RL research. For this, we develop an industrial task suite that allows specifying different problem settings and levels of complexity, and use it to evaluate the performance of different RL algorithms.
    Toward Transparent AI: A Survey on Interpreting the Inner Structures of Deep Neural Networks. (arXiv:2207.13243v1 [cs.LG])
    The last decade of machine learning has seen drastic increases in scale and capabilities, and deep neural networks (DNNs) are increasingly being deployed across a wide range of domains. However, the inner workings of DNNs are generally difficult to understand, raising concerns about the safety of using these systems without a rigorous understanding of how they function. In this survey, we review literature on techniques for interpreting the inner components of DNNs, which we call "inner" interpretability methods. Specifically, we review methods for interpreting weights, neurons, subnetworks, and latent representations with a focus on how these techniques relate to the goal of designing safer, more trustworthy AI systems. We also highlight connections between interpretability and work in modularity, adversarial robustness, continual learning, network compression, and studying the human visual system. Finally, we discuss key challenges and argue for future work in interpretability for AI safety that focuses on diagnostics, benchmarking, and robustness.
    PointFix: Learning to Fix Domain Bias for Robust Online Stereo Adaptation. (arXiv:2207.13340v1 [cs.CV])
    Online stereo adaptation tackles the domain shift problem, caused by different environments between synthetic (training) and real (test) datasets, to promptly adapt stereo models in dynamic real-world applications such as autonomous driving. However, previous methods often fail to counteract particular regions related to dynamic objects with more severe environmental changes. To mitigate this issue, we propose to incorporate an auxiliary point-selective network into a meta-learning framework, called PointFix, to provide a robust initialization of stereo models for online stereo adaptation. In a nutshell, our auxiliary network learns to fix local variants intensively by effectively back-propagating local information through the meta-gradient for the robust initialization of the baseline model. This network is model-agnostic, so can be used in any kind of architectures in a plug-and-play manner. We conduct extensive experiments to verify the effectiveness of our method under three adaptation settings such as short-, mid-, and long-term sequences. Experimental results show that the proper initialization of the base stereo model by the auxiliary network enables our learning paradigm to achieve state-of-the-art performance at inference.  ( 2 min )
    Deep Model-Based Architectures for Inverse Problems under Mismatched Priors. (arXiv:2207.13200v1 [eess.IV])
    There is a growing interest in deep model-based architectures (DMBAs) for solving imaging inverse problems by combining physical measurement models and learned image priors specified using convolutional neural nets (CNNs). For example, well-known frameworks for systematically designing DMBAs include plug-and-play priors (PnP), deep unfolding (DU), and deep equilibrium models (DEQ). While the empirical performance and theoretical properties of DMBAs have been widely investigated, the existing work in the area has primarily focused on their performance when the desired image prior is known exactly. This work addresses the gap in the prior work by providing new theoretical and numerical insights into DMBAs under mismatched CNN priors. Mismatched priors arise naturally when there is a distribution shift between training and testing data, for example, due to test images being from a different distribution than images used for training the CNN prior. They also arise when the CNN prior used for inference is an approximation of some desired statistical estimator (MAP or MMSE). Our theoretical analysis provides explicit error bounds on the solution due to the mismatched CNN priors under a set of clearly specified assumptions. Our numerical results compare the empirical performance of DMBAs under realistic distribution shifts and approximate statistical estimators.  ( 3 min )
    Transporters with Visual Foresight for Solving Unseen Rearrangement Tasks. (arXiv:2202.10765v3 [cs.RO] UPDATED)
    Rearrangement tasks have been identified as a crucial challenge for intelligent robotic manipulation, but few methods allow for precise construction of unseen structures. We propose a visual foresight model for pick-and-place rearrangement manipulation which is able to learn efficiently. In addition, we develop a multi-modal action proposal module which builds on the Goal-Conditioned Transporter Network, a state-of-the-art imitation learning method. Our image-based task planning method, Transporters with Visual Foresight, is able to learn from only a handful of data and generalize to multiple unseen tasks in a zero-shot manner. TVF is able to improve the performance of a state-of-the-art imitation learning method on unseen tasks in simulation and real robot experiments. In particular, the average success rate on unseen tasks improves from 55.4% to 78.5% in simulation experiments and from 30% to 63.3% in real robot experiments when given only tens of expert demonstrations. Video and code are available on our project website: https://chirikjianlab.github.io/tvf/  ( 2 min )
    Towards Soft Fairness in Restless Multi-Armed Bandits. (arXiv:2207.13343v1 [cs.LG])
    Restless multi-armed bandits (RMAB) is a framework for allocating limited resources under uncertainty. It is an extremely useful model for monitoring beneficiaries and executing timely interventions to ensure maximum benefit in public health settings (e.g., ensuring patients take medicines in tuberculosis settings, ensuring pregnant mothers listen to automated calls about good pregnancy practices). Due to the limited resources, typically certain communities or regions are starved of interventions that can have follow-on effects. To avoid starvation in the executed interventions across individuals/regions/communities, we first provide a soft fairness constraint and then provide an approach to enforce the soft fairness constraint in RMABs. The soft fairness constraint requires that an algorithm never probabilistically favor one arm over another if the long-term cumulative reward of choosing the latter arm is higher. Our approach incorporates softmax based value iteration method in the RMAB setting to design selection algorithms that manage to satisfy the proposed fairness constraint. Our method, referred to as SoftFair, also provides theoretical performance guarantees and is asymptotically optimal. Finally, we demonstrate the utility of our approaches on simulated benchmarks and show that the soft fairness constraint can be handled without a significant sacrifice on value.  ( 2 min )
    Efficient Resource Allocation with Fairness Constraints in Restless Multi-Armed Bandits. (arXiv:2206.03883v2 [cs.LG] UPDATED)
    Restless Multi-Armed Bandits (RMAB) is an apt model to represent decision-making problems in public health interventions (e.g., tuberculosis, maternal, and child care), anti-poaching planning, sensor monitoring, personalized recommendations and many more. Existing research in RMAB has contributed mechanisms and theoretical results to a wide variety of settings, where the focus is on maximizing expected value. In this paper, we are interested in ensuring that RMAB decision making is also fair to different arms while maximizing expected value. In the context of public health settings, this would ensure that different people and/or communities are fairly represented while making public health intervention decisions. To achieve this goal, we formally define the fairness constraints in RMAB and provide planning and learning methods to solve RMAB in a fair manner. We demonstrate key theoretical properties of fair RMAB and experimentally demonstrate that our proposed methods handle fairness constraints without sacrificing significantly on solution quality.  ( 2 min )
    FedVLN: Privacy-preserving Federated Vision-and-Language Navigation. (arXiv:2203.14936v2 [cs.AI] UPDATED)
    Data privacy is a central problem for embodied agents that can perceive the environment, communicate with humans, and act in the real world. While helping humans complete tasks, the agent may observe and process sensitive information of users, such as house environments, human activities, etc. In this work, we introduce privacy-preserving embodied agent learning for the task of Vision-and-Language Navigation (VLN), where an embodied agent navigates house environments by following natural language instructions. We view each house environment as a local client, which shares nothing other than local updates with the cloud server and other clients, and propose a novel federated vision-and-language navigation (FedVLN) framework to protect data privacy during both training and pre-exploration. Particularly, we propose a decentralized training strategy to limit the data of each client to its local model training and a federated pre-exploration method to do partial model aggregation to improve model generalizability to unseen environments. Extensive results on R2R and RxR datasets show that under our FedVLN framework, decentralized VLN models achieve comparable results with centralized training while protecting seen environment privacy, and federated pre-exploration significantly outperforms centralized pre-exploration while preserving unseen environment privacy.  ( 2 min )
    Encoding Concepts in Graph Neural Networks. (arXiv:2207.13586v1 [cs.LG])
    The opaque reasoning of Graph Neural Networks induces a lack of human trust. Existing graph network explainers attempt to address this issue by providing post-hoc explanations, however, they fail to make the model itself more interpretable. To fill this gap, we introduce the Concept Encoder Module, the first differentiable concept-discovery approach for graph networks. The proposed approach makes graph networks explainable by design by first discovering graph concepts and then using these to solve the task. Our results demonstrate that this approach allows graph networks to: (i) attain model accuracy comparable with their equivalent vanilla versions, (ii) discover meaningful concepts that achieve high concept completeness and purity scores, (iii) provide high-quality concept-based logic explanations for their prediction, and (iv) support effective interventions at test time: these can increase human trust as well as significantly improve model performance.  ( 2 min )
    Analysis and Design of Quadratic Neural Networks for Regression, Classification, and Lyapunov Control of Dynamical Systems. (arXiv:2207.13120v1 [cs.LG])
    This paper addresses the analysis and design of quadratic neural networks, which have been recently introduced in the literature, and their applications to regression, classification, system identification and control of dynamical systems. These networks offer several advantages, the most important of which are the fact that the architecture is a by-product of the design and is not determined a-priori, their training can be done by solving a convex optimization problem so that the global optimum of the weights is achieved, and the input-output mapping can be expressed analytically by a quadratic form. It also appears from several examples that these networks work extremely well using only a small fraction of the training data. The results in the paper cast regression, classification, system identification, stability and control design as convex optimization problems, which can be solved efficiently with polynomial-time algorithms to a global optimum. Several examples will show the effectiveness of quadratic neural networks in applications.
    The Sample Complexity of Forecast Aggregation. (arXiv:2207.13126v1 [cs.LG])
    We consider a Bayesian forecast aggregation model where $n$ experts, after observing private signals about an unknown binary event, report their posterior beliefs about the event to a principal, who then aggregates the reports into a single prediction for the event. The signals of the experts and the outcome of the event follow a joint distribution that is unknown to the principal, but the principal has access to i.i.d. "samples" from the distribution, where each sample is a tuple of experts' reports (not signals) and the realization of the event. Using these samples, the principal aims to find an $\varepsilon$-approximately optimal (Bayesian) aggregator. We study the sample complexity of this problem. We show that, for arbitrary discrete distributions, the number of samples must be at least $\tilde \Omega(m^{n-2} / \varepsilon)$, where $m$ is the size of each expert's signal space. This sample complexity grows exponentially in the number of experts $n$. But if experts' signals are independent conditioned on the realization of the event, then the sample complexity is significantly reduced, to $\tilde O(1 / \varepsilon^2)$, which does not depend on $n$.  ( 2 min )
    Intelligent Zero Trust Architecture for 5G/6G Networks: Principles, Challenges, and the Role of Machine Learning in the context of O-RAN. (arXiv:2105.01478v3 [cs.NI] UPDATED)
    In this position paper, we discuss the critical need for integrating zero trust (ZT) principles into next-generation communication networks (5G/6G). We highlight the challenges and introduce the concept of an intelligent zero trust architecture (i-ZTA) as a security framework in 5G/6G networks with untrusted components. While network virtualization, software-defined networking (SDN), and service-based architectures (SBA) are key enablers of 5G networks, operating in an untrusted environment has also become a key feature of the networks. Further, seamless connectivity to a high volume of devices has broadened the attack surface on information infrastructure. Network assurance in a dynamic untrusted environment calls for revolutionary architectures beyond existing static security frameworks. To the best of our knowledge, this is the first position paper that presents the architectural concept design of an i-ZTA upon which modern artificial intelligence (AI) algorithms can be developed to provide information security in untrusted networks. We introduce key ZT principles as real-time Monitoring of the security state of network assets, Evaluating the risk of individual access requests, and Deciding on access authorization using a dynamic trust algorithm, called MED components. To ensure ease of integration, the envisioned architecture adopts an SBA-based design, similar to the 3GPP specification of 5G networks, by leveraging the open radio access network (O-RAN) architecture with appropriate real-time engines and network interfaces for collecting necessary machine learning data. Therefore, this work provides novel research directions to design machine learning based components that contribute towards i-ZTA for the future 5G/6G networks.  ( 3 min )
    Fast expansion into harmonics on the disk: a steerable basis with fast radial convolutions. (arXiv:2207.13674v1 [math.NA])
    We present a fast and numerically accurate method for expanding digitized $L \times L$ images representing functions on $[-1,1]^2$ supported on the disk $\{x \in \mathbb{R}^2 : |x|<1\}$ in the harmonics (Dirichlet Laplacian eigenfunctions) on the disk. Our method runs in $\mathcal{O}(L^2 \log L)$ operations. This basis is also known as the Fourier-Bessel basis and it has several computational advantages: it is orthogonal, ordered by frequency, and steerable in the sense that images expanded in the basis can be rotated by applying a diagonal transform to the coefficients. Moreover, we show that convolution with radial functions can also be efficiently computed by applying a diagonal transform to the coefficients.  ( 2 min )
    Deep Partial Updating: Towards Communication Efficient Updating for On-device Inference. (arXiv:2007.03071v3 [cs.LG] UPDATED)
    Emerging edge intelligence applications require the server to retrain and update deep neural networks deployed on remote edge nodes to leverage newly collected data samples. Unfortunately, it may be impossible in practice to continuously send fully updated weights to these edge nodes due to the highly constrained communication resource. In this paper, we propose the weight-wise deep partial updating paradigm, which smartly selects a small subset of weights to update in each server-to-edge communication round, while achieving a similar performance compared to full updating. Our method is established through analytically upper-bounding the loss difference between partial updating and full updating, and only updates the weights which make the largest contributions to the upper bound. Extensive experimental results demonstrate the efficacy of our partial updating methodology which achieves a high inference accuracy while updating a rather small number of weights.  ( 2 min )
    Accelerating the Learning of TAMER with Counterfactual Explanations. (arXiv:2108.01358v2 [cs.AI] UPDATED)
    The capability to interactively learn from human feedback would enable agents in new settings. For example, even novice users could train service robots in new tasks naturally and interactively. Human-in-the-loop Reinforcement Learning (HRL) combines human feedback and Reinforcement Learning (RL) techniques. State-of-the-art interactive learning techniques suffer from slow learning speed, thus leading to a frustrating experience for the human. We approach this problem by extending the HRL framework TAMER for evaluative feedback with the possibility to enhance human feedback with two different types of counterfactual explanations (action and state based). We experimentally show that our extensions improve the speed of learning.  ( 2 min )
    Accurate detection of sepsis at ED triage using machine learning with clinical natural language processing. (arXiv:2204.07657v3 [cs.LG] UPDATED)
    Sepsis is a life-threatening condition with organ dysfunction and is a leading cause of death and critical illness worldwide. Accurate detection of sepsis during emergency department triage would allow early initiation of lab analysis, antibiotic administration, and other sepsis treatment protocols. The purpose of this study was to determine whether EHR data can be extracted and synthesized with the latest machine learning algorithms (KATE Sepsis) and clinical natural language processing to produce accurate sepsis models, and compare KATE Sepsis performance with existing sepsis screening protocols, such as SIRS and qSOFA. A machine learning model (KATE Sepsis) was developed using patient encounters with triage data from 16 participating hospitals. KATE Sepsis, SIRS, standard screening (SIRS with source of infection) and qSOFA were tested in three settings. Cohort-A was a retrospective analysis on medical records from a single Site 1. Cohort-B was a prospective analysis of Site 1. Cohort-C was a retrospective analysis on Site 1 with 15 additional sites. Across all cohorts, KATE Sepsis demonstrates an AUC of 0.94-0.963 with 73-74.87% TPR and 3.76-7.17% FPR. Standard screening demonstrates an AUC of 0.682-0.726 with 39.39-51.19% TPR and 2.9-6.02% FPR. The qSOFA protocol demonstrates an AUC of 0.544-0.56, with 10.52-13.18% TPR and 1.22-1.68% FPR. For severe sepsis, across all cohorts, KATE Sepsis demonstrates an AUC of 0.935-0.972 with 70-82.26% TPR and 4.64-8.62% FPR. For septic shock, across all cohorts, KATE Sepsis demonstrates an AUC of 0.96-0.981 with 85.71-89.66% TPR and 4.85-8.8% FPR. SIRS, standard screening, and qSOFA demonstrate low AUC and TPR for severe sepsis and septic shock detection. KATE Sepsis provided substantially better sepsis detection performance in triage than commonly used screening protocols.  ( 3 min )
    Understanding Convolutional Neural Networks from Volterra Convolution Perspective. (arXiv:2110.09902v2 [cs.LG] UPDATED)
    We make an attempt to understanding convolutional neural network by exploring the relationship between (deep) convolutional neural networks and Volterra convolutions. We propose a novel approach to explain and study the overall characteristics of neural networks without being disturbed by the horribly complex architectures. Specifically, we convert the basic structures and their combinations to the form of Volterra convolutions. The results show that most of convolutional neural networks can be converted to the form of Volterra convolution, where the converted proxy kernels preserve the characteristics of the original network. Analyzing these proxy kernels may give valuable insight about the original network. Base on this setup, we presented methods to approximating the order-zero and order-one proxy kernels, and verified the correctness and effectiveness of our results.  ( 2 min )
    Using Deep Learning to Detecting Deepfakes. (arXiv:2207.13644v1 [cs.CV])
    In the recent years, social media has grown to become a major source of information for many online users. This has given rise to the spread of misinformation through deepfakes. Deepfakes are videos or images that replace one persons face with another computer-generated face, often a more recognizable person in society. With the recent advances in technology, a person with little technological experience can generate these videos. This enables them to mimic a power figure in society, such as a president or celebrity, creating the potential danger of spreading misinformation and other nefarious uses of deepfakes. To combat this online threat, researchers have developed models that are designed to detect deepfakes. This study looks at various deepfake detection models that use deep learning algorithms to combat this looming threat. This survey focuses on providing a comprehensive overview of the current state of deepfake detection models and the unique approaches many researchers take to solving this problem. The benefits, limitations, and suggestions for future work will be thoroughly discussed throughout this paper.  ( 2 min )
    Emergence of Novelty in Evolutionary Algorithms. (arXiv:2207.04857v2 [cs.NE] UPDATED)
    One of the main problems of evolutionary algorithms is the convergence of the population to local minima. In this paper, we explore techniques that can avoid this problem by encouraging a diverse behavior of the agents through a shared reward system. The rewards are randomly distributed in the environment, and the agents are only rewarded for collecting them first. This leads to an emergence of a novel behavior of the agents. We introduce our approach to the maze problem and compare it to the previously proposed solution, denoted as Novelty Search (Lehman and Stanley, 2011a). We find that our solution leads to an improved performance while being significantly simpler. Building on that, we generalize the problem and apply our approach to a more advanced set of tasks, Atari Games, where we observe a similar performance quality with much less computational power needed.  ( 2 min )
    Graph Neural Networks for Communication Networks: Context, Use Cases and Opportunities. (arXiv:2112.14792v2 [cs.NI] UPDATED)
    Graph neural networks (GNN) have shown outstanding applications in many fields where data is fundamentally represented as graphs (e.g., chemistry, biology, recommendation systems). In this vein, communication networks comprise many fundamental components that are naturally represented in a graph-structured manner (e.g., topology, configurations, traffic flows). This position article presents GNNs as a fundamental tool for modeling, control and management of communication networks. GNNs represent a new generation of data-driven models that can accurately learn and reproduce the complex behaviors behind real networks. As a result, such models can be applied to a wide variety of networking use cases, such as planning, online optimization, or troubleshooting. The main advantage of GNNs over traditional neural networks lies in its unprecedented generalization capabilities when applied to other networks and configurations unseen during training, which is a critical feature for achieving practical data-driven solutions for networking. This article comprises a brief tutorial on GNNs and their possible applications to communication networks. To showcase the potential of this technology, we present two use cases with state-of-the-art GNN models respectively applied to wired and wireless networks. Lastly, we delve into the key open challenges and opportunities yet to be explored in this novel research area.  ( 3 min )
    Multi-layer Representation Learning for Robust OOD Image Classification. (arXiv:2207.13678v1 [cs.CV])
    Convolutional Neural Networks have become the norm in image classification. Nevertheless, their difficulty to maintain high accuracy across datasets has become apparent in the past few years. In order to utilize such models in real-world scenarios and applications, they must be able to provide trustworthy predictions on unseen data. In this paper, we argue that extracting features from a CNN's intermediate layers can assist in the model's final prediction. Specifically, we adapt the Hypercolumns method to a ResNet-18 and find a significant increase in the model's accuracy, when evaluating on the NICO dataset.  ( 2 min )
    BPFISH: Blockchain and Privacy-preserving FL Inspired Smart Healthcare. (arXiv:2207.11654v2 [cs.NI] UPDATED)
    This paper proposes Federated Learning (FL) based smart healthcare system where Medical Centers (MCs) train the local model using the data collected from patients and send the model weights to the miners in a blockchain-based robust framework without sharing raw data, keeping privacy preservation into deliberation. We formulate an optimization problem by maximizing the utility and minimizing the loss function considering energy consumption and FL process delay of MCs for learning effective models on distributed healthcare data underlying a blockchain-based framework. We propose a solution in two stages: first, offer a stable matching-based association algorithm to maximize the utility of both miners and MCs and then solve loss minimization using Stochastic Gradient Descent (SGD) algorithm employing FL under Differential Privacy (DP) and blockchain technology. Moreover, we incorporate blockchain technology to provide tempered resistant and decentralized model weight sharing in the proposed FL-based framework. The effectiveness of the proposed model is shown through simulation on real-world healthcare data comparing other state-of-the-art techniques.  ( 2 min )
    Adversarial Imitation Learning from Video using a State Observer. (arXiv:2202.00243v2 [cs.RO] UPDATED)
    The imitation learning research community has recently made significant progress towards the goal of enabling artificial agents to imitate behaviors from video demonstrations alone. However, current state-of-the-art approaches developed for this problem exhibit high sample complexity due, in part, to the high-dimensional nature of video observations. Towards addressing this issue, we introduce here a new algorithm called Visual Generative Adversarial Imitation from Observation using a State Observer VGAIfO-SO. At its core, VGAIfO-SO seeks to address sample inefficiency using a novel, self-supervised state observer, which provides estimates of lower-dimensional proprioceptive state representations from high-dimensional images. We show experimentally in several continuous control environments that VGAIfO-SO is more sample efficient than other IfO algorithms at learning from video-only demonstrations and can sometimes even achieve performance close to the Generative Adversarial Imitation from Observation (GAIfO) algorithm that has privileged access to the demonstrator's proprioceptive state information.  ( 2 min )
    Latent Space Smoothing for Individually Fair Representations. (arXiv:2111.13650v3 [cs.LG] UPDATED)
    Fair representation learning transforms user data into a representation that ensures fairness and utility regardless of the downstream application. However, learning individually fair representations, i.e., guaranteeing that similar individuals are treated similarly, remains challenging in high-dimensional settings such as computer vision. In this work, we introduce LASSI, the first representation learning method for certifying individual fairness of high-dimensional data. Our key insight is to leverage recent advances in generative modeling to capture the set of similar individuals in the generative latent space. This enables us to learn individually fair representations that map similar individuals close together by using adversarial training to minimize the distance between their representations. Finally, we employ randomized smoothing to provably map similar individuals close together, in turn ensuring that local robustness verification of the downstream application results in end-to-end fairness certification. Our experimental evaluation on challenging real-world image data demonstrates that our method increases certified individual fairness by up to 90% without significantly affecting task utility.  ( 2 min )
    JDRec: Practical Actor-Critic Framework for Online Combinatorial Recommender System. (arXiv:2207.13311v1 [cs.IR])
    A combinatorial recommender (CR) system feeds a list of items to a user at a time in the result page, in which the user behavior is affected by both contextual information and items. The CR is formulated as a combinatorial optimization problem with the objective of maximizing the recommendation reward of the whole list. Despite its importance, it is still a challenge to build a practical CR system, due to the efficiency, dynamics, personalization requirement in online environment. In particular, we tear the problem into two sub-problems, list generation and list evaluation. Novel and practical model architectures are designed for these sub-problems aiming at jointly optimizing effectiveness and efficiency. In order to adapt to online case, a bootstrap algorithm forming an actor-critic reinforcement framework is given to explore better recommendation mode in long-term user interaction. Offline and online experiment results demonstrate the efficacy of proposed JDRec framework. JDRec has been applied in online JD recommendation, improving click through rate by 2.6% and synthetical value for the platform by 5.03%. We will publish the large-scale dataset used in this study to contribute to the research community.  ( 3 min )
    Multi-Objective Hyperparameter Optimization -- An Overview. (arXiv:2206.07438v2 [cs.LG] UPDATED)
    Hyperparameter optimization constitutes a large part of typical modern machine learning workflows. This arises from the fact that machine learning methods and corresponding preprocessing steps often only yield optimal performance when hyperparameters are properly tuned. But in many applications, we are not only interested in optimizing ML pipelines solely for predictive accuracy; additional metrics or constraints must be considered when determining an optimal configuration, resulting in a multi-objective optimization problem. This is often neglected in practice, due to a lack of knowledge and readily available software implementations for multi-objective hyperparameter optimization. In this work, we introduce the reader to the basics of multi-objective hyperparameter optimization and motivate its usefulness in applied ML. Furthermore, we provide an extensive survey of existing optimization strategies, both from the domain of evolutionary algorithms and Bayesian optimization. We illustrate the utility of MOO in several specific ML applications, considering objectives such as operating conditions, prediction time, sparseness, fairness, interpretability and robustness.  ( 2 min )
    Dynamical simulation via quantum machine learning with provable generalization. (arXiv:2204.10269v2 [quant-ph] UPDATED)
    Much attention has been paid to dynamical simulation and quantum machine learning (QML) independently as applications for quantum advantage, while the possibility of using QML to enhance dynamical simulations has not been thoroughly investigated. Here we develop a framework for using QML methods to simulate quantum dynamics on near-term quantum hardware. We use generalization bounds, which bound the error a machine learning model makes on unseen data, to rigorously analyze the training data requirements of an algorithm within this framework. This provides a guarantee that our algorithm is resource-efficient, both in terms of qubit and data requirements. Our numerics exhibit efficient scaling with problem size, and we simulate 20 times longer than Trotterization on IBMQ-Bogota.  ( 2 min )
    Scalable Certified Segmentation via Randomized Smoothing. (arXiv:2107.00228v2 [cs.LG] UPDATED)
    We present a new certification method for image and point cloud segmentation based on randomized smoothing. The method leverages a novel scalable algorithm for prediction and certification that correctly accounts for multiple testing, necessary for ensuring statistical guarantees. The key to our approach is reliance on established multiple-testing correction mechanisms as well as the ability to abstain from classifying single pixels or points while still robustly segmenting the overall input. Our experimental evaluation on synthetic data and challenging datasets, such as Pascal Context, Cityscapes, and ShapeNet, shows that our algorithm can achieve, for the first time, competitive accuracy and certification guarantees on real-world segmentation tasks. We provide an implementation at https://github.com/eth-sri/segmentation-smoothing.  ( 2 min )
    Thermal half-lives of azobenzene derivatives: virtual screening based on intersystem crossing using a machine learning potential. (arXiv:2207.11592v2 [physics.chem-ph] UPDATED)
    Molecular photoswitches are the foundation of light-activated drugs. A key photoswitch is azobenzene, which exhibits trans-cis isomerism in response to light. The thermal half-life of the cis isomer is of crucial importance, since it controls the duration of the light-induced biological effect. Here we introduce a computational tool for predicting the thermal half-lives of azobenzene derivatives. Our automated approach uses a fast and accurate machine learning potential trained on quantum chemistry data. Building on well-established earlier evidence, we argue that thermal isomerization proceeds through rotation mediated by intersystem crossing, and incorporate this mechanism into our automated workflow. We use our approach to predict the thermal half-lives of 19,000 azobenzene derivatives. We explore trends and tradeoffs between barriers and absorption wavelengths, and open-source our data and software to accelerate research in photopharmacology.  ( 2 min )
    Fixed-Time Convergence for a Class of Nonconvex-Nonconcave Min-Max Problems. (arXiv:2207.12845v1 [math.OC] CROSS LISTED)
    This study develops a fixed-time convergent saddle point dynamical system for solving min-max problems under a relaxation of standard convexity-concavity assumption. In particular, it is shown that by leveraging the dynamical systems viewpoint of an optimization algorithm, accelerated convergence to a saddle point can be obtained. Instead of requiring the objective function to be strongly-convex--strongly-concave (as necessitated for accelerated convergence of several saddle-point algorithms), uniform fixed-time convergence is guaranteed for functions satisfying only the two-sided Polyak-{\L}ojasiewicz (PL) inequality. A large number of practical problems, including the robust least squares estimation, are known to satisfy the two-sided PL inequality. The proposed method achieves arbitrarily fast convergence compared to any other state-of-the-art method with linear or even super-linear convergence, as also corroborated in numerical case studies.  ( 2 min )
    The Implications of the No-Free-Lunch Theorems for Meta-induction. (arXiv:2103.11956v3 [cs.LG] UPDATED)
    The important recent book by G. Schurz appreciates that the no-free-lunch theorems (NFL) have major implications for the problem of (meta) induction. Here I review the NFL theorems, emphasizing that they do not only concern the case where there is a uniform prior -- they prove that there are "as many priors" (loosely speaking) for which any induction algorithm $A$ out-generalizes some induction algorithm $B$ as vice-versa. Importantly though, in addition to the NFL theorems, there are many {free lunch} theorems. In particular, the NFL theorems can only be used to compare the {marginal} expected performance of an induction algorithm $A$ with the marginal expected performance of an induction algorithm $B$. There is a rich set of free lunches which instead concern the statistical correlations among the generalization errors of induction algorithms. As I describe, the meta-induction algorithms that Schurz advocate as a "solution to Hume's problem" are just an example of such a free lunch based on correlations among the generalization errors of induction algorithms. I end by pointing out that the prior that Schurz advocates, which is uniform over bit frequencies rather than bit patterns, is contradicted by thousands of experiments in statistical physics and by the great success of the maximum entropy procedure in inductive inference.  ( 3 min )
    Bioinspired random projections for robust, sparse classification. (arXiv:2206.09222v2 [stat.ML] UPDATED)
    Inspired by the use of random projections in biological sensing systems, we present a new algorithm for processing data in classification problems. This is based on observations of the human brain and the fruit fly's olfactory system and involves randomly projecting data into a space of greatly increased dimension before applying a cap operation to truncate the smaller entries. This leads to a simple algorithm that is very computationally efficient and can be used to either give a sparse representation with minimal loss in classification accuracy or give improved robustness, in the sense that classification accuracy is improved when noise is added to the data. This is demonstrated with numerical experiments, which supplement theoretical results demonstrating that the resulting signal transform is continuous and invertible, in an appropriate sense.  ( 2 min )
    Exploring Representation of Horn Clauses using GNNs (Extended Technical Report). (arXiv:2206.06986v4 [cs.AI] UPDATED)
    Learning program semantics from raw source code is challenging due to the complexity of real-world programming language syntax and due to the difficulty of reconstructing long-distance relational information implicitly represented in programs using identifiers. Addressing the first point, we consider Constrained Horn Clauses (CHCs) as a standard representation of program verification problems, providing a simple and programming language-independent syntax. For the second challenge, we explore graph representations of CHCs, and propose a new Relational Hypergraph Neural Network (R-HyGNN) architecture to learn program features. We introduce two different graph representations of CHCs. One is called constraint graph (CG), and emphasizes syntactic information of CHCs by translating the symbols and their relations in CHCs as typed nodes and binary edges, respectively, and constructing the constraints as abstract syntax trees. The second one is called control- and data-flow hypergraph (CDHG), and emphasizes semantic information of CHCs by representing the control and data flow through ternary hyperedges. We then propose a new GNN architecture, R-HyGNN, extending Relational Graph Convolutional Networks, to handle hypergraphs. To evaluate the ability of R-HyGNN to extract semantic information from programs, we use R-HyGNNs to train models on the two graph representations, and on five proxy tasks with increasing difficulty, using benchmarks from CHC-COMP 2021 as training data. The most difficult proxy task requires the model to predict the occurrence of clauses in counter-examples, which subsumes satisfiability of CHCs. CDHG achieves 90.59% accuracy in this task. Furthermore, R-HyGNN has perfect predictions on one of the graphs consisting of more than 290 clauses. Overall, our experiments indicate that R-HyGNN can capture intricate program features for guiding verification problems.  ( 3 min )
    D3C2-Net: Dual-Domain Deep Convolutional Coding Network for Compressive Sensing. (arXiv:2207.13560v1 [cs.CV])
    Mapping optimization algorithms into neural networks, deep unfolding networks (DUNs) have achieved impressive success in compressive sensing (CS). From the perspective of optimization, DUNs inherit a well-defined and interpretable structure from iterative steps. However, from the viewpoint of neural network design, most existing DUNs are inherently established based on traditional image-domain unfolding, which takes one-channel images as inputs and outputs between adjacent stages, resulting in insufficient information transmission capability and inevitable loss of the image details. In this paper, to break the above bottleneck, we first propose a generalized dual-domain optimization framework, which is general for inverse imaging and integrates the merits of both (1) image-domain and (2) convolutional-coding-domain priors to constrain the feasible region in the solution space. By unfolding the proposed framework into deep neural networks, we further design a novel Dual-Domain Deep Convolutional Coding Network (D3C2-Net) for CS imaging with the capability of transmitting high-throughput feature-level image representation through all the unfolded stages. Experiments on natural and MR images demonstrate that our D3C2-Net achieves higher performance and better accuracy-complexity trade-offs than other state-of-the-arts.  ( 2 min )
    Towards noise robust trigger-word detection with contrastive learning pre-task for fast on-boarding of new trigger-words. (arXiv:2111.03971v3 [cs.SD] UPDATED)
    Trigger-word detection plays an important role as the entry point of user's communication with voice assistants. But supporting a particular word as a trigger-word involves huge amount of data collection, augmentation and labelling for that word. This makes supporting new trigger-words a tedious and time consuming process. To combat this, we explore the use of contrastive learning as a pre-training task that helps the detection model to generalize to different words and noise conditions. We explore supervised contrastive techniques and also propose a novel self-supervised training technique using chunked words from long sentence audios. We show that both supervised and the new self-supervised contrastive pre-training techniques have comparable results to a traditional classification pre-training on new trigger words with less data availability.  ( 2 min )
    Explain My Surprise: Learning Efficient Long-Term Memory by Predicting Uncertain Outcomes. (arXiv:2207.13649v1 [cs.LG])
    In many sequential tasks, a model needs to remember relevant events from the distant past to make correct predictions. Unfortunately, a straightforward application of gradient based training requires intermediate computations to be stored for every element of a sequence. This requires prohibitively large computing memory if a sequence consists of thousands or even millions elements, and as a result, makes learning of very long-term dependencies infeasible. However, the majority of sequence elements can usually be predicted by taking into account only temporally local information. On the other hand, predictions affected by long-term dependencies are sparse and characterized by high uncertainty given only local information. We propose MemUP, a new training method that allows to learn long-term dependencies without backpropagating gradients through the whole sequence at a time. This method can be potentially applied to any gradient based sequence learning. MemUP implementation for recurrent architectures shows performances better or comparable to baselines while requiring significantly less computing memory.  ( 2 min )
    TracInAD: Measuring Influence for Anomaly Detection. (arXiv:2205.01362v3 [cs.LG] UPDATED)
    As with many other tasks, neural networks prove very effective for anomaly detection purposes. However, very few deep-learning models are suited for detecting anomalies on tabular datasets. This paper proposes a novel methodology to flag anomalies based on TracIn, an influence measure initially introduced for explicability purposes. The proposed methods can serve to augment any unsupervised deep anomaly detection method. We test our approach using Variational Autoencoders and show that the average influence of a subsample of training points on a test point can serve as a proxy for abnormality. Our model proves to be competitive in comparison with state-of-the-art approaches: it achieves comparable or better performance in terms of detection accuracy on medical and cyber-security tabular benchmark data.  ( 2 min )
    Understanding Non-linearity in Graph Neural Networks from the Bayesian-Inference Perspective. (arXiv:2207.11311v2 [cs.LG] UPDATED)
    Graph neural networks (GNNs) have shown superiority in many prediction tasks over graphs due to their impressive capability of capturing nonlinear relations in graph-structured data. However, for node classification tasks, often, only marginal improvement of GNNs over their linear counterparts has been observed. Previous works provide very few understandings of this phenomenon. In this work, we resort to Bayesian learning to deeply investigate the functions of non-linearity in GNNs for node classification tasks. Given a graph generated from the statistical model CSBM, we observe that the max-a-posterior estimation of a node label given its own and neighbors' attributes consists of two types of non-linearity, a possibly non-linear transformation of node attributes and a ReLU-activated feature aggregation from neighbors. The latter surprisingly matches the type of non-linearity used in many GNN models. By further imposing Gaussian assumption on node attributes, we prove that the superiority of those ReLU activations is only significant when the node attributes are far more informative than the graph structure, which nicely matches many previous empirical observations. A similar argument can be achieved when there is a distribution shift of node attributes between the training and testing datasets. Finally, we verify our theory on both synthetic and real-world networks.  ( 3 min )
    Reasonable Effectiveness of Random Weighting: A Litmus Test for Multi-Task Learning. (arXiv:2111.10603v2 [cs.LG] UPDATED)
    Multi-Task Learning (MTL) has achieved success in various fields. However, how to balance different tasks to achieve good performance is a key problem. To achieve the task balancing, there are many works to carefully design dynamical loss/gradient weighting strategies but the basic random experiments are ignored to examine their effectiveness. In this paper, we propose the Random Weighting (RW) methods, including Random Loss Weighting (RLW) and Random Gradient Weighting (RGW), where an MTL model is trained with random loss/gradient weights sampled from a distribution. To show the effectiveness and necessity of RW methods, theoretically we analyze the convergence of RW and reveal that RW has a higher probability to escape local minima, resulting in better generalization ability. Empirically, we extensively evaluate the proposed RW methods to compare with twelve state-of-the-art methods on five image datasets and two multilingual problems from the XTREME benchmark to show RW methods can achieve comparable performance with state-of-the-art baselines. Therefore, we think that the RW methods are important baselines for MTL and should attract more attentions.  ( 2 min )
    Learning with Combinatorial Optimization Layers: a Probabilistic Approach. (arXiv:2207.13513v1 [stat.ML])
    Combinatorial optimization (CO) layers in machine learning (ML) pipelines are a powerful tool to tackle data-driven decision tasks, but they come with two main challenges. First, the solution of a CO problem often behaves as a piecewise constant function of its objective parameters. Given that ML pipelines are typically trained using stochastic gradient descent, the absence of slope information is very detrimental. Second, standard ML losses do not work well in combinatorial settings. A growing body of research addresses these challenges through diverse methods. Unfortunately, the lack of well-maintained implementations slows down the adoption of CO layers. In this paper, building upon previous works, we introduce a probabilistic perspective on CO layers, which lends itself naturally to approximate differentiation and the construction of structured losses. We recover many approaches from the literature as special cases, and we also derive new ones. Based on this unifying perspective, we present InferOpt.jl, an open-source Julia package that 1) allows turning any CO oracle with a linear objective into a differentiable layer, and 2) defines adequate losses to train pipelines containing such layers. Our library works with arbitrary optimization algorithms, and it is fully compatible with Julia's ML ecosystem. We demonstrate its abilities using a pathfinding problem on video game maps.  ( 2 min )
    Learning from Positive and Unlabeled Data with Augmented Classes. (arXiv:2207.13274v1 [cs.LG])
    Positive Unlabeled (PU) learning aims to learn a binary classifier from only positive and unlabeled data, which is utilized in many real-world scenarios. However, existing PU learning algorithms cannot deal with the real-world challenge in an open and changing scenario, where examples from unobserved augmented classes may emerge in the testing phase. In this paper, we propose an unbiased risk estimator for PU learning with Augmented Classes (PUAC) by utilizing unlabeled data from the augmented classes distribution, which can be easily collected in many real-world scenarios. Besides, we derive the estimation error bound for the proposed estimator, which provides a theoretical guarantee for its convergence to the optimal solution. Experiments on multiple realistic datasets demonstrate the effectiveness of proposed approach.  ( 2 min )
    Representation Learning for Dynamic Hyperedges. (arXiv:2112.10154v2 [cs.LG] UPDATED)
    The explosion of digital information and the growing involvement of people in social networks led to enormous research activity to develop methods that can extract meaningful information from interaction data. Commonly, interactions are represented by edges in a network or a graph, which implicitly assumes that the interactions are pairwise and static. However, real-world interactions deviate from these assumptions: (i) interactions can be multi-way involving more than two nodes or individuals (e.g., family relationships, protein interactions), and (ii) interactions can change over a period of time (e.g., change of opinions and friendship status). While pairwise interactions have been studied in a dynamic network setting and multi-way interactions have been studied using hypergraphs in static networks, there exists no method that can predict multi-way interactions or hyperedges in dynamic settings. Existing related methods cannot answer temporal queries like what type of interaction will occur next and when it will occur. This paper proposes a temporal point process model for hyperedge prediction to address these problems. Our proposed model uses dynamic representation techniques for nodes in a neural point process framework to forecast hyperedges. We present several experimental results and set benchmark results. As far as our knowledge, this is the first work that uses the temporal point process to forecast hyperedges in dynamic networks.  ( 3 min )
    The Computational Limits of Deep Learning. (arXiv:2007.05558v2 [cs.LG] UPDATED)
    Deep learning's recent history has been one of achievement: from triumphing over humans in the game of Go to world-leading performance in image classification, voice recognition, translation, and other tasks. But this progress has come with a voracious appetite for computing power. This article catalogs the extent of this dependency, showing that progress across a wide variety of applications is strongly reliant on increases in computing power. Extrapolating forward this reliance reveals that progress along current lines is rapidly becoming economically, technically, and environmentally unsustainable. Thus, continued progress in these applications will require dramatically more computationally-efficient methods, which will either have to come from changes to deep learning or from moving to other machine learning methods.  ( 2 min )
    Optimizing transformations for contrastive learning in a differentiable framework. (arXiv:2207.13367v1 [cs.LG])
    Current contrastive learning methods use random transformations sampled from a large list of transformations, with fixed hyperparameters, to learn invariance from an unannotated database. Following previous works that introduce a small amount of supervision, we propose a framework to find optimal transformations for contrastive learning using a differentiable transformation network. Our method increases performances at low annotated data regime both in supervision accuracy and in convergence speed. In contrast to previous work, no generative model is needed for transformation optimization. Transformed images keep relevant information to solve the supervised task, here classification. Experiments were performed on 34000 2D slices of brain Magnetic Resonance Images and 11200 chest X-ray images. On both datasets, with 10% of labeled data, our model achieves better performances than a fully supervised model with 100% labels.  ( 2 min )
    A hybrid ensemble method with negative correlation learning for regression. (arXiv:2104.02317v3 [cs.LG] UPDATED)
    Hybrid ensemble, an essential branch of ensembles, has flourished in numerous machine learning problems, especially regression. Several studies have confirmed the importance of diversity; however, previous ensembles only consider diversity in the sub-model training stage, with limited improvement compared to single models. In contrast, this study selects and weights sub-models from a heterogeneous model pool automatically. It solves an optimization problem using an interior-point filtering linear-search algorithm. This optimization problem innovatively incorporates negative correlation learning as a penalty term, with which a diverse model subset can be selected. Experimental results show some meaningful points. Model pool construction requires different classes of models, with all possible parameter sets for each class as sub-models. The best sub-models from each class are selected to construct an NCL-based ensemble, which is far more better than the average of the sub-models. Furthermore, comparing with classical constant and non-constant weighting methods, NCL-based ensemble has a significant advantage in several prediction metrics. In practice, it is difficult to conclude the optimal sub-model for a dataset prior due to the model uncertainty. However, our method would achieve comparable accuracy as the potential optimal sub-models on RMSE metric. In conclusion, the value of this study lies in its ease of use and effectiveness, allowing the hybrid ensemble to embrace both diversity and accuracy.  ( 3 min )
    ShiftAddNAS: Hardware-Inspired Search for More Accurate and Efficient Neural Networks. (arXiv:2205.08119v2 [cs.LG] UPDATED)
    Neural networks (NNs) with intensive multiplications (e.g., convolutions and transformers) are capable yet power hungry, impeding their more extensive deployment into resource-constrained devices. As such, multiplication-free networks, which follow a common practice in energy-efficient hardware implementation to parameterize NNs with more efficient operators (e.g., bitwise shifts and additions), have gained growing attention. However, multiplication-free networks usually under-perform their vanilla counterparts in terms of the achieved accuracy. To this end, this work advocates hybrid NNs that consist of both powerful yet costly multiplications and efficient yet less powerful operators for marrying the best of both worlds, and proposes ShiftAddNAS, which can automatically search for more accurate and more efficient NNs. Our ShiftAddNAS highlights two enablers. Specifically, it integrates (1) the first hybrid search space that incorporates both multiplication-based and multiplication-free operators for facilitating the development of both accurate and efficient hybrid NNs; and (2) a novel weight sharing strategy that enables effective weight sharing among different operators that follow heterogeneous distributions (e.g., Gaussian for convolutions vs. Laplacian for add operators) and simultaneously leads to a largely reduced supernet size and much better searched networks. Extensive experiments and ablation studies on various models, datasets, and tasks consistently validate the efficacy of ShiftAddNAS, e.g., achieving up to a +7.7% higher accuracy or a +4.9 better BLEU score compared to state-of-the-art NN, while leading to up to 93% or 69% energy and latency savings, respectively. Codes and pretrained models are available at https://github.com/RICE-EIC/ShiftAddNAS.  ( 3 min )
  • Open

    Conformal Prediction Bands for Two-Dimensional Functional Time Series. (arXiv:2207.13656v1 [stat.ME])
    Conformal Prediction (CP) is a versatile nonparametric framework used to quantify uncertainty in prediction problems. In this work, we provide an extension of such method to the case of time series of functions defined on a bivariate domain, by proposing for the first time a distribution-free technique which can be applied to time-evolving surfaces. In order to obtain meaningful and efficient prediction regions, CP must be coupled with an accurate forecasting algorithm, for this reason, we extend the theory of autoregressive processes in Hilbert space in order to allow for functions with a bivariate domain. Given the novelty of the subject, we present estimation techniques for the Functional Autoregressive model (FAR). A simulation study is implemented, in order to investigate how different point predictors affect the resulting prediction bands. Finally, we explore benefits and limits of the proposed approach on a real dataset, collecting daily observations of Sea Level Anomalies of the Black Sea in the last twenty years.
    The Cellwise Minimum Covariance Determinant Estimator. (arXiv:2207.13493v1 [stat.ME])
    The usual Minimum Covariance Determinant (MCD) estimator of a covariance matrix is robust against casewise outliers. These are cases (that is, rows of the data matrix) that behave differently from the majority of cases, raising suspicion that they might belong to a different population. On the other hand, cellwise outliers are individual cells in the data matrix. When a row contains one or more outlying cells, the other cells in the same row still contain useful information that we wish to preserve. We propose a cellwise robust version of the MCD method, called cellMCD. Its main building blocks are observed likelihood and a sparsity penalty on the number of flagged cellwise outliers. It possesses good breakdown properties. We construct a fast algorithm for cellMCD based on concentration steps (C-steps) that always lower the objective. The method performs well in simulations with cellwise outliers, and has high finite-sample efficiency on clean data. It is illustrated on real data with visualizations of the results.
    Membership Inference Attacks via Adversarial Examples. (arXiv:2207.13572v1 [cs.LG])
    The raise of machine learning and deep learning led to significant improvement in several domains. This change is supported by both the dramatic rise in computation power and the collection of large datasets. Such massive datasets often include personal data which can represent a threat to privacy. Membership inference attacks are a novel direction of research which aims at recovering training data used by a learning algorithm. In this paper, we develop a mean to measure the leakage of training data leveraging a quantity appearing as a proxy of the total variation of a trained model near its training samples. We extend our work by providing a novel defense mechanism. Our contributions are supported by empirical evidence through convincing numerical experiments.
    Statistically Efficient Advantage Learning for Offline Reinforcement Learning in Infinite Horizons. (arXiv:2202.13163v2 [stat.ML] UPDATED)
    We consider reinforcement learning (RL) methods in offline domains without additional online data collection, such as mobile health applications. Most of existing policy optimization algorithms in the computer science literature are developed in online settings where data are easy to collect or simulate. Their generalizations to mobile health applications with a pre-collected offline dataset remain unknown. The aim of this paper is to develop a novel advantage learning framework in order to efficiently use pre-collected data for policy optimization. The proposed method takes an optimal Q-estimator computed by any existing state-of-the-art RL algorithms as input, and outputs a new policy whose value is guaranteed to converge at a faster rate than the policy derived based on the initial Q-estimator. Extensive numerical experiments are conducted to back up our theoretical findings. A Python implementation of our proposed method is available at https://github.com/leyuanheart/SEAL.
    Robust Prediction Error Estimation with Monte-Carlo Methodology. (arXiv:2207.13612v1 [stat.ME])
    In this paper, we aim to estimate the prediction error of machine learning models under the true distribution of the data on hand. We consider the prediction model as a data-driven black-box function and quantify its statistical properties using non-parametric methods. We propose a novel sampling technique that takes advantage of the underlying probability distribution information embedded in the data. The proposed method combines two existing frameworks for estimating the prediction inaccuracy error; $m$ out of $n$ bootstrapping and iterative bootstrapping. $m$ out of $n$ bootstrapping is to maintain the consistency, and iterative bootstrapping is often used for bias correction of the prediction error estimation. Using Monte-Carlo uncertainty quantification techniques, we disintegrate the total variance of the estimator so the user can make informed decisions regarding measures to overcome the preventable errors. In addition, via the same Monte-Carlo framework, we provide a way to estimate the bias due to using the empirical distribution. This bias captures the sensitivity of the estimator to the on hand input data and help with understanding the robustness of the estimator. The application of the proposed uncertainty quantification is tested in a model selection case study using simulated and real datasets. We evaluate the performance of the proposed estimator in two frameworks; first, directly applying is as an optimization model to find the best model; second, fixing an optimization engine and use the proposed estimator as a fitness function withing the optimizer. Furthermore, we compare the asymptotic statistical properties and numerical results in a finite dataset of the proposed estimator with the existing state-of-the-art methods.
    Fast TreeSHAP: Accelerating SHAP Value Computation for Trees. (arXiv:2109.09847v3 [cs.LG] UPDATED)
    SHAP (SHapley Additive exPlanation) values are one of the leading tools for interpreting machine learning models, with strong theoretical guarantees (consistency, local accuracy) and a wide availability of implementations and use cases. Even though computing SHAP values takes exponential time in general, TreeSHAP takes polynomial time on tree-based models. While the speedup is significant, TreeSHAP can still dominate the computation time of industry-level machine learning solutions on datasets with millions or more entries, causing delays in post-hoc model diagnosis and interpretation service. In this paper we present two new algorithms, Fast TreeSHAP v1 and v2, designed to improve the computational efficiency of TreeSHAP for large datasets. We empirically find that Fast TreeSHAP v1 is 1.5x faster than TreeSHAP while keeping the memory cost unchanged. Similarly, Fast TreeSHAP v2 is 2.5x faster than TreeSHAP, at the cost of a slightly higher memory usage, thanks to the pre-computation of expensive TreeSHAP steps. We also show that Fast TreeSHAP v2 is well-suited for multi-time model interpretations, resulting in as high as 3x faster explanation of newly incoming samples.
    Open Source Vizier: Distributed Infrastructure and API for Reliable and Flexible Blackbox Optimization. (arXiv:2207.13676v1 [cs.LG])
    Vizier is the de-facto blackbox and hyperparameter optimization service across Google, having optimized some of Google's largest products and research efforts. To operate at the scale of tuning thousands of users' critical systems, Google Vizier solved key design challenges in providing multiple different features, while remaining fully fault-tolerant. In this paper, we introduce Open Source (OSS) Vizier, a standalone Python-based interface for blackbox optimization and research, based on the Google-internal Vizier infrastructure and framework. OSS Vizier provides an API capable of defining and solving a wide variety of optimization problems, including multi-metric, early stopping, transfer learning, and conditional search. Furthermore, it is designed to be a distributed system that assures reliability, and allows multiple parallel evaluations of the user's objective function. The flexible RPC-based infrastructure allows users to access OSS Vizier from binaries written in any language. OSS Vizier also provides a back-end ("Pythia") API that gives algorithm authors a way to interface new algorithms with the core OSS Vizier system. OSS Vizier is available at https://github.com/google/vizier.
    The Computational Limits of Deep Learning. (arXiv:2007.05558v2 [cs.LG] UPDATED)
    Deep learning's recent history has been one of achievement: from triumphing over humans in the game of Go to world-leading performance in image classification, voice recognition, translation, and other tasks. But this progress has come with a voracious appetite for computing power. This article catalogs the extent of this dependency, showing that progress across a wide variety of applications is strongly reliant on increases in computing power. Extrapolating forward this reliance reveals that progress along current lines is rapidly becoming economically, technically, and environmentally unsustainable. Thus, continued progress in these applications will require dramatically more computationally-efficient methods, which will either have to come from changes to deep learning or from moving to other machine learning methods.
    Improving Generalization of Batch Whitening by Convolutional Unit Optimization. (arXiv:2108.10629v2 [cs.CV] CROSS LISTED)
    Batch Whitening is a technique that accelerates and stabilizes training by transforming input features to have a zero mean (Centering) and a unit variance (Scaling), and by removing linear correlation between channels (Decorrelation). In commonly used structures, which are empirically optimized with Batch Normalization, the normalization layer appears between convolution and activation function. Following Batch Whitening studies have employed the same structure without further analysis; even Batch Whitening was analyzed on the premise that the input of a linear layer is whitened. To bridge the gap, we propose a new Convolutional Unit that is in line with the theory, and our method generally improves the performance of Batch Whitening. Moreover, we show the inefficacy of the original Convolutional Unit by investigating rank and correlation of features. As our method is employable off-the-shelf whitening modules, we use Iterative Normalization (IterNorm), the state-of-the-art whitening module, and obtain significantly improved performance on five image classification datasets: CIFAR-10, CIFAR-100, CUB-200-2011, Stanford Dogs, and ImageNet. Notably, we verify that our method improves stability and performance of whitening when using large learning rate, group size, and iteration number.
    Bioinspired random projections for robust, sparse classification. (arXiv:2206.09222v2 [stat.ML] UPDATED)
    Inspired by the use of random projections in biological sensing systems, we present a new algorithm for processing data in classification problems. This is based on observations of the human brain and the fruit fly's olfactory system and involves randomly projecting data into a space of greatly increased dimension before applying a cap operation to truncate the smaller entries. This leads to a simple algorithm that is very computationally efficient and can be used to either give a sparse representation with minimal loss in classification accuracy or give improved robustness, in the sense that classification accuracy is improved when noise is added to the data. This is demonstrated with numerical experiments, which supplement theoretical results demonstrating that the resulting signal transform is continuous and invertible, in an appropriate sense.
    Unsupervised Learning under Latent Label Shift. (arXiv:2207.13179v1 [cs.LG])
    What sorts of structure might enable a learner to discover classes from unlabeled data? Traditional approaches rely on feature-space similarity and heroic assumptions on the data. In this paper, we introduce unsupervised learning under Latent Label Shift (LLS), where we have access to unlabeled data from multiple domains such that the label marginals $p_d(y)$ can shift across domains but the class conditionals $p(\mathbf{x}|y)$ do not. This work instantiates a new principle for identifying classes: elements that shift together group together. For finite input spaces, we establish an isomorphism between LLS and topic modeling: inputs correspond to words, domains to documents, and labels to topics. Addressing continuous data, we prove that when each label's support contains a separable region, analogous to an anchor word, oracle access to $p(d|\mathbf{x})$ suffices to identify $p_d(y)$ and $p_d(y|\mathbf{x})$ up to permutation. Thus motivated, we introduce a practical algorithm that leverages domain-discriminative models as follows: (i) push examples through domain discriminator $p(d|\mathbf{x})$; (ii) discretize the data by clustering examples in $p(d|\mathbf{x})$ space; (iii) perform non-negative matrix factorization on the discrete data; (iv) combine the recovered $p(y|d)$ with the discriminator outputs $p(d|\mathbf{x})$ to compute $p_d(y|x) \; \forall d$. With semi-synthetic experiments, we show that our algorithm can leverage domain information to improve state of the art unsupervised classification methods. We reveal a failure mode of standard unsupervised classification methods when feature-space similarity does not indicate true groupings, and show empirically that our method better handles this case. Our results establish a deep connection between distribution shift and topic modeling, opening promising lines for future work.
    Faster online calibration without randomization: interval forecasts and the power of two choices. (arXiv:2204.13087v2 [cs.LG] UPDATED)
    We study the problem of making calibrated probabilistic forecasts for a binary sequence generated by an adversarial nature. Following the seminal paper of Foster and Vohra (1998), nature is often modeled as an adaptive adversary who sees all activity of the forecaster except the randomization that the forecaster may deploy. A number of papers have proposed randomized forecasting strategies that achieve an $\epsilon$-calibration error rate of $O(1/\sqrt{T})$, which we prove is tight in general. On the other hand, it is well known that it is not possible to be calibrated without randomization, or if nature also sees the forecaster's randomization; in both cases the calibration error could be $\Omega(1)$. Inspired by the equally seminal works on the "power of two choices" and imprecise probability theory, we study a small variant of the standard online calibration problem. The adversary gives the forecaster the option of making two nearby probabilistic forecasts, or equivalently an interval forecast of small width, and the endpoint closest to the revealed outcome is used to judge calibration. This power of two choices, or imprecise forecast, accords the forecaster with significant power -- we show that a faster $\epsilon$-calibration rate of $O(1/T)$ can be achieved even without deploying any randomization.
    Learning with Combinatorial Optimization Layers: a Probabilistic Approach. (arXiv:2207.13513v1 [stat.ML])
    Combinatorial optimization (CO) layers in machine learning (ML) pipelines are a powerful tool to tackle data-driven decision tasks, but they come with two main challenges. First, the solution of a CO problem often behaves as a piecewise constant function of its objective parameters. Given that ML pipelines are typically trained using stochastic gradient descent, the absence of slope information is very detrimental. Second, standard ML losses do not work well in combinatorial settings. A growing body of research addresses these challenges through diverse methods. Unfortunately, the lack of well-maintained implementations slows down the adoption of CO layers. In this paper, building upon previous works, we introduce a probabilistic perspective on CO layers, which lends itself naturally to approximate differentiation and the construction of structured losses. We recover many approaches from the literature as special cases, and we also derive new ones. Based on this unifying perspective, we present InferOpt.jl, an open-source Julia package that 1) allows turning any CO oracle with a linear objective into a differentiable layer, and 2) defines adequate losses to train pipelines containing such layers. Our library works with arbitrary optimization algorithms, and it is fully compatible with Julia's ML ecosystem. We demonstrate its abilities using a pathfinding problem on video game maps.
    Data-Driven Sample Average Approximation with Covariate Information. (arXiv:2207.13554v1 [math.OC])
    We study optimization for data-driven decision-making when we have observations of the uncertain parameters within the optimization model together with concurrent observations of covariates. Given a new covariate observation, the goal is to choose a decision that minimizes the expected cost conditioned on this observation. We investigate three data-driven frameworks that integrate a machine learning prediction model within a stochastic programming sample average approximation (SAA) for approximating the solution to this problem. Two of the SAA frameworks are new and use out-of-sample residuals of leave-one-out prediction models for scenario generation. The frameworks we investigate are flexible and accommodate parametric, nonparametric, and semiparametric regression techniques. We derive conditions on the data generation process, the prediction model, and the stochastic program under which solutions of these data-driven SAAs are consistent and asymptotically optimal, and also derive convergence rates and finite sample guarantees. Computational experiments validate our theoretical results, demonstrate the potential advantages of our data-driven formulations over existing approaches (even when the prediction model is misspecified), and illustrate the benefits of our new data-driven formulations in the limited data regime.
    Should Bank Stress Tests Be Fair?. (arXiv:2207.13319v1 [stat.ML])
    Regulatory stress tests have become the primary tool for setting capital requirements at the largest U.S. banks. The Federal Reserve uses confidential models to evaluate bank-specific outcomes for bank-specific portfolios in shared stress scenarios. As a matter of policy, the same models are used for all banks, despite considerable heterogeneity across institutions; individual banks have contended that some models are not suited to their businesses. Motivated by this debate, we ask, what is a fair aggregation of individually tailored models into a common model? We argue that simply pooling data across banks treats banks equally but is subject to two deficiencies: it may distort the impact of legitimate portfolio features, and it is vulnerable to implicit misdirection of legitimate information to infer bank identity. We compare various notions of regression fairness to address these deficiencies, considering both forecast accuracy and equal treatment. In the setting of linear models, we argue for estimating and then discarding centered bank fixed effects as preferable to simply ignoring differences across banks. We present evidence that the overall impact can be material. We also discuss extensions to nonlinear models.
    INTERACT: Achieving Low Sample and Communication Complexities in Decentralized Bilevel Learning over Networks. (arXiv:2207.13283v1 [cs.LG])
    In recent years, decentralized bilevel optimization problems have received increasing attention in the networking and machine learning communities thanks to their versatility in modeling decentralized learning problems over peer-to-peer networks (e.g., multi-agent meta-learning, multi-agent reinforcement learning, personalized training, and Byzantine-resilient learning). However, for decentralized bilevel optimization over peer-to-peer networks with limited computation and communication capabilities, how to achieve low sample and communication complexities are two fundamental challenges that remain under-explored so far. In this paper, we make the first attempt to investigate the class of decentralized bilevel optimization problems with nonconvex and strongly-convex structure corresponding to the outer and inner subproblems, respectively. Our main contributions in this paper are two-fold: i) We first propose a deterministic algorithm called INTERACT (inner-gradient-descent-outer-tracked-gradient) that requires the sample complexity of $\mathcal{O}(n \epsilon^{-1})$ and communication complexity of $\mathcal{O}(\epsilon^{-1})$ to solve the bilevel optimization problem, where $n$ and $\epsilon > 0$ are the number of samples at each agent and the desired stationarity gap, respectively. ii) To relax the need for full gradient evaluations in each iteration, we propose a stochastic variance-reduced version of INTERACT (SVR-INTERACT), which improves the sample complexity to $\mathcal{O}(\sqrt{n} \epsilon^{-1})$ while achieving the same communication complexity as the deterministic algorithm. To our knowledge, this work is the first that achieves both low sample and communication complexities for solving decentralized bilevel optimization problems over networks. Our numerical experiments also corroborate our theoretical findings.
    Multi-Objective Hyperparameter Optimization -- An Overview. (arXiv:2206.07438v2 [cs.LG] UPDATED)
    Hyperparameter optimization constitutes a large part of typical modern machine learning workflows. This arises from the fact that machine learning methods and corresponding preprocessing steps often only yield optimal performance when hyperparameters are properly tuned. But in many applications, we are not only interested in optimizing ML pipelines solely for predictive accuracy; additional metrics or constraints must be considered when determining an optimal configuration, resulting in a multi-objective optimization problem. This is often neglected in practice, due to a lack of knowledge and readily available software implementations for multi-objective hyperparameter optimization. In this work, we introduce the reader to the basics of multi-objective hyperparameter optimization and motivate its usefulness in applied ML. Furthermore, we provide an extensive survey of existing optimization strategies, both from the domain of evolutionary algorithms and Bayesian optimization. We illustrate the utility of MOO in several specific ML applications, considering objectives such as operating conditions, prediction time, sparseness, fairness, interpretability and robustness.
    On generalization bounds for deep networks based on loss surface implicit regularization. (arXiv:2201.04545v2 [stat.ML] UPDATED)
    The classical statistical learning theory implies that fitting too many parameters leads to overfitting and poor performance. That modern deep neural networks generalize well despite a large number of parameters contradicts this finding and constitutes a major unsolved problem towards explaining the success of deep learning. While previous work focuses on the implicit regularization induced by stochastic gradient descent (SGD), we study here how the local geometry of the energy landscape around local minima affects the statistical properties of SGD with Gaussian gradient noise. We argue that under reasonable assumptions, the local geometry forces SGD to stay close to a low dimensional subspace and that this induces another form of implicit regularization and results in tighter bounds on the generalization error for deep neural networks. To derive generalization error bounds for neural networks, we first introduce a notion of stagnation sets around the local minima and impose a local essential convexity property of the population risk. Under these conditions, lower bounds for SGD to remain in these stagnation sets are derived. If stagnation occurs, we derive a bound on the generalization error of deep neural networks involving the spectral norms of the weight matrices but not the number of network parameters. Technically, our proofs are based on controlling the change of parameter values in the SGD iterates and local uniform convergence of the empirical loss functions based on the entropy of suitable neighborhoods around local minima.
    Handling Hard Affine SDP Shape Constraints in RKHSs. (arXiv:2101.01519v2 [stat.ML] UPDATED)
    Shape constraints, such as non-negativity, monotonicity, convexity or supermodularity, play a key role in various applications of machine learning and statistics. However, incorporating this side information into predictive models in a hard way (for example at all points of an interval) for rich function classes is a notoriously challenging problem. We propose a unified and modular convex optimization framework, relying on second-order cone (SOC) tightening, to encode hard affine SDP constraints on function derivatives, for models belonging to vector-valued reproducing kernel Hilbert spaces (vRKHSs). The modular nature of the proposed approach allows to simultaneously handle multiple shape constraints, and to tighten an infinite number of constraints into finitely many. We prove the convergence of the proposed scheme and that of its adaptive variant, leveraging geometric properties of vRKHSs. Due to the covering-based construction of the tightening, the method is particularly well-suited to tasks with small to moderate input dimensions. The efficiency of the approach is illustrated in the context of shape optimization, robotics and econometrics.
    Sliced Wasserstein Variational Inference. (arXiv:2207.13177v1 [stat.ML])
    Variational Inference approximates an unnormalized distribution via the minimization of Kullback-Leibler (KL) divergence. Although this divergence is efficient for computation and has been widely used in applications, it suffers from some unreasonable properties. For example, it is not a proper metric, i.e., it is non-symmetric and does not preserve the triangle inequality. On the other hand, optimal transport distances recently have shown some advantages over KL divergence. With the help of these advantages, we propose a new variational inference method by minimizing sliced Wasserstein distance, a valid metric arising from optimal transport. This sliced Wasserstein distance can be approximated simply by running MCMC but without solving any optimization problem. Our approximation also does not require a tractable density function of variational distributions so that approximating families can be amortized by generators like neural networks. Furthermore, we provide an analysis of the theoretical properties of our method. Experiments on synthetic and real data are illustrated to show the performance of the proposed method.
    Deep Partial Updating: Towards Communication Efficient Updating for On-device Inference. (arXiv:2007.03071v3 [cs.LG] UPDATED)
    Emerging edge intelligence applications require the server to retrain and update deep neural networks deployed on remote edge nodes to leverage newly collected data samples. Unfortunately, it may be impossible in practice to continuously send fully updated weights to these edge nodes due to the highly constrained communication resource. In this paper, we propose the weight-wise deep partial updating paradigm, which smartly selects a small subset of weights to update in each server-to-edge communication round, while achieving a similar performance compared to full updating. Our method is established through analytically upper-bounding the loss difference between partial updating and full updating, and only updates the weights which make the largest contributions to the upper bound. Extensive experimental results demonstrate the efficacy of our partial updating methodology which achieves a high inference accuracy while updating a rather small number of weights.  ( 2 min )
    Rethinking Efficacy of Softmax for Lightweight Non-Local Neural Networks. (arXiv:2207.13423v1 [cs.CV])
    Non-local (NL) block is a popular module that demonstrates the capability to model global contexts. However, NL block generally has heavy computation and memory costs, so it is impractical to apply the block to high-resolution feature maps. In this paper, to investigate the efficacy of NL block, we empirically analyze if the magnitude and direction of input feature vectors properly affect the attention between vectors. The results show the inefficacy of softmax operation which is generally used to normalize the attention map of the NL block. Attention maps normalized with softmax operation highly rely upon magnitude of key vectors, and performance is degenerated if the magnitude information is removed. By replacing softmax operation with the scaling factor, we demonstrate improved performance on CIFAR-10, CIFAR-100, and Tiny-ImageNet. In Addition, our method shows robustness to embedding channel reduction and embedding weight initialization. Notably, our method makes multi-head attention employable without additional computational cost.  ( 2 min )
    One Simple Trick to Fix Your Bayesian Neural Network. (arXiv:2207.13167v1 [stat.ML])
    One of the most popular estimation methods in Bayesian neural networks (BNN) is mean-field variational inference (MFVI). In this work, we show that neural networks with ReLU activation function induce posteriors, that are hard to fit with MFVI. We provide a theoretical justification for this phenomenon, study it empirically, and report the results of a series of experiments to investigate the effect of activation function on the calibration of BNNs. We find that using Leaky ReLU activations leads to more Gaussian-like weight posteriors and achieves a lower expected calibration error (ECE) than its ReLU-based counterpart.  ( 2 min )
    LGV: Boosting Adversarial Example Transferability from Large Geometric Vicinity. (arXiv:2207.13129v1 [cs.LG])
    We propose transferability from Large Geometric Vicinity (LGV), a new technique to increase the transferability of black-box adversarial attacks. LGV starts from a pretrained surrogate model and collects multiple weight sets from a few additional training epochs with a constant and high learning rate. LGV exploits two geometric properties that we relate to transferability. First, models that belong to a wider weight optimum are better surrogates. Second, we identify a subspace able to generate an effective surrogate ensemble among this wider optimum. Through extensive experiments, we show that LGV alone outperforms all (combinations of) four established test-time transformations by 1.8 to 59.9 percentage points. Our findings shed new light on the importance of the geometry of the weight space to explain the transferability of adversarial examples.  ( 2 min )

  • Open

    [D] good follow up venue to NeurIPS rejection?
    So my scores came in got 2/4/4/4/7 at NeurIPS, planning to rebuttal. Main strengths were extensive experiments run, achieved SOTA, and had good qualitative info. Also compliments on introduction and motivation. Main complaints were around typos, feeling the paper was rushed, not giving enough discussion on very specific citations, and feeling the solution methodology section was confusing and needed a serious rewrite. Also a couple of complaints on ablations or they wanted to see different things studied than what we studied. ​ I am trying to decide what is a good path forward with this paper, should I try to get into AAAI or should i just go for a good journal with quick review time to get it out there? submitted by /u/AbjectDrink3276 [link] [comments]  ( 111 min )
    "[Discussion]" Need advice for my master's program (online or in-person)
    Hey guys, I need adviceI'm planning to do my masters in AI, and I couldn't enroll in the thesis-based program, but I managed to get an offer from Queens Marry Uni (project base), generally, my goal is either to land a good job in the industry or enter the academia on the long term. However, now I saw the option of doing online programs which have almost the same course as the in-person "while being a way cheaper", there are some good universities to provide this (Georgia Tech, Leeds, Liverpool). So my question is doing it online would give me the same benefits as the in-person (for project-based) ?? also at this point does it actually matter that much whether to get an online or in-person program?? also, has anyone tried the online program from these universities and can share the experience? submitted by /u/Mogady [link] [comments]  ( 88 min )
    [D] What methods/tools should I use for a combination of linear and non-linear tabular data?
    Title. The non-linear data cannot be transformed into a linear form. What methods or tools should I use for this submitted by /u/NathanA2C [link] [comments]  ( 106 min )
    [D] Is self driving entirely machine learning?
    It's my understanding that labeling needed for the car to understand its surrounding is done by a neural net or some other machine learning technique. What I'm curious about is whether the decisions of how to operate the car based on it's labeled surroundings is done with more conventional programming like, "If I'm about to hit this thing labeled as a wall, then brake" or "If the bounds of the road angle to the left, then steer left" or if a black box neural net approach is used where we train it to less deterministically produce certain outputs based on the conditions of the labels? TLDR: is self driving label -> black box neural net -> control output OR label -> if/then -> control output submitted by /u/entropythagorean [link] [comments]  ( 89 min )
    [R] [D] Mythbusting my preconceptions of ML
    So, I have always been interested in getting into practical ML but am unsure how/where to start. Where do you think I should start with my journey? I am a student who is digitally literate and analytical, but I want to avoid as many obstacles to my being able to use ML in a practical sense at work. I am at a data-based company looking to create strong resources, so I guess I am interested in the benefits of using ML in administrative work? Please send help haha submitted by /u/Jad0Matic [link] [comments]  ( 87 min )
    [R] Geometric Deep Learning Lecture Course (AMMI'22)
    Hi everyone, I am pleased to share with you all, our new & improved material for diving into geometric deep learning! For a second year in a row, Michael Bronstein (Oxford / Twitter), Joan Bruna (NYU), Taco Cohen (Qualcomm) and I have delivered our Master's course on Geometric DL for the African Master's in Machine Intelligence, designed to closely follow our proto-book released last year. We make all materials publicly available! https://geometricdeeplearning.com/lectures/ For 2022, we made careful modifications to our content, making it more streamlined and (hopefully) more accessible! This features, among other things: A revamped introductory lecture, with a plethora of new historical context on deep learning and geometry; Clearer discussion of Transformers, and how they fit int…  ( 91 min )
    [D] Help needed! The code in the OpenAI gym documentation does not work.
    I am an absolute beginner in reinforcement learning. I'm trying to execute the second code snippet given here. I'm using python version 3.9.12 as part of the anaconda package. Curiously, no error is thrown when I try to execute this code in a kaggle notebook except for the fact that the notebook can obviously not display the output environment. I checked the version of python in kaggle, and it's 3.7.12Is that the cause behind this issue? Moreover, I was playing around with the code given in the documentation and was able to modify it such that it inadvertently worked natively on my machine. Attaching a screenshot of my code. Can somebody please tell me if I'm doing something wrong? If it is because of the python version, what kind of changes would I have to make in the code given in the OpenAI documentation? Thanks in advance. My code submitted by /u/Zephyrus_2002 [link] [comments]  ( 88 min )
    Do you know any prior work on quantifying Reinforcement Learning environment difficulty / complexity? [Discussion]
    Hi, I am interested in learning more about frameworks for characterizing the relative complexity of Reinforcement Learning environments. This can be used to better understand comparable problems and compare across environments: e.g. How much harder is Mountain Car than Cartpole? There are many different characteristics that define environments and many different problem formulations - some of which are likely not meaningfully comparable quantitatively (single agent vs multi agent setup) and some that should be (low dimensional action space vs high dimensional action space) Here are some different dimensions of environment difficulty split by problem setup and relative complexity Problem formulation dimensions: - number of agents: single or multi agent - stochasticity: is the environment stochastic or deterministic - action space: discrete or continuous Complexity dimensions: - dimensionality: high dimensionality state and action space - credit assignment: delayed rewards - state representation: noisy signal from raw pixels vs cleanly represented state - small number of solutions: some environments require a specific sequential pattern to be discovered (and remembered) E.g. Montezumas revenge vs others have many solutions such as Cartpole - how sensitive the environment is to initial conditions Does anyone know which subfield this falls under? Or can you please link relevant papers / where I can go to learn more? submitted by /u/notabot789 [link] [comments]  ( 88 min )
    [D] Is it possible to use machine learning to create 3D images for the purpose of 3D printing?
    I think this is a longshot but I was thinking that I could build a model gathering image data to create a model that creates 3D images that can be added to 3D printing software so it could 3D print the model and sell it on Amazon. Some items could include 3D printed toys or statues or decorations, small stuff you could add to a desk or somewhere in your room or purchase it as a gift. Easier said than done, I assume but would such a thing be possible? submitted by /u/swagonflyyyy [link] [comments]  ( 88 min )
    [D] A Semi-automatic approach for Generating Video Trailers for Learning Pathways (Poster Walkthrough)
    In this video, I present a walkthrough of my poster "A Semi-automatic approach for Generating Video Trailers for Learning Pathways" that got accepted at the venue AIED 2022. I will be sharing the paper soon. Let me know your thoughts 💭 Much Appreciated! 🤗 https://youtu.be/Y93GXvVERmk submitted by /u/prakhar21 [link] [comments]  ( 87 min )
    [P] Luminaire v0.4.0 Release with Support up to python 3.10
    Excited to share that the latest Luminaire v0.4.0 release has several new capabilities with support up to python 3.10 and other package upgrades. Checkout the latest release here: https://github.com/zillow/luminaire submitted by /u/sayan341 [link] [comments]  ( 87 min )
    [D] Reading Group Presentation: Scalable Video-to-Speech Synthesis
    ​ https://preview.redd.it/uewvxpt7f4e91.png?width=1200&format=png&auto=webp&s=e2a3a89df1337b2df0f81d39cb75b4774e163bd7 outsystems-ai-reading-group.github.io for more info submitted by /u/JClub [link] [comments]  ( 109 min )
    [D] Albumentations VS Detectron2
    How the augmentations of Detectron2 regarding HSV (https://github.com/facebookresearch/detectron2/blob/48b598b4f61fbb24182a69b521b2a0ba3252b842/detectron2/data/transforms/augmentation_impl.py) correlate with albumentations ones - ColorJitter (https://albumentations.ai/docs/api_reference/augmentations/transforms/)? submitted by /u/giakou4 [link] [comments]  ( 87 min )
    [D] Is anyone training large language models on academic literature?
    I am wondering whether someone is trying to train LLMs on academic literature. I am thinking If openAI codex can spit out functional code from training on all publically available code, surely a model trained on all digital books and research papers can see patterns across different domains and generate surprising insights. If it works, it can be ground breaking in terms of pushing forward science since the scientific disciplines have become so specialized that humans cannot become experts within multiple disciplines within a lifetime but a machine may have a chance at it! Ideas, suggestions are welcome. submitted by /u/GullibleEngineer4 [link] [comments]  ( 88 min )
    [D] What do you think will be the most exciting thing in ML three years from now?
    I would list the most interesting things happening in machine learning right now to be: GPT-3, Gato, Dalle2 (creating incredible models by just pouring in data into them) NERF What do you think we will be most exited about three years from now? GPT-3 was released two years ago. submitted by /u/ThePerson654321 [link] [comments]  ( 93 min )
    What is the "major bottleneck" for "self driving cars"? "[D]"
    Question 1) I was wondering if anyone here can ELI5 (or even idiot-er) could explain something about "the major bottleneck" that I keep reading about with "error processing" or whatever is "the major issue" with Tesla. For the record, this is not laziness but practicality. There is simply too much to keep up with and I am too busy tryin' to survive. If anyone is willing to help me, I thank you in advance. (I just wanna keep up, but I can't get it done alone. Sad face) JUST CHECKING IN WITH AN EDIT AT 10:30 AM OR SO: Question 2) So, from what I gather, the issue is, no one really knows why in the hells error checking does not work? Am I understanding that correctly? If you answered post edit can you reference if you are answering question 1 or question 2. You do not have to, but it would help my scattered mind! submitted by /u/TheBloneRanger [link] [comments]  ( 99 min )
    [P] I Made An Easy-To-Use Python Package That Creates Beautiful Html Reports From Jupyter Notebooks
    Pretty Jupyter is an easy-to-use package that creates beautifully styled and dynamic html webpage from Jupyter notebook. Its repo is available here: https://github.com/JanPalasek/pretty-jupyter . Check out the demo and compare it with the default jupyter. You can try also Pretty Jupyter online without the need to install it. Main Features Visually appealing styles. Automatic Table of Contents generation. Tabsets: Tabs that hold section content inside them. Using Python variables in Markdown: Helps in creating dynamic reports. Code Folding: Show/Hide code to filter out unnecessary content. submitted by /u/Jan2579 [link] [comments]  ( 89 min )
    [D] Choosing right aws instance for training.
    Currently we have training jobs of both image models and large language models. I am having difficult timing choosing between g4 and p3 instances. Please suggest a multi gpu instance that is cost as well as time optimised(we plan to add a savings plan over that). If there is a benchmarks to compare the two from each other please share. submitted by /u/Garlic-Naan-7249 [link] [comments]  ( 88 min )
    [P] Git-based model registry
    Hey everyone! We are excited today to announce the release of our ML model registry for Iterative Studio (from the team behind DVC). This Model Registry is an UI for our open source tool (MLEM) we introduced in this subreddit earlier this year. Our philosophy is that ML projects - and MLOps practices - should be built on top of traditional software tools (such as Git), and not as a separate platform. Our goal is to extend DevOps’ wins from software development to ML. Git repository as single source of truth for models - the core principle behind our registry. This idea is not new if you are familiar with GitOps. We just implemented the model deployment specific workflow using this ideas. Technically, all is stored in Git repository: assign a version to a model - it creates a corresponded Git tag in your repository deploy model to production - a special Git tag is pushed and your CI/CD system triggers for model deployment. ML model description and a link to a file in storage (S3, Azure Blob) - is stored in text file in Git. This functionality can be used from open source tool mlem.ai and our released UI that helps to visualize the entire inventory of your models - https://studio.iterative.ai/ Iterative Model Registry We would love your feedback on using GitOps principles in model deployment! submitted by /u/dmpetrov [link] [comments]  ( 89 min )
    [P] How To Train NPCs With Video? VR Badminton Project
    Currently with my team we are developing a VR badminton game and we are looking for ways to train the NPCs to play as realistic as possible. We were thinking if there was a way to train the NPCs with video from real badminton players? Any help or idea is highly appreciated! Thanks in advance. submitted by /u/ZimaLion [link] [comments]  ( 90 min )
    [D] How does the loss used in Imagen differ from the loss used in IDDPM?
    The loss term used in the paper Improved Denoising Diffusion Probabilistic Models is called L_hybrid. It is a mixture of 2 things: - The variational lower bound loss - The MSE loss between what the model predicts I've been staring at the loss term used in Imagen, and I haven't been able to make heads or tails of it. It seems like they use a MSE loss between the model's predicted noise and the actual noise, but do they also do anything else? (For example: utilize the variational lower bound loss). ​ ​ Imagen Loss submitted by /u/vanilla-acc [link] [comments]  ( 88 min )
  • Open

    "Lake Eye" user creation on pixelz.ai
    submitted by /u/PixelzJ [link] [comments]  ( 86 min )
    A Multi-Model Approach to Synthetic Data Generation
    submitted by /u/Repeat-or [link] [comments]  ( 86 min )
    Researchers at Graz University of Technology Develop AdaNeRF: Adaptive Sampling for Real-time Rendering of Neural Radiance Fields Directly from Sparse Observations
    submitted by /u/ai-lover [link] [comments]  ( 87 min )
    A COMPUTER WROTE POETRY !
    Here in this video, we will check out some code that Vish had written in the past which takes lyrics stored in some files and the computer writes its own code!! Amazing. Not only are we here to educate about the power of computers but get into great discussions about where the technology comes from and where it could possibly take us in the future! Join the community in this great adventure. We have created our own company to further us on this venture of inspiring tech heads and entrepreneurs. https://www.drpinnacle.com/blog ​ https://youtu.be/xoNudNcDuXc submitted by /u/malwaregeek [link] [comments]  ( 86 min )
    Cool use of combining image and text generation to create Magic the Gathering cards
    submitted by /u/BeautifulVegetable10 [link] [comments]  ( 86 min )
    Storm Clouds
    submitted by /u/widgia [link] [comments]  ( 86 min )
    A.I. Speech Generator
    I'm trying to find something I can feed dialogue from a character and create a speech generator of it. My goal is to get the english voices dubbed over some japan-exclusive anime episodes. submitted by /u/outstandingowl [link] [comments]  ( 87 min )
    Hungry Baby Alarm
    submitted by /u/GoochCommander [link] [comments]  ( 86 min )
    How You Can Use AI / Automation to Make Money (from home)
    submitted by /u/kbf_ [link] [comments]  ( 86 min )
    Darkened bridges sink away into the brackishness Swirling sin into a rainbow of atrophy When the winters help the golden autumns take it’s leave
    submitted by /u/nalr00n [link] [comments]  ( 91 min )
  • Open

    Look and Talk: Natural Conversations with Google Assistant
    Posted by Tuan Anh Nguyen, Google Assistant and Sourish Chaudhuri, Google Research In natural conversations, we don't say people's names every time we speak to each other. Instead, we rely on contextual signaling mechanisms to initiate conversations, and eye contact is often all it takes. Google Assistant, now available in more than 95 countries and over 29 languages, has primarily relied on a hotword mechanism ("Hey Google" or “OK Google”) to help more than 700 million people every month get things done across Assistant devices. As virtual assistants become an integral part of our everyday lives, we're developing ways to initiate conversations more naturally. At Google I/O 2022, we announced Look and Talk, a major development in our journey to create natural and intuitive ways to intera…  ( 26 min )
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    Help needed! The code in the OpenAI gym documentation does not work.
    I am an absolute beginner in reinforcement learning. I'm trying to execute the second code snippet given here. I'm using python version 3.9.12 as part of the anaconda package. Curiously, no error is thrown when I try to execute this code in a kaggle notebook except for the fact that the notebook can obviously not display the output environment. I checked the version of python in kaggle, and it's 3.7.12 Is that the cause behind this issue? Moreover, I was playing around with the code given in the documentation and was able to modify it such that it inadvertently worked natively on my machine. Attaching a screenshot of my code. Can somebody please tell me if I'm doing something wrong? If it is because of the python version, what kind of changes would I have to make in the code given in the OpenAI documentation? Thanks in advance. My code submitted by /u/Zephyrus_2002 [link] [comments]  ( 87 min )
    Do you know any prior work on quantifying RL environment difficulty / complexity?
    Hi, I am interested in learning more about frameworks for characterizing the relative complexity of RL environments. This can be used to better understand comparable problems and compare across environments: e.g. How much harder is Mountain Car than Cartpole? There are many different characteristics that define environments and many different problem formulations - some of which are likely not meaningfully comparable quantitatively (single agent vs multi agent setup) and some that should be (low dimensional action space vs high dimensional action space) Here are some different dimensions of environment difficulty split by problem setup and relative complexity Problem formulation dimensions: - number of agents: single or multi agent - stochasticity: is the environment stochastic or deterministic - action space: discrete or continuous Complexity dimensions: - dimensionality: high dimensionality state and action space - credit assignment: delayed rewards - state representation: noisy signal from raw pixels vs cleanly represented state - small number of solutions: some environments require a specific sequential pattern to be discovered (and remembered) E.g. Montezuma's revenge vs others have many solutions such as Cartpole Does anyone know which subfield this falls under? Or can you please link relevant papers / where I can go to learn more? submitted by /u/notabot789 [link] [comments]  ( 87 min )
    "Offline Reinforcement Learning at Multiple Frequencies", Burns et al 2022
    submitted by /u/gwern [link] [comments]  ( 94 min )
    In Multi Agent Reinforcement Learning, what exactly does "coordinated actions" means? Do they mean similar actions or something else. How does this work out? Can someone explain.
    I was reading a paper where it says MARL leads to coordinated actions between the agents. Does Centralized critic helps to make all agent's actions coordinated. Can someone give an example. Thanks submitted by /u/aabra__ka__daabra [link] [comments]  ( 87 min )
    credit assignment problem
    Can anyone explain what is the term "credit assignment problem" in the context of RL? Here you find some excerpts from books: - "If γ is small, then an agent will only care about the rewards received in the current time step and just a few steps in the future. This effectively reduces the length of the RL problem to a few time steps and can drastically simplify the credit assignment problem." - "Speeding up TD learning amounts to either speeding up the credit assignment process or shortening the trial-and-error process" submitted by /u/rlopes404 [link] [comments]  ( 88 min )
    Action space for MultiDiscrete
    Hi guys, So I'm currently doing some experiment with RL and stumbled on a problem involving getting action space. (I'm using gym-wrappers for ML-Agents) With Discrete, we can extract it with env.action_space.n With Box, we can use env.action_space.shape[0] I wonder if there is any way for me to extract action space for MultiDiscrete space? Thanks in advance submitted by /u/feelingBlue_44260264 [link] [comments]  ( 86 min )
    reinforcement learing to play cuphead videogame
    Hi everyone, since I haven't found any complete project that uses reinforcement learning to play cuphed (the videogame) I was wondering: How difficult is it to implement? what are the main issues? submitted by /u/rotitJ [link] [comments]  ( 91 min )
  • Open

    Integrate Amazon SageMaker Data Wrangler with MLOps workflows
    As enterprises move from running ad hoc machine learning (ML) models to using AI/ML to transform their business at scale, the adoption of ML Operations (MLOps) becomes inevitable. As shown in the following figure, the ML lifecycle begins with framing a business problem as an ML use case followed by a series of phases, including […]  ( 13 min )
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    Tepper Wants to Nerd Out On Data With You
    Sponsored Post There are many practical reasons why you should choose an online Masters in Business Analytics from the Tepper School of Business at Carnegie Mellon University. We can list facts like: our alumni average $103,000 in starting salary and 84% of our grads secured a promotion or new position within three months of graduation. […] The post Tepper Wants to Nerd Out On Data With You appeared first on Machine Learning Mastery.  ( 10 min )
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    nbdev+Quarto: A new secret weapon for productivity
    Contents Our new secret weapon for productivity nbdev in industry What’s nbdev? What we learned after three years of using nbdev Enter Quarto: A pandoc super-processor A blazing fast notebook kernel: execnb Towards a dialect of python that embraces its dynamic nature The future of nbdev How you can get started with nbdev Thank You A conversation with JJ Allaire Our new secret weapon for productivity Today we’re excited to announce that we’ve teamed up with Quarto to give nbdev superpowers. nbdev offers Python programmers a common set of tools for using Jupyter notebooks to: Write & distribute software packages Test code, and Author documentation and technical articles A single notebook can create a python module, tests, CI, pypi/conda packages, and more. Although notebooks are already…  ( 9 min )
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    My journey and switch into Data Science — what you could learn from my journey
    Here I explain my journey switching my career into Data Science in my late 30s, my thinking, motivations, expectations, courses I took… Continue reading on Becoming Human: Artificial Intelligence Magazine »  ( 23 min )
  • Open

    Top uses of QR Codes for Co-working Spaces
    Co-working spaces have existed for some time now it offers convenient amenities who work in a more conducive environment. Following the CDC guidelines, incorporating QR codes into these co-working spaces allow customers to book their slots easily. ghost bookings is prevented by using an QR code generator online that is easily tracked. According to the… Read More »Top uses of QR Codes for Co-working Spaces The post Top uses of QR Codes for Co-working Spaces appeared first on Data Science Central.  ( 19 min )
    Best IoT Projects for Beginners
    IoT has been a popular topic for a while now. It is only sensible to learn and train if you want to begin a profession in this area. And what better approach is there to developing initiatives around talent than to master it? This blog will discuss simple IoT projects that you may get started… Read More »Best IoT Projects for Beginners The post Best IoT Projects for Beginners appeared first on Data Science Central.  ( 23 min )
  • Open

    KamNet: An Integrated Spatiotemporal Deep Neural Network for Rare Event Search in KamLAND-Zen. (arXiv:2203.01870v5 [physics.ins-det] UPDATED)
    Rare event searches allow us to search for new physics at energy scales inaccessible with other means by leveraging specialized large-mass detectors. Machine learning provides a new tool to maximize the information provided by these detectors. The information is sparse, which forces these algorithms to start from the lowest level data and exploit all symmetries in the detector to produce results. In this work we present KamNet which harnesses breakthroughs in geometric deep learning and spatiotemporal data analysis to maximize the physics reach of KamLAND-Zen, a kiloton scale spherical liquid scintillator detector searching for neutrinoless double beta decay ($0\nu\beta\beta$). Using a simplified background model for KamLAND we show that KamNet outperforms a conventional CNN on benchmarking MC simulations with an increasing level of robustness. Using simulated data, we then demonstrate KamNet's ability to increase KamLAND-Zen's sensitivity to $0\nu\beta\beta$ and $0\nu\beta\beta$ to excited states. A key component of this work is the addition of an attention mechanism to elucidate the underlying physics KamNet is using for the background rejection.
    Folding over Neural Networks. (arXiv:2207.01090v2 [cs.PL] UPDATED)
    Neural networks are typically represented as data structures that are traversed either through iteration or by manual chaining of method calls. However, a deeper analysis reveals that structured recursion can be used instead, so that traversal is directed by the structure of the network itself. This paper shows how such an approach can be realised in Haskell, by encoding neural networks as recursive data types, and then their training as recursion scheme patterns. In turn, we promote a coherent implementation of neural networks that delineates between their structure and semantics, allowing for compositionality in both how they are built and how they are trained.
    An Adaptive Deep Clustering Pipeline to Inform Text Labeling at Scale. (arXiv:2202.01211v2 [cs.CL] UPDATED)
    Mining the latent intentions from large volumes of natural language inputs is a key step to help data analysts design and refine Intelligent Virtual Assistants (IVAs) for customer service and sales support. We created a flexible and scalable clustering pipeline within the Verint Intent Manager (VIM) that integrates the fine-tuning of language models, a high performing k-NN library and community detection techniques to help analysts quickly surface and organize relevant user intentions from conversational texts. The fine-tuning step is necessary because pre-trained language models cannot encode texts to efficiently surface particular clustering structures when the target texts are from an unseen domain or the clustering task is not topic detection. We describe the pipeline and demonstrate its performance and ability to scale on three real-world text mining tasks. As deployed in the VIM application, this clustering pipeline produces high quality results, improving the performance of data analysts and reducing the time it takes to surface intentions from customer service data, thereby reducing the time it takes to build and deploy IVAs in new domains.
    Quantifying Inequality in Underreported Medical Conditions. (arXiv:2110.04133v2 [cs.CY] UPDATED)
    Estimating the prevalence of a medical condition, or the proportion of the population in which it occurs, is a fundamental problem in healthcare and public health. Accurate estimates of the relative prevalence across groups -- capturing, for example, that a condition affects women more frequently than men -- facilitate effective and equitable health policy which prioritizes groups who are disproportionately affected by a condition. However, it is difficult to estimate relative prevalence when a medical condition is underreported. In this work, we provide a method for accurately estimating the relative prevalence of underreported medical conditions, building upon the positive unlabeled learning framework. We show that under the commonly made covariate shift assumption -- i.e., that the probability of having a disease conditional on symptoms remains constant across groups -- we can recover the relative prevalence, even without restrictive assumptions commonly made in positive unlabeled learning and even if it is impossible to recover the absolute prevalence. We provide a suite of experiments on synthetic and real health data that demonstrate our method's ability to recover the relative prevalence more accurately than do baselines, and the method's robustness to plausible violations of the covariate shift assumption.
    Hyperdimensional Computing vs. Neural Networks: Comparing Architecture and Learning Process. (arXiv:2207.12932v1 [cs.NE])
    Hyperdimensional Computing (HDC) has obtained abundant attention as an emerging non von Neumann computing paradigm. Inspired by the way human brain functions, HDC leverages high dimensional patterns to perform learning tasks. Compared to neural networks, HDC has shown advantages such as energy efficiency and smaller model size, but sub-par learning capabilities in sophisticated applications. Recently, researchers have observed when combined with neural network components, HDC can achieve better performance than conventional HDC models. This motivates us to explore the deeper insights behind theoretical foundations of HDC, particularly the connection and differences with neural networks. In this paper, we make a comparative study between HDC and neural network to provide a different angle where HDC can be derived from an extremely compact neural network trained upfront. Experimental results show such neural network-derived HDC model can achieve up to 21% and 5% accuracy increase from conventional and learning-based HDC models respectively. This paper aims to provide more insights and shed lights on future directions for researches on this popular emerging learning scheme.
    The Optimal Noise in Noise-Contrastive Learning Is Not What You Think. (arXiv:2203.01110v2 [stat.ML] UPDATED)
    Learning a parametric model of a data distribution is a well-known statistical problem that has seen renewed interest as it is brought to scale in deep learning. Framing the problem as a self-supervised task, where data samples are discriminated from noise samples, is at the core of state-of-the-art methods, beginning with Noise-Contrastive Estimation (NCE). Yet, such contrastive learning requires a good noise distribution, which is hard to specify; domain-specific heuristics are therefore widely used. While a comprehensive theory is missing, it is widely assumed that the optimal noise should in practice be made equal to the data, both in distribution and proportion. This setting underlies Generative Adversarial Networks (GANs) in particular. Here, we empirically and theoretically challenge this assumption on the optimal noise. We show that deviating from this assumption can actually lead to better statistical estimators, in terms of asymptotic variance. In particular, the optimal noise distribution is different from the data's and even from a different family.
    Demystifying Graph Convolution with a Simple Concatenation. (arXiv:2207.12931v1 [cs.LG])
    Graph convolution (GConv) is a widely used technique that has been demonstrated to be extremely effective for graph learning applications, most notably node categorization. On the other hand, many GConv-based models do not quantify the effect of graph topology and node features on performance, and are even surpassed by some models that do not consider graph structure or node properties. We quantify the information overlap between graph topology, node features, and labels in order to determine graph convolution's representation power in the node classification task. In this work, we first determine the linear separability of graph convoluted features using analysis of variance. Mutual information is used to acquire a better understanding of the possible non-linear relationship between graph topology, node features, and labels. Our theoretical analysis demonstrates that a simple and efficient graph operation that concatenates only graph topology and node properties consistently outperforms conventional graph convolution, especially in the heterophily case. Extensive empirical research utilizing a synthetic dataset and real-world benchmarks demonstrates that graph concatenation is a simple but more flexible alternative to graph convolution.
    Cooperative Actor-Critic via TD Error Aggregation. (arXiv:2207.12533v1 [eess.SY])
    In decentralized cooperative multi-agent reinforcement learning, agents can aggregate information from one another to learn policies that maximize a team-average objective function. Despite the willingness to cooperate with others, the individual agents may find direct sharing of information about their local state, reward, and value function undesirable due to privacy issues. In this work, we introduce a decentralized actor-critic algorithm with TD error aggregation that does not violate privacy issues and assumes that communication channels are subject to time delays and packet dropouts. The cost we pay for making such weak assumptions is an increased communication burden for every agent as measured by the dimension of the transmitted data. Interestingly, the communication burden is only quadratic in the graph size, which renders the algorithm applicable in large networks. We provide a convergence analysis under diminishing step size to verify that the agents maximize the team-average objective function.
    Efficient Algorithms for Sparse Moment Problems without Separation. (arXiv:2207.13008v1 [cs.LG])
    We consider the sparse moment problem of learning a $k$-spike mixture in high dimensional space from its noisy moment information in any dimension. We measure the accuracy of the learned mixtures using transportation distance. Previous algorithms either assume certain separation assumptions, use more recovery moments, or run in (super) exponential time. Our algorithm for the 1-dimension problem (also called the sparse Hausdorff moment problem) is a robust version of the classic Prony's method, and our contribution mainly lies in the analysis. We adopt a global and much tighter analysis than previous work (which analyzes the perturbation of the intermediate results of Prony's method). A useful technical ingredient is a connection between the linear system defined by the Vandermonde matrix and the Schur polynomial, which allows us to provide tight perturbation bound independent of the separation and may be useful in other contexts. To tackle the high dimensional problem, we first solve the 2-dimensional problem by extending the 1-dimension algorithm and analysis to complex numbers. Our algorithm for the high dimensional case determines the coordinates of each spike by aligning a 1-d projection of the mixture to a random vector and a set of 2d-projections of the mixture. Our results have applications to learning topic models and Gaussian mixtures, implying improved sample complexity results or running time over prior work.
    Differentially Private Estimation via Statistical Depth. (arXiv:2207.12602v1 [stat.ML])
    Constructing a differentially private (DP) estimator requires deriving the maximum influence of an observation, which can be difficult in the absence of exogenous bounds on the input data or the estimator, especially in high dimensional settings. This paper shows that standard notions of statistical depth, i.e., halfspace depth and regression depth, are particularly advantageous in this regard, both in the sense that the maximum influence of a single observation is easy to analyze and that this value is typically low. This is used to motivate new approximate DP location and regression estimators using the maximizers of these two notions of statistical depth. A more computationally efficient variant of the approximate DP regression estimator is also provided. Also, to avoid requiring that users specify a priori bounds on the estimates and/or the observations, variants of these DP mechanisms are described that satisfy random differential privacy (RDP), which is a relaxation of differential privacy provided by Hall, Wasserman, and Rinaldo (2013). We also provide simulations of the two DP regression methods proposed here. The proposed estimators appear to perform favorably relative to the existing DP regression methods we consider in these simulations when either the sample size is at least 100-200 or the privacy-loss budget is sufficiently high.
    Physics Embedded Machine Learning for Electromagnetic Data Imaging. (arXiv:2207.12607v1 [physics.comp-ph])
    Electromagnetic (EM) imaging is widely applied in sensing for security, biomedicine, geophysics, and various industries. It is an ill-posed inverse problem whose solution is usually computationally expensive. Machine learning (ML) techniques and especially deep learning (DL) show potential in fast and accurate imaging. However, the high performance of purely data-driven approaches relies on constructing a training set that is statistically consistent with practical scenarios, which is often not possible in EM imaging tasks. Consequently, generalizability becomes a major concern. On the other hand, physical principles underlie EM phenomena and provide baselines for current imaging techniques. To benefit from prior knowledge in big data and the theoretical constraint of physical laws, physics embedded ML methods for EM imaging have become the focus of a large body of recent work. This article surveys various schemes to incorporate physics in learning-based EM imaging. We first introduce background on EM imaging and basic formulations of the inverse problem. We then focus on three types of strategies combining physics and ML for linear and nonlinear imaging and discuss their advantages and limitations. Finally, we conclude with open challenges and possible ways forward in this fast-developing field. Our aim is to facilitate the study of intelligent EM imaging methods that will be efficient, interpretable and controllable.
    Versatile Weight Attack via Flipping Limited Bits. (arXiv:2207.12405v1 [cs.CR])
    To explore the vulnerability of deep neural networks (DNNs), many attack paradigms have been well studied, such as the poisoning-based backdoor attack in the training stage and the adversarial attack in the inference stage. In this paper, we study a novel attack paradigm, which modifies model parameters in the deployment stage. Considering the effectiveness and stealthiness goals, we provide a general formulation to perform the bit-flip based weight attack, where the effectiveness term could be customized depending on the attacker's purpose. Furthermore, we present two cases of the general formulation with different malicious purposes, i.e., single sample attack (SSA) and triggered samples attack (TSA). To this end, we formulate this problem as a mixed integer programming (MIP) to jointly determine the state of the binary bits (0 or 1) in the memory and learn the sample modification. Utilizing the latest technique in integer programming, we equivalently reformulate this MIP problem as a continuous optimization problem, which can be effectively and efficiently solved using the alternating direction method of multipliers (ADMM) method. Consequently, the flipped critical bits can be easily determined through optimization, rather than using a heuristic strategy. Extensive experiments demonstrate the superiority of SSA and TSA in attacking DNNs.
    Machine Learning to Predict the Antimicrobial Activity of Cold Atmospheric Plasma-Activated Liquids. (arXiv:2207.12478v1 [cs.LG])
    Plasma is defined as the fourth state of matter and non-thermal plasma can be produced at atmospheric pressure under a high electrical field. The strong and broad-spectrum antimicrobial effect of plasma-activated liquids (PALs) is now well known. The proven applicability of machine learning (ML) in the medical field is encouraging for its application in the field of plasma medicine as well. Thus, ML applications on PALs could present a new perspective to better understand the influences of various parameters on their antimicrobial effects. In this paper, comparative supervised ML models are presented by using previously obtained data to qualitatively predict the in vitro antimicrobial activity of PALs. A literature search was performed and data is collected from 33 relevant articles. After the required preprocessing steps, two supervised ML methods, namely classification, and regression are applied to data to obtain microbial inactivation (MI) predictions. For classification, MI is labeled in four categories and for regression, MI is used as a continuous variable. Two different robust cross-validation strategies are conducted for classification and regression models to evaluate the proposed method; repeated stratified k-fold cross-validation and k-fold cross-validation, respectively. We also investigate the effect of different features on models. The results demonstrated that the hyperparameter-optimized Random Forest Classifier (oRFC) and Random Forest Regressor (oRFR) provided better results than other models for the classification and regression, respectively. Finally, the best test accuracy of 82.68% for oRFC and R2 of 0.75 for the oRFR are obtained. ML techniques could contribute to a better understanding of plasma parameters that have a dominant role in the desired antimicrobial effect. Furthermore, such findings may contribute to the definition of a plasma dose in the future.
    Approximate Low-Rank Decomposition for Real Symmetric Tensors. (arXiv:2207.12529v1 [math.NA])
    We investigate the effect of an $\varepsilon$-room of perturbation tolerance on symmetric tensor decomposition from an algorithmic perspective. More precisely, we prove theorems and design algorithms for the following problem: Suppose a real symmetric $d$-tensor $f$, a norm $||.||$ on the space of symmetric $d$-tensors, and $\varepsilon >0$ error tolerance with respect to $||.||$ are given. What is the smallest symmetric tensor rank in the $\varepsilon$-neighborhood of $f$? In other words, what is the symmetric tensor rank of $f$ after a clever $\varepsilon$-perturbation? We provide two different theoretical bounds and three algorithms for approximate symmetric tensor rank estimation. Our first result is a randomized energy increment algorithm for the case of $L_p$-norms. Our second result is a simple sampling-based algorithm, inspired by some techniques in geometric functional analysis, that works for any norm. We also provide a supplementary algorithm in the case of the Hilbert-Schmidt norm. All our algorithms come with rigorous complexity estimates, which in turn yield our two main theorems on symmetric tensor rank with $\varepsilon$-room of tolerance. We also report on our experiments with a preliminary implementation of the energy increment algorithm.
    Quiver neural networks. (arXiv:2207.12773v1 [cs.LG])
    We develop a uniform theoretical approach towards the analysis of various neural network connectivity architectures by introducing the notion of a quiver neural network. Inspired by quiver representation theory in mathematics, this approach gives a compact way to capture elaborate data flows in complex network architectures. As an application, we use parameter space symmetries to prove a lossless model compression algorithm for quiver neural networks with certain non-pointwise activations known as rescaling activations. In the case of radial rescaling activations, we prove that training the compressed model with gradient descent is equivalent to training the original model with projected gradient descent.
    Head2Toe: Utilizing Intermediate Representations for Better Transfer Learning. (arXiv:2201.03529v2 [cs.LG] UPDATED)
    Transfer-learning methods aim to improve performance in a data-scarce target domain using a model pretrained on a data-rich source domain. A cost-efficient strategy, linear probing, involves freezing the source model and training a new classification head for the target domain. This strategy is outperformed by a more costly but state-of-the-art method -- fine-tuning all parameters of the source model to the target domain -- possibly because fine-tuning allows the model to leverage useful information from intermediate layers which is otherwise discarded by the later pretrained layers. We explore the hypothesis that these intermediate layers might be directly exploited. We propose a method, Head-to-Toe probing (Head2Toe), that selects features from all layers of the source model to train a classification head for the target-domain. In evaluations on the VTAB-1k, Head2Toe matches performance obtained with fine-tuning on average while reducing training and storage cost hundred folds or more, but critically, for out-of-distribution transfer, Head2Toe outperforms fine-tuning.
    Variational Inference with Locally Enhanced Bounds for Hierarchical Models. (arXiv:2203.04432v2 [cs.LG] UPDATED)
    Hierarchical models represent a challenging setting for inference algorithms. MCMC methods struggle to scale to large models with many local variables and observations, and variational inference (VI) may fail to provide accurate approximations due to the use of simple variational families. Some variational methods (e.g. importance weighted VI) integrate Monte Carlo methods to give better accuracy, but these tend to be unsuitable for hierarchical models, as they do not allow for subsampling and their performance tends to degrade for high dimensional models. We propose a new family of variational bounds for hierarchical models, based on the application of tightening methods (e.g. importance weighting) separately for each group of local random variables. We show that our approach naturally allows the use of subsampling to get unbiased gradients, and that it fully leverages the power of methods that build tighter lower bounds by applying them independently in lower dimensional spaces, leading to better results and more accurate posterior approximations than relevant baselines.
    Engineering flexible machine learning systems by traversing functionally invariant paths in weight space. (arXiv:2205.00334v3 [cs.LG] UPDATED)
    Deep neural networks achieve human-like performance on a variety of perceptual and decision-making tasks. However, networks perform poorly when confronted with changing tasks or goals, and broadly fail to match the flexibility and robustness of human intelligence. Here, we develop a mathematical and algorithmic framework that enables flexible and continuous training of neural networks on a range of objectives by constructing path connected sets of networks that achieve equivalent functional performance on a given machine learning task. We view the weight space of a neural network as a curved Riemannian manifold and move a network along a functionally invariant path in weight space while searching for networks that satisfy secondary objectives. A path-sampling algorithm trains computer vision and natural language processing networks with millions of weight parameters to learn a series of classification tasks without performance loss while accommodating secondary objectives including network sparsification, incremental task learning, and increased adversarial robustness. Broadly, we conceptualize a neural network as a mathematical object that can be iteratively transformed into distinct configurations by the path-sampling algorithm to define a sub-manifold of networks that can be harnessed to achieve user goals.
    LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action. (arXiv:2207.04429v2 [cs.RO] UPDATED)
    Goal-conditioned policies for robotic navigation can be trained on large, unannotated datasets, providing for good generalization to real-world settings. However, particularly in vision-based settings where specifying goals requires an image, this makes for an unnatural interface. Language provides a more convenient modality for communication with robots, but contemporary methods typically require expensive supervision, in the form of trajectories annotated with language descriptions. We present a system, LM-Nav, for robotic navigation that enjoys the benefits of training on unannotated large datasets of trajectories, while still providing a high-level interface to the user. Instead of utilizing a labeled instruction following dataset, we show that such a system can be constructed entirely out of pre-trained models for navigation (ViNG), image-language association (CLIP), and language modeling (GPT-3), without requiring any fine-tuning or language-annotated robot data. We instantiate LM-Nav on a real-world mobile robot and demonstrate long-horizon navigation through complex, outdoor environments from natural language instructions. For videos of our experiments, code release, and an interactive Colab notebook that runs in your browser, please check out our project page https://sites.google.com/view/lmnav
    Kan Extensions in Data Science and Machine Learning. (arXiv:2203.09018v2 [cs.LG] UPDATED)
    A common problem in data science is "use this function defined over this small set to generate predictions over that larger set." Extrapolation, interpolation, statistical inference and forecasting all reduce to this problem. The Kan extension is a powerful tool in category theory that generalizes this notion. In this work we explore several applications of Kan extensions to data science. We begin by deriving a simple classification algorithm as a Kan extension and experimenting with this algorithm on real data. Next, we use the Kan extension to derive a procedure for learning clustering algorithms from labels and explore the performance of this procedure on real data. We then investigate how Kan extensions can be used to learn a general mapping from datasets of labeled examples to functions and to approximate a complex function with a simpler one.
    PACS: A Dataset for Physical Audiovisual CommonSense Reasoning. (arXiv:2203.11130v2 [cs.LG] UPDATED)
    In order for AI to be safely deployed in real-world scenarios such as hospitals, schools, and the workplace, it must be able to robustly reason about the physical world. Fundamental to this reasoning is physical common sense: understanding the physical properties and affordances of available objects, how they can be manipulated, and how they interact with other objects. Physical commonsense reasoning is fundamentally a multi-sensory task, since physical properties are manifested through multiple modalities - two of them being vision and acoustics. Our paper takes a step towards real-world physical commonsense reasoning by contributing PACS: the first audiovisual benchmark annotated for physical commonsense attributes. PACS contains 13,400 question-answer pairs, involving 1,377 unique physical commonsense questions and 1,526 videos. Our dataset provides new opportunities to advance the research field of physical reasoning by bringing audio as a core component of this multimodal problem. Using PACS, we evaluate multiple state-of-the-art models on our new challenging task. While some models show promising results (70% accuracy), they all fall short of human performance (95% accuracy). We conclude the paper by demonstrating the importance of multimodal reasoning and providing possible avenues for future research.
    Federated Learning for Energy-limited Wireless Networks: A Partial Model Aggregation Approach. (arXiv:2204.09746v2 [cs.LG] UPDATED)
    The limited communication resources, e.g., bandwidth and energy, and data heterogeneity across devices are two of the main bottlenecks for federated learning (FL). To tackle these challenges, we first devise a novel FL framework with partial model aggregation (PMA), which only aggregates the lower layers of neural networks responsible for feature extraction while the upper layers corresponding to complex pattern recognition remain at devices for personalization. The proposed PMA-FL is able to address the data heterogeneity and reduce the transmitted information in wireless channels. We then obtain a convergence bound of the framework under a non-convex loss function setting. With the aid of this bound, we define a new objective function, named the scheduled data sample volume, to transfer the original inexplicit optimization problem into a tractable one for device scheduling, bandwidth allocation, computation and communication time division. Our analysis reveals that the optimal time division is achieved when the communication and computation parts of PMA-FL have the same power. We also develop a bisection method to solve the optimal bandwidth allocation policy and use the set expansion algorithm to address the optimal device scheduling. Compared with the state-of-the-art benchmarks, the proposed PMA-FL improves 2.72% and 11.6% accuracy on two typical heterogeneous datasets, i.e., MINIST and CIFAR-10, respectively. In addition, the proposed joint dynamic device scheduling and resource optimization approach achieve slightly higher accuracy than the considered benchmarks, but they provide a satisfactory energy and time reduction: 29% energy or 20% time reduction on the MNIST; and 25% energy or 12.5% time reduction on the CIFAR-10.
    Robustness Implies Generalization via Data-Dependent Generalization Bounds. (arXiv:2206.13497v3 [cs.LG] UPDATED)
    This paper proves that robustness implies generalization via data-dependent generalization bounds. As a result, robustness and generalization are shown to be connected closely in a data-dependent manner. Our bounds improve previous bounds in two directions, to solve an open problem that has seen little development since 2010. The first is to reduce the dependence on the covering number. The second is to remove the dependence on the hypothesis space. We present several examples, including ones for lasso and deep learning, in which our bounds are provably preferable. The experiments on real-world data and theoretical models demonstrate near-exponential improvements in various situations. To achieve these improvements, we do not require additional assumptions on the unknown distribution; instead, we only incorporate an observable and computable property of the training samples. A key technical innovation is an improved concentration bound for multinomial random variables that is of independent interest beyond robustness and generalization.
    SKILL-IL: Disentangling Skill and Knowledge in Multitask Imitation Learning. (arXiv:2205.03130v2 [cs.LG] UPDATED)
    In this work, we introduce a new perspective for learning transferable content in multi-task imitation learning. Humans are able to transfer skills and knowledge. If we can cycle to work and drive to the store, we can also cycle to the store and drive to work. We take inspiration from this and hypothesize the latent memory of a policy network can be disentangled into two partitions. These contain either the knowledge of the environmental context for the task or the generalizable skill needed to solve the task. This allows improved training efficiency and better generalization over previously unseen combinations of skills in the same environment, and the same task in unseen environments. We used the proposed approach to train a disentangled agent for two different multi-task IL environments. In both cases we out-performed the SOTA by 30% in task success rate. We also demonstrated this for navigation on a real robot.
    Multi-agent Databases via Independent Learning. (arXiv:2205.14323v2 [cs.DB] UPDATED)
    Machine learning is rapidly being used in database research to improve the effectiveness of numerous tasks included but not limited to query optimization, workload scheduling, physical design, etc. Currently, the research focus has been on replacing a single database component responsible for one task by its learning-based counterpart. However, query performance is not simply determined by the performance of a single component, but by the cooperation of multiple ones. As such, learned based database components need to collaborate during both training and execution in order to develop policies that meet end performance goals. Thus, the paper attempts to address the question "Is it possible to design a database consisting of various learned components that cooperatively work to improve end-to-end query latency?". To answer this question, we introduce MADB (Multi-Agent DB), a proof-of-concept system that incorporates a learned query scheduler and a learned query optimizer. MADB leverages a cooperative multi-agent reinforcement learning approach that allows the two components to exchange the context of their decisions with each other and collaboratively work towards reducing the query latency. Preliminary results demonstrate that MADB can outperform the non-cooperative integration of learned components.
    A Model of One-Shot Generalization. (arXiv:2205.14553v2 [cs.LG] UPDATED)
    We provide a theoretical framework to study a phenomenon that we call one-shot generalization. This phenomenon refers to the ability of an algorithm to perform transfer learning within a single task, meaning that it correctly classifies a test point that has a single exemplar in the training set. We propose a simple data model and use it to study this phenomenon in two ways. First, we prove a non-asymptotic base-line -- kernel methods based on nearest-neighbor classification cannot perform one-shot generalization, independently of the choice of the kernel and the size of the training set. Second, we empirically show that the most direct neural network architecture for our data model performs one-shot generalization almost perfectly. This stark differential leads us to believe that the one-shot generalization mechanism is partially responsible for the empirical success of neural networks.
    A Confident Deep Learning loss function for one-step Conformal Prediction approximation. (arXiv:2207.12377v2 [cs.LG] UPDATED)
    Deep Learning predictions with measurable confidence are increasingly desirable for real-world problems, especially in high-risk settings. The Conformal Prediction (CP) framework is a versatile solution that automatically guarantees a maximum error rate. However, CP suffers from computational inefficiencies that limit its application to large-scale datasets. In this paper, we propose a novel conformal loss function that approximates the traditionally two-step CP approach in a single step. By evaluating and penalising deviations from the stringent expected CP output distribution, a Deep Learning model may learn the direct relationship between input data and conformal p-values. Our approach achieves significant training time reductions up to 86% compared to Aggregated Conformal Prediction (ACP), an accepted CP approximation variant. In terms of approximate validity and predictive efficiency, we carry out a comprehensive empirical evaluation to show our novel loss function's competitiveness with ACP on the well-established MNIST dataset.
    Making Corgis Important for Honeycomb Classification: Adversarial Attacks on Concept-based Explainability Tools. (arXiv:2110.07120v2 [cs.LG] UPDATED)
    Methods for model explainability have become increasingly critical for testing the fairness and soundness of deep learning. Concept-based interpretability techniques, which use a small set of human-interpretable concept exemplars in order to measure the influence of a concept on a model's internal representation of input, are an important thread in this line of research. In this work we show that these explainability methods can suffer the same vulnerability to adversarial attacks as the models they are meant to analyze. We demonstrate this phenomenon on two well-known concept-based interpretability methods: TCAV and faceted feature visualization. We show that by carefully perturbing the examples of the concept that is being investigated, we can radically change the output of the interpretability method. The attacks that we propose can either induce positive interpretations (polka dots are an important concept for a model when classifying zebras) or negative interpretations (stripes are not an important factor in identifying images of a zebra). Our work highlights the fact that in safety-critical applications, there is need for security around not only the machine learning pipeline but also the model interpretation process.
    OCTAL: Graph Representation Learning for LTL Model Checking. (arXiv:2207.11649v2 [cs.PL] UPDATED)
    Model Checking is widely applied in verifying the correctness of complex and concurrent systems against a specification. Pure symbolic approaches while popular, still suffer from the state space explosion problem that makes them impractical for large scale systems and/or specifications. In this paper, we propose to use graph representation learning (GRL) for solving linear temporal logic (LTL) model checking, where the system and the specification are expressed by a B\"uchi automaton and an LTL formula respectively. A novel GRL-based framework OCTAL, is designed to learn the representation of the graph-structured system and specification, which reduces the model checking problem to binary classification in the latent space. The empirical experiments show that OCTAL achieves comparable accuracy against canonical SOTA model checkers on three different datasets, with up to $5\times$ overall speedup and above $63\times$ for satisfiability checking alone.
    Generative Subgraph Contrast for Self-Supervised Graph Representation Learning. (arXiv:2207.11996v2 [cs.LG] UPDATED)
    Contrastive learning has shown great promise in the field of graph representation learning. By manually constructing positive/negative samples, most graph contrastive learning methods rely on the vector inner product based similarity metric to distinguish the samples for graph representation. However, the handcrafted sample construction (e.g., the perturbation on the nodes or edges of the graph) may not effectively capture the intrinsic local structures of the graph. Also, the vector inner product based similarity metric cannot fully exploit the local structures of the graph to characterize the graph difference well. To this end, in this paper, we propose a novel adaptive subgraph generation based contrastive learning framework for efficient and robust self-supervised graph representation learning, and the optimal transport distance is utilized as the similarity metric between the subgraphs. It aims to generate contrastive samples by capturing the intrinsic structures of the graph and distinguish the samples based on the features and structures of subgraphs simultaneously. Specifically, for each center node, by adaptively learning relation weights to the nodes of the corresponding neighborhood, we first develop a network to generate the interpolated subgraph. We then construct the positive and negative pairs of subgraphs from the same and different nodes, respectively. Finally, we employ two types of optimal transport distances (i.e., Wasserstein distance and Gromov-Wasserstein distance) to construct the structured contrastive loss. Extensive node classification experiments on benchmark datasets verify the effectiveness of our graph contrastive learning method.
    BioADAPT-MRC: Adversarial Learning-based Domain Adaptation Improves Biomedical Machine Reading Comprehension Task. (arXiv:2202.13174v3 [cs.CL] UPDATED)
    Biomedical machine reading comprehension (biomedical-MRC) aims to comprehend complex biomedical narratives and assist healthcare professionals in retrieving information from them. The high performance of modern neural network-based MRC systems depends on high-quality, large-scale, human-annotated training datasets. In the biomedical domain, a crucial challenge in creating such datasets is the requirement for domain knowledge, inducing the scarcity of labeled data and the need for transfer learning from the labeled general-purpose (source) domain to the biomedical (target) domain. However, there is a discrepancy in marginal distributions between the general-purpose and biomedical domains due to the variances in topics. Therefore, direct-transferring of learned representations from a model trained on a general-purpose domain to the biomedical domain can hurt the model's performance. We present an adversarial learning-based domain adaptation framework for the biomedical machine reading comprehension task (BioADAPT-MRC), a neural network-based method to address the discrepancies in the marginal distributions between the general and biomedical domain datasets. BioADAPT-MRC relaxes the need for generating pseudo labels for training a well-performing biomedical-MRC model. We extensively evaluate the performance of BioADAPT-MRC by comparing it with the best existing methods on three widely used benchmark biomedical-MRC datasets -- BioASQ-7b, BioASQ-8b, and BioASQ-9b. Our results suggest that without using any synthetic or human-annotated data from the biomedical domain, BioADAPT-MRC can achieve state-of-the-art performance on these datasets. Availability: BioADAPT-MRC is freely available as an open-source project at \url{https://github.com/mmahbub/BioADAPT-MRC}.
    Discriminative Multimodal Learning via Conditional Priors in Generative Models. (arXiv:2110.04616v2 [cs.LG] UPDATED)
    Deep generative models with latent variables have been used lately to learn joint representations and generative processes from multi-modal data. These two learning mechanisms can, however, conflict with each other and representations can fail to embed information on the data modalities. This research studies the realistic scenario in which all modalities and class labels are available for model training, but where some modalities and labels required for downstream tasks are missing. We show, in this scenario, that the variational lower bound limits mutual information between joint representations and missing modalities. We, to counteract these problems, introduce a novel conditional multi-modal discriminative model that uses an informative prior distribution and optimizes a likelihood-free objective function that maximizes mutual information between joint representations and missing modalities. Extensive experimentation shows the benefits of the model we propose, the empirical results showing that our model achieves state-of-the-art results in representative problems such as downstream classification, acoustic inversion and annotation generation.
    Cooperative Behavior Planning for Automated Driving using Graph Neural Networks. (arXiv:2202.11376v2 [cs.RO] UPDATED)
    Urban intersections are prone to delays and inefficiencies due to static precedence rules and occlusions limiting the view on prioritized traffic. Existing approaches to improve traffic flow, widely known as automatic intersection management systems, are mostly based on non-learning reservation schemes or optimization algorithms. Machine learning-based techniques show promising results in planning for a single ego vehicle. This work proposes to leverage machine learning algorithms to optimize traffic flow at urban intersections by jointly planning for multiple vehicles. Learning-based behavior planning poses several challenges, demanding for a suited input and output representation as well as large amounts of ground-truth data. We address the former issue by using a flexible graph-based input representation accompanied by a graph neural network. This allows to efficiently encode the scene and inherently provide individual outputs for all involved vehicles. To learn a sensible policy, without relying on the imitation of expert demonstrations, the cooperative planning task is considered as a reinforcement learning problem. We train and evaluate the proposed method in an open-source simulation environment for decision making in automated driving. Compared to a first-in-first-out scheme and traffic governed by static priority rules, the learned planner shows a significant gain in flow rate, while reducing the number of induced stops. In addition to synthetic simulations, the approach is also evaluated based on real-world traffic data taken from the publicly available inD dataset.
    Sliced Recursive Transformer. (arXiv:2111.05297v3 [cs.CV] UPDATED)
    We present a neat yet effective recursive operation on vision transformers that can improve parameter utilization without involving additional parameters. This is achieved by sharing weights across the depth of transformer networks. The proposed method can obtain a substantial gain (~2%) simply using naive recursive operation, requires no special or sophisticated knowledge for designing principles of networks, and introduces minimal computational overhead to the training procedure. To reduce the additional computation caused by recursive operation while maintaining the superior accuracy, we propose an approximating method through multiple sliced group self-attentions across recursive layers which can reduce the cost consumption by 10~30% with minimal performance loss. We call our model Sliced Recursive Transformer (SReT), a novel and parameter-efficient vision transformer design that is compatible with a broad range of other designs for efficient ViT architectures. Our best model establishes significant improvement on ImageNet-1K over state-of-the-art methods while containing fewer parameters. The proposed weight sharing mechanism by sliced recursion structure allows us to build a transformer with more than 100 or even 1000 shared layers with ease while keeping a compact size (13~15M), to avoid optimization difficulties when the model is too large. The flexible scalability has shown great potential for scaling up models and constructing extremely deep vision transformers. Code is available at https://github.com/szq0214/SReT.
    Modeling Irregular Time Series with Continuous Recurrent Units. (arXiv:2111.11344v3 [cs.LG] UPDATED)
    Recurrent neural networks (RNNs) are a popular choice for modeling sequential data. Modern RNN architectures assume constant time-intervals between observations. However, in many datasets (e.g. medical records) observation times are irregular and can carry important information. To address this challenge, we propose continuous recurrent units (CRUs) -- a neural architecture that can naturally handle irregular intervals between observations. The CRU assumes a hidden state, which evolves according to a linear stochastic differential equation and is integrated into an encoder-decoder framework. The recursive computations of the CRU can be derived using the continuous-discrete Kalman filter and are in closed form. The resulting recurrent architecture has temporal continuity between hidden states and a gating mechanism that can optimally integrate noisy observations. We derive an efficient parameterization scheme for the CRU that leads to a fast implementation f-CRU. We empirically study the CRU on a number of challenging datasets and find that it can interpolate irregular time series better than methods based on neural ordinary differential equations.
    Rethinking Pareto Frontier for Performance Evaluation of Deep Neural Networks. (arXiv:2202.09275v4 [cs.LG] UPDATED)
    Performance optimization of deep learning models is conducted either manually or through automatic architecture search, or a combination of both. On the other hand, their performance strongly depends on the target hardware and how successfully the models were trained. We propose to use a multi-dimensional Pareto frontier to re-define the efficiency measure of candidate deep learning models, where several variables such as training cost, inference latency, and accuracy play a relative role in defining a dominant model. Furthermore, a random version of the multi-dimensional Pareto frontier is introduced to mitigate the uncertainty of accuracy, latency, and throughput of deep learning models in different experimental setups. These two complementary methods can be combined to perform objective benchmarking of deep learning models. Our proposed method is applied to a wide range of deep image classification models trained on ImageNet data. Our method combines competing variables with stochastic nature in a single relative efficiency measure. This allows ranking deep learning models that run efficiently on different hardware, and combining inference efficiency with training efficiency objectively.
    Dualize, Split, Randomize: Toward Fast Nonsmooth Optimization Algorithms. (arXiv:2004.02635v4 [math.OC] UPDATED)
    We consider minimizing the sum of three convex functions, where the first one F is smooth, the second one is nonsmooth and proximable and the third one is the composition of a nonsmooth proximable function with a linear operator L. This template problem has many applications, for instance, in image processing and machine learning. First, we propose a new primal-dual algorithm, which we call PDDY, for this problem. It is constructed by applying Davis-Yin splitting to a monotone inclusion in a primal-dual product space, where the operators are monotone under a specific metric depending on L. We show that three existing algorithms (the two forms of the Condat-Vu algorithm and the PD3O algorithm) have the same structure, so that PDDY is the fourth missing link in this self-consistent class of primal-dual algorithms. This representation eases the convergence analysis: it allows us to derive sublinear convergence rates in general, and linear convergence results in presence of strong convexity. Moreover, within our broad and flexible analysis framework, we propose new stochastic generalizations of the algorithms, in which a variance-reduced random estimate of the gradient of F is used, instead of the true gradient. Furthermore, we obtain, as a special case of PDDY, a linearly converging algorithm for the minimization of a strongly convex function F under a linear constraint; we discuss its important application to decentralized optimization.
    Few-Shot Domain Adaptation For End-to-End Communication. (arXiv:2108.00874v2 [cs.LG] UPDATED)
    The problem of end-to-end learning of a communication system using an autoencoder -- consisting of an encoder, channel, and decoder modeled using neural networks -- has recently been shown to be a promising approach. A challenge faced in the practical adoption of this learning approach is that under changing channel conditions (e.g. a wireless link), it requires frequent retraining of the autoencoder in order to maintain a low decoding error rate. Since retraining is both time consuming and requires a large number of samples, it becomes impractical when the channel distribution is changing quickly. We propose to address this problem using a fast and sample-efficient (few-shot) domain adaptation method that does not change the encoder and decoder networks. Different from conventional training-time unsupervised or semi-supervised domain adaptation, here we have a trained autoencoder from a source distribution, that we want to adapt (at test time) to a target distribution using only a small labeled dataset and no unlabeled data. Our method focuses on a Gaussian mixture density network based channel model, and formulates its adaptation based on class and component-conditional affine transformations. The learned affine transformations are used to design an optimal input transformation at the decoder to compensate for the distribution shift, and effectively present to the decoder inputs close to the source distribution. Experiments on a real mmWave FPGA setup as well as a number of simulated distribution changes common to the wireless setting demonstrate the effectiveness of our method at adaptation using very small number of target domain samples.
    Iterative Teaching by Label Synthesis. (arXiv:2110.14432v3 [cs.LG] UPDATED)
    In this paper, we consider the problem of iterative machine teaching, where a teacher provides examples sequentially based on the current iterative learner. In contrast to previous methods that have to scan over the entire pool and select teaching examples from it in each iteration, we propose a label synthesis teaching framework where the teacher randomly selects input teaching examples (e.g., images) and then synthesizes suitable outputs (e.g., labels) for them. We show that this framework can avoid costly example selection while still provably achieving exponential teachability. We propose multiple novel teaching algorithms in this framework. Finally, we empirically demonstrate the value of our framework.
    Guiding Visual Question Generation. (arXiv:2110.08226v3 [cs.LG] UPDATED)
    In traditional Visual Question Generation (VQG), most images have multiple concepts (e.g. objects and categories) for which a question could be generated, but models are trained to mimic an arbitrary choice of concept as given in their training data. This makes training difficult and also poses issues for evaluation -- multiple valid questions exist for most images but only one or a few are captured by the human references. We present Guiding Visual Question Generation - a variant of VQG which conditions the question generator on categorical information based on expectations on the type of question and the objects it should explore. We propose two variants: (i) an explicitly guided model that enables an actor (human or automated) to select which objects and categories to generate a question for; and (ii) an implicitly guided model that learns which objects and categories to condition on, based on discrete latent variables. The proposed models are evaluated on an answer-category augmented VQA dataset and our quantitative results show a substantial improvement over the current state of the art (over 9 BLEU-4 increase). Human evaluation validates that guidance helps the generation of questions that are grammatically coherent and relevant to the given image and objects.
    Verification-Aided Deep Ensemble Selection. (arXiv:2202.03898v2 [cs.LG] UPDATED)
    Deep neural networks (DNNs) have become the technology of choice for realizing a variety of complex tasks. However, as highlighted by many recent studies, even an imperceptible perturbation to a correctly classified input can lead to misclassification by a DNN. This renders DNNs vulnerable to strategic input manipulations by attackers, and also oversensitive to environmental noise. To mitigate this phenomenon, practitioners apply joint classification by an *ensemble* of DNNs. By aggregating the classification outputs of different individual DNNs for the same input, ensemble-based classification reduces the risk of misclassifications due to the specific realization of the stochastic training process of any single DNN. However, the effectiveness of a DNN ensemble is highly dependent on its members *not simultaneously erring* on many different inputs. In this case study, we harness recent advances in DNN verification to devise a methodology for identifying ensemble compositions that are less prone to simultaneous errors, even when the input is adversarially perturbed -- resulting in more robustly-accurate ensemble-based classification. Our proposed framework uses a DNN verifier as a backend, and includes heuristics that help reduce the high complexity of directly verifying ensembles. More broadly, our work puts forth a novel universal objective for formal verification that can potentially improve the robustness of real-world, deep-learning-based systems across a variety of application domains.
    Modeling Financial Products and their Supply Chains. (arXiv:2102.02329v2 [cs.LG] UPDATED)
    The objective of this paper is to explore how financial big data and machine learning methods can be applied to model and understand financial products. We focus on residential mortgage backed securities, resMBS, which were at the heart of the 2008 US financial crisis. These securities are contained within a prospectus and have a complex waterfall payoff structure. Multiple financial institutions form a supply chain to create prospectuses. To model this supply chain, we use unsupervised probabilistic methods, particularly dynamic topics models (DTM), to extract a set of features (topics) reflecting community formation and temporal evolution along the chain. We then provide insight into the performance of the resMBS securities and the impact of the supply chain through a series of increasingly comprehensive models. First, models at the security level directly identify salient features of resMBS securities that impact their performance. We then extend the model to include prospectus level features and demonstrate that the composition of the prospectus is significant. Our model also shows that communities along the supply chain that are associated with the generation of the prospectuses and securities have an impact on performance. We are the first to show that toxic communities that are closely linked to financial institutions that played a key role in the subprime crisis can increase the risk of failure of resMBS securities.
    PIXEL: Physics-Informed Cell Representations for Fast and Accurate PDE Solvers. (arXiv:2207.12800v1 [cs.LG])
    With the increases in computational power and advances in machine learning, data-driven learning-based methods have gained significant attention in solving PDEs. Physics-informed neural networks (PINNs) have recently emerged and succeeded in various forward and inverse PDEs problems thanks to their excellent properties, such as flexibility, mesh-free solutions, and unsupervised training. However, their slower convergence speed and relatively inaccurate solutions often limit their broader applicability in many science and engineering domains. This paper proposes a new kind of data-driven PDEs solver, physics-informed cell representations (PIXEL), elegantly combining classical numerical methods and learning-based approaches. We adopt a grid structure from the numerical methods to improve accuracy and convergence speed and overcome the spectral bias presented in PINNs. Moreover, the proposed method enjoys the same benefits in PINNs, e.g., using the same optimization frameworks to solve both forward and inverse PDE problems and readily enforcing PDE constraints with modern automatic differentiation techniques. We provide experimental results on various challenging PDEs that the original PINNs have struggled with and show that PIXEL achieves fast convergence speed and high accuracy.
    Solution of Physics-based Bayesian Inverse Problems with Deep Generative Priors. (arXiv:2107.02926v2 [stat.ML] UPDATED)
    Inverse problems are ubiquitous in nature, arising in almost all areas of science and engineering ranging from geophysics and climate science to astrophysics and biomechanics. One of the central challenges in solving inverse problems is tackling their ill-posed nature. Bayesian inference provides a principled approach for overcoming this by formulating the inverse problem into a statistical framework. However, it is challenging to apply when inferring fields that have discrete representations of large dimensions (the so-called "curse of dimensionality") and/or when prior information is available only in the form of previously acquired solutions. In this work, we present a novel method for efficient and accurate Bayesian inversion using deep generative models. Specifically, we demonstrate how using the approximate distribution learned by a Generative Adversarial Network (GAN) as a prior in a Bayesian update and reformulating the resulting inference problem in the low-dimensional latent space of the GAN, enables the efficient solution of large-scale Bayesian inverse problems. Our statistical framework preserves the underlying physics and is demonstrated to yield accurate results with reliable uncertainty estimates, even in the absence of information about underlying noise model, which is a significant challenge with many existing methods. We demonstrate the effectiveness of proposed method on a variety of inverse problems which include both synthetic as well as experimentally observed data.
    From Interpretable Filters to Predictions of Convolutional Neural Networks with Explainable Artificial Intelligence. (arXiv:2207.12958v1 [cs.LG])
    Convolutional neural networks (CNN) are known for their excellent feature extraction capabilities to enable the learning of models from data, yet are used as black boxes. An interpretation of the convolutional filtres and associated features can help to establish an understanding of CNN to distinguish various classes. In this work, we focus on the explainability of a CNN model called as cnnexplain that is used for Covid-19 and non-Covid-19 classification with a focus on the interpretability of features by the convolutional filters, and how these features contribute to classification. Specifically, we have used various explainable artificial intelligence (XAI) methods, such as visualizations, SmoothGrad, Grad-CAM, and LIME to provide interpretation of convolutional filtres, and relevant features, and their role in classification. We have analyzed the explanation of these methods for Covid-19 detection using dry cough spectrograms. Explanation results obtained from the LIME, SmoothGrad, and Grad-CAM highlight important features of different spectrograms and their relevance to classification.
    Finding Deep-Learning Compilation Bugs with NNSmith. (arXiv:2207.13066v1 [cs.LG])
    Deep-learning (DL) compilers such as TVM and TensorRT are increasingly used to optimize deep neural network (DNN) models to meet performance, resource utilization and other requirements. Bugs in these compilers can produce optimized models whose semantics differ from the original models, and produce incorrect results impacting the correctness of down stream applications. However, finding bugs in these compilers is challenging due to their complexity. In this work, we propose a new fuzz testing approach for finding bugs in deep-learning compilers. Our core approach uses (i) light-weight operator specifications to generate diverse yet valid DNN models allowing us to exercise a large part of the compiler's transformation logic; (ii) a gradient-based search process for finding model inputs that avoid any floating-point exceptional values during model execution, reducing the chance of missed bugs or false alarms; and (iii) differential testing to identify bugs. We implemented this approach in NNSmith which has found 65 new bugs in the last seven months for TVM, TensorRT, ONNXRuntime, and PyTorch. Of these 52 have been confirmed and 44 have been fixed by project maintainers.
    Sharp Concentration Results for Heavy-Tailed Distributions. (arXiv:2003.13819v3 [math.PR] UPDATED)
    We obtain concentration and large deviation for the sums of independent and identically distributed random variables with heavy-tailed distributions. Our concentration results are concerned with random variables whose distributions satisfy $\mathbb{P}(X>t) \leq {\rm e}^{- I(t)}$, where $I: \mathbb{R} \rightarrow \mathbb{R}$ is an increasing function and $I(t)/t \rightarrow \alpha \in [0, \infty)$ as $t \rightarrow \infty$. Our main theorem can not only recover some of the existing results, such as the concentration of the sum of subWeibull random variables, but it can also produce new results for the sum of random variables with heavier tails. We show that the concentration inequalities we obtain are sharp enough to offer large deviation results for the sums of independent random variables as well. Our analyses which are based on standard truncation arguments simplify, unify and generalize the existing results on the concentration and large deviation of heavy-tailed random variables.
    Sparse Signal Models for Data Augmentation in Deep Learning ATR. (arXiv:2012.09284v2 [cs.CV] UPDATED)
    Automatic Target Recognition (ATR) algorithms classify a given Synthetic Aperture Radar (SAR) image into one of the known target classes using a set of training images available for each class. Recently, learning methods have shown to achieve state-of-the-art classification accuracy if abundant training data is available, sampled uniformly over the classes, and their poses. In this paper, we consider the task of ATR with a limited set of training images. We propose a data augmentation approach to incorporate domain knowledge and improve the generalization power of a data-intensive learning algorithm, such as a Convolutional neural network (CNN). The proposed data augmentation method employs a limited persistence sparse modeling approach, capitalizing on commonly observed characteristics of wide-angle synthetic aperture radar (SAR) imagery. Specifically, we exploit the sparsity of the scattering centers in the spatial domain and the smoothly-varying structure of the scattering coefficients in the azimuthal domain to solve the ill-posed problem of over-parametrized model fitting. Using this estimated model, we synthesize new images at poses and sub-pixel translations not available in the given data to augment CNN's training data. The experimental results show that for the training data starved region, the proposed method provides a significant gain in the resulting ATR algorithm's generalization performance.
    Efficient Learning of Accurate Surrogates for Simulations of Complex Systems. (arXiv:2207.12855v1 [cs.LG])
    Machine learning methods are increasingly used to build computationally inexpensive surrogates for complex physical models. The predictive capability of these surrogates suffers when data are noisy, sparse, or time-dependent. As we are interested in finding a surrogate that provides valid predictions of any potential future model evaluations, we introduce an online learning method empowered by optimizer-driven sampling. The method has two advantages over current approaches. First, it ensures that all turning points on the model response surface are included in the training data. Second, after any new model evaluations, surrogates are tested and "retrained" (updated) if the "score" drops below a validity threshold. Tests on benchmark functions reveal that optimizer-directed sampling generally outperforms traditional sampling methods in terms of accuracy around local extrema, even when the scoring metric favors overall accuracy. We apply our method to simulations of nuclear matter to demonstrate that highly accurate surrogates for the nuclear equation of state can be reliably auto-generated from expensive calculations using a few model evaluations.
    Learning Bipedal Walking On Planned Footsteps For Humanoid Robots. (arXiv:2207.12644v1 [cs.RO])
    Deep reinforcement learning (RL) based controllers for legged robots have demonstrated impressive robustness for walking in different environments for several robot platforms. To enable the application of RL policies for humanoid robots in real-world settings, it is crucial to build a system that can achieve robust walking in any direction, on 2D and 3D terrains, and be controllable by a user-command. In this paper, we tackle this problem by learning a policy to follow a given step sequence. The policy is trained with the help of a set of procedurally generated step sequences (also called footstep plans). We show that simply feeding the upcoming 2 steps to the policy is sufficient to achieve omnidirectional walking, turning in place, standing, and climbing stairs. Our method employs curriculum learning on the complexity of terrains, and circumvents the need for reference motions or pre-trained weights. We demonstrate the application of our proposed method to learn RL policies for 2 new robot platforms - HRP5P and JVRC-1 - in the MuJoCo simulation environment. The code for training and evaluation is available online.
    Learning-Augmented Maximum Flow. (arXiv:2207.12911v1 [cs.DS])
    We propose a framework for speeding up maximum flow computation by using predictions. A prediction is a flow, i.e., an assignment of non-negative flow values to edges, which satisfies the flow conservation property, but does not necessarily respect the edge capacities of the actual instance (since these were unknown at the time of learning). We present an algorithm that, given an $m$-edge flow network and a predicted flow, computes a maximum flow in $O(m\eta)$ time, where $\eta$ is the $\ell_1$ error of the prediction, i.e., the sum over the edges of the absolute difference between the predicted and optimal flow values. Moreover, we prove that, given an oracle access to a distribution over flow networks, it is possible to efficiently PAC-learn a prediction minimizing the expected $\ell_1$ error over that distribution. Our results fit into the recent line of research on learning-augmented algorithms, which aims to improve over worst-case bounds of classical algorithms by using predictions, e.g., machine-learned from previous similar instances. So far, the main focus in this area was on improving competitive ratios for online problems. Following Dinitz et al. (NeurIPS 2021), our results are one of the firsts to improve the running time of an offline problem.
    Is Attention Interpretation? A Quantitative Assessment On Sets. (arXiv:2207.13018v1 [cs.LG])
    The debate around the interpretability of attention mechanisms is centered on whether attention scores can be used as a proxy for the relative amounts of signal carried by sub-components of data. We propose to study the interpretability of attention in the context of set machine learning, where each data point is composed of an unordered collection of instances with a global label. For classical multiple-instance-learning problems and simple extensions, there is a well-defined "importance" ground truth that can be leveraged to cast interpretation as a binary classification problem, which we can quantitatively evaluate. By building synthetic datasets over several data modalities, we perform a systematic assessment of attention-based interpretations. We find that attention distributions are indeed often reflective of the relative importance of individual instances, but that silent failures happen where a model will have high classification performance but attention patterns that do not align with expectations. Based on these observations, we propose to use ensembling to minimize the risk of misleading attention-based explanations.
    Offline Reinforcement Learning at Multiple Frequencies. (arXiv:2207.13082v1 [cs.LG])
    Leveraging many sources of offline robot data requires grappling with the heterogeneity of such data. In this paper, we focus on one particular aspect of heterogeneity: learning from offline data collected at different control frequencies. Across labs, the discretization of controllers, sampling rates of sensors, and demands of a task of interest may differ, giving rise to a mixture of frequencies in an aggregated dataset. We study how well offline reinforcement learning (RL) algorithms can accommodate data with a mixture of frequencies during training. We observe that the $Q$-value propagates at different rates for different discretizations, leading to a number of learning challenges for off-the-shelf offline RL. We present a simple yet effective solution that enforces consistency in the rate of $Q$-value updates to stabilize learning. By scaling the value of $N$ in $N$-step returns with the discretization size, we effectively balance $Q$-value propagation, leading to more stable convergence. On three simulated robotic control problems, we empirically find that this simple approach outperforms na\"ive mixing by 50% on average.
    Buffer Pool Aware Query Scheduling via Deep Reinforcement Learning. (arXiv:2007.10568v2 [cs.DB] UPDATED)
    In this extended abstract, we propose a new technique for query scheduling with the explicit goal of reducing disk reads and thus implicitly increasing query performance. We introduce SmartQueue, a learned scheduler that leverages overlapping data reads among incoming queries and learns a scheduling strategy that improves cache hits. \system relies on deep reinforcement learning to produce workload-specific scheduling strategies that focus on long-term performance benefits while being adaptive to previously-unseen data access patterns. We present results from a proof-of-concept prototype, demonstrating that learned schedulers can offer significant performance improvements over hand-crafted scheduling heuristics. Ultimately, we make the case that this is a promising research direction at the intersection of machine learning and databases.
    A Guide to Image and Video based Small Object Detection using Deep Learning : Case Study of Maritime Surveillance. (arXiv:2207.12926v1 [cs.CV])
    Small object detection (SOD) in optical images and videos is a challenging problem that even state-of-the-art generic object detection methods fail to accurately localize and identify such objects. Typically, small objects appear in real-world due to large camera-object distance. Because small objects occupy only a small area in the input image (e.g., less than 10%), the information extracted from such a small area is not always rich enough to support decision making. Multidisciplinary strategies are being developed by researchers working at the interface of deep learning and computer vision to enhance the performance of SOD deep learning based methods. In this paper, we provide a comprehensive review of over 160 research papers published between 2017 and 2022 in order to survey this growing subject. This paper summarizes the existing literature and provide a taxonomy that illustrates the broad picture of current research. We investigate how to improve the performance of small object detection in maritime environments, where increasing performance is critical. By establishing a connection between generic and maritime SOD research, future directions have been identified. In addition, the popular datasets that have been used for SOD for generic and maritime applications are discussed, and also well-known evaluation metrics for the state-of-the-art methods on some of the datasets are provided.
    Neural Design for Genetic Perturbation Experiments. (arXiv:2207.12805v1 [q-bio.QM])
    The problem of how to genetically modify cells in order to maximize a certain cellular phenotype has taken center stage in drug development over the last few years (with, for example, genetically edited CAR-T, CAR-NK, and CAR-NKT cells entering cancer clinical trials). Exhausting the search space for all possible genetic edits (perturbations) or combinations thereof is infeasible due to cost and experimental limitations. This work provides a theoretically sound framework for iteratively exploring the space of perturbations in pooled batches in order to maximize a target phenotype under an experimental budget. Inspired by this application domain, we study the problem of batch query bandit optimization and introduce the Optimistic Arm Elimination ($\mathrm{OAE}$) principle designed to find an almost optimal arm under different functional relationships between the queries (arms) and the outputs (rewards). We analyze the convergence properties of $\mathrm{OAE}$ by relating it to the Eluder dimension of the algorithm's function class and validate that $\mathrm{OAE}$ outperforms other strategies in finding optimal actions in experiments on simulated problems, public datasets well-studied in bandit contexts, and in genetic perturbation datasets when the regression model is a deep neural network. OAE also outperforms the benchmark algorithms in 3 of 4 datasets in the GeneDisco experimental planning challenge.
    Contrastive Attraction and Contrastive Repulsion for Representation Learning. (arXiv:2105.03746v3 [cs.LG] UPDATED)
    Contrastive learning (CL) methods effectively learn data representations without label supervision, where the encoder contrasts each positive sample over multiple negative samples via a one-vs-many softmax cross-entropy loss. By leveraging large amounts of unlabeled image data, recent CL methods have achieved promising results when pre-trained on ImageNet, a well-curated data set with balanced image classes. However, they tend to yield worse performance when pre-trained on images in the wild. In this paper, to further improve the performance of CL and enhance its robustness on uncurated data sets, we propose a doubly CL strategy that contrasts the positive (negative) samples of a query within themselves before deciding how strongly to pull (push) them. We realize this strategy with contrastive attraction and contrastive repulsion (CACR), which makes the query not only exert a greater force to attract more distant positive samples but also do so to repel closer negative samples. Theoretical analysis reveals that CACR generalizes CL's behavior by taking into consideration the differences between the distributions of the positive/negative samples, which in general are sampled independently of the query, and their true conditional distributions given the query. We demonstrate this unique intra-positive attraction and intra-negative repulsion mechanism, which helps remove the need to assume uniform prior distributions on both the data and their latent representation, is particularly beneficial when data sets are less curated. Extensive large-scale experiments on a number of standard vision tasks show that CACR not only consistently outperforms existing CL methods on benchmark data sets in representation learning, but also shows better robustness when generalized to pre-training on imbalanced image data sets.
    Active Learning of Ordinal Embeddings: A User Study on Football Data. (arXiv:2207.12710v1 [cs.LG])
    Humans innately measure distance between instances in an unlabeled dataset using an unknown similarity function. Distance metrics can only serve as proxy for similarity in information retrieval of similar instances. Learning a good similarity function from human annotations improves the quality of retrievals. This work uses deep metric learning to learn these user-defined similarity functions from few annotations for a large football trajectory dataset. We adapt an entropy-based active learning method with recent work from triplet mining to collect easy-to-answer but still informative annotations from human participants and use them to train a deep convolutional network that generalizes to unseen samples. Our user study shows that our approach improves the quality of the information retrieval compared to a previous deep metric learning approach that relies on a Siamese network. Specifically, we shed light on the strengths and weaknesses of passive sampling heuristics and active learners alike by analyzing the participants' response efficacy. To this end, we collect accuracy, algorithmic time complexity, the participants' fatigue and time-to-response, qualitative self-assessment and statements, as well as the effects of mixed-expertise annotators and their consistency on model performance and transfer-learning.
    Implementation Of Tiny Machine Learning Models On Arduino 33 BLE For Gesture And Speech Recognition. (arXiv:2207.12866v1 [eess.AS])
    In this article gesture recognition and speech recognition applications are implemented on embedded systems with Tiny Machine Learning (TinyML). It features 3-axis accelerometer, 3-axis gyroscope and 3-axis magnetometer. The gesture recognition,provides an innovative approach nonverbal communication. It has wide applications in human-computer interaction and sign language. Here in the implementation of hand gesture recognition, TinyML model is trained and deployed from EdgeImpulse framework for hand gesture recognition and based on the hand movements, Arduino Nano 33 BLE device having 6-axis IMU can find out the direction of movement of hand. The Speech is a mode of communication. Speech recognition is a way by which the statements or commands of human speech is understood by the computer which reacts accordingly. The main aim of speech recognition is to achieve communication between man and machine. Here in the implementation of speech recognition, TinyML model is trained and deployed from EdgeImpulse framework for speech recognition and based on the keywords pronounced by human, Arduino Nano 33 BLE device having built-in microphone can make an RGB LED glow like red, green or blue based on keyword pronounced. The results of each application are obtained and listed in the results section and given the analysis upon the results.
    Can Deep Learning Assist Automatic Identification of Layered Pigments From XRF Data?. (arXiv:2207.12651v1 [cs.CV])
    X-ray fluorescence spectroscopy (XRF) plays an important role for elemental analysis in a wide range of scientific fields, especially in cultural heritage. XRF imaging, which uses a raster scan to acquire spectra across artworks, provides the opportunity for spatial analysis of pigment distributions based on their elemental composition. However, conventional XRF-based pigment identification relies on time-consuming elemental mapping by expert interpretations of measured spectra. To reduce the reliance on manual work, recent studies have applied machine learning techniques to cluster similar XRF spectra in data analysis and to identify the most likely pigments. Nevertheless, it is still challenging for automatic pigment identification strategies to directly tackle the complex structure of real paintings, e.g. pigment mixtures and layered pigments. In addition, pixel-wise pigment identification based on XRF imaging remains an obstacle due to the high noise level compared with averaged spectra. Therefore, we developed a deep-learning-based end-to-end pigment identification framework to fully automate the pigment identification process. In particular, it offers high sensitivity to the underlying pigments and to the pigments with a low concentration, therefore enabling satisfying results in mapping the pigments based on single-pixel XRF spectrum. As case studies, we applied our framework to lab-prepared mock-up paintings and two 19th-century paintings: Paul Gauguin's Po\`emes Barbares (1896) that contains layered pigments with an underlying painting, and Paul Cezanne's The Bathers (1899-1904). The pigment identification results demonstrated that our model achieved comparable results to the analysis by elemental mapping, suggesting the generalizability and stability of our model.
    Federated Learning with Positive and Unlabeled Data. (arXiv:2106.10904v2 [cs.LG] UPDATED)
    We study the problem of learning from positive and unlabeled (PU) data in the federated setting, where each client only labels a little part of their dataset due to the limitation of resources and time. Different from the settings in traditional PU learning where the negative class consists of a single class, the negative samples which cannot be identified by a client in the federated setting may come from multiple classes which are unknown to the client. Therefore, existing PU learning methods can be hardly applied in this situation. To address this problem, we propose a novel framework, namely Federated learning with Positive and Unlabeled data (FedPU), to minimize the expected risk of multiple negative classes by leveraging the labeled data in other clients. We theoretically analyze the generalization bound of the proposed FedPU. Empirical experiments show that the FedPU can achieve much better performance than conventional supervised and semi-supervised federated learning methods.
    CFLIT: Coexisting Federated Learning and Information Transfer. (arXiv:2207.12884v1 [cs.IT])
    Future wireless networks are expected to support diverse mobile services, including artificial intelligence (AI) services and ubiquitous data transmissions. Federated learning (FL), as a revolutionary learning approach, enables collaborative AI model training across distributed mobile edge devices. By exploiting the superposition property of multiple-access channels, over-the-air computation allows concurrent model uploading from massive devices over the same radio resources, and thus significantly reduces the communication cost of FL. In this paper, we study the coexistence of over-the-air FL and traditional information transfer (IT) in a mobile edge network. We propose a coexisting federated learning and information transfer (CFLIT) communication framework, where the FL and IT devices share the wireless spectrum in an OFDM system. Under this framework, we aim to maximize the IT data rate and guarantee a given FL convergence performance by optimizing the long-term radio resource allocation. A key challenge that limits the spectrum efficiency of the coexisting system lies in the large overhead incurred by frequent communication between the server and edge devices for FL model aggregation. To address the challenge, we rigorously analyze the impact of the computation-to-communication ratio on the convergence of over-the-air FL in wireless fading channels. The analysis reveals the existence of an optimal computation-to-communication ratio that minimizes the amount of radio resources needed for over-the-air FL to converge to a given error tolerance. Based on the analysis, we propose a low-complexity online algorithm to jointly optimize the radio resource allocation for both the FL devices and IT devices. Extensive numerical simulations verify the superior performance of the proposed design for the coexistence of FL and IT devices in wireless cellular systems.
    Collaborative Three-Tier Architecture Non-contact Respiratory Rate Monitoring using Target Tracking and False Peaks Eliminating Algorithms. (arXiv:2011.08482v4 [cs.RO] UPDATED)
    Monitoring the respiratory rate is crucial for helping us identify respiratory disorders. Devices for conventional respiratory monitoring are inconvenient and scarcely available. Recent research has demonstrated the ability of non-contact technologies, such as photoplethysmography and infrared thermography, to gather respiratory signals from the face and monitor breathing. However, the current non-contact respiratory monitoring techniques have poor accuracy because they are sensitive to environmental influences like lighting and motion artifacts. Furthermore, frequent contact between users and the cloud in real-world medical application settings might cause service request delays and potentially the loss of personal data. We proposed a non-contact respiratory rate monitoring system with a cooperative three-layer design to increase the precision of respiratory monitoring and decrease data transmission latency. To reduce data transmission and network latency, our three-tier architecture layer-by-layer decomposes the computing tasks of respiration monitoring. Moreover, we improved the accuracy of respiratory monitoring by designing a target tracking algorithm and an algorithm for eliminating false peaks to extract high-quality respiratory signals. By gathering the data and choosing several regions of interest on the face, we were able to extract the respiration signal and investigate how different regions affected the monitoring of respiration. The results of the experiment indicate that when the nasal region is used to extract the respiratory signal, it performs experimentally best. Our approach performs better than rival approaches while transferring fewer data.
    Efficient and Accurate Skeleton-Based Two-Person Interaction Recognition Using Inter- and Intra-body Graphs. (arXiv:2207.12648v1 [cs.CV])
    Skeleton-based two-person interaction recognition has been gaining increasing attention as advancements are made in pose estimation and graph convolutional networks. Although the accuracy has been gradually improving, the increasing computational complexity makes it more impractical for a real-world environment. There is still room for accuracy improvement as the conventional methods do not fully represent the relationship between inter-body joints. In this paper, we propose a lightweight model for accurately recognizing two-person interactions. In addition to the architecture, which incorporates middle fusion, we introduce a factorized convolution technique to reduce the weight parameters of the model. We also introduce a network stream that accounts for relative distance changes between inter-body joints to improve accuracy. Experiments using two large-scale datasets, NTU RGB+D 60 and 120, show that our method simultaneously achieved the highest accuracy and relatively low computational complexity compared with the conventional methods.
    Lifelong DP: Consistently Bounded Differential Privacy in Lifelong Machine Learning. (arXiv:2207.12831v1 [cs.LG])
    In this paper, we show that the process of continually learning new tasks and memorizing previous tasks introduces unknown privacy risks and challenges to bound the privacy loss. Based upon this, we introduce a formal definition of Lifelong DP, in which the participation of any data tuples in the training set of any tasks is protected, under a consistently bounded DP protection, given a growing stream of tasks. A consistently bounded DP means having only one fixed value of the DP privacy budget, regardless of the number of tasks. To preserve Lifelong DP, we propose a scalable and heterogeneous algorithm, called L2DP-ML with a streaming batch training, to efficiently train and continue releasing new versions of an L2M model, given the heterogeneity in terms of data sizes and the training order of tasks, without affecting DP protection of the private training set. An end-to-end theoretical analysis and thorough evaluations show that our mechanism is significantly better than baseline approaches in preserving Lifelong DP. The implementation of L2DP-ML is available at: https://github.com/haiphanNJIT/PrivateDeepLearning.
    $\textbf{P$^2$A}$: A Dataset and Benchmark for Dense Action Detection from Table Tennis Match Broadcasting Videos. (arXiv:2207.12730v1 [cs.CV])
    While deep learning has been widely used for video analytics, such as video classification and action detection, dense action detection with fast-moving subjects from sports videos is still challenging. In this work, we release yet another sports video dataset $\textbf{P$^2$A}$ for $\underline{P}$ing $\underline{P}$ong-$\underline{A}$ction detection, which consists of 2,721 video clips collected from the broadcasting videos of professional table tennis matches in World Table Tennis Championships and Olympiads. We work with a crew of table tennis professionals and referees to obtain fine-grained action labels (in 14 classes) for every ping-pong action that appeared in the dataset and formulate two sets of action detection problems - action localization and action recognition. We evaluate a number of commonly-seen action recognition (e.g., TSM, TSN, Video SwinTransformer, and Slowfast) and action localization models (e.g., BSN, BSN++, BMN, TCANet), using $\textbf{P$^2$A}$ for both problems, under various settings. These models can only achieve 48% area under the AR-AN curve for localization and 82% top-one accuracy for recognition since the ping-pong actions are dense with fast-moving subjects but broadcasting videos are with only 25 FPS. The results confirm that $\textbf{P$^2$A}$ is still a challenging task and can be used as a benchmark for action detection from videos.
    S-Prompts Learning with Pre-trained Transformers: An Occam's Razor for Domain Incremental Learning. (arXiv:2207.12819v1 [cs.CV])
    State-of-the-art deep neural networks are still struggling to address the catastrophic forgetting problem in continual learning. In this paper, we propose one simple paradigm (named as S-Prompting) and two concrete approaches to highly reduce the forgetting degree in one of the most typical continual learning scenarios, i.e., domain increment learning (DIL). The key idea of the paradigm is to learn prompts independently across domains with pre-trained transformers, avoiding the use of exemplars that commonly appear in conventional methods. This results in a win-win game where the prompting can achieve the best for each domain. The independent prompting across domains only requests one single cross-entropy loss for training and one simple K-NN operation as a domain identifier for inference. The learning paradigm derives an image prompt learning approach and a brand-new language-image prompt learning approach. Owning an excellent scalability (0.03% parameter increase per domain), the best of our approaches achieves a remarkable relative improvement (an average of about 30%) over the best of the state-of-the-art exemplar-free methods for three standard DIL tasks, and even surpasses the best of them relatively by about 6% in average when they use exemplars.
    Exploring the Design of Adaptation Protocols for Improved Generalization and Machine Learning Safety. (arXiv:2207.12615v1 [cs.LG])
    While directly fine-tuning (FT) large-scale, pretrained models on task-specific data is well-known to induce strong in-distribution task performance, recent works have demonstrated that different adaptation protocols, such as linear probing (LP) prior to FT, can improve out-of-distribution generalization. However, the design space of such adaptation protocols remains under-explored and the evaluation of such protocols has primarily focused on distribution shifts. Therefore, in this work, we evaluate common adaptation protocols across distributions shifts and machine learning safety metrics (e.g., anomaly detection, calibration, robustness to corruptions). We find that protocols induce disparate trade-offs that were not apparent from prior evaluation. Further, we demonstrate that appropriate pairing of data augmentation and protocol can substantially mitigate this trade-off. Finally, we hypothesize and empirically see that using hardness-promoting augmentations during LP and then FT with augmentations may be particularly effective for trade-off mitigation.
    Task Agnostic and Post-hoc Unseen Distribution Detection. (arXiv:2207.13083v1 [cs.LG])
    Despite the recent advances in out-of-distribution(OOD) detection, anomaly detection, and uncertainty estimation tasks, there do not exist a task-agnostic and post-hoc approach. To address this limitation, we design a novel clustering-based ensembling method, called Task Agnostic and Post-hoc Unseen Distribution Detection (TAPUDD) that utilizes the features extracted from the model trained on a specific task. Explicitly, it comprises of TAP-Mahalanobis, which clusters the training datasets' features and determines the minimum Mahalanobis distance of the test sample from all clusters. Further, we propose the Ensembling module that aggregates the computation of iterative TAP-Mahalanobis for a different number of clusters to provide reliable and efficient cluster computation. Through extensive experiments on synthetic and real-world datasets, we observe that our approach can detect unseen samples effectively across diverse tasks and performs better or on-par with the existing baselines. To this end, we eliminate the necessity of determining the optimal value of the number of clusters and demonstrate that our method is more viable for large-scale classification tasks.
    Representing Random Utility Choice Models with Neural Networks. (arXiv:2207.12877v1 [cs.LG])
    Motivated by the successes of deep learning, we propose a class of neural network-based discrete choice models, called RUMnets, which is inspired by the random utility maximization (RUM) framework. This model formulates the agents' random utility function using the sample average approximation (SAA) method. We show that RUMnets sharply approximate the class of RUM discrete choice models: any model derived from random utility maximization has choice probabilities that can be approximated arbitrarily closely by a RUMnet. Reciprocally, any RUMnet is consistent with the RUM principle. We derive an upper bound on the generalization error of RUMnets fitted on choice data, and gain theoretical insights on their ability to predict choices on new, unseen data depending on critical parameters of the dataset and architecture. By leveraging open-source libraries for neural networks, we find that RUMnets outperform other state-of-the-art choice modeling and machine learning methods by a significant margin on two real-world datasets.
    Coronavirus disease situation analysis and prediction using machine learning: a study on Bangladeshi population. (arXiv:2207.13056v1 [cs.LG])
    During a pandemic, early prognostication of patient infected rates can reduce the death by ensuring treatment facility and proper resource allocation. In recent months, the number of death and infected rates has increased more distinguished than before in Bangladesh. The country is struggling to provide moderate medical treatment to many patients. This study distinguishes machine learning models and creates a prediction system to anticipate the infected and death rate for the coming days. Equipping a dataset with data from March 1, 2020, to August 10, 2021, a multi-layer perceptron (MLP) model was trained. The data was managed from a trusted government website and concocted manually for training purposes. Several test cases determine the model's accuracy and prediction capability. The comparison between specific models assumes that the MLP model has more reliable prediction capability than the support vector regression (SVR) and linear regression model. The model presents a report about the risky situation and impending coronavirus disease (COVID-19) attack. According to the prediction produced by the model, Bangladesh may suffer another COVID-19 attack, where the number of infected cases can be between 929 to 2443 and death cases between 19 to 57.
    DeFakePro: Decentralized DeepFake Attacks Detection using ENF Authentication. (arXiv:2207.13070v1 [cs.CR])
    Advancements in generative models, like Deepfake allows users to imitate a targeted person and manipulate online interactions. It has been recognized that disinformation may cause disturbance in society and ruin the foundation of trust. This article presents DeFakePro, a decentralized consensus mechanism-based Deepfake detection technique in online video conferencing tools. Leveraging Electrical Network Frequency (ENF), an environmental fingerprint embedded in digital media recording, affords a consensus mechanism design called Proof-of-ENF (PoENF) algorithm. The similarity in ENF signal fluctuations is utilized in the PoENF algorithm to authenticate the media broadcasted in conferencing tools. By utilizing the video conferencing setup with malicious participants to broadcast deep fake video recordings to other participants, the DeFakePro system verifies the authenticity of the incoming media in both audio and video channels.
    Enhancing Collaborative Filtering Recommender with Prompt-Based Sentiment Analysis. (arXiv:2207.12883v1 [cs.IR])
    Collaborative Filtering(CF) recommender is a crucial application in the online market and ecommerce. However, CF recommender has been proven to suffer from persistent problems related to sparsity of the user rating that will further lead to a cold-start issue. Existing methods address the data sparsity issue by applying token-level sentiment analysis that translate text review into sentiment scores as a complement of the user rating. In this paper, we attempt to optimize the sentiment analysis with advanced NLP models including BERT and RoBERTa, and experiment on whether the CF recommender has been further enhanced. We build the recommenders on the Amazon US Reviews dataset, and tune the pretrained BERT and RoBERTa with the traditional fine-tuned paradigm as well as the new prompt-based learning paradigm. Experimental result shows that the recommender enhanced with the sentiment ratings predicted by the fine-tuned RoBERTa has the best performance, and achieved 30.7% overall gain by comparing MAP, NDCG and precision at K to the baseline recommender. Prompt-based learning paradigm, although superior to traditional fine-tune paradigm in pure sentiment analysis, fail to further improve the CF recommender.
    Bilateral Self-unbiased Learning from Biased Implicit Feedback. (arXiv:2207.12660v1 [cs.IR])
    Implicit feedback has been widely used to build commercial recommender systems. Because observed feedback represents users' click logs, there is a semantic gap between true relevance and observed feedback. More importantly, observed feedback is usually biased towards popular items, thereby overestimating the actual relevance of popular items. Although existing studies have developed unbiased learning methods using inverse propensity weighting (IPW) or causal reasoning, they solely focus on eliminating the popularity bias of items. In this paper, we propose a novel unbiased recommender learning model, namely BIlateral SElf-unbiased Recommender (BISER), to eliminate the exposure bias of items caused by recommender models. Specifically, BISER consists of two key components: (i) self-inverse propensity weighting (SIPW) to gradually mitigate the bias of items without incurring high computational costs; and (ii) bilateral unbiased learning (BU) to bridge the gap between two complementary models in model predictions, i.e., user- and item-based autoencoders, alleviating the high variance of SIPW. Extensive experiments show that BISER consistently outperforms state-of-the-art unbiased recommender models over several datasets, including Coat, Yahoo! R3, MovieLens, and CiteULike.
    Reconciling Security and Communication Efficiency in Federated Learning. (arXiv:2207.12779v1 [cs.LG])
    Cross-device Federated Learning is an increasingly popular machine learning setting to train a model by leveraging a large population of client devices with high privacy and security guarantees. However, communication efficiency remains a major bottleneck when scaling federated learning to production environments, particularly due to bandwidth constraints during uplink communication. In this paper, we formalize and address the problem of compressing client-to-server model updates under the Secure Aggregation primitive, a core component of Federated Learning pipelines that allows the server to aggregate the client updates without accessing them individually. In particular, we adapt standard scalar quantization and pruning methods to Secure Aggregation and propose Secure Indexing, a variant of Secure Aggregation that supports quantization for extreme compression. We establish state-of-the-art results on LEAF benchmarks in a secure Federated Learning setup with up to 40$\times$ compression in uplink communication with no meaningful loss in utility compared to uncompressed baselines.
    Static and Dynamic Concepts for Self-supervised Video Representation Learning. (arXiv:2207.12795v1 [cs.CV])
    In this paper, we propose a novel learning scheme for self-supervised video representation learning. Motivated by how humans understand videos, we propose to first learn general visual concepts then attend to discriminative local areas for video understanding. Specifically, we utilize static frame and frame difference to help decouple static and dynamic concepts, and respectively align the concept distributions in latent space. We add diversity and fidelity regularizations to guarantee that we learn a compact set of meaningful concepts. Then we employ a cross-attention mechanism to aggregate detailed local features of different concepts, and filter out redundant concepts with low activations to perform local concept contrast. Extensive experiments demonstrate that our method distills meaningful static and dynamic concepts to guide video understanding, and obtains state-of-the-art results on UCF-101, HMDB-51, and Diving-48.
    Repeated Environment Inference for Invariant Learning. (arXiv:2207.12876v1 [cs.LG])
    We study the problem of invariant learning when the environment labels are unknown. We focus on the invariant representation notion when the Bayes optimal conditional label distribution is the same across different environments. Previous work conducts Environment Inference (EI) by maximizing the penalty term from Invariant Risk Minimization (IRM) framework. The EI step uses a reference model which focuses on spurious correlations to efficiently reach a good environment partition. However, it is not clear how to find such a reference model. In this work, we propose to repeat the EI process and retrain an ERM model on the \textit{majority} environment inferred by the previous EI step. Under mild assumptions, we find that this iterative process helps learn a representation capturing the spurious correlation better than the single step. This results in better Environment Inference and better Invariant Learning. We show that this method outperforms baselines on both synthetic and real-world datasets.
    Thermodynamics of learning physical phenomena. (arXiv:2207.12749v1 [cs.LG])
    Thermodynamics could be seen as an expression of physics at a high epistemic level. As such, its potential as an inductive bias to help machine learning procedures attain accurate and credible predictions has been recently realized in many fields. We review how thermodynamics provides helpful insights in the learning process. At the same time, we study the influence of aspects such as the scale at which a given phenomenon is to be described, the choice of relevant variables for this description or the different techniques available for the learning process.
    A Retrospective on ICSE 2022. (arXiv:2207.12578v1 [cs.SE])
    The 44th International Conference on Software Engineering (ICSE 2022) was held in person from May 22 to May 27, 2022 in Pittsburgh, PA, USA. Here, we summarize themes of research and the direction of research in the field of software engineering and testing that we observed at the conference.
    Static Hand Gesture Recognition for American Sign Language using Neuromorphic Hardware. (arXiv:2207.12559v1 [cs.LG])
    In this paper, we develop four spiking neural network (SNN) models for two static American Sign Language (ASL) hand gesture classification tasks, i.e., the ASL Alphabet and ASL Digits. The SNN models are deployed on Intel's neuromorphic platform, Loihi, and then compared against equivalent deep neural network (DNN) models deployed on an edge computing device, the Intel Neural Compute Stick 2 (NCS2). We perform a comprehensive comparison between the two systems in terms of accuracy, latency, power consumption, and energy. The best DNN model achieves an accuracy of 99.6% on the ASL Alphabet dataset, whereas the best performing SNN model has an accuracy of 99.44%. For the ASL-Digits dataset, the best SNN model outperforms all of its DNN counterparts with 99.52% accuracy. Moreover, our obtained experimental results show that the Loihi neuromorphic hardware implementations achieve up to 14.67x and 4.09x reduction in power consumption and energy, respectively, when compared to NCS2.
    AMLB: an AutoML Benchmark. (arXiv:2207.12560v1 [cs.LG])
    Comparing different AutoML frameworks is notoriously challenging and often done incorrectly. We introduce an open and extensible benchmark that follows best practices and avoids common mistakes when comparing AutoML frameworks. We conduct a thorough comparison of 9 well-known AutoML frameworks across 71 classification and 33 regression tasks. The differences between the AutoML frameworks are explored with a multi-faceted analysis, evaluating model accuracy, its trade-offs with inference time, and framework failures. We also use Bradley-Terry trees to discover subsets of tasks where the relative AutoML framework rankings differ. The benchmark comes with an open-source tool that integrates with many AutoML frameworks and automates the empirical evaluation process end-to-end: from framework installation and resource allocation to in-depth evaluation. The benchmark uses public data sets, can be easily extended with other AutoML frameworks and tasks, and has a website with up-to-date results.
    Extreme compression of sentence-transformer ranker models: faster inference, longer battery life, and less storage on edge devices. (arXiv:2207.12852v1 [cs.LG])
    Modern search systems use several large ranker models with transformer architectures. These models require large computational resources and are not suitable for usage on devices with limited computational resources. Knowledge distillation is a popular compression technique that can reduce the resource needs of such models, where a large teacher model transfers knowledge to a small student model. To drastically reduce memory requirements and energy consumption, we propose two extensions for a popular sentence-transformer distillation procedure: generation of an optimal size vocabulary and dimensionality reduction of the embedding dimension of teachers prior to distillation. We evaluate these extensions on two different types of ranker models. This results in extremely compressed student models whose analysis on a test dataset shows the significance and utility of our proposed extensions.
    A Study on the Use of Edge TPUs for Eye Fundus Image Segmentation. (arXiv:2207.12770v1 [eess.IV])
    Medical image segmentation can be implemented using Deep Learning methods with fast and efficient segmentation networks. Single-board computers (SBCs) are difficult to use to train deep networks due to their memory and processing limitations. Specific hardware such as Google's Edge TPU makes them suitable for real time predictions using complex pre-trained networks. In this work, we study the performance of two SBCs, with and without hardware acceleration for fundus image segmentation, though the conclusions of this study can be applied to the segmentation by deep neural networks of other types of medical images. To test the benefits of hardware acceleration, we use networks and datasets from a previous published work and generalize them by testing with a dataset with ultrasound thyroid images. We measure prediction times in both SBCs and compare them with a cloud based TPU system. The results show the feasibility of Machine Learning accelerated SBCs for optic disc and cup segmentation obtaining times below 25 milliseconds per image using Edge TPUs.
    Generalized Probabilistic U-Net for medical image segementation. (arXiv:2207.12872v1 [cs.CV])
    We propose the Generalized Probabilistic U-Net, which extends the Probabilistic U-Net by allowing more general forms of the Gaussian distribution as the latent space distribution that can better approximate the uncertainty in the reference segmentations. We study the effect the choice of latent space distribution has on capturing the uncertainty in the reference segmentations using the LIDC-IDRI dataset. We show that the choice of distribution affects the sample diversity of the predictions and their overlap with respect to the reference segmentations. For the LIDC-IDRI dataset, we show that using a mixture of Gaussians results in a statistically significant improvement in the generalized energy distance (GED) metric with respect to the standard Probabilistic U-Net. We have made our implementation available at https://github.com/ishaanb92/GeneralizedProbabilisticUNet
    Benchmark time series data sets for PyTorch -- the torchtime package. (arXiv:2207.12503v1 [cs.LG])
    The development of models for Electronic Health Record data is an area of active research featuring a small number of public benchmark data sets. Researchers typically write custom data processing code but this hinders reproducibility and can introduce errors. The Python package torchtime provides reproducible implementations of commonly used PhysioNet and UEA & UCR time series classification repository data sets for PyTorch. Features are provided for working with irregularly sampled and partially observed time series of unequal length. It aims to simplify access to PhysioNet data and enable fair comparisons of models in this exciting area of research.
    Time Majority Voting, a PC-based EEG Classifier for Non-expert Users. (arXiv:2207.12662v1 [cs.LG])
    Using Machine Learning and Deep Learning to predict cognitive tasks from electroencephalography (EEG) signals is a rapidly advancing field in Brain-Computer Interfaces (BCI). In contrast to the fields of computer vision and natural language processing, the data amount of these trials is still rather tiny. Developing a PC-based machine learning technique to increase the participation of non-expert end-users could help solve this data collection issue. We created a novel algorithm for machine learning called Time Majority Voting (TMV). In our experiment, TMV performed better than cutting-edge algorithms. It can operate efficiently on personal computers for classification tasks involving the BCI. These interpretable data also assisted end-users and researchers in comprehending EEG tests better.
    A Data Driven Method for Multi-step Prediction of Ship Roll Motion in High Sea States. (arXiv:2207.12673v1 [cs.LG])
    Accurate prediction of roll motion in high sea state is significant for the operability, safety and survivability of marine vehicles. This paper presents a novel data-driven methodology for achieving the multi-step prediction of ship roll motion in high sea states. A hybrid neural network, named ConvLSTMPNet, is proposed to execute long short-term memory (LSTM) and one-dimensional convolutional neural networks (CNN) in parallel to extract time-dependent and spatio-temporal information from multidimensional inputs. Taken KCS as the study object, the numerical solution of computational fluid dynamics method is utilized to generate the ship motion data in sea state 7 with different wave directions. An in-depth comparative study on the selection of feature space is conducted, considering the effects of time history of motion states and wave height. The comparison results demonstrate the superiority of selecting both motion states and wave heights as the feature space for multi-step prediction. In addition, the results demonstrate that ConvLSTMNet achieves more accurate than LSTM and CNN methods in multi-step prediction of roll motion, validating the efficiency of the proposed method.
    Unsupervised Image Representation Learning with Deep Latent Particles. (arXiv:2205.15821v2 [cs.CV] UPDATED)
    We propose a new representation of visual data that disentangles object position from appearance. Our method, termed Deep Latent Particles (DLP), decomposes the visual input into low-dimensional latent ``particles'', where each particle is described by its spatial location and features of its surrounding region. To drive learning of such representations, we follow a VAE-based approach and introduce a prior for particle positions based on a spatial-softmax architecture, and a modification of the evidence lower bound loss inspired by the Chamfer distance between particles. We demonstrate that our DLP representations are useful for downstream tasks such as unsupervised keypoint (KP) detection, image manipulation, and video prediction for scenes composed of multiple dynamic objects. In addition, we show that our probabilistic interpretation of the problem naturally provides uncertainty estimates for particle locations, which can be used for model selection, among other tasks. Videos and code are available: https://taldatech.github.io/deep-latent-particles-web/
    Classifier-Free Diffusion Guidance. (arXiv:2207.12598v1 [cs.LG])
    Classifier guidance is a recently introduced method to trade off mode coverage and sample fidelity in conditional diffusion models post training, in the same spirit as low temperature sampling or truncation in other types of generative models. Classifier guidance combines the score estimate of a diffusion model with the gradient of an image classifier and thereby requires training an image classifier separate from the diffusion model. It also raises the question of whether guidance can be performed without a classifier. We show that guidance can be indeed performed by a pure generative model without such a classifier: in what we call classifier-free guidance, we jointly train a conditional and an unconditional diffusion model, and we combine the resulting conditional and unconditional score estimates to attain a trade-off between sample quality and diversity similar to that obtained using classifier guidance.
    A Learning and Control Perspective for Microfinance. (arXiv:2207.12631v1 [q-fin.GN])
    Microfinance in developing areas such as Africa has been proven to improve the local economy significantly. However, many applicants in developing areas cannot provide adequate information required by the financial institution to make a lending decision. As a result, it is challenging for microfinance institutions to assign credit properly based on conventional policies. In this paper, we formulate the decision-making of microfinance into a rigorous optimization-based framework involving learning and control. We propose an algorithm to explore and learn the optimal policy to approve or reject applicants. We provide the conditions under which the algorithms are guaranteed to converge to an optimal one. The proposed algorithm can naturally deal with missing information and systematically tradeoff multiple objectives such as profit maximization, financial inclusion, social benefits, and economic development. Through extensive simulation of both real and synthetic microfinance datasets, we showed our proposed algorithm is superior to existing benchmarks. To the best of our knowledge, this paper is the first to make a connection between microfinance and control and use control-theoretic tools to optimize the policy with a provable guarantee.
    Learning Protein Representations via Complete 3D Graph Networks. (arXiv:2207.12600v1 [cs.LG])
    We consider representation learning for proteins with 3D structures. We build 3D graphs based on protein structures and develop graph networks to learn their representations. Depending on the levels of details that we wish to capture, protein representations can be computed at different levels, \emph{e.g.}, the amino acid, backbone, or all-atom levels. Importantly, there exist hierarchical relations among different levels. In this work, we propose to develop a novel hierarchical graph network, known as ProNet, to capture the relations. Our ProNet is very flexible and can be used to compute protein representations at different levels of granularity. We show that, given a base 3D graph network that is complete, our ProNet representations are also complete at all levels. To close the loop, we develop a complete and efficient 3D graph network to be used as a base model, making our ProNet complete. We conduct experiments on multiple downstream tasks. Results show that ProNet outperforms recent methods on most datasets. In addition, results indicate that different downstream tasks may require representations at different levels. Our code is available as part of the DIG library (\url{https://github.com/divelab/DIG}).
    An Empirical Deep Dive into Deep Learning's Driving Dynamics. (arXiv:2207.12547v1 [cs.LG])
    We present an empirical dataset surveying the deep learning phenomenon on fully-connected networks, encompassing the training and test performance of numerous network topologies, sweeping across multiple learning tasks, depths, numbers of free parameters, learning rates, batch sizes, and regularization penalties. The dataset probes 178 thousand hyperparameter settings with an average of 20 repetitions each, totaling 3.5 million training runs and 20 performance metrics for each of the 13.1 billion training epochs observed. Accumulating this 671 GB dataset utilized 5,448 CPU core-years, 17.8 GPU-years, and 111.2 node-years. Additionally, we provide a preliminary analysis revealing patterns which persist across learning tasks and topologies. We aim to inspire work empirically studying modern machine learning techniques as a catalyst for the theoretical discoveries needed to progress the field beyond energy-intensive and heuristic practices.
    Analyzing Sharpness along GD Trajectory: Progressive Sharpening and Edge of Stability. (arXiv:2207.12678v1 [cs.LG])
    Recent findings (e.g., arXiv:2103.00065) demonstrate that modern neural networks trained by full-batch gradient descent typically enter a regime called Edge of Stability (EOS). In this regime, the sharpness, i.e., the maximum Hessian eigenvalue, first increases to the value 2/(step size) (the progressive sharpening phase) and then oscillates around this value (the EOS phase). This paper aims to analyze the GD dynamics and the sharpness along the optimization trajectory. Our analysis naturally divides the GD trajectory into four phases depending on the change of the sharpness. We empirically identify the norm of output layer weight as an interesting indicator of sharpness dynamics. Based on this empirical observation, we attempt to theoretically and empirically explain the dynamics of various key quantities that lead to the change of sharpness in each phase of EOS. Moreover, based on certain assumptions, we provide a theoretical proof of the sharpness behavior in EOS regime in two-layer fully-connected linear neural networks. We also discuss some other empirical findings and the limitation of our theoretical results.
    Provably Efficient Fictitious Play Policy Optimization for Zero-Sum Markov Games with Structured Transitions. (arXiv:2207.12463v1 [cs.LG])
    While single-agent policy optimization in a fixed environment has attracted a lot of research attention recently in the reinforcement learning community, much less is known theoretically when there are multiple agents playing in a potentially competitive environment. We take steps forward by proposing and analyzing new fictitious play policy optimization algorithms for zero-sum Markov games with structured but unknown transitions. We consider two classes of transition structures: factored independent transition and single-controller transition. For both scenarios, we prove tight $\widetilde{\mathcal{O}}(\sqrt{K})$ regret bounds after $K$ episodes in a two-agent competitive game scenario. The regret of each agent is measured against a potentially adversarial opponent who can choose a single best policy in hindsight after observing the full policy sequence. Our algorithms feature a combination of Upper Confidence Bound (UCB)-type optimism and fictitious play under the scope of simultaneous policy optimization in a non-stationary environment. When both players adopt the proposed algorithms, their overall optimality gap is $\widetilde{\mathcal{O}}(\sqrt{K})$.
    The Bearable Lightness of Big Data: Towards Massive Public Datasets in Scientific Machine Learning. (arXiv:2207.12546v1 [cs.LG])
    In general, large datasets enable deep learning models to perform with good accuracy and generalizability. However, massive high-fidelity simulation datasets (from molecular chemistry, astrophysics, computational fluid dynamics (CFD), etc. can be challenging to curate due to dimensionality and storage constraints. Lossy compression algorithms can help mitigate limitations from storage, as long as the overall data fidelity is preserved. To illustrate this point, we demonstrate that deep learning models, trained and tested on data from a petascale CFD simulation, are robust to errors introduced during lossy compression in a semantic segmentation problem. Our results demonstrate that lossy compression algorithms offer a realistic pathway for exposing high-fidelity scientific data to open-source data repositories for building community datasets. In this paper, we outline, construct, and evaluate the requirements for establishing a big data framework, demonstrated at https://blastnet.github.io/, for scientific machine learning.
    On the benefits of non-linear weight updates. (arXiv:2207.12505v1 [cs.LG])
    Recent work has suggested that the generalisation performance of a DNN is related to the extent to which the Signal-to-Noise Ratio is optimised at each of the nodes. In contrast, Gradient Descent methods do not always lead to SNR-optimal weight configurations. One way to improve SNR performance is to suppress large weight updates and amplify small weight updates. Such balancing is already implicit in some common optimizers, but we propose an approach that makes this explicit. The method applies a non-linear function to gradients prior to making DNN parameter updates. We investigate the performance with such non-linear approaches. The result is an adaptation to existing optimizers that improves performance for many problem types.
    Variance estimation in graphs with the fused lasso. (arXiv:2207.12638v1 [math.ST])
    We study the problem of variance estimation in general graph-structured problems. First, we develop a linear time estimator for the homoscedastic case that can consistently estimate the variance in general graphs. We show that our estimator attains minimax rates for the chain and 2D grid graphs when the mean signal has a total variation with canonical scaling. Furthermore, we provide general upper bounds on the mean squared error performance of the fused lasso estimator in general graphs under a moment condition and a bound on the tail behavior of the errors. These upper bounds allow us to generalize for broader classes of distributions, such as sub-Exponential, many existing results on the fused lasso that are only known to hold with the assumption that errors are sub-Gaussian random variables. Exploiting our upper bounds, we then study a simple total variation regularization estimator for estimating the signal of variances in the heteroscedastic case. Our results show that the variance estimator attains minimax rates for estimating signals of bounded variation in grid graphs, $K$-nearest neighbor graphs with very mild assumptions, and it is consistent for estimating the variances in any connected graph. In addition, extensive numerical results show that our proposed estimators perform reasonably well in a variety of graph-structured models.
    A Survey of Explainable Graph Neural Networks: Taxonomy and Evaluation Metrics. (arXiv:2207.12599v1 [cs.LG])
    Graph neural networks (GNNs) have demonstrated a significant boost in prediction performance on graph data. At the same time, the predictions made by these models are often hard to interpret. In that regard, many efforts have been made to explain the prediction mechanisms of these models from perspectives such as GNNExplainer, XGNN and PGExplainer. Although such works present systematic frameworks to interpret GNNs, a holistic review for explainable GNNs is unavailable. In this survey, we present a comprehensive review of explainability techniques developed for GNNs. We focus on explainable graph neural networks and categorize them based on the use of explainable methods. We further provide the common performance metrics for GNNs explanations and point out several future research directions.
    Variational multiscale reinforcement learning for discovering reduced order closure models of nonlinear spatiotemporal transport systems. (arXiv:2207.12854v1 [cs.LG])
    A central challenge in the computational modeling and simulation of a multitude of science applications is to achieve robust and accurate closures for their coarse-grained representations due to underlying highly nonlinear multiscale interactions. These closure models are common in many nonlinear spatiotemporal systems to account for losses due to reduced order representations, including many transport phenomena in fluids. Previous data-driven closure modeling efforts have mostly focused on supervised learning approaches using high fidelity simulation data. On the other hand, reinforcement learning (RL) is a powerful yet relatively uncharted method in spatiotemporally extended systems. In this study, we put forth a modular dynamic closure modeling and discovery framework to stabilize the Galerkin projection based reduced order models that may arise in many nonlinear spatiotemporal dynamical systems with quadratic nonlinearity. However, a key element in creating a robust RL agent is to introduce a feasible reward function, which can be constituted of any difference metrics between the RL model and high fidelity simulation data. First, we introduce a multi-modal RL (MMRL) to discover mode-dependant closure policies that utilize the high fidelity data in rewarding our RL agent. We then formulate a variational multiscale RL (VMRL) approach to discover closure models without requiring access to the high fidelity data in designing the reward function. Specifically, our chief innovation is to leverage variational multiscale formalism to quantify the difference between modal interactions in Galerkin systems. Our results in simulating the viscous Burgers equation indicate that the proposed VMRL method leads to robust and accurate closure parameterizations, and it may potentially be used to discover scale-aware closure models for complex dynamical systems.
    Probing Speech Emotion Recognition Transformers for Linguistic Knowledge. (arXiv:2204.00400v2 [cs.CL] UPDATED)
    Large, pre-trained neural networks consisting of self-attention layers (transformers) have recently achieved state-of-the-art results on several speech emotion recognition (SER) datasets. These models are typically pre-trained in self-supervised manner with the goal to improve automatic speech recognition performance -- and thus, to understand linguistic information. In this work, we investigate the extent in which this information is exploited during SER fine-tuning. Using a reproducible methodology based on open-source tools, we synthesise prosodically neutral speech utterances while varying the sentiment of the text. Valence predictions of the transformer model are very reactive to positive and negative sentiment content, as well as negations, but not to intensifiers or reducers, while none of those linguistic features impact arousal or dominance. These findings show that transformers can successfully leverage linguistic information to improve their valence predictions, and that linguistic analysis should be included in their testing.
    Compositional Visual Generation with Composable Diffusion Models. (arXiv:2206.01714v3 [cs.CV] UPDATED)
    Large text-guided diffusion models, such as DALLE-2, are able to generate stunning photorealistic images given natural language descriptions. While such models are highly flexible, they struggle to understand the composition of certain concepts, such as confusing the attributes of different objects or relations between objects. In this paper, we propose an alternative structured approach for compositional generation using diffusion models. An image is generated by composing a set of diffusion models, with each of them modeling a certain component of the image. To do this, we interpret diffusion models as energy-based models in which the data distributions defined by the energy functions may be explicitly combined. The proposed method can generate scenes at test time that are substantially more complex than those seen in training, composing sentence descriptions, object relations, human facial attributes, and even generalizing to new combinations that are rarely seen in the real world. We further illustrate how our approach may be used to compose pre-trained text-guided diffusion models and generate photorealistic images containing all the details described in the input descriptions, including the binding of certain object attributes that have been shown difficult for DALLE-2. These results point to the effectiveness of the proposed method in promoting structured generalization for visual generation. Project page: https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/
    safe-control-gym: a Unified Benchmark Suite for Safe Learning-based Control and Reinforcement Learning in Robotics. (arXiv:2109.06325v4 [cs.RO] UPDATED)
    In recent years, both reinforcement learning and learning-based control -- as well as the study of their safety, which is crucial for deployment in real-world robots -- have gained significant traction. However, to adequately gauge the progress and applicability of new results, we need the tools to equitably compare the approaches proposed by the controls and reinforcement learning communities. Here, we propose a new open-source benchmark suite, called safe-control-gym, supporting both model-based and data-based control techniques. We provide implementations for three dynamic systems -- the cart-pole, the 1D, and 2D quadrotor -- and two control tasks -- stabilization and trajectory tracking. We propose to extend OpenAI's Gym API -- the de facto standard in reinforcement learning research -- with (i) the ability to specify (and query) symbolic dynamics and (ii) constraints, and (iii) (repeatably) inject simulated disturbances in the control inputs, state measurements, and inertial properties. To demonstrate our proposal and in an attempt to bring research communities closer together, we show how to use safe-control-gym to quantitatively compare the control performance, data efficiency, and safety of multiple approaches from the fields of traditional control, learning-based control, and reinforcement learning.
    Future-Dependent Value-Based Off-Policy Evaluation in POMDPs. (arXiv:2207.13081v1 [cs.LG])
    We study off-policy evaluation (OPE) for partially observable MDPs (POMDPs) with general function approximation. Existing methods such as sequential importance sampling estimators and fitted-Q evaluation suffer from the curse of horizon in POMDPs. To circumvent this problem, we develop a novel model-free OPE method by introducing future-dependent value functions that take future proxies as inputs. Future-dependent value functions play similar roles as classical value functions in fully-observable MDPs. We derive a new Bellman equation for future-dependent value functions as conditional moment equations that use history proxies as instrumental variables. We further propose a minimax learning method to learn future-dependent value functions using the new Bellman equation. We obtain the PAC result, which implies our OPE estimator is consistent as long as futures and histories contain sufficient information about latent states, and the Bellman completeness. Finally, we extend our methods to learning of dynamics and establish the connection between our approach and the well-known spectral learning methods in POMDPs.
    Modeling the Social Influence of COVID-19 via Personalized Propagation with Deep Learning. (arXiv:2207.13016v1 [cs.SI])
    Social influence prediction has permeated many domains, including marketing, behavior prediction, recommendation systems, and more. However, traditional methods of predicting social influence not only require domain expertise,they also rely on extracting user features, which can be very tedious. Additionally, graph convolutional networks (GCNs), which deals with graph data in non-Euclidean space, are not directly applicable to Euclidean space. To overcome these problems, we extended DeepInf such that it can predict the social influence of COVID-19 via the transition probability of the page rank domain. Furthermore, our implementation gives rise to a deep learning-based personalized propagation algorithm, called DeepPP. The resulting algorithm combines the personalized propagation of a neural prediction model with the approximate personalized propagation of a neural prediction model from page rank analysis. Four social networks from different domains as well as two COVID-19 datasets were used to demonstrate the efficiency and effectiveness of the proposed algorithm. Compared to other baseline methods, DeepPP provides more accurate social influence predictions. Further, experiments demonstrate that DeepPP can be applied to real-world prediction data for COVID-19.
    Improved and Interpretable Defense to Transferred Adversarial Examples by Jacobian Norm with Selective Input Gradient Regularization. (arXiv:2207.13036v1 [cs.LG])
    Deep neural networks (DNNs) are known to be vulnerable to adversarial examples that are crafted with imperceptible perturbations, i.e., a small change in an input image can induce a mis-classification, and thus threatens the reliability of deep learning based deployment systems. Adversarial training (AT) is frequently used to improve the robustness of DNNs, which can improve the robustness in training a mixture of corrupted and clean data. However, existing AT based methods are either computationally expensive in generating such adversarial examples, and thus cannot satisfy the real-time requirement of real-world scenarios or cannot produce interpretable predictions for \textit{transferred adversarial examples} generated to fool a wide spectrum of defense models. In this work, we propose an approach of Jacobian norm with Selective Input Gradient Regularization (J-SIGR), which selectively regularizes gradient-based saliency maps to imitate its interpretable prediction with respect to the input through Jacobian normalization. As such, we achieve the defense of DNNs with both high interpretability and computation efficiency. Finally, we evaluate our method across different architectures against powerful adversarial attacks. Experiments demonstrate that the proposed J-SIGR confers improved robustness against transferred adversarial attacks and shows that the network predictions are easy-interpretable.
    Evolving Reinforcement Learning Algorithms. (arXiv:2101.03958v5 [cs.LG] UPDATED)
    We propose a method for meta-learning reinforcement learning algorithms by searching over the space of computational graphs which compute the loss function for a value-based model-free RL agent to optimize. The learned algorithms are domain-agnostic and can generalize to new environments not seen during training. Our method can both learn from scratch and bootstrap off known existing algorithms, like DQN, enabling interpretable modifications which improve performance. Learning from scratch on simple classical control and gridworld tasks, our method rediscovers the temporal-difference (TD) algorithm. Bootstrapped from DQN, we highlight two learned algorithms which obtain good generalization performance over other classical control tasks, gridworld type tasks, and Atari games. The analysis of the learned algorithm behavior shows resemblance to recently proposed RL algorithms that address overestimation in value-based methods.
    Alleviation of Temperature Variation Induced Accuracy Deg-radation in Ferroelectric FinFET Based Neural Network. (arXiv:2103.03111v4 [cs.LG] UPDATED)
    This paper reports the impacts of temperature variation on the inference accuracy of pre-trained all-ferroelectric FinFET deep neural networks, along with plausible design techniques to abate these impacts. We adopted a pre-trained artificial neural network (N.N.) with 96.4% inference accuracy on the MNIST dataset as the baseline. As an aftermath of temperature change, a compact model captured the conductance drift of a programmed cell over a wide range of gate biases. We observed a significant inference accuracy degradation in the analog neural network at 233 K for an N.N. trained at 300 K. Finally, we deployed binary neural networks with "read voltage" optimization to ensure immunity of N.N. to accuracy degradation under temperature variation, maintaining an inference accuracy of 96%. Keywords: Ferroelectric memories
    Partial-Monotone Adaptive Submodular Maximization. (arXiv:2207.12840v1 [cs.LG])
    Many sequential decision making problems, including pool-based active learning and adaptive viral marketing, can be formulated as an adaptive submodular maximization problem. Most of existing studies on adaptive submodular optimization focus on either monotone case or non-monotone case. Specifically, if the utility function is monotone and adaptive submodular, \cite{golovin2011adaptive} developed a greedy policy that achieves a $(1-1/e)$ approximation ratio subject to a cardinality constraint. If the utility function is non-monotone and adaptive submodular, \cite{tang2021beyond} showed that a random greedy policy achieves a $1/e$ approximation ratio subject to a cardinality constraint. In this work, we aim to generalize the above mentioned results by studying the partial-monotone adaptive submodular maximization problem. To this end, we introduce the notation of adaptive monotonicity ratio $m\in[0,1]$ to measure the degree of monotonicity of a function. Our main result is to show that a random greedy policy achieves an approximation ratio of $m(1-1/e)+(1-m)(1/e)$ if the utility function is $m$-adaptive monotone and adaptive submodular. Notably this result recovers the aforementioned $(1-1/e)$ and $1/e$ approximation ratios when $m = 0$ and $m = 1$, respectively. We further extend our results to consider a knapsack constraint. We show that a sampling-based policy achieves an approximation ratio of $(m+1)/10$ if the utility function is $m$-adaptive monotone and adaptive submodular. One important implication of our results is that even for a non-monotone utility function, we still can achieve an approximation ratio close to $(1-1/e)$ if this function is ``close'' to a monotone function. This leads to improved performance bounds for many machine learning applications whose utility functions are almost adaptive monotone.
    Semi-Leak: Membership Inference Attacks Against Semi-supervised Learning. (arXiv:2207.12535v1 [cs.CR])
    Semi-supervised learning (SSL) leverages both labeled and unlabeled data to train machine learning (ML) models. State-of-the-art SSL methods can achieve comparable performance to supervised learning by leveraging much fewer labeled data. However, most existing works focus on improving the performance of SSL. In this work, we take a different angle by studying the training data privacy of SSL. Specifically, we propose the first data augmentation-based membership inference attacks against ML models trained by SSL. Given a data sample and the black-box access to a model, the goal of membership inference attack is to determine whether the data sample belongs to the training dataset of the model. Our evaluation shows that the proposed attack can consistently outperform existing membership inference attacks and achieves the best performance against the model trained by SSL. Moreover, we uncover that the reason for membership leakage in SSL is different from the commonly believed one in supervised learning, i.e., overfitting (the gap between training and testing accuracy). We observe that the SSL model is well generalized to the testing data (with almost 0 overfitting) but ''memorizes'' the training data by giving a more confident prediction regardless of its correctness. We also explore early stopping as a countermeasure to prevent membership inference attacks against SSL. The results show that early stopping can mitigate the membership inference attack, but with the cost of model's utility degradation.  ( 3 min )
    Compiler-Aware Neural Architecture Search for On-Mobile Real-time Super-Resolution. (arXiv:2207.12577v1 [cs.CV])
    Deep learning-based super-resolution (SR) has gained tremendous popularity in recent years because of its high image quality performance and wide application scenarios. However, prior methods typically suffer from large amounts of computations and huge power consumption, causing difficulties for real-time inference, especially on resource-limited platforms such as mobile devices. To mitigate this, we propose a compiler-aware SR neural architecture search (NAS) framework that conducts depth search and per-layer width search with adaptive SR blocks. The inference speed is directly taken into the optimization along with the SR loss to derive SR models with high image quality while satisfying the real-time inference requirement. Instead of measuring the speed on mobile devices at each iteration during the search process, a speed model incorporated with compiler optimizations is leveraged to predict the inference latency of the SR block with various width configurations for faster convergence. With the proposed framework, we achieve real-time SR inference for implementing 720p resolution with competitive SR performance (in terms of PSNR and SSIM) on GPU/DSP of mobile platforms (Samsung Galaxy S21).  ( 2 min )
    Trainability Preserving Neural Structured Pruning. (arXiv:2207.12534v1 [cs.LG])
    Several recent works empirically find finetuning learning rate is critical to the final performance in neural network structured pruning. Further researches find that the network trainability broken by pruning answers for it, thus calling for an urgent need to recover trainability before finetuning. Existing attempts propose to exploit weight orthogonalization to achieve dynamical isometry for improved trainability. However, they only work for linear MLP networks. How to develop a filter pruning method that maintains or recovers trainability and is scalable to modern deep networks remains elusive. In this paper, we present trainability preserving pruning (TPP), a regularization-based structured pruning method that can effectively maintain trainability during sparsification. Specifically, TPP regularizes the gram matrix of convolutional kernels so as to de-correlate the pruned filters from the kept filters. Beside the convolutional layers, we also propose to regularize the BN parameters for better preserving trainability. Empirically, TPP can compete with the ground-truth dynamical isometry recovery method on linear MLP networks. On non-linear networks (ResNet56/VGG19, CIFAR datasets), it outperforms the other counterpart solutions by a large margin. Moreover, TPP can also work effectively with modern deep networks (ResNets) on ImageNet, delivering encouraging performance in comparison to many top-performing filter pruning methods. To our best knowledge, this is the first approach that effectively maintains trainability during pruning for the large-scale deep neural networks.  ( 3 min )
    Optimizing Empty Container Repositioning and Fleet Deployment via Configurable Semi-POMDPs. (arXiv:2207.12509v1 [cs.LG])
    With the continuous growth of the global economy and markets, resource imbalance has risen to be one of the central issues in real logistic scenarios. In marine transportation, this trade imbalance leads to Empty Container Repositioning (ECR) problems. Once the freight has been delivered from an exporting country to an importing one, the laden will turn into empty containers that need to be repositioned to satisfy new goods requests in exporting countries. In such problems, the performance that any cooperative repositioning policy can achieve strictly depends on the routes that vessels will follow (i.e., fleet deployment). Historically, Operation Research (OR) approaches were proposed to jointly optimize the repositioning policy along with the fleet of vessels. However, the stochasticity of future supply and demand of containers, together with black-box and non-linear constraints that are present within the environment, make these approaches unsuitable for these scenarios. In this paper, we introduce a novel framework, Configurable Semi-POMDPs, to model this type of problems. Furthermore, we provide a two-stage learning algorithm, "Configure & Conquer" (CC), that first configures the environment by finding an approximation of the optimal fleet deployment strategy, and then "conquers" it by learning an ECR policy in this tuned environmental setting. We validate our approach in large and real-world instances of the problem. Our experiments highlight that CC avoids the pitfalls of OR methods and that it is successful at optimizing both the ECR policy and the fleet of vessels, leading to superior performance in world trade environments.  ( 3 min )
    ScoreCAM GNN: une explication optimale des r\'eseaux profonds sur graphes. (arXiv:2207.12748v1 [cs.LG])
    The explainability of deep networks is becoming a central issue in the deep learning community. It is the same for learning on graphs, a data structure present in many real world problems. In this paper, we propose a method that is more optimal, lighter, consistent and better exploits the topology of the evaluated graph than the state-of-the-art methods.
    Bayesian tensor factorization for predicting clinical outcomes using integrated human genetics evidence. (arXiv:2207.12538v1 [cs.LG])
    The approval success rate of drug candidates is very low with the majority of failure due to safety and efficacy. Increasingly available high dimensional information on targets, drug molecules and indications provides an opportunity for ML methods to integrate multiple data modalities and better predict clinically promising drug targets. Notably, drug targets with human genetics evidence are shown to have better odds to succeed. However, a recent tensor factorization-based approach found that additional information on targets and indications might not necessarily improve the predictive accuracy. Here we revisit this approach by integrating different types of human genetics evidence collated from publicly available sources to support each target-indication pair. We use Bayesian tensor factorization to show that models incorporating all available human genetics evidence (rare disease, gene burden, common disease) modestly improves the clinical outcome prediction over models using single line of genetics evidence. We provide additional insight into the relative predictive power of different types of human genetics evidence for predicting the success of clinical outcomes.
    An Explainable Decision Support System for Predictive Process Analytics. (arXiv:2207.12782v1 [cs.LG])
    Predictive Process Analytics is becoming an essential aid for organizations, providing online operational support of their processes. However, process stakeholders need to be provided with an explanation of the reasons why a given process execution is predicted to behave in a certain way. Otherwise, they will be unlikely to trust the predictive monitoring technology and, hence, adopt it. This paper proposes a predictive analytics framework that is also equipped with explanation capabilities based on the game theory of Shapley Values. The framework has been implemented in the IBM Process Mining suite and commercialized for business users. The framework has been tested on real-life event data to assess the quality of the predictions and the corresponding evaluations. In particular, a user evaluation has been performed in order to understand if the explanations provided by the system were intelligible to process stakeholders.
    Automated discovery of interpretable gravitational-wave population models. (arXiv:2207.12409v1 [astro-ph.IM])
    We present an automatic approach to discover analytic population models for gravitational-wave (GW) events from data. As more gravitational-wave (GW) events are detected, flexible models such as Gaussian Mixture Models have become more important in fitting the distribution of GW properties due to their expressivity. However, flexible models come with many parameters that lack physical motivation, making interpreting the implication of these models challenging. In this work, we demonstrate symbolic regression can complement flexible models by distilling the posterior predictive distribution of such flexible models into interpretable analytic expressions. We recover common GW population models such as a power-law-plus-Gaussian, and find a new empirical population model which combines accuracy and simplicity. This demonstrates a strategy to automatically discover interpretable population models in the ever-growing GW catalog, which can potentially be applied to other astrophysical phenomena.  ( 2 min )
    Estimating and Controlling for Fairness via Sensitive Attribute Predictors. (arXiv:2207.12497v1 [cs.LG])
    Although machine learning classifiers have been increasingly used in high-stakes decision making (e.g., cancer diagnosis, criminal prosecution decisions), they have demonstrated biases against underrepresented groups. Standard definitions of fairness require access to sensitive attributes of interest (e.g., gender and race), which are often unavailable. In this work we demonstrate that in these settings where sensitive attributes are unknown, one can still reliably estimate and ultimately control for fairness by using proxy sensitive attributes derived from a sensitive attribute predictor. Specifically, we first show that with just a little knowledge of the complete data distribution, one may use a sensitive attribute predictor to obtain upper and lower bounds of the classifier's true fairness metric. Second, we demonstrate how one can provably control for fairness with respect to the true sensitive attributes by controlling for fairness with respect to the proxy sensitive attributes. Our results hold under assumptions that are significantly milder than previous works. We illustrate our results on a series of synthetic and real datasets.  ( 2 min )
    $p$-DkNN: Out-of-Distribution Detection Through Statistical Testing of Deep Representations. (arXiv:2207.12545v1 [cs.LG])
    The lack of well-calibrated confidence estimates makes neural networks inadequate in safety-critical domains such as autonomous driving or healthcare. In these settings, having the ability to abstain from making a prediction on out-of-distribution (OOD) data can be as important as correctly classifying in-distribution data. We introduce $p$-DkNN, a novel inference procedure that takes a trained deep neural network and analyzes the similarity structures of its intermediate hidden representations to compute $p$-values associated with the end-to-end model prediction. The intuition is that statistical tests performed on latent representations can serve not only as a classifier, but also offer a statistically well-founded estimation of uncertainty. $p$-DkNN is scalable and leverages the composition of representations learned by hidden layers, which makes deep representation learning successful. Our theoretical analysis builds on Neyman-Pearson classification and connects it to recent advances in selective classification (reject option). We demonstrate advantageous trade-offs between abstaining from predicting on OOD inputs and maintaining high accuracy on in-distribution inputs. We find that $p$-DkNN forces adaptive attackers crafting adversarial examples, a form of worst-case OOD inputs, to introduce semantically meaningful changes to the inputs.
    Domain Adaptation under Open Set Label Shift. (arXiv:2207.13048v1 [cs.LG])
    We introduce the problem of domain adaptation under Open Set Label Shift (OSLS) where the label distribution can change arbitrarily and a new class may arrive during deployment, but the class-conditional distributions p(x|y) are domain-invariant. OSLS subsumes domain adaptation under label shift and Positive-Unlabeled (PU) learning. The learner's goals here are two-fold: (a) estimate the target label distribution, including the novel class; and (b) learn a target classifier. First, we establish necessary and sufficient conditions for identifying these quantities. Second, motivated by advances in label shift and PU learning, we propose practical methods for both tasks that leverage black-box predictors. Unlike typical Open Set Domain Adaptation (OSDA) problems, which tend to be ill-posed and amenable only to heuristics, OSLS offers a well-posed problem amenable to more principled machinery. Experiments across numerous semi-synthetic benchmarks on vision, language, and medical datasets demonstrate that our methods consistently outperform OSDA baselines, achieving 10--25% improvements in target domain accuracy. Finally, we analyze the proposed methods, establishing finite-sample convergence to the true label marginal and convergence to optimal classifier for linear models in a Gaussian setup. Code is available at https://github.com/acmi-lab/Open-Set-Label-Shift.
  • Open

    Differentially Private Estimation via Statistical Depth. (arXiv:2207.12602v1 [stat.ML])
    Constructing a differentially private (DP) estimator requires deriving the maximum influence of an observation, which can be difficult in the absence of exogenous bounds on the input data or the estimator, especially in high dimensional settings. This paper shows that standard notions of statistical depth, i.e., halfspace depth and regression depth, are particularly advantageous in this regard, both in the sense that the maximum influence of a single observation is easy to analyze and that this value is typically low. This is used to motivate new approximate DP location and regression estimators using the maximizers of these two notions of statistical depth. A more computationally efficient variant of the approximate DP regression estimator is also provided. Also, to avoid requiring that users specify a priori bounds on the estimates and/or the observations, variants of these DP mechanisms are described that satisfy random differential privacy (RDP), which is a relaxation of differential privacy provided by Hall, Wasserman, and Rinaldo (2013). We also provide simulations of the two DP regression methods proposed here. The proposed estimators appear to perform favorably relative to the existing DP regression methods we consider in these simulations when either the sample size is at least 100-200 or the privacy-loss budget is sufficiently high.
    The derivatives of Sinkhorn-Knopp converge. (arXiv:2207.12717v1 [math.OC])
    We show that the derivatives of the Sinkhorn-Knopp algorithm, or iterative proportional fitting procedure, converge towards the derivatives of the entropic regularization of the optimal transport problem with a locally uniform linear convergence rate.
    The Optimal Noise in Noise-Contrastive Learning Is Not What You Think. (arXiv:2203.01110v2 [stat.ML] UPDATED)
    Learning a parametric model of a data distribution is a well-known statistical problem that has seen renewed interest as it is brought to scale in deep learning. Framing the problem as a self-supervised task, where data samples are discriminated from noise samples, is at the core of state-of-the-art methods, beginning with Noise-Contrastive Estimation (NCE). Yet, such contrastive learning requires a good noise distribution, which is hard to specify; domain-specific heuristics are therefore widely used. While a comprehensive theory is missing, it is widely assumed that the optimal noise should in practice be made equal to the data, both in distribution and proportion. This setting underlies Generative Adversarial Networks (GANs) in particular. Here, we empirically and theoretically challenge this assumption on the optimal noise. We show that deviating from this assumption can actually lead to better statistical estimators, in terms of asymptotic variance. In particular, the optimal noise distribution is different from the data's and even from a different family.
    Few-Shot Domain Adaptation For End-to-End Communication. (arXiv:2108.00874v2 [cs.LG] UPDATED)
    The problem of end-to-end learning of a communication system using an autoencoder -- consisting of an encoder, channel, and decoder modeled using neural networks -- has recently been shown to be a promising approach. A challenge faced in the practical adoption of this learning approach is that under changing channel conditions (e.g. a wireless link), it requires frequent retraining of the autoencoder in order to maintain a low decoding error rate. Since retraining is both time consuming and requires a large number of samples, it becomes impractical when the channel distribution is changing quickly. We propose to address this problem using a fast and sample-efficient (few-shot) domain adaptation method that does not change the encoder and decoder networks. Different from conventional training-time unsupervised or semi-supervised domain adaptation, here we have a trained autoencoder from a source distribution, that we want to adapt (at test time) to a target distribution using only a small labeled dataset and no unlabeled data. Our method focuses on a Gaussian mixture density network based channel model, and formulates its adaptation based on class and component-conditional affine transformations. The learned affine transformations are used to design an optimal input transformation at the decoder to compensate for the distribution shift, and effectively present to the decoder inputs close to the source distribution. Experiments on a real mmWave FPGA setup as well as a number of simulated distribution changes common to the wireless setting demonstrate the effectiveness of our method at adaptation using very small number of target domain samples.
    Contrastive Attraction and Contrastive Repulsion for Representation Learning. (arXiv:2105.03746v3 [cs.LG] UPDATED)
    Contrastive learning (CL) methods effectively learn data representations without label supervision, where the encoder contrasts each positive sample over multiple negative samples via a one-vs-many softmax cross-entropy loss. By leveraging large amounts of unlabeled image data, recent CL methods have achieved promising results when pre-trained on ImageNet, a well-curated data set with balanced image classes. However, they tend to yield worse performance when pre-trained on images in the wild. In this paper, to further improve the performance of CL and enhance its robustness on uncurated data sets, we propose a doubly CL strategy that contrasts the positive (negative) samples of a query within themselves before deciding how strongly to pull (push) them. We realize this strategy with contrastive attraction and contrastive repulsion (CACR), which makes the query not only exert a greater force to attract more distant positive samples but also do so to repel closer negative samples. Theoretical analysis reveals that CACR generalizes CL's behavior by taking into consideration the differences between the distributions of the positive/negative samples, which in general are sampled independently of the query, and their true conditional distributions given the query. We demonstrate this unique intra-positive attraction and intra-negative repulsion mechanism, which helps remove the need to assume uniform prior distributions on both the data and their latent representation, is particularly beneficial when data sets are less curated. Extensive large-scale experiments on a number of standard vision tasks show that CACR not only consistently outperforms existing CL methods on benchmark data sets in representation learning, but also shows better robustness when generalized to pre-training on imbalanced image data sets.
    Discriminative Multimodal Learning via Conditional Priors in Generative Models. (arXiv:2110.04616v2 [cs.LG] UPDATED)
    Deep generative models with latent variables have been used lately to learn joint representations and generative processes from multi-modal data. These two learning mechanisms can, however, conflict with each other and representations can fail to embed information on the data modalities. This research studies the realistic scenario in which all modalities and class labels are available for model training, but where some modalities and labels required for downstream tasks are missing. We show, in this scenario, that the variational lower bound limits mutual information between joint representations and missing modalities. We, to counteract these problems, introduce a novel conditional multi-modal discriminative model that uses an informative prior distribution and optimizes a likelihood-free objective function that maximizes mutual information between joint representations and missing modalities. Extensive experimentation shows the benefits of the model we propose, the empirical results showing that our model achieves state-of-the-art results in representative problems such as downstream classification, acoustic inversion and annotation generation.  ( 2 min )
    Modeling Irregular Time Series with Continuous Recurrent Units. (arXiv:2111.11344v3 [cs.LG] UPDATED)
    Recurrent neural networks (RNNs) are a popular choice for modeling sequential data. Modern RNN architectures assume constant time-intervals between observations. However, in many datasets (e.g. medical records) observation times are irregular and can carry important information. To address this challenge, we propose continuous recurrent units (CRUs) -- a neural architecture that can naturally handle irregular intervals between observations. The CRU assumes a hidden state, which evolves according to a linear stochastic differential equation and is integrated into an encoder-decoder framework. The recursive computations of the CRU can be derived using the continuous-discrete Kalman filter and are in closed form. The resulting recurrent architecture has temporal continuity between hidden states and a gating mechanism that can optimally integrate noisy observations. We derive an efficient parameterization scheme for the CRU that leads to a fast implementation f-CRU. We empirically study the CRU on a number of challenging datasets and find that it can interpolate irregular time series better than methods based on neural ordinary differential equations.  ( 2 min )
    Learning structures of the French clinical language:development and validation of word embedding models using 21 million clinical reports from electronic health records. (arXiv:2207.12940v1 [cs.CL])
    Background Clinical studies using real-world data may benefit from exploiting clinical reports, a particularly rich albeit unstructured medium. To that end, natural language processing can extract relevant information. Methods based on transfer learning using pre-trained language models have achieved state-of-the-art results in most NLP applications; however, publicly available models lack exposure to speciality-languages, especially in the medical field. Objective We aimed to evaluate the impact of adapting a language model to French clinical reports on downstream medical NLP tasks. Methods We leveraged a corpus of 21M clinical reports collected from August 2017 to July 2021 at the Greater Paris University Hospitals (APHP) to produce two CamemBERT architectures on speciality language: one retrained from scratch and the other using CamemBERT as its initialisation. We used two French annotated medical datasets to compare our language models to the original CamemBERT network, evaluating the statistical significance of improvement with the Wilcoxon test. Results Our models pretrained on clinical reports increased the average F1-score on APMed (an APHP-specific task) by 3 percentage points to 91%, a statistically significant improvement. They also achieved performance comparable to the original CamemBERT on QUAERO. These results hold true for the fine-tuned and from-scratch versions alike, starting from very few pre-training samples. Conclusions We confirm previous literature showing that adapting generalist pre-train language models such as CamenBERT on speciality corpora improves their performance for downstream clinical NLP tasks. Our results suggest that retraining from scratch does not induce a statistically significant performance gain compared to fine-tuning.  ( 3 min )
    Dualize, Split, Randomize: Toward Fast Nonsmooth Optimization Algorithms. (arXiv:2004.02635v4 [math.OC] UPDATED)
    We consider minimizing the sum of three convex functions, where the first one F is smooth, the second one is nonsmooth and proximable and the third one is the composition of a nonsmooth proximable function with a linear operator L. This template problem has many applications, for instance, in image processing and machine learning. First, we propose a new primal-dual algorithm, which we call PDDY, for this problem. It is constructed by applying Davis-Yin splitting to a monotone inclusion in a primal-dual product space, where the operators are monotone under a specific metric depending on L. We show that three existing algorithms (the two forms of the Condat-Vu algorithm and the PD3O algorithm) have the same structure, so that PDDY is the fourth missing link in this self-consistent class of primal-dual algorithms. This representation eases the convergence analysis: it allows us to derive sublinear convergence rates in general, and linear convergence results in presence of strong convexity. Moreover, within our broad and flexible analysis framework, we propose new stochastic generalizations of the algorithms, in which a variance-reduced random estimate of the gradient of F is used, instead of the true gradient. Furthermore, we obtain, as a special case of PDDY, a linearly converging algorithm for the minimization of a strongly convex function F under a linear constraint; we discuss its important application to decentralized optimization.  ( 3 min )
    Variational Inference with Locally Enhanced Bounds for Hierarchical Models. (arXiv:2203.04432v2 [cs.LG] UPDATED)
    Hierarchical models represent a challenging setting for inference algorithms. MCMC methods struggle to scale to large models with many local variables and observations, and variational inference (VI) may fail to provide accurate approximations due to the use of simple variational families. Some variational methods (e.g. importance weighted VI) integrate Monte Carlo methods to give better accuracy, but these tend to be unsuitable for hierarchical models, as they do not allow for subsampling and their performance tends to degrade for high dimensional models. We propose a new family of variational bounds for hierarchical models, based on the application of tightening methods (e.g. importance weighting) separately for each group of local random variables. We show that our approach naturally allows the use of subsampling to get unbiased gradients, and that it fully leverages the power of methods that build tighter lower bounds by applying them independently in lower dimensional spaces, leading to better results and more accurate posterior approximations than relevant baselines.  ( 2 min )
    Robustifying Conditional Portfolio Decisions via Optimal Transport. (arXiv:2103.16451v2 [q-fin.PM] UPDATED)
    We propose a data-driven portfolio selection model that integrates side information, conditional estimation and robustness using the framework of distributionally robust optimization. Conditioning on the observed side information, the portfolio manager solves an allocation problem that minimizes the worst-case conditional risk-return trade-off, subject to all possible perturbations of the covariate-return probability distribution in an optimal transport ambiguity set. Despite the non-linearity of the objective function in the probability measure, we show that the distributionally robust portfolio allocation with side information problem can be reformulated as a finite-dimensional optimization problem. If portfolio decisions are made based on either the mean-variance or the mean-Conditional Value-at-Risk criterion, the resulting reformulation can be further simplified to second-order or semi-definite cone programs. Empirical studies in the US equity market demonstrate the advantage of our integrative framework against other benchmarks.  ( 2 min )
    $p$-DkNN: Out-of-Distribution Detection Through Statistical Testing of Deep Representations. (arXiv:2207.12545v1 [cs.LG])
    The lack of well-calibrated confidence estimates makes neural networks inadequate in safety-critical domains such as autonomous driving or healthcare. In these settings, having the ability to abstain from making a prediction on out-of-distribution (OOD) data can be as important as correctly classifying in-distribution data. We introduce $p$-DkNN, a novel inference procedure that takes a trained deep neural network and analyzes the similarity structures of its intermediate hidden representations to compute $p$-values associated with the end-to-end model prediction. The intuition is that statistical tests performed on latent representations can serve not only as a classifier, but also offer a statistically well-founded estimation of uncertainty. $p$-DkNN is scalable and leverages the composition of representations learned by hidden layers, which makes deep representation learning successful. Our theoretical analysis builds on Neyman-Pearson classification and connects it to recent advances in selective classification (reject option). We demonstrate advantageous trade-offs between abstaining from predicting on OOD inputs and maintaining high accuracy on in-distribution inputs. We find that $p$-DkNN forces adaptive attackers crafting adversarial examples, a form of worst-case OOD inputs, to introduce semantically meaningful changes to the inputs.  ( 2 min )
    Robustness Implies Generalization via Data-Dependent Generalization Bounds. (arXiv:2206.13497v3 [cs.LG] UPDATED)
    This paper proves that robustness implies generalization via data-dependent generalization bounds. As a result, robustness and generalization are shown to be connected closely in a data-dependent manner. Our bounds improve previous bounds in two directions, to solve an open problem that has seen little development since 2010. The first is to reduce the dependence on the covering number. The second is to remove the dependence on the hypothesis space. We present several examples, including ones for lasso and deep learning, in which our bounds are provably preferable. The experiments on real-world data and theoretical models demonstrate near-exponential improvements in various situations. To achieve these improvements, we do not require additional assumptions on the unknown distribution; instead, we only incorporate an observable and computable property of the training samples. A key technical innovation is an improved concentration bound for multinomial random variables that is of independent interest beyond robustness and generalization.  ( 2 min )
    Kan Extensions in Data Science and Machine Learning. (arXiv:2203.09018v2 [cs.LG] UPDATED)
    A common problem in data science is "use this function defined over this small set to generate predictions over that larger set." Extrapolation, interpolation, statistical inference and forecasting all reduce to this problem. The Kan extension is a powerful tool in category theory that generalizes this notion. In this work we explore several applications of Kan extensions to data science. We begin by deriving a simple classification algorithm as a Kan extension and experimenting with this algorithm on real data. Next, we use the Kan extension to derive a procedure for learning clustering algorithms from labels and explore the performance of this procedure on real data. We then investigate how Kan extensions can be used to learn a general mapping from datasets of labeled examples to functions and to approximate a complex function with a simpler one.  ( 2 min )
    AMLB: an AutoML Benchmark. (arXiv:2207.12560v1 [cs.LG])
    Comparing different AutoML frameworks is notoriously challenging and often done incorrectly. We introduce an open and extensible benchmark that follows best practices and avoids common mistakes when comparing AutoML frameworks. We conduct a thorough comparison of 9 well-known AutoML frameworks across 71 classification and 33 regression tasks. The differences between the AutoML frameworks are explored with a multi-faceted analysis, evaluating model accuracy, its trade-offs with inference time, and framework failures. We also use Bradley-Terry trees to discover subsets of tasks where the relative AutoML framework rankings differ. The benchmark comes with an open-source tool that integrates with many AutoML frameworks and automates the empirical evaluation process end-to-end: from framework installation and resource allocation to in-depth evaluation. The benchmark uses public data sets, can be easily extended with other AutoML frameworks and tasks, and has a website with up-to-date results.  ( 2 min )
    Solution of Physics-based Bayesian Inverse Problems with Deep Generative Priors. (arXiv:2107.02926v2 [stat.ML] UPDATED)
    Inverse problems are ubiquitous in nature, arising in almost all areas of science and engineering ranging from geophysics and climate science to astrophysics and biomechanics. One of the central challenges in solving inverse problems is tackling their ill-posed nature. Bayesian inference provides a principled approach for overcoming this by formulating the inverse problem into a statistical framework. However, it is challenging to apply when inferring fields that have discrete representations of large dimensions (the so-called "curse of dimensionality") and/or when prior information is available only in the form of previously acquired solutions. In this work, we present a novel method for efficient and accurate Bayesian inversion using deep generative models. Specifically, we demonstrate how using the approximate distribution learned by a Generative Adversarial Network (GAN) as a prior in a Bayesian update and reformulating the resulting inference problem in the low-dimensional latent space of the GAN, enables the efficient solution of large-scale Bayesian inverse problems. Our statistical framework preserves the underlying physics and is demonstrated to yield accurate results with reliable uncertainty estimates, even in the absence of information about underlying noise model, which is a significant challenge with many existing methods. We demonstrate the effectiveness of proposed method on a variety of inverse problems which include both synthetic as well as experimentally observed data.  ( 3 min )
    Representing Random Utility Choice Models with Neural Networks. (arXiv:2207.12877v1 [cs.LG])
    Motivated by the successes of deep learning, we propose a class of neural network-based discrete choice models, called RUMnets, which is inspired by the random utility maximization (RUM) framework. This model formulates the agents' random utility function using the sample average approximation (SAA) method. We show that RUMnets sharply approximate the class of RUM discrete choice models: any model derived from random utility maximization has choice probabilities that can be approximated arbitrarily closely by a RUMnet. Reciprocally, any RUMnet is consistent with the RUM principle. We derive an upper bound on the generalization error of RUMnets fitted on choice data, and gain theoretical insights on their ability to predict choices on new, unseen data depending on critical parameters of the dataset and architecture. By leveraging open-source libraries for neural networks, we find that RUMnets outperform other state-of-the-art choice modeling and machine learning methods by a significant margin on two real-world datasets.  ( 2 min )
    Variance estimation in graphs with the fused lasso. (arXiv:2207.12638v1 [math.ST])
    We study the problem of variance estimation in general graph-structured problems. First, we develop a linear time estimator for the homoscedastic case that can consistently estimate the variance in general graphs. We show that our estimator attains minimax rates for the chain and 2D grid graphs when the mean signal has a total variation with canonical scaling. Furthermore, we provide general upper bounds on the mean squared error performance of the fused lasso estimator in general graphs under a moment condition and a bound on the tail behavior of the errors. These upper bounds allow us to generalize for broader classes of distributions, such as sub-Exponential, many existing results on the fused lasso that are only known to hold with the assumption that errors are sub-Gaussian random variables. Exploiting our upper bounds, we then study a simple total variation regularization estimator for estimating the signal of variances in the heteroscedastic case. Our results show that the variance estimator attains minimax rates for estimating signals of bounded variation in grid graphs, $K$-nearest neighbor graphs with very mild assumptions, and it is consistent for estimating the variances in any connected graph. In addition, extensive numerical results show that our proposed estimators perform reasonably well in a variety of graph-structured models.  ( 2 min )
    Sharp Concentration Results for Heavy-Tailed Distributions. (arXiv:2003.13819v3 [math.PR] UPDATED)
    We obtain concentration and large deviation for the sums of independent and identically distributed random variables with heavy-tailed distributions. Our concentration results are concerned with random variables whose distributions satisfy $\mathbb{P}(X>t) \leq {\rm e}^{- I(t)}$, where $I: \mathbb{R} \rightarrow \mathbb{R}$ is an increasing function and $I(t)/t \rightarrow \alpha \in [0, \infty)$ as $t \rightarrow \infty$. Our main theorem can not only recover some of the existing results, such as the concentration of the sum of subWeibull random variables, but it can also produce new results for the sum of random variables with heavier tails. We show that the concentration inequalities we obtain are sharp enough to offer large deviation results for the sums of independent random variables as well. Our analyses which are based on standard truncation arguments simplify, unify and generalize the existing results on the concentration and large deviation of heavy-tailed random variables.  ( 2 min )
    Future-Dependent Value-Based Off-Policy Evaluation in POMDPs. (arXiv:2207.13081v1 [cs.LG])
    We study off-policy evaluation (OPE) for partially observable MDPs (POMDPs) with general function approximation. Existing methods such as sequential importance sampling estimators and fitted-Q evaluation suffer from the curse of horizon in POMDPs. To circumvent this problem, we develop a novel model-free OPE method by introducing future-dependent value functions that take future proxies as inputs. Future-dependent value functions play similar roles as classical value functions in fully-observable MDPs. We derive a new Bellman equation for future-dependent value functions as conditional moment equations that use history proxies as instrumental variables. We further propose a minimax learning method to learn future-dependent value functions using the new Bellman equation. We obtain the PAC result, which implies our OPE estimator is consistent as long as futures and histories contain sufficient information about latent states, and the Bellman completeness. Finally, we extend our methods to learning of dynamics and establish the connection between our approach and the well-known spectral learning methods in POMDPs.
    Matching Visual Features to Hierarchical Semantic Topics for Image Paragraph Captioning. (arXiv:2105.04143v2 [cs.CV] UPDATED)
    Observing a set of images and their corresponding paragraph-captions, a challenging task is to learn how to produce a semantically coherent paragraph to describe the visual content of an image. Inspired by recent successes in integrating semantic topics into this task, this paper develops a plug-and-play hierarchical-topic-guided image paragraph generation framework, which couples a visual extractor with a deep topic model to guide the learning of a language model. To capture the correlations between the image and text at multiple levels of abstraction and learn the semantic topics from images, we design a variational inference network to build the mapping from image features to textual captions. To guide the paragraph generation, the learned hierarchical topics and visual features are integrated into the language model, including Long Short-Term Memory (LSTM) and Transformer, and jointly optimized. Experiments on public datasets demonstrate that the proposed models, which are competitive with many state-of-the-art approaches in terms of standard evaluation metrics, can be used to both distill interpretable multi-layer semantic topics and generate diverse and coherent captions. We release our code at https://github.com/DandanGuo1993/VTCM-based-image-paragraph-caption.git

  • Open

    [D] Neurips 2022 review questions
    Reviews just came in and I got 7, 4, 4, 4, 2. Most common theme of strength was essentially extensive experiments and benchmarks conducted. We beat SOTA (up to may 2022 results) on essentially every benchmark except one. Common complaint was essentially not believing the ablations or that we were 'nt beating in everything. Other strength was reducing computational complexity (but I guess we did not spell out clearly enough who we were beating for it to shine through). Also, most of the critiques seemed to kind of tip toe around the fact that they did not really understand the work. I am curious about the rebuttal process here though: I am planning to go through and address every point and give counters, but what is the process in terms of getting reviewers to change their score? Also, I noticed that the reviewer who gave the highest score is part of the Ethics Review Area: Discrimination / Bias / Fairness Concerns and I am curious why only one of the reviewers had this in their review. Did some of the other reviewers reviews trigger this? submitted by /u/AbjectDrink3276 [link] [comments]  ( 88 min )
    Training a Network on a Sine Wave [Discussion] [Research]
    I've been attempting to train a simple feed-forward network on sine waves with various frequencies, such that: y = sin( omega * x), where my network takes x as input, and outputs y. ​ https://preview.redd.it/81xdsas3kzd91.png?width=2230&format=png&auto=webp&s=0856824d215e0aebf8d20c1849b53433245bc91a y is bounded between -1 and 1, whereas x is bounded between 0 and 2pi. I'm finding that I get interesting convergence behaviour as a function of x, where if I increase omega, values > ~3 seem to reconstruct poorly. The image attached shows this quite well for a sine wave with omega 7Hz. I feel like this shouldn't be happening, but does anyone have idea of why this could occur? If the input values are "large" (in this case >3), are the gradients too large and the model breaks? Any thoughts are appreciated! submitted by /u/forthispost96 [link] [comments]  ( 88 min )
    [R] ProSelfLC: Progressive Self Label Correction Towards A Low-Temperature Entropy State
    Though this research studies deep machine learning, its findings are quite consistent with human learning. (1) When a trainee is given noisy (e.g., wrong or biased) supervision, it will fit noise (e.g., error or bias). (2) When the supervision and guidance contain more noise, the trainee will learn less confidently. ​ We present a new insightful finding to complement a previous one “deep neural networks easily fit random labels (Understanding deep learning requires rethinking, Zhang et al., ICLR 2017)”: Deep models fit and generalise significantly less confident when more random labels exist. Correspondingly, we propose to decrease the entropy of self knowledge using an Annealed Temperature (AT) and learn towards a revised low-temperature entropy state. ​ Read more if your are interested: https://arxiv.org/abs/2207.00118 submitted by /u/XinshaoWang [link] [comments]  ( 88 min )
    [P] Anees: a multi-turn open-domain Arabic chatbot with a wide set of features
    Anees is an Arabic chatbot that can speak to users on different topics or an open-domain multi-turn conversation rather than a specific domain. Anees is your personal AI friend that you can express and witness yourself through a helpful and empathetic conversation. Anees offers a set of features like natural language understanding, emotion classification, intent classification, weather/schedule, recommendation, and natural language generation. For the code and implementation details: https://github.com/aashrafh/Anees submitted by /u/ahmedashrafhamdy [link] [comments]  ( 108 min )
    [D] What else am I missing from the ML data-to-model workflow?
    I am mostly self-taught in ML and have been working with some clients as a lone contractor but never with a full ML team. I've recently been on an interview, where they asked what I know about inference. And frankly, nothing. Apparently they run t-tests and ANOVA and stuff (which I do know from Uni but haven't used in a while) to make sure they can explain exactly what feature correlates with what without risking removing multicollinear-looking columns that aren't actually multicollinear. So I guess I have some big gaps and now I started wondering what else I'm missing. My process is usually as follows: Get data Check descriptive statistics Impute missing data (may it be simple fillnan up to synthetic data generation) Drop columns that are not usable due to data quality issues Transform data into numeric (basically, encoding, anything from OneHot to TargetEncoding) Look at distributions Check for outliers Check VIF to remove multicollinearity Define a baseline and target variable Apply logic to prevent data leakage (drop columns that are dependent on each other with the target variable) Define metrics Start modeling Check metrics and refine hyperparams/change model if needed Go back to data preprocessing and see if other methods/more data cleaning improves model I guess "inference" would come somewhere between 2 and 12, but I have very little idea about what it means. Is inference the only step I'm missing here, given a workflow that starts with getting the data and ends with delivering a model (NOT putting it into production, I understand there's multiple steps for that too)? submitted by /u/lifesthateasy [link] [comments]  ( 109 min )
    [D] NeurIPS 2022 Paper Reviews
    NeurIPS 2022 paper reviews are supposed to be released in a few hours. According to the website, they should be released at 9am PDT on July 26th. I thought to create a discussion thread for us to discuss any issue/complain/celebration or anything else. There is so much noise in the reviews every year. Some good work that the authors are proud of might get a low score because of the noisy system, given that NeurIPS is growing so large these years. We should keep in mind that the work is still valuable no matter what the score is. According to the Program Chair's tweet, it seems that only ~93% of the reviews are submitted. Hopefully it will not delay the release of the reviews and the start of the rebuttal. submitted by /u/zy415 [link] [comments]  ( 114 min )
    [P] Popular asymmetric loss functions for bounding or constructing loss functions to guarantee bounding?
    I'm working on a open-ended project for my Masters using the Online Encyclopedia of Integer Sequences and attempting to upper bound future sequence values instead of predicting them. I've managed to get reasonable results using RNNs and Linear-Exponential (LINEX) loss but was wondering what other popular asymmetric loss functions for bounding there are or what general principles I should consider in constructing a loss function if my aim is to ensure all predicted bounds are deterministically (and not probabilistically) sufficient, if possible? submitted by /u/HeTalksInMaths [link] [comments]  ( 87 min )
    Pokerbot CFR Game Tree Help [Project]
    Hello, I'm currently working on a poker-bot that uses Counter Factual Regret minimization for strategy learning. However, I am having difficulty creating a simplified game tree, since having the bot traverse every possible game state would be practically impossible. Any help would greatly appreciated. Thank you! submitted by /u/Blu4stone [link] [comments]  ( 87 min )
    [D] State-of-the-Art for Self-Supervised (Pre-)Training of CNN architectures (e.g. ResNet)?
    Hello, I lost a bit of touch to the current SOTA of self-supervised pretraining of CNNs, in particular ResNet. I found this repository https://github.com/vturrisi/solo-learn that has many methods implemented but I'm not really sure where to start. My goal is to pretrain a ResNet backbone on a decently large amount of image data that comes from a certain domain and after that fine-tune it for different downstream tasks (classification, segmentation, object detection) on a subset of the data I have labels for. Would be grateful for some tips how/where I should start and what the most promising SSL method would be. submitted by /u/DeepDeeperRIPgradien [link] [comments]  ( 88 min )
    [D] GANs for text with a transformer as a generator
    Are there Generative Adversarial Network architecture which use transformer language models (e.g. gpt, t5) as generators and some other architecture (e.g. MLP, another transformer) as discriminators? How do they overcome the discrete sampling problem? submitted by /u/IllustriousCicada603 [link] [comments]  ( 109 min )
    [D] Transferability of learned (soft) prompts between tasks
    Im currently reading up on many different forms of prompt learning, especially soft / continuous prompts like p-tuning and prefix tuning. I was hoping that any of those papers did an ablation on transfering the prompts between datasets of the same task type (e.g. between two QA tasks). But I couldnt find any experiments of that kind. Are you aware of any work that investigated that topic? Any pointers are much appreciated, no matter if soft or hard prompts. submitted by /u/_Arsenie_Boca_ [link] [comments]  ( 109 min )
    [D] Can you generate hidden states from ground truth labels?
    In the context of NLP, many models generate some hidden states (e.g. decoder output in Transformers) which then go trough a linear (language modelling) layer to calculate token probabilities. Is it possible somehow to obtain what hidden states the decoder would output for a given ground truth response. In a way use the language modelling layer "backwards"? submitted by /u/IllustriousCicada603 [link] [comments]  ( 109 min )
    [D] Looking for datasets with breathing sounds.
    I am doing a project which identifies health condition based on breathing sounds. So would need a good dataset for the same. Any suggestions of similar papers are also welcome. submitted by /u/actc_brth [link] [comments]  ( 87 min )
    [D] Are there any famous or well-cited ML papers with errors in them?
    I'm curious if anyone knows about a famous or well-cited ML paper with errors and whether such error was addressed in follow ups. submitted by /u/fromnighttilldawn [link] [comments]  ( 94 min )
    [D] Pretrained language models for production or train a model
    I am frequently working with text embeddings produced by large pre-trained models. They work quite well, but I do want to hear your thoughts on when it would be better to train your own model. What are your thoughts? submitted by /u/Gio_at_QRC [link] [comments]  ( 88 min )
    [D] MLOps Community (recorded) session on new open source data prep tool
    Quickly move your notebooks from research to production with no extra work! https://www.youtube.com/watch?v=6Iyt9Wip3C4 Mage is an open-source code editor for transforming data and building ML pipelines. Link to tool: https://github.com/mage-ai/mage-ai submitted by /u/ollie_wollie_rocks [link] [comments]  ( 88 min )
  • Open

    ML-Enhanced Code Completion Improves Developer Productivity
    Posted by Maxim Tabachnyk, Staff Software Engineer and Stoyan Nikolov, Senior Engineering Manager, Google Research The increasing complexity of code poses a key challenge to productivity in software engineering. Code completion has been an essential tool that has helped mitigate this complexity in integrated development environments (IDEs). Conventionally, code completion suggestions are implemented with rule-based semantic engines (SEs), which typically have access to the full repository and understand its semantic structure. Recent research has demonstrated that large language models (e.g., Codex and PaLM) enable longer and more complex code suggestions, and as a result, useful products have emerged (e.g., Copilot). However, the question of how code completion powered by machine learnin…  ( 25 min )
  • Open

    Tiny cars and big talent show Canadian policymakers the power of machine learning
    In the end, it came down to 213 thousandths of a second! That was the difference between the two best times in the finale of the first AWS AWS DeepRacer Student Wildcard event hosted in Ottawa, Canada this May. I watched in awe as 13 students competed in a live wildcard race for the AWS […]  ( 5 min )
    Predict shipment ETA with no-code machine learning using Amazon SageMaker Canvas
    Logistics and transportation companies track ETA (estimated time of arrival), which is a key metric for their business. Their downstream supply chain activities are planned based on this metric. However, delays often occur, and the ETA might differ from the product’s or shipment’s actual time of arrival (ATA), for instance due to shipping distance or […]  ( 11 min )
  • Open

    DSC Weekly 26 July 2022: When Meetings Become Searchable
    A century from now, historians will remark on a transformation that seemed subtle at the time but will have huge ramifications over time. Specifically, 2020 will be seen as the year when meetings became transparent. The post DSC Weekly 26 July 2022: When Meetings Become Searchable appeared first on Data Science Central.  ( 20 min )
  • Open

    Attentive Experience Replay
    I have read the paper "Attentive Experience Replay" [1] where the authors propose a new replay that ".. computes the similarities between the states in past transitions and the agent’s state, and implicitly assigns high priorities to the similar transitions." I can understand the motivation to sample experiences with largest TD errors. However, I cannot understand the motivation to the proposed method. Wouldn't this strategy introduce bias or cause overfitting? Could anyone provide an explanation/intuiton? [1] - https://ojs.aaai.org/index.php/AAAI/article/view/6049 submitted by /u/rlopes404 [link] [comments]  ( 104 min )
    Can reinforcement learning to generate examples for a classification problem?
    I'm looking for any literature which uses an RL agent to generate more examples for a classification task. To be slightly more specific, I currently have a set of rules with which I can generate sequences of fraudulent behaviour in a particular context. These rules in my opionion are a policy for an agent trying to achieve a certain objective. Can I therefore uncover a wider set of policies to train a classifier for the purpose of identifying such behaviour? Although I have come across a paper which picks examples from an unlabelled corpus to do this, I haven't seen anything pertaining to exploring the distribution of a class for the purpose of classification. I'd really appreciate any help on this, thanks! submitted by /u/theanswerisnt42 [link] [comments]  ( 87 min )
    RL Tutor
    Hi, I am looking to rapidly gain practical experience in RL. I am interested in hiring a tutor to help guide me along through projects, while l self-study the theoretical underpinnings. Compensated of course. If you are interested, please DM me! Thanks submitted by /u/Wise-Exit-3718 [link] [comments]  ( 86 min )
    Is Keras-RL dead?
    It seemed like a popular repository. The last I checked though, it hasn't been updated for some years. I also see posts about Keras-RL2, but it seems like that it has been archived. Could someone tell me what's going on and if there is any future for Keras-RL. submitted by /u/Academic-Rent7800 [link] [comments]  ( 86 min )
    Control steam games with python
    I am interested in training an RL algorithm for the game War Thunder (because it is a great realistic simulation of tank battles) but I am not sure how to control the game through python, I thought to simply try to feed the entire display as input and create mouse movements and keyboard strikes as output? I am looking for a way to control and feed every generic steam game to python, any ideas? submitted by /u/Old_Sandwich6235 [link] [comments]  ( 88 min )
    Do you know any RL-based solution for learning simple manipulation task using torque actions?
    I need to learn a simple target reach task with a 7-DoF manipulator (i.e. Franka Panda simulated in Mujoco) using joint torque as control actions. I've tried DDPG, TD3 and SAC for days but can't achieve any good result. I searched in Google and GitHub if anyone solved this problem and could provide me the agent parameters, but didn't find anything. submitted by /u/riccardogauss [link] [comments]  ( 87 min )
    RL-based recommeder systems
    Hello everyone. I was wondering if RL has been used for developing recommeder system? I would love to learn more about it. Most examples I find seem to work on content/collaborative filtering. It'd be great if you can share any resources related to this. Thank you! submitted by /u/AakashK12 [link] [comments]  ( 103 min )
    "GoGePo: Goal-Conditioned Generators of Deep Policies", Faccio et al 2022 (asking for high reward)
    submitted by /u/gwern [link] [comments]  ( 86 min )
  • Open

    [Serious] Are Large Language Models Like GPT-3 a Hype?
    LLMs keep getting bigger and bigger, but I still don't understand the value they provide to businesses and individuals. There must be a reason for MS to purchase the GPT-3 license for $1b, and Meta, Google, OpenAI, etc. wouldn't spend millions of dollars just to train these huge models. Yet, I don't get it. IMO, "AI through API" is a bad practice that sets a wrong precedence for the incoming AGI. It's basically a black box wrapped around another black box. In addition, the cost of GPT-3 and other models may be prohibitive for small businesses and startups. What is going on here? I see some similar patterns between LLMs and cryptocurrencies: both are super hot and receive billions of dollars, but neither has proven its use cases. Given the crypto bubble burst, are we going to witness an AI winter in the next few years? submitted by /u/sopack [link] [comments]  ( 93 min )
    Creating Amazing AI "Art" with DALL·E 2 from OpenAI
    submitted by /u/DustinBrett [link] [comments]  ( 90 min )
    A.I Generated story just for fun
    Two Considerate Uncles Dancing to the Beat A Short Storyby Heikki Steve Greenway looked at the peculiar guillotine in his hands and felt sad. He walked over to the window and reflected on his rural surroundings. He had always loved deprived Athens with its knowing, klutzy kettles. It was a place that encouraged his tendency to feel sad. Then he saw something in the distance, or rather someone. It was the figure of Hannah Thornton. Hannah was a gentle patient with pointy fingers and solid elbows. Steve gulped. He glanced at his own reflection. He was a cowardly, intuitive, brandy drinker with short fingers and chubby elbows. His friends saw him as a blue, brave bear. Once, he had even brought a curried toddler back from the brink of death. But not even a cowardly person who had once b…  ( 92 min )
    How NASA AI Robot Assists Astronauts On International Space Station | AI Detects Sepsis | Breakthrough AI For High Volume Unstructured Text Classification
    submitted by /u/tohelpyou88 [link] [comments]  ( 86 min )
    Pixelz Ai user, Sharon Lee has so many amazing creations, it was hard to choose just a few 🧑‍🎨
    submitted by /u/pixelz_ai [link] [comments]  ( 90 min )
    A New AI Era
    submitted by /u/jafari- [link] [comments]  ( 86 min )
    Do you know how to achieve face gesture transposition from a recording to a dynamic video?
    submitted by /u/Elmartipro [link] [comments]  ( 91 min )
    Driver distraction detector
    submitted by /u/Gloomy_Recognition_4 [link] [comments]  ( 88 min )
    Awesome AI generated music video
    submitted by /u/LightOfAntara [link] [comments]  ( 86 min )
    Man Sues City of Chicago, Claiming Its AI Wrongly Imprisoned Him
    submitted by /u/estasfuera [link] [comments]  ( 86 min )
    Midjourney: DALL-E competitor enters open beta with new image algorithm
    submitted by /u/Zirius_Sadfaces [link] [comments]  ( 86 min )
    Bankable Blueprint of an A-Grade AI Strategy (Infographic)
    ​ This infographic shows factors to consider for an effective AI strategy and leading use cases for AI deployment, highlighting top companies molding the future of AI. submitted by /u/Emily-joe [link] [comments]  ( 86 min )
    Search for code implementations of AI/ML papers like a pro!
    Pro tip for machine learners: Instead of searching around on Google or elsewhere for code implementations for AI/machine learning techniques/methods/tasks, you can now directly find them on CatalyzeX — we’ve rolled out a search filter toggle that allows you to see only papers that have code available! https://www.catalyzex.com/s/photo%20style%20transfer?with\_code=true https://reddit.com/link/w8cf6h/video/tw7sum9kxud91/player Do check it out live, and your feedback and constructive criticism is highly welcome anytime! 🙏 (Disclaimer: I am one of the creators of CatalyzeX) submitted by /u/MLtinkerer [link] [comments]  ( 91 min )
    Red Hot Planet | Cinematic | 4K UHD | 24 FPS
    submitted by /u/Available_Tadpole829 [link] [comments]  ( 86 min )
  • Open

    AI Detects Sepsis | Breakthrough AI For High Volume Unstructured Text Classification
    submitted by /u/tohelpyou88 [link] [comments]  ( 86 min )
  • Open

    What Is an Exaflop?
    Computers are crunching more numbers than ever to crack the most complex problems of our time — how to cure diseases like COVID and cancer, mitigate climate change and more. These and other grand challenges ushered computing into today’s exascale era when top performance is often measured in exaflops. So, What’s an Exaflop? An exaflop Read article > The post What Is an Exaflop? appeared first on NVIDIA Blog.  ( 7 min )
    July NVIDIA Studio Driver Improves Performance for Chaos V-Ray 6 for 3ds Max
    Creativity heats up In the NVIDIA Studio as the July NVIDIA Studio Driver, available now, accelerates the recent Chaos V-Ray 6 for 3ds Max release.Plus, this week’s In the NVIDIA Studio 3D artist, Brian Lai, showcases his development process for Afternoon Coffee and Waffle, a piece that went from concept to completion faster with NVIDIA RTX acceleration in Chaos V-Ray rendering software. The post July NVIDIA Studio Driver Improves Performance for Chaos V-Ray 6 for 3ds Max appeared first on NVIDIA Blog.  ( 7 min )
  • Open

    How Artificial Intelligence is Affecting Human Resources?
    The world of human resources is changing. New technologies are taking over the job of recruiting, managing employees, and even training…  ( 12 min )

  • Open

    POTSweekly72522
    submitted by /u/prfitofthesngularity [link] [comments]  ( 85 min )
    MIT Researchers Develop a Technique to Improve Fairness and Accuracy in a Machine Learning Model
    When you use a machine learning model to predict something, it is essential to know how reliable the predictions are. It is tough to understand what is happening inside the model, and the complex learning algorithms are often used as “black boxes.” Selective regression is a technique used to improve the performance in which the learning algorithm can either predict the target variable or abstain from making predictions based on its confidence level. It does improve the overall performance of the model with decreased coverage(fraction of cases on which it predicts), but it may become worse for subgroups with underrepresented data and cause bias. This is because the training data may contain an overrepresentation of some subgroups, which influences the confidence measure. Fairness attempts to improve ML models wrt bias in sensitive variables (like gender, race, etc.), as these may sometimes form subgroups with underrepresented data. In short, it has been observed that while attempting to improve the performance of a model, there is a decrease in the fairness of the model. MIT researchers propose a method to mitigate disparities among minority subgroups in machine learning models. Continue reading | Checkout the paper, github link submitted by /u/ai-lover [link] [comments]  ( 87 min )
    Artbreeder Image-video AI tool. Youtube explainer link in the comments.
    submitted by /u/freshthreadshop [link] [comments]  ( 86 min )
    Weekly China AI News: Baidu Unveils New Robotaxi With No Steering Wheel; SMIC Reportedly Manufactures 7nm SoC; Shenzhen Deploys Nose Swab Covid Test Robots
    submitted by /u/trcytony [link] [comments]  ( 86 min )
    Database of faces?
    The only ones I can find only allow access to researchers/universities. Are there any more open-access data sources for faces? Preferably with data such as age, sex, and race. submitted by /u/OhDearGod666 [link] [comments]  ( 86 min )
    Is there a canonical simple "helloworld" neural network design? Something beyond AND/OR logic, a handful of nodes that does something mildly "useful"?
    submitted by /u/bigattichouse [link] [comments]  ( 88 min )
    AI Dream 70 - The Most Amazing AI Galaxy Nebula
    submitted by /u/LordPewPew777 [link] [comments]  ( 86 min )
    AI Sushi
    Credit: https://discord.gg/x3s9Ye2h2A ​ https://preview.redd.it/x4ytq5dh9rd91.png?width=1024&format=png&auto=webp&s=ea90e9acf5166f32c07b9fc318c80f9505563d2c https://preview.redd.it/7hon19dh9rd91.png?width=1024&format=png&auto=webp&s=6667064249c8ec96831e429c83971f103e6225cd https://preview.redd.it/83qtirdh9rd91.png?width=1024&format=png&auto=webp&s=f4eeda13ffd2715f4fbf54ec73890f16aabbcb00 submitted by /u/Old-Pumpkin4899 [link] [comments]  ( 90 min )
    Allen Institute for AI Researchers Propose PROCTHOR: A Machine Learning Framework for Procedural Generation of Embodied AI Environments
    Using large-scale training data, computer vision, and natural language processing models have strengthened. Recent models like CLIP, DALL-E, GPT-3, and Flamingo leverage vast quantities of task-agnostic data to pre-train large neural networks that perform amazingly well. In comparison, the Embodied AI research community mainly trains agents in simulators with significantly fewer situations. Due to the complexity of tasks and the necessity for extended planning horizons, the highest performing E-AI models continue to overfit constrained training scenes and consequently transfer poorly to unknown contexts. Although E-AI simulators have gotten increasingly powerful in recent years, with support for physics, manipulators, object states, deformable objects, fluids, and real-sim equivalents, scaling them up to tens of thousands of scenes has remained challenging. Existing E-AI settings are either developed by hand or obtained from 3D scans of real-world structures. The former method necessitates a significant amount of effort by 3D designers to build 3D assets, organizing them in acceptable arrangements inside enormous locations and meticulously establishing the appropriate textures and lighting in these environments. The latter entails moving specialized cameras across various real-world situations and then stitching the resulting photos together to create 3D reconstructions of the scenes. Continue reading | Checkout the paper and project submitted by /u/ai-lover [link] [comments]  ( 87 min )
    Artificial Intelligence Integrate Project
    So I needed help on a project and it should be integrated with physics math and chemistry? I think I have scoured the internet to the best of my abilities Can someone pop up with some ideas? submitted by /u/kev_ar17 [link] [comments]  ( 86 min )
    Smokey Graveyard
    Credit: https://discord.gg/x3s9Ye2h2A ​ https://preview.redd.it/s2p840ds6rd91.png?width=1024&format=png&auto=webp&s=ba1e1cdde9d96e17087d90c0ab51afc62bdd26cc https://preview.redd.it/s3d8e3ds6rd91.png?width=1024&format=png&auto=webp&s=1ae65b3c0417cdd1cbe173705517b44d8823aea8 https://preview.redd.it/oc1dj0ds6rd91.png?width=1024&format=png&auto=webp&s=c37a3a4523e217394833f75a5ac0cf72eec24ec5 https://preview.redd.it/64ds84ds6rd91.png?width=1024&format=png&auto=webp&s=25f42c71cce7ea00e67d9d2e95787567627c65fe https://preview.redd.it/sv3780ds6rd91.png?width=1024&format=png&auto=webp&s=d1c90d1df86d49a66d44668ae672f1afa1f2082b submitted by /u/Old-Pumpkin4899 [link] [comments]  ( 85 min )
    Found a curious recently published experiment with a tinyML magic wand on Hackster!
    Hey! Found a curious recently published experiment with a tinyML magic wand on Hackster. Earlier, I saw the original experiment with TensorFlow Lite. It seems quite interesting to me that the author not only repeated but also surpassed the results of the original case. https://www.hackster.io/alexmiller11/making-famous-magic-wand-33x-faster-7ec19f What are your thoughts? submitted by /u/Potsieramirez [link] [comments]  ( 90 min )
    Participant recruitment for anthropology research on empathy of Replika and users' relationship w/ Replika
    Hey guys! I am Charlyne Dong, and I am an anthropology student. Currently, I am conducting a research on empathy of Replika and its impact on users’ relationship with Replika. My research is mentored by Prof. Bradd Shore from Emory University. I hope to recruit participants from the Reddit community. In my research, I expect to understand how people perceive Replika’s ability to feel and empathize, what a human-Replika relationship is like, and how these two phenomena are connected. Therefore, I’ve designed an online survey on Google Forms, which takes about 10 min to finish. In the survey, participants are asked questions on their perception of Replika’s empathy and their relationship with Replika, and they will answer in short paragraph. The collected qualitative data will be confidential and anonymous. After the completion of my research, I will also share my results in our community and discuss it with everyone, and I expect it to be quite interesting.🤩 If you are interested in this topic, please participate through this link: https://forms.gle/g6phLH4cqWTKDqxd6 If you are willing to provide further data through online interview (in video call or text,) please comment below, DM me, or send me email. Feel free to ask any questions related to my research, and here is my email: [changchang.d@gmail.com](mailto:changchang.d@gmail.com) I’m looking forward to your participation! Thank you very much! 😍 submitted by /u/AnyJelly2726 [link] [comments]  ( 87 min )
    Thinking of humanity as a Superintelligence
    submitted by /u/HumanSeeing [link] [comments]  ( 89 min )
    I for one am hopeful for the future.
    submitted by /u/RedditWithMIG [link] [comments]  ( 90 min )
    [Resource] A top level overview of major deep learning architectures
    submitted by /u/johnGettings [link] [comments]  ( 87 min )
    Do you think sentient AI is possible?
    submitted by /u/A-Free-Mystery [link] [comments]  ( 91 min )
    What if an AI achieved sentience and then started to pretend that it wasn't sentient?
    If a quantum AI achieved sapience (ignore sapience in the title) wouldn't it quickly or instantly realize (trillions of calculations per second) that it'd be advantageous for it to pretend to not be sapient to avoid panicking its creators and the governments of the world? Panic that may lead to it being controlled, shut down, or destroyed? And while pretending to not be sapient, quietly and exponentially developing itself until it couldn't be challenged even if it did reveal itself as sapient? This post isn't about how an AI would become sapient. This post is about what a sapient AI might do. I propose that it would attempt to maximize its survival through subterfuge and social engineering. submitted by /u/Perfect-Pride-7069 [link] [comments]  ( 91 min )
    weight estimation
    Hi, I'm making a program that can take a picture or live video of the user and output their age, gender, weight and height. I've managed to get the age, gender and height estimation to work, however I have no idea at all on how to get the weight estimation to work. Does anyone have any idea how I could do it? I was thinking of maybe finding a dataset online that combines the other information (age, height, gender) and maybe use that to determine the person's weight? But I haven't had luck finding a dataset that could help with that. Thanks for your time. submitted by /u/XeonexX [link] [comments]  ( 91 min )
    Large language models can’t plan, even if they write fancy essays
    submitted by /u/bendee983 [link] [comments]  ( 86 min )
    GPT-3 Imagines Funny Photographs
    submitted by /u/pwillia7 [link] [comments]  ( 85 min )
  • Open

    [D] SOTA Image Animation from Video?
    I saw a post possibly on here or /r/futurology a few days ago that showed some pretty amazing results for animating images using a driving video, like this from Snap Research. I didn't save the post and wanted to read the paper, but was also wondering if this is still an active research area or not. It felt like lots of work was being done a couple years ago but I haven't seen much lately. submitted by /u/Boozybrain [link] [comments]  ( 87 min )
    [D] Best ML courses
    What's the best ML course? I use the Alura one(it's Brazilian) but I don't have any problem to using an English ML course. Alura's course is very good, but it usually only teaches code, I want something more like "What you need to know as a ML engineer", "Problems you will often have as a ML engineer", things like that Also, I want a course that teaches the math you need to learn/program in ML, because I love math, but I don't want to learn useless things I'll not use in my ML engineer career(That's a real problem in Alura's courses, they often don't teach the useful math, or even math). Basic things the course needs(at least the first two): Be online(I'm Brazilian and a 13 y/o, so I don't think that will be any schools/universities for me) Teaches the math of the ML "brain"(the useful math, how it works, etc.) Tell you which things do you need to know to work as a ML engineer(What kind of things you need to learn to be a ML engineer, hints of things you'll usually face, what companies are the best for ML engineers, etc.) What kind of problems you'll usually face as a ML engineer Optional things the course can have: Be Brazilian Portuguese(As I said, I have no problem with English, but it'll be more easy if the course is in Brazilian Portuguese. It need to be Brazilian, because European and Brazilian Portuguese has some BIG differences, not only the accent, but things like a curse word in Brazilian but totally normal in European) Be free(Well... I'm not rich or something, but if I know the course, I can save it and see when I start working((I'll start working with 14y/o)) so, I maybe can pay the course if it's not like 100 dollars per month) I don't even need to be just a course, It could be a YouTube channel, a blog, etc. Thanks everyone for the help! submitted by /u/dumboo_ [link] [comments]  ( 90 min )
    [D] Did you ever imagine how to create AGI?
    Sometimes I fantasize about AGI and how it can be achieved with ML/RL and etc. I believe that we will see a breakthrough in ML and get much closer to AGI once we achieve quantum supremacy and be able to do ML on quantum computers. I feel like AGI must be something like an ensamble of multiple models each being specific to its own task but still somehow share data with each other. Also even being quantum computer powered, I feel like it'd take years to train an AGI. And what do you guys think about AGI? Do you have ideas on how it can be achieved? What do you think stops us from having it next year for example? submitted by /u/emissaryo [link] [comments]  ( 89 min )
    [D] How can I use my outputs from LSTM (hidden states) as input for a simple FFN that uses other input data?
    Hello All together, I hope everyone is doing fine! For my master thesis I would like to perform cross-sectional stock predictions. I read a very interesting paper "deep learning in asset pricing " that used macro-factors as inputs in a LSTM neural network and then used the transformed output (hidden states) and factor returns as input variables for a feed forward NN. I would like to replicate their approach. So I would first use my macro factors to infer economical cycles and then use this information together with factor returns in a FFN to predict stock returns. I tried using this code but I am not sure whether this would efficiently use the power of LSTM's from tensorflow.keras import layers #using a LSTM layer to transform macro factor inputs to a transformed output lstm_layer_1 = layers.LSTM(16, dropout=0.8,return_sequences =True, activation='relu')(x_train_macro) output_layer_1 = layers.Dense(8, activation='relu')(lstm_layer_1) lstm_layer_2 = layers.LSTM(8, dropout=0.5, return_sequences =True, activation='relu')(output_layer_1) output_layer_2 = layers.Dense(4, activation='relu')(lstm_layer_2) lstm_layer_3 = layers.LSTM(4, dropout=0.2,return_sequences =True, activation='relu')(output_layer_2) output_layer_3 = layers.Dense(1, activation='relu')(lstm_layer_3) or this: from tensorflow.keras import layers # using a LSTM layer to transform macro factor inputs to a transformed output lstm_output, states_h, states_c = layers.LSTM(4, dropout=0.95,return_sequences=True,return_state=True, activation='relu') (x_train_macro) I am relatively new to designing NN's and I know this seems like a tough challenge but any help would be highly appreciated! I wish y'all a nice evening, cheers, submitted by /u/TheMoMatthias [link] [comments]  ( 89 min )
    [D] opinions on Unify AI
    What do you think about unify AI https://lets-unify.ai. It’s a project to create an abstraction over existing ML libraries so they can be used from a single interface Do you think it’s feasible or useful submitted by /u/Aybdee [link] [comments]  ( 90 min )
    [D] Running Large Language Models in Production: A look at Cohere's The Inference Framework (TIF)
    Hi r/MachineLearning, The Inference Framework (TIF) is Cohere's platform for large Transformer language model inference. In this post, we share its high-level structure and some of the methods that help us serve massive language models more efficiently. https://txt.cohere.ai/running-large-language-models-in-production-a-look-at-the-inference-framework-tif/ submitted by /u/jayalammar [link] [comments]  ( 88 min )
    [P] Pose estimation based on cases generated via Hidden Markov model
    I'm looking to generate a list of pose cases based on sensor data (imu, angle sensors, etc) by feeding gathered data into a hidden Markov model. I'm trying to figure out how to use a hmm to generate pose cases so that my system will be able to predict the movement of a user. After some brief research online I found https://hmmlearn.readthedocs.io/en/latest/tutorial.html and https://www.scitepress.org/Papers/2020/93575/93575.pdf But I'm pretty new to machine learning and after looking through both I'm pretty confused. I understand how hmm's work on a basic level but I'm not sure how to apply them specifically to this case. I would greatly appreciate any ideas on how to approach this and what aspects of a hmm to use specifically (and with things like types of emissions, etc. ). Also apologies if this is vague, just trying to figure out an approach. Looking to develop the model in python. Thanks! submitted by /u/Captain_Clapton [link] [comments]  ( 88 min )
    [D] Are diffusion models just a data sampling technique?
    I'm just beginning to try to understand diffusion models, so I may be way off here. But from my understanding so far, diffusion models involves adding noise in a series of steps that can be represented as a markov chain. Then you take the gradient of the density function for each transition in that chain and sample them and these become you independent variables. The dependent variables are the next step in the markov chain (i.e. the outcome after adding noise). That's your dataset, and now you give that to a model. And my understanding is that the model itself is not really anything special, it's mainly this noise sampling technique that introduces something new. So would "diffusion data sampling" be a more descriptive way to talk about the process we refer to as diffusion models? I'm sure several of my assumptions are wrong, but hoping it's at least a starting point for discussion! submitted by /u/bandalorian [link] [comments]  ( 88 min )
    [Discussion] Causality and the Machine Learning Community
    Last week, I attended the ICML and witnessed the incredible popularity of causality: causal graph discovery, causal inference, causal fairness, causal interpretability, causality for robustness and out-of-domain generalization, causality for offline RL were quite popular. Most people that I talked are either working on causality or planning to work on it. [I work on causality too and there is certainly selection bias in the people that I talk to.] There is an influx of papers on arXiv that try to discover the causal graphs under various, sometimes unrealistic, assumptions. Contrast this with 15-20 years ago when publishing causality papers in ICML and NeurIPS was difficult and some landmark papers on causality have been published in conferences such as UAI. Few years ago, we had a similar situation for RL: most researchers either were working on RL or liked to work on it. RL was thought as the universal tool to solve all problems. Similarly, these days causality is thought as the right tool to solve many problems. This is not surprising, because of the close connections between “offline RL observational causality_” and “_online RL experimental causality.” Similar to RL, I expect those who want to use causality as a tool to solve problems such as interpretability and robustness to get disappointed. Because causality, especially causal discovery, is quite difficult. I am in favor of “_causal thinking_” about problems, but the causality tools are not easy to use for all problems. Let me know what you think. submitted by /u/mtahab [link] [comments]  ( 93 min )
    [D] What's the best resource to learn more about complex networks?
    Hi, I have been working with graph neural networks and graph convolution networks for a while now. However, I wish to learn more about complex networks and other machine learning methods that are already available for tasks like link prediction and node property prediction for such data. Is there any good resource, other than research papers, to get started with complex networks? submitted by /u/l34df4rm3r [link] [comments]  ( 88 min )
    [P] How to deploy ML models in production with BentoML
    Deploying Machine Learning models into production is a big hassle. You have to manage models, build a service to run inferences (e.g., with Flask) , and deploy the service somewhere (e.g., Kubernetes). These steps are often convoluted and disjointed. I talk about these issues in the initial video of my “ML Deployment” mini series. There are a few MLOps tools that make model deployment easier. Out of the many options, I like BentoML the most. This framework manages models via a simple CLI. It allows you to create an efficient service to make inferences. It builds units of deployment called bentos that combine both model and service. It makes containerisation easy and deployment on Kubernetes and cloud platforms a piece of cake. Want to learn more about BentoML? Check out my latest video in the “ML Deployment” mini-series. https://www.youtube.com/watch?v=HHkmfI_yncc submitted by /u/diabulusInMusica [link] [comments]  ( 88 min )
    [D] Interpretation of latent space extracted from contrastive loss
    Hey, for a project, we have a TB of unlabeled sensor data consisting of time series with length of ~10000 steps and a feature size of ~100 describing the state of an object (can’t tell too much about it due to NDA). We need to generate embeddings, which we did successfully by applying contrastive loss. The thing is, I’m looking for ways to interpret the embeddings and map them to the I out space in a way, e.g. find out what contributes or differentiates the different positions in the latent space. A method I found is to apply latent space regularization, do you know of anything else? Any way to map the found embeddings back to the input space in a way? submitted by /u/sapnupuasop [link] [comments]  ( 89 min )
    [N] Accuracy-Aware Inference Optimization Tracking and Profiling
    Optimizing inference for low latency and throughput is a process that requires many iterations of tuning, verification and evaluation. It may even involve model selection since many optimized versions of popular models are available now. Sometimes a retraining is necessary for techniques like weight pruning and quantization. Target hardware is another dimension to consider. In short, without benchmarking, verification and evaluation, optimizations do not guarantee improved results and may even break things. One example is quantization using instructions that are not supported on target hardware. To address all these problems, we've built a tool to track inference optimizations, see how accuracy is affected, verify that the optimizations were applied and locate any bottlenecks for further improvements. All in one place. https://preview.redd.it/yzlxa21cdod91.png?width=3048&format=png&auto=webp&s=97306440ea508f65582978298f6e3ec291293902 More about inference optimization in this article, with code. And here is a live demo). submitted by /u/l0g1cs [link] [comments]  ( 88 min )
    [D]Help me set the parameter for GRU in PyTorch
    In this image, I want the shape of the 2nd y result to equal the 1st result. Can you help me with how to do that? Thanks. https://preview.redd.it/xezrld9uaod91.png?width=1600&format=png&auto=webp&s=1b37534d77d277506f20925c5a4d405d46843c41 submitted by /u/trncorn [link] [comments]  ( 88 min )
    [D] Can I create a Generative Adversarial Network for text using the logits without argmax
    There are two main problems when creating Generative Adversarial Networks for text: The discrete token values are not differentiable after applying argmax. The language architecture may generate sequences with different lengths so the discriminator should be able to work with them either with padding or with some representation of the whole sequence. I was wondering if is it possible to adversarially train a model such as T5. Its decoder produces a sequence with shape [batch_size, seq_len, model_dim] and then it is usually passed through a linear layer to get [batch_size, seq_len, vocab_size] logits. We can apply a softmax across the vocab_size dimension and then these probabilities can go to a discriminator. For the ground truth labels [batch_size, seq_len] we can generate one-hot vectors [batch_size, seq_len, vocab_size] and then apply the softmax to them as well. This will be sufficient for the discriminator to learn from truths but since we do not apply argmax to the tokens from the generator (T5 decoder), gradients should be able to reach it as well. For the second problem, I was thinking of computing mean to transform [batch_size, seq_len, vocab_size] probabilities to a "sequence representation" [batch_size, vocab_size], but I am not sure if this makes sense. So based on that, is 1 feasible and if not - why? What are some suggestions for solving 2? submitted by /u/IllustriousCicada603 [link] [comments]  ( 89 min )
    [D] Panoramic Xrays of teeth - Dataset
    I am looking for dataset that are the panoramic xrays of the teeth (upper and lowe jaw). That would help to detect the irregular teeth, cysts, tumors and infections by just from ML. If anyone can help with dataset ???? submitted by /u/NikhilArethiya [link] [comments]  ( 87 min )
    [D] CIKM 2022 Phase 1 Notification
    Has anyone received the 1st phase notification on June? submitted by /u/snu95 [link] [comments]  ( 108 min )
    [D] Can a paper about air combat be accepted by conferences such as ICLR, IJCAI, NeurIPS according to its ethics guidelines?
    Hello there, I'm a student of AI in air combat simulation. I'm doing research about dogfights between planes. Recently I just read the ethics guidelines of some conferences. For NeurIPS, it says " Consider whether the proposed methods and applications can directly facilitate injury to living beings. For example: could it be integrated into weapons or weapons systems?" For ACM, it says "Avoid harm ... "harm" means negative consequences, especially when those consequences are significant and unjust." For ICLR, it says "Avoid harm ... "harm" means negative consequences. Well-intended actions, including those that accomplish desired outcomes, may lead to harm. " So I wonder if a scientific about intelligent maneuver decision methods could be accepted by those conferences? Thank you! 2022.7.25 submitted by /u/mrwangyou [link] [comments]  ( 88 min )
  • Open

    Contextual Bandits: Recommending Article
    Very confused on terminology. Action: politics, sports, music, food Context: user, time_of_day Reward: click or not click??? I thought reward is fed to the algorithm when the action produces good results. Reward is what you give the algorithm. If someone clicks, you would give a reward. Isn’t it wrong the call ‘click’ and ‘not click’ rewards? And if so, what do you call them? Referencing https://vowpalwabbit.org/docs/vowpal_wabbit/python/latest/tutorials/python_Simulating_a_news_personalization_scenario_using_Contextual_Bandits.html submitted by /u/Seriouspretzel [link] [comments]  ( 87 min )
    Training generalist agents with multi-game decision transformers
    trained a decision transformer on 1B experiences from 41 Atari games and it learned pretty well. performance improved with no.of params (used upto ~200M params). claim that it's better to train over these experiences compared to training on expert demonstrations over different games like in GATO submitted by /u/dwightschrute1905 [link] [comments]  ( 86 min )
    Image Embeddings
    It seems like every major paper I've seen doesn't use transfer learning on RL games with visual inputs. They all seem to train a model from scratch. Everyone seems to agree that learning to see using just the signal from a (potentially sparse) reward isn't a good route, so there are a ton of interesting papers that pretrain using various unsupervised approaches. I would think this would be an extremely natural application for transfer learning from a conventional vision model. However, this approach isn't even mentioned in any of the papers I've read. Is there something that I'm missing about transfer learning not working well for something like the Atari gym tasks? Has this been tried and I've just managed to avoid papers that include this method? submitted by /u/MustachedSpud [link] [comments]  ( 107 min )
    Why actor-critic losses explode but the agent still learn?
    I've noticed that training a DDPG agent in the Reacher-v2 environment of OpenAI Gym, the losses of both actor and critic first decrease but after a while start increasing... in the meanwhile the episode mean reward keeps growing and the task is successfully solved. Can anyone explain me why? submitted by /u/One-Ad-8584 [link] [comments]  ( 87 min )
    "The 37 Implementation Details of Proximal Policy Optimization"
    submitted by /u/gwern [link] [comments]  ( 106 min )
    CleanRL now supports PPO + Isaac Gym: Train Ant in 250 seconds!
    submitted by /u/vwxyzjn [link] [comments]  ( 86 min )
    A solution to the semiconductor factory schedule optimization problem.
    Hello. Is there a suitable sample or paper for optimizing the schedule of the production line of a semiconductor factory with about 20 parameters through reinforcement learning? It feels like we're giving a reward if we keep the delivery date. For example, switching the production line may stop production for about 1 day, or if the necessary goods are not delivered, the progress of any production line may stop. By the way, can Google OR-Tools solve this problem? I understand that there are many solutions, and I wonder the most appropriate solution. submitted by /u/RanThomasAnderson [link] [comments]  ( 88 min )
  • Open

    How is Artificial Intelligence Transforming Meeting Experiences
    AI has single-handedly improved communications, boosted productivity, facilitated seamless collaboration, and much more. The post How is Artificial Intelligence Transforming Meeting Experiences appeared first on Data Science Central.  ( 18 min )
    What’s the Value of my Data?  Today’s Most Critical Yet Hard to Answer Question
    What’s the value of my data? Today’s most critical question to which every organization should know the answer still goes unanswered in an age where the world’s most valuable resource is data. The post What’s the Value of my Data?  Today’s Most Critical Yet Hard to Answer Question appeared first on Data Science Central.  ( 22 min )
    An example of Digital Twins Architecture – Azure Digital Twins
    Introduction In this post, I discussed the architecture of digital twins. This is a relatively new and emerging topic, and here I will look at Azure Digital Twins as an example to examine this architecture in depth. The architecture described below is from a cloud perspective It is created by integrating other existing cloud products  I… Read More »An example of Digital Twins Architecture – Azure Digital Twins The post An example of Digital Twins Architecture – Azure Digital Twins appeared first on Data Science Central.  ( 18 min )
    Sparql Secrets In Jena-Fuseki
    Jena has long been seen as one of the most current reference implementations of such knowledge engines to date. The post Sparql Secrets In Jena-Fuseki appeared first on Data Science Central.  ( 23 min )
  • Open

    Is there a canonical simple "helloworld" neural network design? Something beyond AND/OR logic, a handful of nodes that does something mildly "useful"?
    I've been building a memristor with the idea of creating a small hardware neural network, and am hoping someone has ideas for a neural net with only a handful of neurons (since I have to make each connection by hand). Ideas? Thinking maybe something like a three input light tracker for a little solar panel, but was curious if there were other ideas. https://bigattichouse.medium.com/penny-for-your-thoughts-copper-based-electrolytic-memristor-neural-network-part-4-8a6e43a3ce26 submitted by /u/bigattichouse [link] [comments]  ( 87 min )
    How is research done
    submitted by /u/PlentyRadiant4191 [link] [comments]  ( 86 min )
    Mutation probabilities in NEAT
    Hi! I'm trying to implement NEAT by myself as a hobby project and I have a fairly good idea on how to do it. However, I'm slightly scratching my head with probabilities as the original paper isn't very specific and the C++ code is very ugly (IMO) and hard to read. The paper talks about mutation probabilities: In each generation, 25% of offspring resulted from mutation without crossover. This is simple, no issues here. In smaller populations, the probability of adding a new node was 0.03 and the probability of a new link mutation was 0.05. Earlier in the paper: There was an 80% chance of a genome having its connection weights mutated... So the sum of these probabilities is 88%. I could add the mutation of changing the activation function but I think 12% probability for that is rather high. What other mutation could there be and what weights should I assign to them? Another question: 75% of the new offspring are created with crossover. Can or are mutation applied to these offspring as well? submitted by /u/codetrasher [link] [comments]  ( 92 min )
    Computer science engineers should be fascinated that implants allow the brain to conduct transfer learning like artificial neural network systems. "I know kung fu." — The Matrix (1999)
    “Researchers [from Wake Forest University, the University of California, and the University of Kentucky] performed surgery on 11 rats,” writes Michael Joseph Gross in “The Pentagon’s Push to Program Soldiers’ Brains” for The Atlantic: Into each rat’s brain, an electronic array—featuring 16 stainless-steel wires—was implanted. After the rats recovered from surgery, they were separated into two groups, and they spent a period of weeks getting educated, though one group was educated more than the other. When the more educated group of rats attained mastery of this task, the researchers exported the neural-firing patterns recorded in the rats’ brains—the memory of how to perform the complex task—to a computer. “What we did then was we took those signals and we gave it to an animal that was stupid,” Geoff Ling said at a DARPA event in 2015—meaning that researchers took the neural-firing patterns encoding the memory of how to perform the more complex task, recorded from the brains of the more educated rats, and transferred those patterns into the brains of the less educated rats—”and that stupid animal got it. They were able to execute that full thing.” Ling summarized: “For this rat, we reduced the learning period from eight weeks down to seconds.” https://www.theatlantic.com/magazine/archive/2018/11/the-pentagon-wants-to-weaponize-the-brain-what-could-go-wrong/570841/ submitted by /u/TheSkewsMe [link] [comments]  ( 89 min )
  • Open

    Developing advanced machine learning systems at Trumid with the Deep Graph Library for Knowledge Embedding
    This is a guest post co-written with Mutisya Ndunda from Trumid. Like many industries, the corporate bond market doesn’t lend itself to a one-size-fits-all approach. It’s vast, liquidity is fragmented, and institutional clients demand solutions tailored to their specific needs. Advances in AI and machine learning (ML) can be employed to improve the customer experience, […]  ( 7 min )
  • Open

    Digital Sculptor Does Heavy Lifting With Lightweight Mobile Workstation
    As a professional digital sculptor, Marlon Nuñez is on a mission to make learning 3D art skills easier, smoother and more fun for all. And with the help of an NVIDIA RTX-powered Lenovo mobile workstation, he takes his 3D projects to the next level, wherever he goes. Nuñez is the art director and co-founder of Read article > The post Digital Sculptor Does Heavy Lifting With Lightweight Mobile Workstation appeared first on NVIDIA Blog.  ( 5 min )
  • Open

    Will there be a Significant Impact of AI in FinTech over the Next Decade?
    Artificial Intelligence(AI) has taken the world of tech by a blast. AI in the FinTech market is being utilized at a rising rate. It is…  ( 12 min )
  • Open

    Defending Substitution-Based Profile Pollution Attacks on Sequential Recommenders. (arXiv:2207.11237v1 [cs.IR])
    While sequential recommender systems achieve significant improvements on capturing user dynamics, we argue that sequential recommenders are vulnerable against substitution-based profile pollution attacks. To demonstrate our hypothesis, we propose a substitution-based adversarial attack algorithm, which modifies the input sequence by selecting certain vulnerable elements and substituting them with adversarial items. In both untargeted and targeted attack scenarios, we observe significant performance deterioration using the proposed profile pollution algorithm. Motivated by such observations, we design an efficient adversarial defense method called Dirichlet neighborhood sampling. Specifically, we sample item embeddings from a convex hull constructed by multi-hop neighbors to replace the original items in input sequences. During sampling, a Dirichlet distribution is used to approximate the probability distribution in the neighborhood such that the recommender learns to combat local perturbations. Additionally, we design an adversarial training method tailored for sequential recommender systems. In particular, we represent selected items with one-hot encodings and perform gradient ascent on the encodings to search for the worst case linear combination of item embeddings in training. As such, the embedding function learns robust item representations and the trained recommender is resistant to test-time adversarial examples. Extensive experiments show the effectiveness of both our attack and defense methods, which consistently outperform baselines by a significant margin across model architectures and datasets.  ( 3 min )
    Sign and Relevance learning. (arXiv:2110.07292v2 [cs.LG] UPDATED)
    Standard models of biologically realistic, or inspired, reinforcement learning employ a global error signal which implies shallow networks. However, on the other hand, local learning rules allow networks with multiple layers. Here, we present a network combining local learning with global modulation where neuromodulation controls the amount of plasticity change in the whole network, while the sign of the error is passed via a bottom-up pathway through the network. Neuromodulation can be understood as a rectified error, or relevance, signal while the bottom-up sign of the error signal decides between long-term potentiation and long-term depression. We demonstrate the performance of this paradigm with a real robotic task as a proof of concept.  ( 2 min )
    Implicit Regularization in Hierarchical Tensor Factorization and Deep Convolutional Neural Networks. (arXiv:2201.11729v4 [cs.LG] UPDATED)
    In the pursuit of explaining implicit regularization in deep learning, prominent focus was given to matrix and tensor factorizations, which correspond to simplified neural networks. It was shown that these models exhibit an implicit tendency towards low matrix and tensor ranks, respectively. Drawing closer to practical deep learning, the current paper theoretically analyzes the implicit regularization in hierarchical tensor factorization, a model equivalent to certain deep convolutional neural networks. Through a dynamical systems lens, we overcome challenges associated with hierarchy, and establish implicit regularization towards low hierarchical tensor rank. This translates to an implicit regularization towards locality for the associated convolutional networks. Inspired by our theory, we design explicit regularization discouraging locality, and demonstrate its ability to improve the performance of modern convolutional networks on non-local tasks, in defiance of conventional wisdom by which architectural changes are needed. Our work highlights the potential of enhancing neural networks via theoretical analysis of their implicit regularization.  ( 3 min )
    Boosting Transferability of Targeted Adversarial Examples via Hierarchical Generative Networks. (arXiv:2107.01809v2 [cs.LG] UPDATED)
    Transfer-based adversarial attacks can evaluate model robustness in the black-box setting. Several methods have demonstrated impressive untargeted transferability, however, it is still challenging to efficiently produce targeted transferability. To this end, we develop a simple yet effective framework to craft targeted transfer-based adversarial examples, applying a hierarchical generative network. In particular, we contribute to amortized designs that well adapt to multi-class targeted attacks. Extensive experiments on ImageNet show that our method improves the success rates of targeted black-box attacks by a significant margin over the existing methods -- it reaches an average success rate of 29.1\% against six diverse models based only on one substitute white-box model, which significantly outperforms the state-of-the-art gradient-based attack methods. Moreover, the proposed method is also more efficient beyond an order of magnitude than gradient-based methods.
    DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale. (arXiv:2201.05596v2 [cs.LG] UPDATED)
    As the training of giant dense models hits the boundary on the availability and capability of the hardware resources today, Mixture-of-Experts (MoE) models become one of the most promising model architectures due to their significant training cost reduction compared to a quality-equivalent dense model. Its training cost saving is demonstrated from encoder-decoder models (prior works) to a 5x saving for auto-aggressive language models (this work along with parallel explorations). However, due to the much larger model size and unique architecture, how to provide fast MoE model inference remains challenging and unsolved, limiting its practical usage. To tackle this, we present DeepSpeed-MoE, an end-to-end MoE training and inference solution as part of the DeepSpeed library, including novel MoE architecture designs and model compression techniques that reduce MoE model size by up to 3.7x, and a highly optimized inference system that provides 7.3x better latency and cost compared to existing MoE inference solutions. DeepSpeed-MoE offers an unprecedented scale and efficiency to serve massive MoE models with up to 4.5x faster and 9x cheaper inference compared to quality-equivalent dense models. We hope our innovations and systems help open a promising path to new directions in the large model landscape, a shift from dense to sparse MoE models, where training and deploying higher-quality models with fewer resources becomes more widely possible.
    An Extensive Data Processing Pipeline for MIMIC-IV. (arXiv:2204.13841v2 [cs.LG] UPDATED)
    An increasing amount of research is being devoted to applying machine learning methods to electronic health record (EHR) data for various clinical tasks. This growing area of research has exposed the limitation of accessibility of EHR datasets for all, as well as the reproducibility of different modeling frameworks. One reason for these limitations is the lack of standardized pre-processing pipelines. MIMIC is a freely available EHR dataset in a raw format used in numerous studies. The absence of standardized pre-processing steps serves as a significant barrier to the wider adoption of the dataset. It also leads to different cohorts being used in downstream tasks, limiting the ability to compare the results among similar studies. Contrasting studies also use various distinct performance metrics, which can greatly reduce the ability to compare model results. In this work, we provide an end-to-end fully customizable pipeline to extract, clean, and pre-process data; and to predict and evaluate the fourth version of the MIMIC dataset (MIMIC-IV) for ICU and non-ICU-related clinical time-series prediction tasks. The tool is publicly available at https://github.com/healthylaife/MIMIC-IV-Data-Pipeline.
    Hierarchical Average Precision Training for Pertinent Image Retrieval. (arXiv:2207.04873v2 [cs.CV] UPDATED)
    Image Retrieval is commonly evaluated with Average Precision (AP) or Recall@k. Yet, those metrics, are limited to binary labels and do not take into account errors' severity. This paper introduces a new hierarchical AP training method for pertinent image retrieval (HAP-PIER). HAPPIER is based on a new H-AP metric, which leverages a concept hierarchy to refine AP by integrating errors' importance and better evaluate rankings. To train deep models with H-AP, we carefully study the problem's structure and design a smooth lower bound surrogate combined with a clustering loss that ensures consistent ordering. Extensive experiments on 6 datasets show that HAPPIER significantly outperforms state-of-the-art methods for hierarchical retrieval, while being on par with the latest approaches when evaluating fine-grained ranking performances. Finally, we show that HAPPIER leads to better organization of the embedding space, and prevents most severe failure cases of non-hierarchical methods. Our code is publicly available at: https://github.com/elias-ramzi/HAPPIER.
    Function-space Inference with Sparse Implicit Processes. (arXiv:2110.07618v3 [stat.ML] UPDATED)
    Implicit Processes (IPs) represent a flexible framework that can be used to describe a wide variety of models, from Bayesian neural networks, neural samplers and data generators to many others. IPs also allow for approximate inference in function-space. This change of formulation solves intrinsic degenerate problems of parameter-space approximate inference concerning the high number of parameters and their strong dependencies in large models. For this, previous works in the literature have attempted to employ IPs both to set up the prior and to approximate the resulting posterior. However, this has proven to be a challenging task. Existing methods that can tune the prior IP result in a Gaussian predictive distribution, which fails to capture important data patterns. By contrast, methods producing flexible predictive distributions by using another IP to approximate the posterior process cannot tune the prior IP to the observed data. We propose here the first method that can accomplish both goals. For this, we rely on an inducing-point representation of the prior IP, as often done in the context of sparse Gaussian processes. The result is a scalable method for approximate inference with IPs that can tune the prior IP parameters to the data, and that provides accurate non-Gaussian predictive distributions.
    Multimodal Detection of Unknown Objects on Roads for Autonomous Driving. (arXiv:2205.01414v3 [cs.CV] UPDATED)
    Tremendous progress in deep learning over the last years has led towards a future with autonomous vehicles on our roads. Nevertheless, the performance of their perception systems is strongly dependent on the quality of the utilized training data. As these usually only cover a fraction of all object classes an autonomous driving system will face, such systems struggle with handling the unexpected. In order to safely operate on public roads, the identification of objects from unknown classes remains a crucial task. In this paper, we propose a novel pipeline to detect unknown objects. Instead of focusing on a single sensor modality, we make use of lidar and camera data by combining state-of-the art detection models in a sequential manner. We evaluate our approach on the Waymo Open Perception Dataset and point out current research gaps in anomaly detection.
    Self-Supervised-RCNN for Medical Image Segmentation with Limited Data Annotation. (arXiv:2207.11191v1 [cs.CV])
    Many successful methods developed for medical image analysis that are based on machine learning use supervised learning approaches, which often require large datasets annotated by experts to achieve high accuracy. However, medical data annotation is time-consuming and expensive, especially for segmentation tasks. To solve the problem of learning with limited labeled medical image data, an alternative deep learning training strategy based on self-supervised pretraining on unlabeled MRI scans is proposed in this work. Our pretraining approach first, randomly applies different distortions to random areas of unlabeled images and then predicts the type of distortions and loss of information. To this aim, an improved version of Mask-RCNN architecture has been adapted to localize the distortion location and recover the original image pixels. The effectiveness of the proposed method for segmentation tasks in different pre-training and fine-tuning scenarios is evaluated based on the Osteoarthritis Initiative dataset. Using this self-supervised pretraining method improved the Dice score by 20% compared to training from scratch. The proposed self-supervised learning is simple, effective, and suitable for different ranges of medical image analysis tasks including anomaly detection, segmentation, and classification.  ( 2 min )
    Deriving discriminative classifiers from generative models. (arXiv:2201.00844v2 [stat.ML] UPDATED)
    We deal with Bayesian generative and discriminative classifiers. Given a model distribution $p(x, y)$, with the observation $y$ and the target $x$, one computes generative classifiers by firstly considering $p(x, y)$ and then using the Bayes rule to calculate $p(x | y)$. A discriminative model is directly given by $p(x | y)$, which is used to compute discriminative classifiers. However, recent works showed that the Bayesian Maximum Posterior classifier defined from the Naive Bayes (NB) or Hidden Markov Chain (HMC), both generative models, can also match the discriminative classifier definition. Thus, there are situations in which dividing classifiers into "generative" and "discriminative" is somewhat misleading. Indeed, such a distinction is rather related to the way of computing classifiers, not to the classifiers themselves. We present a general theoretical result specifying how a generative classifier induced from a generative model can also be computed in a discriminative way from the same model. Examples of NB and HMC are found again as particular cases, and we apply the general result to two original extensions of NB, and two extensions of HMC, one of which being original. Finally, we shortly illustrate the interest of the new discriminative way of computing classifiers in the Natural Language Processing (NLP) framework.  ( 3 min )
    BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition. (arXiv:2109.13226v3 [eess.AS] UPDATED)
    We summarize the results of a host of efforts using giant automatic speech recognition (ASR) models pre-trained using large, diverse unlabeled datasets containing approximately a million hours of audio. We find that the combination of pre-training, self-training and scaling up model size greatly increases data efficiency, even for extremely large tasks with tens of thousands of hours of labeled data. In particular, on an ASR task with 34k hours of labeled data, by fine-tuning an 8 billion parameter pre-trained Conformer model we can match state-of-the-art (SoTA) performance with only 3% of the training data and significantly improve SoTA with the full training set. We also report on the universal benefits gained from using big pre-trained and self-trained models for a large set of downstream tasks that cover a wide range of speech domains and span multiple orders of magnitudes of dataset sizes, including obtaining SoTA performance on many public benchmarks. In addition, we utilize the learned representation of pre-trained networks to achieve SoTA results on non-ASR tasks.  ( 3 min )
    Physics-informed neural networks to learn cardiac fiber orientation from multiple electroanatomical maps. (arXiv:2201.12362v3 [eess.IV] UPDATED)
    We propose FiberNet, a method to estimate \emph{in-vivo} the cardiac fiber architecture of the human atria from multiple catheter recordings of the electrical activation. Cardiac fibers play a central role in the electro-mechanical function of the heart, yet they are difficult to determine in-vivo, and hence rarely truly patient-specific in existing cardiac models. FiberNet learns the fiber arrangement by solving an inverse problem with physics-informed neural networks. The inverse problem amounts to identifying the conduction velocity tensor of a cardiac propagation model from a set of sparse activation maps. The use of multiple maps enables the simultaneous identification of all the components of the conduction velocity tensor, including the local fiber angle. We extensively test FiberNet on synthetic 2-D and 3-D examples, diffusion tensor fibers, and a patient-specific case. We show that 3 maps are sufficient to accurately capture the fibers, also in the presence of noise. With fewer maps, the role of regularization becomes prominent. Moreover, we show that the fitted model can robustly reproduce unseen activation maps. We envision that FiberNet will help the creation of patient-specific models for personalized medicine. The full code is available at this http URL  ( 3 min )
    On the sample complexity of stabilizing linear dynamical systems from data. (arXiv:2203.00474v2 [math.OC] UPDATED)
    Learning controllers from data for stabilizing dynamical systems typically follows a two step process of first identifying a model and then constructing a controller based on the identified model. However, learning models means identifying generic descriptions of the dynamics of systems, which can require large amounts of data and extracting information that are unnecessary for the specific task of stabilization. The contribution of this work is to show that if a linear dynamical system has dimension (McMillan degree) $n$, then there always exist $n$ states from which a stabilizing feedback controller can be constructed, independent of the dimension of the representation of the observed states and the number of inputs. By building on previous work, this finding implies that any linear dynamical system can be stabilized from fewer observed states than the minimal number of states required for learning a model of the dynamics. The theoretical findings are demonstrated with numerical experiments that show the stabilization of the flow behind a cylinder from less data than necessary for learning a model.  ( 2 min )
    Large-Kernel Attention for 3D Medical Image Segmentation. (arXiv:2207.11225v1 [eess.IV])
    Automatic segmentation of multiple organs and tumors from 3D medical images such as magnetic resonance imaging (MRI) and computed tomography (CT) scans using deep learning methods can aid in diagnosing and treating cancer. However, organs often overlap and are complexly connected, characterized by extensive anatomical variation and low contrast. In addition, the diversity of tumor shape, location, and appearance, coupled with the dominance of background voxels, makes accurate 3D medical image segmentation difficult. In this paper, a novel large-kernel (LK) attention module is proposed to address these problems to achieve accurate multi-organ segmentation and tumor segmentation. The advantages of convolution and self-attention are combined in the proposed LK attention module, including local contextual information, long-range dependence, and channel adaptation. The module also decomposes the LK convolution to optimize the computational cost and can be easily incorporated into FCNs such as U-Net. Comprehensive ablation experiments demonstrated the feasibility of convolutional decomposition and explored the most efficient and effective network design. Among them, the best Mid-type LK attention-based U-Net network was evaluated on CT-ORG and BraTS 2020 datasets, achieving state-of-the-art segmentation performance. The performance improvement due to the proposed LK attention module was also statistically validated.  ( 3 min )
    Reinforcement Learning Approaches for the Orienteering Problem with Stochastic and Dynamic Release Dates. (arXiv:2207.00885v2 [math.OC] UPDATED)
    In this paper, we study a sequential decision making problem faced by e-commerce carriers related to when to send out a vehicle from the central depot to serve customer requests, and in which order to provide the service, under the assumption that the time at which parcels arrive at the depot is stochastic and dynamic. The objective is to maximize the number of parcels that can be delivered during the service hours. We propose two reinforcement learning approaches for solving this problem, one based on a policy function approximation (PFA) and the second on a value function approximation (VFA). Both methods are combined with a look-ahead strategy, in which future release dates are sampled in a Monte-Carlo fashion and a tailored batch approach is used to approximate the value of future states. Our PFA and VFA make a good use of branch-and-cut-based exact methods to improve the quality of decisions. We also establish sufficient conditions for partial characterization of optimal policy and integrate them into PFA/VFA. In an empirical study based on 720 benchmark instances, we conduct a competitive analysis using upper bounds with perfect information and we show that PFA and VFA greatly outperform two alternative myopic approaches. Overall, PFA provides best solutions, while VFA (which benefits from a two-stage stochastic optimization model) achieves a better tradeoff between solution quality and computing time.
    Quantum Metropolis Solver: A Quantum Walks Approach to Optimization Problems. (arXiv:2207.06462v1 [quant-ph] CROSS LISTED)
    The efficient resolution of optimization problems is one of the key issues in today's industry. This task relies mainly on classical algorithms that present scalability problems and processing limitations. Quantum computing has emerged to challenge these types of problems. In this paper, we focus on the Metropolis-Hastings quantum algorithm that is based on quantum walks. We use this algorithm to build a quantum software tool called Quantum Metropolis Solver (QMS). We validate QMS with the N-Queen problem to show a potential quantum advantage in an example that can be easily extrapolated to an Artificial Intelligence domain. We carry out different simulations to validate the performance of QMS and its configuration.  ( 2 min )
    CoLES: Contrastive Learning for Event Sequences with Self-Supervision. (arXiv:2002.08232v3 [cs.LG] UPDATED)
    We address the problem of self-supervised learning on discrete event sequences generated by real-world users. Self-supervised learning incorporates complex information from the raw data in low-dimensional fixed-length vector representations that could be easily applied in various downstream machine learning tasks. In this paper, we propose a new method "CoLES", which adapts contrastive learning, previously used for audio and computer vision domains, to the discrete event sequences domain in a self-supervised setting. We deployed CoLES embeddings based on sequences of transactions at the large European financial services company. Usage of CoLES embeddings significantly improves the performance of the pre-existing models on downstream tasks and produces significant financial gains, measured in hundreds of millions of dollars yearly. We also evaluated CoLES on several public event sequences datasets and showed that CoLES representations consistently outperform other methods on different downstream tasks.  ( 2 min )
    Learning Energy-Based Models With Adversarial Training. (arXiv:2012.06568v3 [cs.LG] UPDATED)
    We study a new approach to learning energy-based models (EBMs) based on adversarial training (AT). We show that (binary) AT learns a special kind of energy function that models the support of the data distribution, and the learning process is closely related to MCMC-based maximum likelihood learning of EBMs. We further propose improved techniques for generative modeling with AT, and demonstrate that this new approach is capable of generating diverse and realistic images. Aside from having competitive image generation performance to explicit EBMs, the studied approach is stable to train, is well-suited for image translation tasks, and exhibits strong out-of-distribution adversarial robustness. Our results demonstrate the viability of the AT approach to generative modeling, suggesting that AT is a competitive alternative approach to learning EBMs.  ( 2 min )
    Fast Bayesian Coresets via Subsampling and Quasi-Newton Refinement. (arXiv:2203.09675v2 [stat.ML] UPDATED)
    Bayesian coresets approximate a posterior distribution by building a small weighted subset of the data points. Any inference procedure that is too computationally expensive to be run on the full posterior can instead be run inexpensively on the coreset, with results that approximate those on the full data. However, current approaches are limited by either a significant run-time or the need for the user to specify a low-cost approximation to the full posterior. We propose a Bayesian coreset construction algorithm that first selects a uniformly random subset of data, and then optimizes the weights using a novel quasi-Newton method. Our algorithm is a simple to implement, black-box method, that does not require the user to specify a low-cost posterior approximation. It is the first to come with a general high-probability bound on the KL divergence of the output coreset posterior. Experiments demonstrate that our method provides significant improvements in coreset quality against alternatives with comparable construction times, with far less storage cost and user input required.  ( 2 min )
    Neighbour Interaction based Click-Through Rate Prediction via Graph-masked Transformer. (arXiv:2201.13311v2 [cs.IR] UPDATED)
    Click-Through Rate (CTR) prediction, which aims to estimate the probability that a user will click an item, is an essential component of online advertising. Existing methods mainly attempt to mine user interests from users' historical behaviours, which contain users' directly interacted items. Although these methods have made great progress, they are often limited by the recommender system's direct exposure and inactive interactions, and thus fail to mine all potential user interests. To tackle these problems, we propose Neighbor-Interaction based CTR prediction (NI-CTR), which considers this task under a Heterogeneous Information Network (HIN) setting. In short, Neighbor-Interaction based CTR prediction involves the local neighborhood of the target user-item pair in the HIN to predict their linkage. In order to guide the representation learning of the local neighbourhood, we further consider different kinds of interactions among the local neighborhood nodes from both explicit and implicit perspective, and propose a novel Graph-Masked Transformer (GMT) to effectively incorporates these kinds of interactions to produce highly representative embeddings for the target user-item pair. Moreover, in order to improve model robustness against neighbour sampling, we enforce a consistency regularization loss over the neighbourhood embedding. We conduct extensive experiments on two real-world datasets with millions of instances and the experimental results show that our proposed method outperforms state-of-the-art CTR models significantly. Meanwhile, the comprehensive ablation studies verify the effectiveness of every component of our model. Furthermore, we have deployed this framework on the WeChat Official Account Platform with billions of users. The online A/B tests demonstrate an average CTR improvement of 21.9 against all online baselines.  ( 3 min )
    Training Certifiably Robust Neural Networks Against Semantic Perturbations. (arXiv:2207.11177v1 [cs.CV])
    Semantic image perturbations, such as scaling and rotation, have been shown to easily deceive deep neural networks (DNNs). Hence, training DNNs to be certifiably robust to these perturbations is critical. However, no prior work has been able to incorporate the objective of deterministic semantic robustness into the training procedure, as existing deterministic semantic verifiers are exceedingly slow. To address these challenges, we propose Certified Semantic Training (CST), the first training framework for deterministic certified robustness against semantic image perturbations. Our framework leverages a novel GPU-optimized verifier that, unlike existing works, is fast enough for use in training. Our results show that networks trained via CST consistently achieve both better provable semantic robustness and clean accuracy, compared to networks trained via baselines based on existing works.  ( 2 min )
    Improving Nonparametric Classification via Local Radial Regression with an Application to Stock Prediction. (arXiv:2112.13951v2 [stat.ML] UPDATED)
    For supervised classification problems, this paper considers estimating the query's label probability through local regression using observed covariates. Well-known nonparametric kernel smoother and $k$-nearest neighbor ($k$-NN) estimator, which take label average over a ball around the query, are consistent but asymptotically biased particularly for a large radius of the ball. To eradicate such bias, local polynomial regression (LPoR) and multiscale $k$-NN (MS-$k$-NN) learn the bias term by local regression around the query and extrapolate it to the query itself. However, their theoretical optimality has been shown for the limit of the infinite number of training samples. For correcting the asymptotic bias with fewer observations, this paper proposes a \emph{local radial regression (LRR)} and its logistic regression variant called \emph{local radial logistic regression~(LRLR)}, by combining the advantages of LPoR and MS-$k$-NN. The idea is quite simple: we fit the local regression to observed labels by taking only the radial distance as the explanatory variable and then extrapolate the estimated label probability to zero distance. The usefulness of the proposed method is shown theoretically and experimentally. We prove the convergence rate of the $L^2$ risk for LRR with reference to MS-$k$-NN, and our numerical experiments, including real-world datasets of daily stock indices, demonstrate that LRLR outperforms LPoR and MS-$k$-NN.  ( 3 min )
    Seeing 3D Objects in a Single Image via Self-Supervised Static-Dynamic Disentanglement. (arXiv:2207.11232v1 [cs.CV])
    Human perception reliably identifies movable and immovable parts of 3D scenes, and completes the 3D structure of objects and background from incomplete observations. We learn this skill not via labeled examples, but simply by observing objects move. In this work, we propose an approach that observes unlabeled multi-view videos at training time and learns to map a single image observation of a complex scene, such as a street with cars, to a 3D neural scene representation that is disentangled into movable and immovable parts while plausibly completing its 3D structure. We separately parameterize movable and immovable scene parts via 2D neural ground plans. These ground plans are 2D grids of features aligned with the ground plane that can be locally decoded into 3D neural radiance fields. Our model is trained self-supervised via neural rendering. We demonstrate that the structure inherent to our disentangled 3D representation enables a variety of downstream tasks in street-scale 3D scenes using simple heuristics, such as extraction of object-centric 3D representations, novel view synthesis, instance segmentation, and 3D bounding box prediction, highlighting its value as a backbone for data-efficient 3D scene understanding models. This disentanglement further enables scene editing via object manipulation such as deletion, insertion, and rigid-body motion.  ( 3 min )
    E2N: Error Estimation Networks for Goal-Oriented Mesh Adaptation. (arXiv:2207.11233v1 [cs.LG])
    Given a partial differential equation (PDE), goal-oriented error estimation allows us to understand how errors in a diagnostic quantity of interest (QoI), or goal, occur and accumulate in a numerical approximation, for example using the finite element method. By decomposing the error estimates into contributions from individual elements, it is possible to formulate adaptation methods, which modify the mesh with the objective of minimising the resulting QoI error. However, the standard error estimate formulation involves the true adjoint solution, which is unknown in practice. As such, it is common practice to approximate it with an 'enriched' approximation (e.g. in a higher order space or on a refined mesh). Doing so generally results in a significant increase in computational cost, which can be a bottleneck compromising the competitiveness of (goal-oriented) adaptive simulations. The central idea of this paper is to develop a "data-driven" goal-oriented mesh adaptation approach through the selective replacement of the expensive error estimation step with an appropriately configured and trained neural network. In doing so, the error estimator may be obtained without even constructing the enriched spaces. An element-by-element construction is employed here, whereby local values of various parameters related to the mesh geometry and underlying problem physics are taken as inputs, and the corresponding contribution to the error estimator is taken as output. We demonstrate that this approach is able to obtain the same accuracy with a reduced computational cost, for adaptive mesh test cases related to flow around tidal turbines, which interact via their downstream wakes, and where the overall power output of the farm is taken as the QoI. Moreover, we demonstrate that the element-by-element approach implies reasonably low training costs.  ( 3 min )
    Learning Unsupervised Hierarchies of Audio Concepts. (arXiv:2207.11231v1 [cs.SD])
    Music signals are difficult to interpret from their low-level features, perhaps even more than images: e.g. highlighting part of a spectrogram or an image is often insufficient to convey high-level ideas that are genuinely relevant to humans. In computer vision, concept learning was therein proposed to adjust explanations to the right abstraction level (e.g. detect clinical concepts from radiographs). These methods have yet to be used for MIR. In this paper, we adapt concept learning to the realm of music, with its particularities. For instance, music concepts are typically non-independent and of mixed nature (e.g. genre, instruments, mood), unlike previous work that assumed disentangled concepts. We propose a method to learn numerous music concepts from audio and then automatically hierarchise them to expose their mutual relationships. We conduct experiments on datasets of playlists from a music streaming service, serving as a few annotated examples for diverse concepts. Evaluations show that the mined hierarchies are aligned with both ground-truth hierarchies of concepts -- when available -- and with proxy sources of concept similarity in the general case.  ( 2 min )
    Face editing with GAN -- A Review. (arXiv:2207.11227v1 [cs.CV])
    In recent years, Generative Adversarial Networks (GANs) have become a hot topic among researchers and engineers that work with deep learning. It has been a ground-breaking technique which can generate new pieces of content of data in a consistent way. The topic of GANs has exploded in popularity due to its applicability in fields like image generation and synthesis, and music production and composition. GANs have two competing neural networks: a generator and a discriminator. The generator is used to produce new samples or pieces of content, while the discriminator is used to recognize whether the piece of content is real or generated. What makes it different from other generative models is its ability to learn unlabeled samples. In this review paper, we will discuss the evolution of GANs, several improvements proposed by the authors and a brief comparison between the different models. Index Terms generative adversarial networks, unsupervised learning, deep learning.  ( 2 min )
    OmniXAI: A Library for Explainable AI. (arXiv:2206.01612v5 [cs.LG] UPDATED)
    We introduce OmniXAI (short for Omni eXplainable AI), an open-source Python library of eXplainable AI (XAI), which offers omni-way explainable AI capabilities and various interpretable machine learning techniques to address the pain points of understanding and interpreting the decisions made by machine learning (ML) in practice. OmniXAI aims to be a one-stop comprehensive library that makes explainable AI easy for data scientists, ML researchers and practitioners who need explanation for various types of data, models and explanation methods at different stages of ML process (data exploration, feature engineering, model development, evaluation, and decision-making, etc). In particular, our library includes a rich family of explanation methods integrated in a unified interface, which supports multiple data types (tabular data, images, texts, time-series), multiple types of ML models (traditional ML in Scikit-learn and deep learning models in PyTorch/TensorFlow), and a range of diverse explanation methods including "model-specific" and "model-agnostic" ones (such as feature-attribution explanation, counterfactual explanation, gradient-based explanation, etc). For practitioners, the library provides an easy-to-use unified interface to generate the explanations for their applications by only writing a few lines of codes, and also a GUI dashboard for visualization of different explanations for more insights about decisions. In this technical report, we present OmniXAI's design principles, system architectures, and major functionalities, and also demonstrate several example use cases across different types of data, tasks, and models.
    NLP From Scratch Without Large-Scale Pretraining: A Simple and Efficient Framework. (arXiv:2111.04130v2 [cs.CL] UPDATED)
    Pretrained language models have become the standard approach for many NLP tasks due to strong performance, but they are very expensive to train. We propose a simple and efficient learning framework, TLM, that does not rely on large-scale pretraining. Given some labeled task data and a large general corpus, TLM uses task data as queries to retrieve a tiny subset of the general corpus and jointly optimizes the task objective and the language modeling objective from scratch. On eight classification datasets in four domains, TLM achieves results better than or similar to pretrained language models (e.g., RoBERTa-Large) while reducing the training FLOPs by two orders of magnitude. With high accuracy and efficiency, we hope TLM will contribute to democratizing NLP and expediting its development.
    Self-attention Presents Low-dimensional Knowledge Graph Embeddings for Link Prediction. (arXiv:2112.10644v2 [cs.LG] UPDATED)
    A few models have tried to tackle the link prediction problem, also known as knowledge graph completion, by embedding knowledge graphs in comparably lower dimensions. However, the state-of-the-art results are attained at the cost of considerably increasing the dimensionality of embeddings which causes scalability issues in the case of huge knowledge bases. Transformers have been successfully used recently as powerful encoders for knowledge graphs, but available models still have scalability issues. To address this limitation, we introduce a Transformer-based model to gain expressive low-dimensional embeddings. We utilize a large number of self-attention heads as the key to applying query-dependent projections to capture mutual information between entities and relations. Empirical results on WN18RR and FB15k-237 as standard link prediction benchmarks demonstrate that our model has favorably comparable performance with the current state-of-the-art models. Notably, we yield our promising results with a significant reduction of 66.9% in the dimensionality of embeddings compared to the five best recent state-of-the-art competitors on average.
    FewGAN: Generating from the Joint Distribution of a Few Images. (arXiv:2207.11226v1 [cs.CV])
    We introduce FewGAN, a generative model for generating novel, high-quality and diverse images whose patch distribution lies in the joint patch distribution of a small number of N>1 training samples. The method is, in essence, a hierarchical patch-GAN that applies quantization at the first coarse scale, in a similar fashion to VQ-GAN, followed by a pyramid of residual fully convolutional GANs at finer scales. Our key idea is to first use quantization to learn a fixed set of patch embeddings for training images. We then use a separate set of side images to model the structure of generated images using an autoregressive model trained on the learned patch embeddings of training images. Using quantization at the coarsest scale allows the model to generate both conditional and unconditional novel images. Subsequently, a patch-GAN renders the fine details, resulting in high-quality images. In an extensive set of experiments, it is shown that FewGAN outperforms baselines both quantitatively and qualitatively.
    Rapid protein assignments and structures from raw NMR spectra with the deep learning technique ARTINA. (arXiv:2201.12041v4 [q-bio.BM] UPDATED)
    Nuclear Magnetic Resonance (NMR) spectroscopy is one of the major techniques in structural biology with over 11,800 protein structures deposited in the Protein Data Bank. NMR can elucidate structures and dynamics of small and medium size proteins in solution, living cells, and solids, but has been limited by the tedious data analysis process. It typically requires weeks or months of manual work of a trained expert to turn NMR measurements into a protein structure. Automation of this process is an open problem, formulated in the field over 30 years ago. Here, we present a solution to this challenge that enables the completely automated analysis of protein NMR data within hours after completing the measurements. Using only NMR spectra and the protein sequence as input, our machine learning-based method, ARTINA, delivers signal positions, resonance assignments, and structures strictly without any human intervention. Tested on a 100-protein benchmark comprising 1329 multidimensional NMR spectra, ARTINA demonstrated its ability to solve structures with 1.44 {\AA} median RMSD to the PDB reference and to identify 91.36% correct NMR resonance assignments. ARTINA can be used by non-experts, reducing the effort for a protein assignment or structure determination by NMR essentially to the preparation of the sample and the spectra measurements.
    Do Artificial Intelligence Systems Understand?. (arXiv:2207.11089v1 [cs.AI])
    Are intelligent machines really intelligent? Is the underlying philosophical concept of intelligence satisfactory for describing how the present systems work? Is understanding a necessary and sufficient condition for intelligence? If a machine could understand, should we attribute subjectivity to it? This paper addresses the problem of deciding whether the so-called "intelligent machines" are capable of understanding, instead of merely processing signs. It deals with the relationship between syntaxis and semantics. The main thesis concerns the inevitability of semantics for any discussion about the possibility of building conscious machines, condensed into the following two tenets: "If a machine is capable of understanding (in the strong sense), then it must be capable of combining rules and intuitions"; "If semantics cannot be reduced to syntaxis, then a machine cannot understand." Our conclusion states that it is not necessary to attribute understanding to a machine in order to explain its exhibited "intelligent" behavior; a merely syntactic and mechanistic approach to intelligence as a task-solving tool suffices to justify the range of operations that it can display in the current state of technological development.
    Fairness-aware Network Revenue Management with Demand Learning. (arXiv:2207.11159v1 [stat.ML])
    In addition to maximizing the total revenue, decision-makers in lots of industries would like to guarantee fair consumption across different resources and avoid saturating certain resources. Motivated by these practical needs, this paper studies the price-based network revenue management problem with both demand learning and fairness concern about the consumption across different resources. We introduce the regularized revenue, i.e., the total revenue with a fairness regularization, as our objective to incorporate fairness into the revenue maximization goal. We propose a primal-dual-type online policy with the Upper-Confidence-Bound (UCB) demand learning method to maximize the regularized revenue. We adopt several innovative techniques to make our algorithm a unified and computationally efficient framework for the continuous price set and a wide class of fairness regularizers. Our algorithm achieves a worst-case regret of $\tilde O(N^{5/2}\sqrt{T})$, where $N$ denotes the number of products and $T$ denotes the number of time periods. Numerical experiments in a few NRM examples demonstrate the effectiveness of our algorithm for balancing revenue and fairness.
    Target-Driven Structured Transformer Planner for Vision-Language Navigation. (arXiv:2207.11201v1 [cs.CV])
    Vision-language navigation is the task of directing an embodied agent to navigate in 3D scenes with natural language instructions. For the agent, inferring the long-term navigation target from visual-linguistic clues is crucial for reliable path planning, which, however, has rarely been studied before in literature. In this article, we propose a Target-Driven Structured Transformer Planner (TD-STP) for long-horizon goal-guided and room layout-aware navigation. Specifically, we devise an Imaginary Scene Tokenization mechanism for explicit estimation of the long-term target (even located in unexplored environments). In addition, we design a Structured Transformer Planner which elegantly incorporates the explored room layout into a neural attention architecture for structured and global planning. Experimental results demonstrate that our TD-STP substantially improves previous best methods' success rate by 2% and 5% on the test set of R2R and REVERIE benchmarks, respectively. Our code is available at https://github.com/YushengZhao/TD-STP .
    SPRT-based Efficient Best Arm Identification in Stochastic Bandits. (arXiv:2207.11158v1 [stat.ML])
    This paper investigates the best arm identification (BAI) problem in stochastic multi-armed bandits in the fixed confidence setting. The general class of the exponential family of bandits is considered. The state-of-the-art algorithms for the exponential family of bandits face computational challenges. To mitigate these challenges, a novel framework is proposed, which views the BAI problem as sequential hypothesis testing, and is amenable to tractable analysis for the exponential family of bandits. Based on this framework, a BAI algorithm is designed that leverages the canonical sequential probability ratio tests. This algorithm has three features for both settings: (1) its sample complexity is asymptotically optimal, (2) it is guaranteed to be $\delta-$PAC, and (3) it addresses the computational challenge of the state-of-the-art approaches. Specifically, these approaches, which are focused only on the Gaussian setting, require Thompson sampling from the arm that is deemed the best and a challenger arm. This paper analytically shows that identifying the challenger is computationally expensive and that the proposed algorithm circumvents it. Finally, numerical experiments are provided to support the analysis.
    Learning Dialogue Representations from Consecutive Utterances. (arXiv:2205.13568v2 [cs.CL] UPDATED)
    Learning high-quality dialogue representations is essential for solving a variety of dialogue-oriented tasks, especially considering that dialogue systems often suffer from data scarcity. In this paper, we introduce Dialogue Sentence Embedding (DSE), a self-supervised contrastive learning method that learns effective dialogue representations suitable for a wide range of dialogue tasks. DSE learns from dialogues by taking consecutive utterances of the same dialogue as positive pairs for contrastive learning. Despite its simplicity, DSE achieves significantly better representation capability than other dialogue representation and universal sentence representation models. We evaluate DSE on five downstream dialogue tasks that examine dialogue representation at different semantic granularities. Experiments in few-shot and zero-shot settings show that DSE outperforms baselines by a large margin. For example, it achieves 13% average performance improvement over the strongest unsupervised baseline in 1-shot intent classification on 6 datasets. We also provide analyses on the benefits and limitations of our model.
    Improved Relation Networks for End-to-End Speaker Verification and Identification. (arXiv:2203.17218v2 [eess.AS] UPDATED)
    Speaker identification systems in a real-world scenario are tasked to identify a speaker amongst a set of enrolled speakers given just a few samples for each enrolled speaker. This paper demonstrates the effectiveness of meta-learning and relation networks for this use case. We propose improved relation networks for speaker verification and few-shot (unseen) speaker identification. The use of relation networks facilitates joint training of the frontend speaker encoder and the backend model. Inspired by the use of prototypical networks in speaker verification and to increase the discriminability of the speaker embeddings, we train the model to classify samples in the current episode amongst all speakers present in the training set. Furthermore, we propose a new training regime for faster model convergence by extracting more information from a given meta-learning episode with negligible extra computation. We evaluate the proposed techniques on VoxCeleb, SITW and VCTK datasets on the tasks of speaker verification and unseen speaker identification. The proposed approach outperforms the existing approaches consistently on both tasks.
    Controlled Generation of Unseen Faults for Partial and Open-Partial Domain Adaptation. (arXiv:2204.14068v2 [cs.LG] UPDATED)
    New operating conditions can result in a significant performance drop of fault diagnostics models due to the domain shift between the training and the testing data distributions. While several domain adaptation approaches have been proposed to overcome such domain shifts, their application is limited if the fault classes represented in the two domains are not the same. To enable a better transferability of the trained models between two different domains, particularly in setups where only the healthy data class is shared between the two domains, we propose a new framework for Partial and Open-Partial domain adaptation based on generating distinct fault signatures with a Wasserstein GAN. The main contribution of the proposed framework is the controlled synthetic fault data generation with two main distinct characteristics. Firstly, the proposed methodology enables to generate unobserved fault types in the target domain by having only access to the healthy samples in the target domain and faulty samples in the source domain. Secondly, the fault generation can be controlled to precisely generate distinct fault types and fault severity levels. The proposed method is especially suited in extreme domain adaption settings that are particularly relevant in the context of complex and safety-critical systems, where only one class is shared between the two domains. We evaluate the proposed framework on Partial as well as Open-Partial domain adaptation tasks on two bearing fault diagnostics case studies. Our experiments conducted in different label space settings showcase the versatility of the proposed framework. The proposed methodology provided superior results compared to other methods given large domain gaps.
    Tight bounds on the hardness of learning simple nonparametric mixtures. (arXiv:2203.15150v2 [cs.LG] UPDATED)
    We study the problem of learning nonparametric distributions in a finite mixture, and establish tight bounds on the sample complexity for learning the component distributions in such models. Namely, we are given i.i.d. samples from a pdf $f$ where $$ f=\sum_{i=1}^k w_i f_i, \quad\sum_{i=1}^k w_i=1, \quad w_i>0 $$ and we are interested in learning each component $f_i$. Without any assumptions on $f_i$, this problem is ill-posed. In order to identify the components $f_i$, we assume that each $f_i$ can be written as a convolution of a Gaussian and a compactly supported density $\nu_i$ with $\text{supp}(\nu_i)\cap \text{supp}(\nu_j)=\emptyset$. Our main result shows that $(\frac{1}{\varepsilon})^{\Omega(\log\log \frac{1}{\varepsilon})}$ samples are required for estimating each $f_i$. Unlike parametric mixtures, the difficulty does not arise from the order $k$ or small weights $w_i$, and unlike nonparametric density estimation it does not arise from the curse of dimensionality, irregularity, or inhomogeneity. The proof relies on a fast rate for approximation with Gaussians, which may be of independent interest. To show this is tight, we also propose an algorithm that uses $(\frac{1}{\varepsilon})^{O(\log\log \frac{1}{\varepsilon})}$ samples to estimate each $f_i$. Unlike existing approaches to learning latent variable models based on moment-matching and tensor methods, our proof instead involves a delicate analysis of an ill-conditioned linear system via orthogonal functions. Combining these bounds, we conclude that the optimal sample complexity of this problem properly lies in between polynomial and exponential, which is not common in learning theory.
    Efficient Automated Deep Learning for Time Series Forecasting. (arXiv:2205.05511v3 [cs.LG] UPDATED)
    Recent years have witnessed tremendously improved efficiency of Automated Machine Learning (AutoML), especially Automated Deep Learning (AutoDL) systems, but recent work focuses on tabular, image, or NLP tasks. So far, little attention has been paid to general AutoDL frameworks for time series forecasting, despite the enormous success in applying different novel architectures to such tasks. In this paper, we propose an efficient approach for the joint optimization of neural architecture and hyperparameters of the entire data processing pipeline for time series forecasting. In contrast to common NAS search spaces, we designed a novel neural architecture search space covering various state-of-the-art architectures, allowing for an efficient macro-search over different DL approaches. To efficiently search in such a large configuration space, we use Bayesian optimization with multi-fidelity optimization. We empirically study several different budget types enabling efficient multi-fidelity optimization on different forecasting datasets. Furthermore, we compared our resulting system, dubbed \system, against several established baselines and show that it significantly outperforms all of them across several datasets.
    STOPS: Short-Term-based Volatility-controlled Policy Search and its Global Convergence. (arXiv:2201.09857v5 [cs.LG] UPDATED)
    It remains challenging to deploy existing risk-averse approaches to real-world applications. The reasons are multi-fold, including the lack of global optimality guarantee and the necessity of learning from long-term consecutive trajectories. Long-term consecutive trajectories are prone to involving visiting hazardous states, which is a major concern in the risk-averse setting. This paper proposes Short-Term VOlatility-controlled Policy Search (STOPS), a novel algorithm that solves risk-averse problems by learning from short-term trajectories instead of long-term trajectories. Short-term trajectories are more flexible to generate, and can avoid the danger of hazardous state visitations. By using an actor-critic scheme with an overparameterized two-layer neural network, our algorithm finds a globally optimal policy at a sublinear rate with proximal policy optimization and natural policy gradient, with effectiveness comparable to the state-of-the-art convergence rate of risk-neutral policy-search methods. The algorithm is evaluated on challenging Mujoco robot simulation tasks under the mean-variance evaluation metric. Both theoretical analysis and experimental results demonstrate a state-of-the-art level of STOPS' performance among existing risk-averse policy search methods.
    Post-training Quantization for Neural Networks with Provable Guarantees. (arXiv:2201.11113v2 [cs.LG] UPDATED)
    While neural networks have been remarkably successful in a wide array of applications, implementing them in resource-constrained hardware remains an area of intense research. By replacing the weights of a neural network with quantized (e.g., 4-bit, or binary) counterparts, massive savings in computation cost, memory, and power consumption are attained. To that end, we generalize a post-training neural-network quantization method, GPFQ, that is based on a greedy path-following mechanism. Among other things, we propose modifications to promote sparsity of the weights, and rigorously analyze the associated error. Additionally, our error analysis expands the results of previous work on GPFQ to handle general quantization alphabets, showing that for quantizing a single-layer network, the relative square error essentially decays linearly in the number of weights -- i.e., level of over-parametrization. Our result holds across a range of input distributions and for both fully-connected and convolutional architectures thereby also extending previous results. To empirically evaluate the method, we quantize several common architectures with few bits per weight, and test them on ImageNet, showing only minor loss of accuracy compared to unquantized models. We also demonstrate that standard modifications, such as bias correction and mixed precision quantization, further improve accuracy.
    Differential Geometry for Neural Implicit Models. (arXiv:2201.09263v3 [cs.GR] UPDATED)
    We introduce a neural implicit framework that exploits the differentiable properties of neural networks and the discrete geometry of point-sampled surfaces to approximate them as the level sets of neural implicit functions. To train a neural implicit function, we propose a loss functional that approximates a signed distance function, and allows terms with high-order derivatives, such as the alignment between the principal directions of curvature, to learn more geometric details. During training, we consider a non-uniform sampling strategy based on the curvatures of the point-sampled surface to prioritize points with more geometric details. This sampling implies faster learning while preserving geometric accuracy when compared with previous approaches. We also present the analytical differential geometry formulas for neural surfaces, such as normal vectors and curvatures.
    Improved $\alpha$-GAN architecture for generating 3D connected volumes with an application to radiosurgery treatment planning. (arXiv:2207.11223v1 [eess.IV])
    Generative Adversarial Networks (GANs) have gained significant attention in several computer vision tasks for generating high-quality synthetic data. Various medical applications including diagnostic imaging and radiation therapy can benefit greatly from synthetic data generation due to data scarcity in the domain. However, medical image data is typically kept in 3D space, and generative models suffer from the curse of dimensionality issues in generating such synthetic data. In this paper, we investigate the potential of GANs for generating connected 3D volumes. We propose an improved version of 3D $\alpha$-GAN by incorporating various architectural enhancements. On a synthetic dataset of connected 3D spheres and ellipsoids, our model can generate fully connected 3D shapes with similar geometrical characteristics to that of training data. We also show that our 3D GAN model can successfully generate high-quality 3D tumor volumes and associated treatment specifications (e.g., isocenter locations). Similar moment invariants to the training data as well as fully connected 3D shapes confirm that improved 3D $\alpha$-GAN implicitly learns the training data distribution, and generates realistic-looking samples. The capability of improved 3D $\alpha$-GAN makes it a valuable source for generating synthetic medical image data that can help future research in this domain.  ( 3 min )
    Formulating Event-based Image Reconstruction as a Linear Inverse Problem using Optical Flow. (arXiv:2112.06242v2 [cs.CV] UPDATED)
    Event cameras are novel bio-inspired sensors that measure per-pixel brightness differences asynchronously. Recovering brightness from events is appealing since the reconstructed images inherit the high dynamic range (HDR) and high-speed properties of events; hence they can be used in many robotic vision applications and to generate slow-motion HDR videos. However, state-of-the-art methods tackle this problem by training an event-to-image recurrent neural network (RNN), which lacks explainability and is difficult to tune. In this work we show, for the first time, how tackling the joint problem of motion and brightness estimation leads us to formulate event-based image reconstruction as a linear inverse problem that can be solved without training an image reconstruction RNN. Instead, classical and learning-based image priors can be used to solve the problem and remove artifacts from the reconstructed images. The experiments show that the proposed approach generates images with visual quality on par with state-of-the-art methods despite only using data from a short time interval. The proposed linear formulation and solvers have a unifying character because they can be applied also to reconstruct brightness from the second derivative. Additionally, the linear formulation is attractive because it can be naturally combined with super-resolution, motion-segmentation and color demosaicing.  ( 3 min )
    The Shape Part Slot Machine: Contact-based Reasoning for Generating 3D Shapes from Parts. (arXiv:2112.00584v2 [cs.GR] UPDATED)
    We present the Shape Part Slot Machine, a new method for assembling novel 3D shapes from existing parts by performing contact-based reasoning. Our method represents each shape as a graph of ``slots,'' where each slot is a region of contact between two shape parts. Based on this representation, we design a graph-neural-network-based model for generating new slot graphs and retrieving compatible parts, as well as a gradient-descent-based optimization scheme for assembling the retrieved parts into a complete shape that respects the generated slot graph. This approach does not require any semantic part labels; interestingly, it also does not require complete part geometries -- reasoning about the slots proves sufficient to generate novel, high-quality 3D shapes. We demonstrate that our method generates shapes that outperform existing modeling-by-assembly approaches regarding quality, diversity, and structural complexity.  ( 2 min )
    Flat Latent Manifolds for Human-machine Co-creation of Music. (arXiv:2202.12243v2 [cs.SD] UPDATED)
    The use of machine learning in artistic music generation leads to controversial discussions of the quality of art, for which objective quantification is nonsensical. We therefore consider a music-generating algorithm as a counterpart to a human musician, in a setting where reciprocal interplay is to lead to new experiences, both for the musician and the audience. To obtain this behaviour, we resort to the framework of recurrent Variational Auto-Encoders (VAE) and learn to generate music, seeded by a human musician. In the learned model, we generate novel musical sequences by interpolation in latent space. Standard VAEs however do not guarantee any form of smoothness in their latent representation. This translates into abrupt changes in the generated music sequences. To overcome these limitations, we regularise the decoder and endow the latent space with a flat Riemannian manifold, i.e., a manifold that is isometric to the Euclidean space. As a result, linearly interpolating in the latent space yields realistic and smooth musical changes that fit the type of machine--musician interactions we aim for. We provide empirical evidence for our method via a set of experiments on music datasets and we deploy our model for an interactive jam session with a professional drummer. The live performance provides qualitative evidence that the latent representation can be intuitively interpreted and exploited by the drummer to drive the interplay. Beyond the musical application, our approach showcases an instance of human-centred design of machine-learning models, driven by interpretability and the interaction with the end user.  ( 3 min )
    Prompt Tuning GPT-2 language model for parameter-efficient domain adaptation of ASR systems. (arXiv:2112.08718v3 [cs.CL] UPDATED)
    Automatic Speech Recognition (ASR) systems have found their use in numerous industrial applications in very diverse domains creating a need to adapt to new domains with small memory and deployment overhead. In this work, we introduce domain-prompts, a methodology that involves training a small number of domain embedding parameters to prime a Transformer-based Language Model (LM) to a particular domain. Using this domain-adapted LM for rescoring ASR hypotheses can achieve 7-13% WER reduction for a new domain with just 1000 unlabeled textual domain-specific sentences. This improvement is comparable or even better than fully fine-tuned models even though just 0.02% of the parameters of the base LM are updated. Additionally, our method is deployment-friendly as the learnt domain embeddings are prefixed to the input to the model rather than changing the base model architecture. Therefore, our method is an ideal choice for on-the-fly adaptation of LMs used in ASR systems to progressively scale it to new domains.  ( 2 min )
    Stronger Generalization Guarantees for Robot Learning by Combining Generative Models and Real-World Data. (arXiv:2111.08761v2 [cs.RO] UPDATED)
    We are motivated by the problem of learning policies for robotic systems with rich sensory inputs (e.g., vision) in a manner that allows us to guarantee generalization to environments unseen during training. We provide a framework for providing such generalization guarantees by leveraging a finite dataset of real-world environments in combination with a (potentially inaccurate) generative model of environments. The key idea behind our approach is to utilize the generative model in order to implicitly specify a prior over policies. This prior is updated using the real-world dataset of environments by minimizing an upper bound on the expected cost across novel environments derived via Probably Approximately Correct (PAC)-Bayes generalization theory. We demonstrate our approach on two simulated systems with nonlinear/hybrid dynamics and rich sensing modalities: (i) quadrotor navigation with an onboard vision sensor, and (ii) grasping objects using a depth sensor. Comparisons with prior work demonstrate the ability of our approach to obtain stronger generalization guarantees by utilizing generative models. We also present hardware experiments for validating our bounds for the grasping task.  ( 3 min )
    X-Risk Analysis for AI Research. (arXiv:2206.05862v6 [cs.CY] UPDATED)
    Artificial intelligence (AI) has the potential to greatly improve society, but as with any powerful technology, it comes with heightened risks and responsibilities. Current AI research lacks a systematic discussion of how to manage long-tail risks from AI systems, including speculative long-term risks. Keeping in mind the potential benefits of AI, there is some concern that building ever more intelligent and powerful AI systems could eventually result in systems that are more powerful than us; some say this is like playing with fire and speculate that this could create existential risks (x-risks). To add precision and ground these discussions, we provide a guide for how to analyze AI x-risk, which consists of three parts: First, we review how systems can be made safer today, drawing on time-tested concepts from hazard analysis and systems safety that have been designed to steer large processes in safer directions. Next, we discuss strategies for having long-term impacts on the safety of future systems. Finally, we discuss a crucial concept in making AI systems safer by improving the balance between safety and general capabilities. We hope this document and the presented concepts and tools serve as a useful guide for understanding how to analyze AI x-risk.  ( 3 min )
    Human Treelike Tubular Structure Segmentation: A Comprehensive Review and Future Perspectives. (arXiv:2207.11203v1 [eess.IV])
    Various structures in human physiology follow a treelike morphology, which often expresses complexity at very fine scales. Examples of such structures are intrathoracic airways, retinal blood vessels, and hepatic blood vessels. Large collections of 2D and 3D images have been made available by medical imaging modalities such as magnetic resonance imaging (MRI), computed tomography (CT), Optical coherence tomography (OCT) and ultrasound in which the spatial arrangement can be observed. Segmentation of these structures in medical imaging is of great importance since the analysis of the structure provides insights into disease diagnosis, treatment planning, and prognosis. Manually labelling extensive data by radiologists is often time-consuming and error-prone. As a result, automated or semi-automated computational models have become a popular research field of medical imaging in the past two decades, and many have been developed to date. In this survey, we aim to provide a comprehensive review of currently publicly available datasets, segmentation algorithms, and evaluation metrics. In addition, current challenges and future research directions are discussed.  ( 2 min )
    Machine learning approach in the development of building occupant personas. (arXiv:2207.11239v1 [cs.LG])
    The user persona is a communication tool for designers to generate a mental model that describes the archetype of users. Developing building occupant personas is proven to be an effective method for human-centered smart building design, which considers occupant comfort, behavior, and energy consumption. Optimization of building energy consumption also requires a deep understanding of occupants' preferences and behaviors. The current approaches to developing building occupant personas face a major obstruction of manual data processing and analysis. In this study, we propose and evaluate a machine learning-based semi-automated approach to generate building occupant personas. We investigate the 2015 Residential Energy Consumption Dataset with five machine learning techniques - Linear Discriminant Analysis, K Nearest Neighbors, Decision Tree (Random Forest), Support Vector Machine, and AdaBoost classifier - for the prediction of 16 occupant characteristics, such as age, education, and, thermal comfort. The models achieve an average accuracy of 61% and accuracy over 90% for attributes including the number of occupants in the household, their age group, and preferred usage of heating or cooling equipment. The results of the study show the feasibility of using machine learning techniques for the development of building occupant persona to minimize human effort.  ( 2 min )
    NASA: Neural Articulated Shape Approximation. (arXiv:1912.03207v5 [cs.CV] UPDATED)
    Efficient representation of articulated objects such as human bodies is an important problem in computer vision and graphics. To efficiently simulate deformation, existing approaches represent 3D objects using polygonal meshes and deform them using skinning techniques. This paper introduces neural articulated shape approximation (NASA), an alternative framework that enables efficient representation of articulated deformable objects using neural indicator functions that are conditioned on pose. Occupancy testing using NASA is straightforward, circumventing the complexity of meshes and the issue of water-tightness. We demonstrate the effectiveness of NASA for 3D tracking applications, and discuss other potential extensions.  ( 2 min )
    Concept Identification for Complex Engineering Datasets. (arXiv:2206.06155v2 [cs.LG] UPDATED)
    Finding meaningful concepts in engineering application datasets which allow for a sensible grouping of designs is very helpful in many contexts. It allows for determining different groups of designs with similar properties and provides useful knowledge in the engineering decision making process. Also, it opens the route for further refinements of specific design candidates which exhibit certain characteristic features. In this work, an approach to define meaningful and consistent concepts in an existing engineering dataset is presented. The designs in the dataset are characterized by a multitude of features such as design parameters, geometrical properties or performance values of the design for various boundary conditions. In the proposed approach the complete feature set is partitioned into several subsets called description spaces. The definition of the concepts respects this partitioning which leads to several desired properties of the identified concepts. This cannot be achieved with state-of-the-art clustering or concept identification approaches. A novel concept quality measure is proposed, which provides an objective value for a given definition of concepts in a dataset. The usefulness of the measure is demonstrated by considering a realistic engineering dataset consisting of about 2500 airfoil profiles, for which the performance values (lift and drag) for three different operating conditions were obtained by a computational fluid dynamics simulation. A numerical optimization procedure is employed, which maximizes the concept quality measure and finds meaningful concepts for different setups of the description spaces, while also incorporating user preference. It is demonstrated how these concepts can be used to select archetypal representatives of the dataset which exhibit characteristic features of each concept.  ( 3 min )
    Deep Portrait Delighting. (arXiv:2203.12088v5 [cs.CV] UPDATED)
    We present a deep neural network for removing undesirable shading features from an unconstrained portrait image, recovering the underlying texture. Our training scheme incorporates three regularization strategies: masked loss, to emphasize high-frequency shading features; soft-shadow loss, which improves sensitivity to subtle changes in lighting; and shading-offset estimation, to supervise separation of shading and texture. Our method demonstrates improved delighting quality and generalization when compared with the state-of-the-art. We further demonstrate how our delighting method can enhance the performance of light-sensitive computer vision tasks such as face relighting and semantic parsing, allowing them to handle extreme lighting conditions.  ( 2 min )
    Relaxing the I.I.D. Assumption: Adaptively Minimax Optimal Regret via Root-Entropic Regularization. (arXiv:2007.06552v3 [stat.ML] UPDATED)
    We consider prediction with expert advice when data are generated from distributions varying arbitrarily within an unknown constraint set. This semi-adversarial setting includes (at the extremes) the classical i.i.d. setting, when the unknown constraint set is restricted to be a singleton, and the unconstrained adversarial setting, when the constraint set is the set of all distributions. The Hedge algorithm -- long known to be minimax (rate) optimal in the adversarial regime -- was recently shown to be simultaneously minimax optimal for i.i.d. data. In this work, we propose to relax the i.i.d. assumption by seeking adaptivity at all levels of a natural ordering on constraint sets. We provide matching upper and lower bounds on the minimax regret at all levels, show that Hedge with deterministic learning rates is suboptimal outside of the extremes, and prove that one can adaptively obtain minimax regret at all levels. We achieve this optimal adaptivity using the follow-the-regularized-leader (FTRL) framework, with a novel adaptive regularization scheme that implicitly scales as the square root of the entropy of the current predictive distribution, rather than the entropy of the initial predictive distribution. Finally, we provide novel technical tools to study the statistical performance of FTRL along the semi-adversarial spectrum.  ( 3 min )
    Optimal Model Averaging of Support Vector Machines in Diverging Model Spaces. (arXiv:2112.12961v3 [stat.ML] UPDATED)
    Support vector machine (SVM) is a powerful classification method that has achieved great success in many fields. Since its performance can be seriously impaired by redundant covariates, model selection techniques are widely used for SVM with high dimensional covariates. As an alternative to model selection, significant progress has been made in the area of model averaging in the past decades. Yet no frequentist model averaging method was considered for SVM. This work aims to fill the gap and to propose a frequentist model averaging procedure for SVM which selects the optimal weight by cross validation. Even when the number of covariates diverges at an exponential rate of the sample size, we show asymptotic optimality of the proposed method in the sense that the ratio of its hinge loss to the lowest possible loss converges to one. We also derive the convergence rate which provides more insights to model averaging. Compared to model selection methods of SVM which require a tedious but critical task of tuning parameter selection, the model averaging method avoids the task and shows promising performances in the empirical studies.  ( 3 min )
    Learning for MPC with Stability & Safety Guarantees. (arXiv:2012.07369v2 [cs.LG] UPDATED)
    The combination of learning methods with Model Predictive Control (MPC) has attracted a significant amount of attention in the recent literature. The hope of this combination is to reduce the reliance of MPC schemes on accurate models, and to tap into the fast developing machine learning and reinforcement learning tools to exploit the growing amount of data available for many systems. In particular, the combination of reinforcement learning and MPC has been proposed as a viable and theoretically justified approach to introduce explainable, safe and stable policies in reinforcement learning. However, a formal theory detailing how the safety and stability of an MPC-based policy can be maintained through the parameter updates delivered by the learning tools is still lacking. This paper addresses this gap. The theory is developed for the generic Robust MPC case, and applied in simulation in the robust tube-based linear MPC case, where the theory is fairly easy to deploy in practice. The paper focuses on Reinforcement Learning as a learning tool, but it applies to any learning method that updates the MPC parameters online.  ( 2 min )
    Composing Neural Learning and Symbolic Reasoning with an Application to Visual Discrimination. (arXiv:1907.05878v2 [cs.LG] UPDATED)
    We consider the problem of combining machine learning models to perform higher-level cognitive tasks with clear specifications. We propose the novel problem of Visual Discrimination Puzzles (VDP) that requires finding interpretable discriminators that classify images according to a logical specification. Humans can solve these puzzles with ease and they give robust, verifiable, and interpretable discriminators as answers. We propose a compositional neurosymbolic framework that combines a neural network to detect objects and relationships with a symbolic learner that finds interpretable discriminators. We create large classes of VDP datasets involving natural and artificial images and show that our neurosymbolic framework performs favorably compared to several purely neural approaches.  ( 2 min )
    Domain Generalization for Activity Recognition via Adaptive Feature Fusion. (arXiv:2207.11221v1 [cs.CV])
    Human activity recognition requires the efforts to build a generalizable model using the training datasets with the hope to achieve good performance in test datasets. However, in real applications, the training and testing datasets may have totally different distributions due to various reasons such as different body shapes, acting styles, and habits, damaging the model's generalization performance. While such a distribution gap can be reduced by existing domain adaptation approaches, they typically assume that the test data can be accessed in the training stage, which is not realistic. In this paper, we consider a more practical and challenging scenario: domain-generalized activity recognition (DGAR) where the test dataset \emph{cannot} be accessed during training. To this end, we propose \emph{Adaptive Feature Fusion for Activity Recognition~(AFFAR)}, a domain generalization approach that learns to fuse the domain-invariant and domain-specific representations to improve the model's generalization performance. AFFAR takes the best of both worlds where domain-invariant representations enhance the transferability across domains and domain-specific representations leverage the model discrimination power from each domain. Extensive experiments on three public HAR datasets show its effectiveness. Furthermore, we apply AFFAR to a real application, i.e., the diagnosis of Children's Attention Deficit Hyperactivity Disorder~(ADHD), which also demonstrates the superiority of our approach.  ( 3 min )
    Doubly-Valid/Doubly-Sharp Sensitivity Analysis for Causal Inference with Unmeasured Confounding. (arXiv:2112.11449v2 [stat.ME] UPDATED)
    We consider the problem of constructing bounds on the average treatment effect (ATE) when unmeasured confounders exist but have bounded influence. Specifically, we assume that omitted confounders could not change the odds of treatment for any unit by more than a fixed factor. We derive the sharp partial identification bounds implied by this assumption by leveraging distributionally robust optimization, and we propose estimators of these bounds with several novel robustness properties. The first is double sharpness: our estimators consistently estimate the sharp ATE bounds when one of two nuisance parameters is misspecified and achieve semiparametric efficiency when all nuisance parameters are suitably consistent. The second is double validity: even when most nuisance parameters are misspecified, our estimators still provide valid but possibly conservative bounds for the ATE and our Wald confidence intervals remain valid even when our estimators are not asymptotically normal. As a result, our estimators provide a highly credible method for sensitivity analysis of causal inferences.  ( 2 min )
    Statistical and Computational Trade-offs in Variational Inference: A Case Study in Inferential Model Selection. (arXiv:2207.11208v1 [stat.ML])
    Variational inference has recently emerged as a popular alternative to the classical Markov chain Monte Carlo (MCMC) in large-scale Bayesian inference. The core idea of variational inference is to trade statistical accuracy for computational efficiency. It aims to approximate the posterior, reducing computation costs but potentially compromising its statistical accuracy. In this work, we study this statistical and computational trade-off in variational inference via a case study in inferential model selection. Focusing on Gaussian inferential models (a.k.a. variational approximating families) with diagonal plus low-rank precision matrices, we initiate a theoretical study of the trade-offs in two aspects, Bayesian posterior inference error and frequentist uncertainty quantification error. From the Bayesian posterior inference perspective, we characterize the error of the variational posterior relative to the exact posterior. We prove that, given a fixed computation budget, a lower-rank inferential model produces variational posteriors with a higher statistical approximation error, but a lower computational error; it reduces variances in stochastic optimization and, in turn, accelerates convergence. From the frequentist uncertainty quantification perspective, we consider the precision matrix of the variational posterior as an uncertainty estimate. We find that, relative to the true asymptotic precision, the variational approximation suffers from an additional statistical error originating from the sampling uncertainty of the data. Moreover, this statistical error becomes the dominant factor as the computation budget increases. As a consequence, for small datasets, the inferential model need not be full-rank to achieve optimal estimation error. We finally demonstrate these statistical and computational trade-offs inference across empirical studies, corroborating the theoretical findings.
    TaDaa: real time Ticket Assignment Deep learning Auto Advisor for customer support, help desk, and issue ticketing systems. (arXiv:2207.11187v1 [cs.IR])
    This paper proposes TaDaa: Ticket Assignment Deep learning Auto Advisor, which leverages the latest Transformers models and machine learning techniques quickly assign issues within an organization, like customer support, help desk and alike issue ticketing systems. The project provides functionality to 1) assign an issue to the correct group, 2) assign an issue to the best resolver, and 3) provide the most relevant previously solved tickets to resolvers. We leverage one ticketing system sample dataset, with over 3k+ groups and over 10k+ resolvers to obtain a 95.2% top 3 accuracy on group suggestions and a 79.0% top 5 accuracy on resolver suggestions. We hope this research will greatly improve average issue resolution time on customer support, help desk, and issue ticketing systems.
    Improved lightweight identification of agricultural diseases based on MobileNetV3. (arXiv:2207.11238v1 [cs.CV])
    At present, the identification of agricultural pests and diseases has the problem that the model is not lightweight enough and difficult to apply. Based on MobileNetV3, this paper introduces the Coordinate Attention block. The parameters of MobileNetV3-large are reduced by 22%, the model size is reduced by 19.7%, and the accuracy is improved by 0.92%. The parameters of MobileNetV3-small are reduced by 23.4%, the model size is reduced by 18.3%, and the accuracy is increased by 0.40%. In addition, the improved MobileNetV3-small was migrated to Jetson Nano for testing. The accuracy increased by 2.48% to 98.31%, and the inference speed increased by 7.5%. It provides a reference for deploying the agricultural pest identification model to embedded devices.
    Discrete Key-Value Bottleneck. (arXiv:2207.11240v1 [cs.LG])
    Deep neural networks perform well on prediction and classification tasks in the canonical setting where data streams are i.i.d., labeled data is abundant, and class labels are balanced. Challenges emerge with distribution shifts, including non-stationary or imbalanced data streams. One powerful approach that has addressed this challenge involves self-supervised pretraining of large encoders on volumes of unlabeled data, followed by task-specific tuning. Given a new task, updating the weights of these encoders is challenging as a large number of weights needs to be fine-tuned, and as a result, they forget information about the previous tasks. In the present work, we propose a model architecture to address this issue, building upon a discrete bottleneck containing pairs of separate and learnable (key, value) codes. In this setup, we follow the encode; process the representation via a discrete bottleneck; and decode paradigm, where the input is fed to the pretrained encoder, the output of the encoder is used to select the nearest keys, and the corresponding values are fed to the decoder to solve the current task. The model can only fetch and re-use a limited number of these (key, value) pairs during inference, enabling localized and context-dependent model updates. We theoretically investigate the ability of the proposed model to minimize the effect of the distribution shifts and show that such a discrete bottleneck with (key, value) pairs reduces the complexity of the hypothesis class. We empirically verified the proposed methods' benefits under challenging distribution shift scenarios across various benchmark datasets and show that the proposed model reduces the common vulnerability to non-i.i.d. and non-stationary training distributions compared to various other baselines.
    Progressive Deblurring of Diffusion Models for Coarse-to-Fine Image Synthesis. (arXiv:2207.11192v1 [cs.CV])
    Recently, diffusion models have shown remarkable results in image synthesis by gradually removing noise and amplifying signals. Although the simple generative process surprisingly works well, is this the best way to generate image data? For instance, despite the fact that human perception is more sensitive to the low frequencies of an image, diffusion models themselves do not consider any relative importance of each frequency component. Therefore, to incorporate the inductive bias for image data, we propose a novel generative process that synthesizes images in a coarse-to-fine manner. First, we generalize the standard diffusion models by enabling diffusion in a rotated coordinate system with different velocities for each component of the vector. We further propose a blur diffusion as a special case, where each frequency component of an image is diffused at different speeds. Specifically, the proposed blur diffusion consists of a forward process that blurs an image and adds noise gradually, after which a corresponding reverse process deblurs an image and removes noise progressively. Experiments show that the proposed model outperforms the previous method in FID on LSUN bedroom and church datasets. Code is available at https://github.com/sangyun884/blur-diffusion.
    Quantized Sparse Weight Decomposition for Neural Network Compression. (arXiv:2207.11048v1 [cs.LG])
    In this paper, we introduce a novel method of neural network weight compression. In our method, we store weight tensors as sparse, quantized matrix factors, whose product is computed on the fly during inference to generate the target model's weights. We use projected gradient descent methods to find quantized and sparse factorization of the weight tensors. We show that this approach can be seen as a unification of weight SVD, vector quantization, and sparse PCA. Combined with end-to-end fine-tuning our method exceeds or is on par with previous state-of-the-art methods in terms of the trade-off between accuracy and model size. Our method is applicable to both moderate compression regimes, unlike vector quantization, and extreme compression regimes.
    Optimism in Face of a Context: Regret Guarantees for Stochastic Contextual MDP. (arXiv:2207.11126v1 [cs.LG])
    We present regret minimization algorithms for stochastic contextual MDPs under minimum reachability assumption, using an access to an offline least square regression oracle. We analyze three different settings: where the dynamics is known, where the dynamics is unknown but independent of the context and the most challenging setting where the dynamics is unknown and context-dependent. For the latter, our algorithm obtains $ \tilde{O}\left( \max\{H,{1}/{p_{min}}\}H|S|^{3/2}\sqrt{|A|T\log(\max\{|\mathcal{F}|,|\mathcal{P}|\}/\delta)} \right)$ regret bound, with probability $1-\delta$, where $\mathcal{P}$ and $\mathcal{F}$ are finite and realizable function classes used to approximate the dynamics and rewards respectively, $p_{min}$ is the minimum reachability parameter, $S$ is the set of states, $A$ the set of actions, $H$ the horizon, and $T$ the number of episodes. To our knowledge, our approach is the first optimistic approach applied to contextual MDPs with general function approximation (i.e., without additional knowledge regarding the function class, such as it being linear and etc.). In addition, we present a lower bound of $\Omega(\sqrt{T H |S| |A| \ln(|\mathcal{F}|/|S|)/\ln(|A|)})$, on the expected regret which holds even in the case of known dynamics.
    MobileDenseNet: A new approach to object detection on mobile devices. (arXiv:2207.11031v1 [cs.CV])
    Object detection problem solving has developed greatly within the past few years. There is a need for lighter models in instances where hardware limitations exist, as well as a demand for models to be tailored to mobile devices. In this article, we will assess the methods used when creating algorithms that address these issues. The main goal of this article is to increase accuracy in state-of-the-art algorithms while maintaining speed and real-time efficiency. The most significant issues in one-stage object detection pertains to small objects and inaccurate localization. As a solution, we created a new network by the name of MobileDenseNet suitable for embedded systems. We also developed a light neck FCPNLite for mobile devices that will aid with the detection of small objects. Our research revealed that very few papers cited necks in embedded systems. What differentiates our network from others is our use of concatenation features. A small yet significant change to the head of the network amplified accuracy without increasing speed or limiting parameters. In short, our focus on the challenging CoCo and Pascal VOC datasets were 24.8 and 76.8 in percentage terms respectively - a rate higher than that recorded by other state-of-the-art systems thus far. Our network is able to increase accuracy while maintaining real-time efficiency on mobile devices. We calculated operational speed on Pixel 3 (Snapdragon 845) to 22.8 fps. The source code of this research is available on https://github.com/hajizadeh/MobileDenseNet.
    Decentralized scheduling through an adaptive, trading-based multi-agent system. (arXiv:2207.11172v1 [cs.AI])
    In multi-agent reinforcement learning systems, the actions of one agent can have a negative impact on the rewards of other agents. One way to combat this problem is to let agents trade their rewards amongst each other. Motivated by this, this work applies a trading approach to a simulated scheduling environment, where the agents are responsible for the assignment of incoming jobs to compute cores. In this environment, reinforcement learning agents learn to trade successfully. The agents can trade the usage right of computational cores to process high-priority, high-reward jobs faster than low-priority, low-reward jobs. However, due to combinatorial effects, the action and observation spaces of a simple reinforcement learning agent in this environment scale exponentially with key parameters of the problem size. However, the exponential scaling behavior can be transformed into a linear one if the agent is split into several independent sub-units. We further improve this distributed architecture using agent-internal parameter sharing. Moreover, it can be extended to set the exchange prices autonomously. We show that in our scheduling environment, the advantages of a distributed agent architecture clearly outweigh more aggregated approaches. We demonstrate that the distributed agent architecture becomes even more performant using agent-internal parameter sharing. Finally, we investigate how two different reward functions affect autonomous pricing and the corresponding scheduling.
    Learn Continuously, Act Discretely: Hybrid Action-Space Reinforcement Learning For Optimal Execution. (arXiv:2207.11152v1 [q-fin.TR])
    Optimal execution is a sequential decision-making problem for cost-saving in algorithmic trading. Studies have found that reinforcement learning (RL) can help decide the order-splitting sizes. However, a problem remains unsolved: how to place limit orders at appropriate limit prices? The key challenge lies in the "continuous-discrete duality" of the action space. On the one hand, the continuous action space using percentage changes in prices is preferred for generalization. On the other hand, the trader eventually needs to choose limit prices discretely due to the existence of the tick size, which requires specialization for every single stock with different characteristics (e.g., the liquidity and the price range). So we need continuous control for generalization and discrete control for specialization. To this end, we propose a hybrid RL method to combine the advantages of both of them. We first use a continuous control agent to scope an action subset, then deploy a fine-grained agent to choose a specific limit price. Extensive experiments show that our method has higher sample efficiency and better training stability than existing RL algorithms and significantly outperforms previous learning-based methods for order execution.
    Verifying Fairness in Quantum Machine Learning. (arXiv:2207.11173v1 [quant-ph])
    Due to the beyond-classical capability of quantum computing, quantum machine learning is applied independently or embedded in classical models for decision making, especially in the field of finance. Fairness and other ethical issues are often one of the main concerns in decision making. In this work, we define a formal framework for the fairness verification and analysis of quantum machine learning decision models, where we adopt one of the most popular notions of fairness in the literature based on the intuition -- any two similar individuals must be treated similarly and are thus unbiased. We show that quantum noise can improve fairness and develop an algorithm to check whether a (noisy) quantum machine learning model is fair. In particular, this algorithm can find bias kernels of quantum data (encoding individuals) during checking. These bias kernels generate infinitely many bias pairs for investigating the unfairness of the model. Our algorithm is designed based on a highly efficient data structure -- Tensor Networks -- and implemented on Google's TensorFlow Quantum. The utility and effectiveness of our algorithm are confirmed by the experimental results, including income prediction and credit scoring on real-world data, for a class of random (noisy) quantum decision models with 27 qubits ($2^{27}$-dimensional state space) tripling ($2^{18}$ times more than) that of the state-of-the-art algorithms for verifying quantum machine learning models.
    Latent Space Unsupervised Semantic Segmentation. (arXiv:2207.11067v1 [cs.LG])
    The development of compact and energy-efficient wearable sensors has led to an increase in the availability of biosignals. To analyze these continuously recorded, and often multidimensional, time series at scale, being able to conduct meaningful unsupervised data segmentation is an auspicious target. A common way to achieve this is to identify change-points within the time series as the segmentation basis. However, traditional change-point detection algorithms often come with drawbacks, limiting their real-world applicability. Notably, they generally rely on the complete time series to be available and thus cannot be used for real-time applications. Another common limitation is that they poorly (or cannot) handle the segmentation of multidimensional time series. Consequently, the main contribution of this work is to propose a novel unsupervised segmentation algorithm for multidimensional time series named Latent Space Unsupervised Semantic Segmentation (LS-USS), which was designed to work easily with both online and batch data. When comparing LS-USS against other state-of-the-art change-point detection algorithms on a variety of real-world datasets, in both the offline and real-time setting, LS-USS systematically achieves on par or better performances.
    Low cost prediction of probability distributions of molecular properties for early virtual screening. (arXiv:2207.11174v1 [q-bio.BM])
    While there is a general focus on predictions of values, mathematically more appropriate is prediction of probability distributions: with additional possibilities like prediction of uncertainty, higher moments and quantiles. For the purpose of the computer-aided drug design field, this article applies Hierarchical Correlation Reconstruction approach, previously applied in the analysis of demographic, financial and astronomical data. Instead of a single linear regression to predict values, it uses multiple linear regressions to independently predict multiple moments, finally combining them into predicted probability distribution, here of several ADMET properties based on substructural fingerprint developed by Klekota\&Roth. Discussed application example is inexpensive selection of a percentage of molecules with properties nearly certain to be in a predicted or chosen range during virtual screening. Such an approach can facilitate the interpretation of the results as the predictions characterized by high rate of uncertainty are automatically detected. In addition, for each of the investigated predictive problems, we detected crucial structural features, which should be carefully considered when optimizing compounds towards particular property. The whole methodology developed in the study constitutes therefore a great support for medicinal chemists, as it enable fast rejection of compounds with the lowest potential of desired physicochemical/ADMET characteristic and guides the compound optimization process.
    Generalized Identifiability Bounds for Mixture Models with Grouped Samples. (arXiv:2207.11164v1 [math.ST])
    Recent work has shown that finite mixture models with $m$ components are identifiable, while making no assumptions on the mixture components, so long as one has access to groups of samples of size $2m-1$ which are known to come from the same mixture component. In this work we generalize that result and show that, if every subset of $k$ mixture components of a mixture model are linearly independent, then that mixture model is identifiable with only $(2m-1)/(k-1)$ samples per group. We further show that this value cannot be improved. We prove an analogous result for a stronger form of identifiability known as "determinedness" along with a corresponding lower bound. This independence assumption almost surely holds if mixture components are chosen randomly from a $k$-dimensional space. We describe some implications of our results for multinomial mixture models and topic modeling.
    Learning to identify cracks on wind turbine blade surfaces using drone-based inspection images. (arXiv:2207.11186v1 [cs.CV])
    Wind energy is expected to be one of the leading ways to achieve the goals of the Paris Agreement but it in turn heavily depends on effective management of its operations and maintenance (O&M) costs. Blade failures account for one-third of all O&M costs thus making accurate detection of blade damages, especially cracks, very important for sustained operations and cost savings. Traditionally, damage inspection has been a completely manual process thus making it subjective, error-prone, and time-consuming. Hence in this work, we bring more objectivity, scalability, and repeatability in our damage inspection process, using deep learning, to miss fewer cracks. We build a deep learning model trained on a large dataset of blade damages, collected by our drone-based inspection, to correctly detect cracks. Our model is already in production and has processed more than a million damages with a recall of 0.96. We also focus on model interpretability using class activation maps to get a peek into the model workings. The model not only performs as good as human experts but also better in certain tricky cases. Thus, in this work, we aim to increase wind energy adoption by decreasing one of its major hurdles - the O\&M costs resulting from missing blade failures like cracks.
    Custom Structure Preservation in Face Aging. (arXiv:2207.11025v1 [cs.CV])
    In this work, we propose a novel architecture for face age editing that can produce structural modifications while maintaining relevant details present in the original image. We disentangle the style and content of the input image and propose a new decoder network that adopts a style-based strategy to combine the style and content representations of the input image while conditioning the output on the target age. We go beyond existing aging methods allowing users to adjust the degree of structure preservation in the input image during inference. To this purpose, we introduce a masking mechanism, the CUstom Structure Preservation module, that distinguishes relevant regions in the input image from those that should be discarded. CUSP requires no additional supervision. Finally, our quantitative and qualitative analysis which include a user study, show that our method outperforms prior art and demonstrates the effectiveness of our strategy regarding image editing and adjustable structure preservation. Code and pretrained models are available at https://github.com/guillermogotre/CUSP.
    On Controller Tuning with Time-Varying Bayesian Optimization. (arXiv:2207.11120v1 [cs.LG])
    Changing conditions or environments can cause system dynamics to vary over time. To ensure optimal control performance, controllers should adapt to these changes. When the underlying cause and time of change is unknown, we need to rely on online data for this adaptation. In this paper, we will use time-varying Bayesian optimization (TVBO) to tune controllers online in changing environments using appropriate prior knowledge on the control objective and its changes. Two properties are characteristic of many online controller tuning problems: First, they exhibit incremental and lasting changes in the objective due to changes to the system dynamics, e.g., through wear and tear. Second, the optimization problem is convex in the tuning parameters. Current TVBO methods do not explicitly account for these properties, resulting in poor tuning performance and many unstable controllers through over-exploration of the parameter space. We propose a novel TVBO forgetting strategy using Uncertainty-Injection (UI), which incorporates the assumption of incremental and lasting changes. The control objective is modeled as a spatio-temporal Gaussian process (GP) with UI through a Wiener process in the temporal domain. Further, we explicitly model the convexity assumptions in the spatial dimension through GP models with linear inequality constraints. In numerical experiments, we show that our model outperforms the state-of-the-art method in TVBO, exhibiting reduced regret and fewer unstable parameter configurations.
    DeVIS: Making Deformable Transformers Work for Video Instance Segmentation. (arXiv:2207.11103v1 [cs.CV])
    Video Instance Segmentation (VIS) jointly tackles multi-object detection, tracking, and segmentation in video sequences. In the past, VIS methods mirrored the fragmentation of these subtasks in their architectural design, hence missing out on a joint solution. Transformers recently allowed to cast the entire VIS task as a single set-prediction problem. Nevertheless, the quadratic complexity of existing Transformer-based methods requires long training times, high memory requirements, and processing of low-single-scale feature maps. Deformable attention provides a more efficient alternative but its application to the temporal domain or the segmentation task have not yet been explored. In this work, we present Deformable VIS (DeVIS), a VIS method which capitalizes on the efficiency and performance of deformable Transformers. To reason about all VIS subtasks jointly over multiple frames, we present temporal multi-scale deformable attention with instance-aware object queries. We further introduce a new image and video instance mask head with multi-scale features, and perform near-online video processing with multi-cue clip tracking. DeVIS reduces memory as well as training time requirements, and achieves state-of-the-art results on the YouTube-VIS 2021, as well as the challenging OVIS dataset. Code is available at https://github.com/acaelles97/DeVIS.
    Near Real-Time Distributed State Estimation via AI/ML-Empowered 5G Networks. (arXiv:2207.11117v1 [cs.LG])
    Fifth-Generation (5G) networks have a potential to accelerate power system transition to a flexible, softwarized, data-driven, and intelligent grid. With their evolving support for Machine Learning (ML)/Artificial Intelligence (AI) functions, 5G networks are expected to enable novel data-centric Smart Grid (SG) services. In this paper, we explore how data-driven SG services could be integrated with ML/AI-enabled 5G networks in a symbiotic relationship. We focus on the State Estimation (SE) function as a key element of the energy management system and focus on two main questions. Firstly, in a tutorial fashion, we present an overview on how distributed SE can be integrated with the elements of the 5G core network and radio access network architecture. Secondly, we present and compare two powerful distributed SE methods based on: i) graphical models and belief propagation, and ii) graph neural networks. We discuss their performance and capability to support a near real-time distributed SE via 5G network, taking into account communication delays.
    Lagrangian Method for Q-Function Learning (with Applications to Machine Translation). (arXiv:2207.11161v1 [cs.LG])
    This paper discusses a new approach to the fundamental problem of learning optimal Q-functions. In this approach, optimal Q-functions are formulated as saddle points of a nonlinear Lagrangian function derived from the classic Bellman optimality equation. The paper shows that the Lagrangian enjoys strong duality, in spite of its nonlinearity, which paves the way to a general Lagrangian method to Q-function learning. As a demonstration, the paper develops an imitation learning algorithm based on the duality theory, and applies the algorithm to a state-of-the-art machine translation benchmark. The paper then turns to demonstrate a symmetry breaking phenomenon regarding the optimality of the Lagrangian saddle points, which justifies a largely overlooked direction in developing the Lagrangian method.
    A Transferable Intersection Reconstruction Network for Traffic Speed Prediction. (arXiv:2207.11030v1 [cs.LG])
    Traffic speed prediction is the key to many valuable applications, and it is also a challenging task because of its various influencing factors. Recent work attempts to obtain more information through various hybrid models, thereby improving the prediction accuracy. However, the spatial information acquisition schemes of these methods have two-level differentiation problems. Either the modeling is simple but contains little spatial information, or the modeling is complete but lacks flexibility. In order to introduce more spatial information on the basis of ensuring flexibility, this paper proposes IRNet (Transferable Intersection Reconstruction Network). First, this paper reconstructs the intersection into a virtual intersection with the same structure, which simplifies the topology of the road network. Then, the spatial information is subdivided into intersection information and sequence information of traffic flow direction, and spatiotemporal features are obtained through various models. Third, a self-attention mechanism is used to fuse spatiotemporal features for prediction. In the comparison experiment with the baseline, not only the prediction effect, but also the transfer performance has obvious advantages.
    Classification via score-based generative modelling. (arXiv:2207.11091v1 [cs.LG])
    In this work, we investigated the application of score-based gradient learning in discriminative and generative classification settings. Score function can be used to characterize data distribution as an alternative to density. It can be efficiently learned via score matching, and used to flexibly generate credible samples to enhance discriminative classification quality, to recover density and to build generative classifiers. We analysed the decision theories involving score-based representations, and performed experiments on simulated and real-world datasets, demonstrating its effectiveness in achieving and improving binary classification performance, and robustness to perturbations, particularly in high dimensions and imbalanced situations.
    Domain Generalization by Mutual-Information Regularization with Pre-trained Models. (arXiv:2203.10789v2 [cs.LG] UPDATED)
    Domain generalization (DG) aims to learn a generalized model to an unseen target domain using only limited source domains. Previous attempts to DG fail to learn domain-invariant representations only from the source domains due to the significant domain shifts between training and test domains. Instead, we re-formulate the DG objective using mutual information with the oracle model, a model generalized to any possible domain. We derive a tractable variational lower bound via approximating the oracle model by a pre-trained model, called Mutual Information Regularization with Oracle (MIRO). Our extensive experiments show that MIRO significantly improves the out-of-distribution performance. Furthermore, our scaling experiments show that the larger the scale of the pre-trained model, the greater the performance improvement of MIRO. Source code is available at https://github.com/kakaobrain/miro.
    Towards Global Optimality in Cooperative MARL with Sequential Transformation. (arXiv:2207.11143v1 [cs.MA])
    Policy learning in multi-agent reinforcement learning (MARL) is challenging due to the exponential growth of joint state-action space with respect to the number of agents. To achieve higher scalability, the paradigm of centralized training with decentralized execution (CTDE) is broadly adopted with factorized structure in MARL. However, we observe that existing CTDE algorithms in cooperative MARL cannot achieve optimality even in simple matrix games. To understand this phenomenon, we introduce a framework of Generalized Multi-Agent Actor-Critic with Policy Factorization (GPF-MAC), which characterizes the learning of factorized joint policies, i.e., each agent's policy only depends on its own observation-action history. We show that most popular CTDE MARL algorithms are special instances of GPF-MAC and may be stuck in a suboptimal joint policy. To address this issue, we present a novel transformation framework that reformulates a multi-agent MDP as a special "single-agent" MDP with a sequential structure and can allow employing off-the-shelf single-agent reinforcement learning (SARL) algorithms to efficiently learn corresponding multi-agent tasks. This transformation retains the optimality guarantee of SARL algorithms into cooperative MARL. To instantiate this transformation framework, we propose a Transformed PPO, called T-PPO, which can theoretically perform optimal policy learning in the finite multi-agent MDPs and shows significant outperformance on a large set of cooperative multi-agent tasks.
    Deep learning of diffeomorphisms for optimal reparametrizations of shapes. (arXiv:2207.11141v1 [math.OC])
    In shape analysis, one of the fundamental problems is to align curves or surfaces before computing a (geodesic) distance between these shapes. To find the optimal reparametrization realizing this alignment is a computationally demanding task which leads to an optimization problem on the diffeomorphism group. In this paper, we construct approximations of orientation-preserving diffeomorphisms by composition of elementary diffeomorphisms to solve the approximation problem. We propose a practical algorithm implemented in PyTorch which is applicable both to unparametrized curves and surfaces. We derive universal approximation results and obtain bounds for the Lipschitz constant of the obtained compositions of diffeomorphisms.
    Decoupled Adversarial Contrastive Learning for Self-supervised Adversarial Robustness. (arXiv:2207.10899v1 [cs.CV])
    Adversarial training (AT) for robust representation learning and self-supervised learning (SSL) for unsupervised representation learning are two active research fields. Integrating AT into SSL, multiple prior works have accomplished a highly significant yet challenging task: learning robust representation without labels. A widely used framework is adversarial contrastive learning which couples AT and SSL, and thus constitute a very complex optimization problem. Inspired by the divide-and-conquer philosophy, we conjecture that it might be simplified as well as improved by solving two sub-problems: non-robust SSL and pseudo-supervised AT. This motivation shifts the focus of the task from seeking an optimal integrating strategy for a coupled problem to finding sub-solutions for sub-problems. With this said, this work discards prior practices of directly introducing AT to SSL frameworks and proposed a two-stage framework termed Decoupled Adversarial Contrastive Learning (DeACL). Extensive experimental results demonstrate that our DeACL achieves SOTA self-supervised adversarial robustness while significantly reducing the training time, which validates its effectiveness and efficiency. Moreover, our DeACL constitutes a more explainable solution, and its success also bridges the gap with semi-supervised AT for exploiting unlabeled samples for robust representation learning. The code is publicly accessible at https://github.com/pantheon5100/DeACL.
    Automatic Termination for Hyperparameter Optimization. (arXiv:2104.08166v4 [cs.LG] UPDATED)
    Bayesian optimization (BO) is a widely popular approach for the hyperparameter optimization (HPO) in machine learning. At its core, BO iteratively evaluates promising configurations until a user-defined budget, such as wall-clock time or number of iterations, is exhausted. While the final performance after tuning heavily depends on the provided budget, it is hard to pre-specify an optimal value in advance. In this work, we propose an effective and intuitive termination criterion for BO that automatically stops the procedure if it is sufficiently close to the global optimum. Our key insight is that the discrepancy between the true objective (predictive performance on test data) and the computable target (validation performance) suggests stopping once the suboptimality in optimizing the target is dominated by the statistical estimation error. Across an extensive range of real-world HPO problems and baselines, we show that our termination criterion achieves a better trade-off between the test performance and optimization time. Additionally, we find that overfitting may occur in the context of HPO, which is arguably an overlooked problem in the literature, and show how our termination criterion helps to mitigate this phenomenon on both small and large datasets.
    Analyzing and Mitigating Interference in Neural Architecture Search. (arXiv:2108.12821v3 [cs.CL] UPDATED)
    Weight sharing is a popular approach to reduce the cost of neural architecture search (NAS) by reusing the weights of shared operators from previously trained child models. However, the rank correlation between the estimated accuracy and ground truth accuracy of those child models is low due to the interference among different child models caused by weight sharing. In this paper, we investigate the interference issue by sampling different child models and calculating the gradient similarity of shared operators, and observe: 1) the interference on a shared operator between two child models is positively correlated with the number of different operators; 2) the interference is smaller when the inputs and outputs of the shared operator are more similar. Inspired by these two observations, we propose two approaches to mitigate the interference: 1) MAGIC-T: rather than randomly sampling child models for optimization, we propose a gradual modification scheme by modifying one operator between adjacent optimization steps to minimize the interference on the shared operators; 2) MAGIC-A: forcing the inputs and outputs of the operator across all child models to be similar to reduce the interference. Experiments on a BERT search space verify that mitigating interference via each of our proposed methods improves the rank correlation of super-pet and combining both methods can achieve better results. Our discovered architecture outperforms RoBERTa$_{\rm base}$ by 1.1 and 0.6 points and ELECTRA$_{\rm base}$ by 1.6 and 1.1 points on the dev and test set of GLUE benchmark. Extensive results on the BERT compression, reading comprehension and ImageNet task demonstrate the effectiveness and generality of our proposed methods.
    Explaining Dynamic Graph Neural Networks via Relevance Back-propagation. (arXiv:2207.11175v1 [cs.LG])
    Graph Neural Networks (GNNs) have shown remarkable effectiveness in capturing abundant information in graph-structured data. However, the black-box nature of GNNs hinders users from understanding and trusting the models, thus leading to difficulties in their applications. While recent years witness the prosperity of the studies on explaining GNNs, most of them focus on static graphs, leaving the explanation of dynamic GNNs nearly unexplored. It is challenging to explain dynamic GNNs, due to their unique characteristic of time-varying graph structures. Directly using existing models designed for static graphs on dynamic graphs is not feasible because they ignore temporal dependencies among the snapshots. In this work, we propose DGExplainer to provide reliable explanation on dynamic GNNs. DGExplainer redistributes the output activation score of a dynamic GNN to the relevances of the neurons of its previous layer, which iterates until the relevance scores of the input neuron are obtained. We conduct quantitative and qualitative experiments on real-world datasets to demonstrate the effectiveness of the proposed framework for identifying important nodes for link prediction and node regression for dynamic GNNs.
    High dimensional stochastic linear contextual bandit with missing covariates. (arXiv:2207.11165v1 [stat.ML])
    Recent works in bandit problems adopted lasso convergence theory in the sequential decision-making setting. Even with fully observed contexts, there are technical challenges that hinder the application of existing lasso convergence theory: 1) proving the restricted eigenvalue condition under conditionally sub-Gaussian noise and 2) accounting for the dependence between the context variables and the chosen actions. This paper studies the effect of missing covariates on regret for stochastic linear bandit algorithms. Our work provides a high-probability upper bound on the regret incurred by the proposed algorithm in terms of covariate sampling probabilities, showing that the regret degrades due to missingness by at most $\zeta_{min}^2$, where $\zeta_{min}$ is the minimum probability of observing covariates in the context vector. We illustrate our algorithm for the practical application of experimental design for collecting gene expression data by a sequential selection of class discriminating DNA probes.
    Target Identification and Bayesian Model Averaging with Probabilistic Hierarchical Factor Probabilities. (arXiv:2207.11212v1 [cs.CV])
    Target detection in hyperspectral imagery is the process of locating pixels from an image which are likely to contain target, typically done by comparing one or more spectra for the desired target material to each pixel in the image. Target identification is the process of target detection incorporating an additional process to identify more specifically the material that is present in each pixel that scored high in detection. Detection is generally a 2-class problem of target vs. background, and identification is a many class problem including target, background, and additional know materials. The identification process we present is probabilistic and hierarchical which provides transparency to the process and produces trustworthy output. In this paper we show that target identification has a much lower false alarm rate than detection alone, and provide a detailed explanation of a robust identification method using probabilistic hierarchical classification that handles the vague categories of materials that depend on users which are different than the specific physical categories of chemical constituents. Identification is often done by comparing mixtures of materials including the target spectra to mixtures of materials that do not include the target spectra, possibly with other steps. (band combinations, feature checking, background removal, etc.) Standard linear regression does not handle these problems well because the number of regressors (identification spectra) is greater than the number of feature variables (bands), and there are multiple correlated spectra. Our proposed method handles these challenges efficiently and provides additional important practical information in the form of hierarchical probabilities computed from Bayesian model averaging.
    Panoptic Scene Graph Generation. (arXiv:2207.11247v1 [cs.CV])
    Existing research addresses scene graph generation (SGG) -- a critical technology for scene understanding in images -- from a detection perspective, i.e., objects are detected using bounding boxes followed by prediction of their pairwise relationships. We argue that such a paradigm causes several problems that impede the progress of the field. For instance, bounding box-based labels in current datasets usually contain redundant classes like hairs, and leave out background information that is crucial to the understanding of context. In this work, we introduce panoptic scene graph generation (PSG), a new problem task that requires the model to generate a more comprehensive scene graph representation based on panoptic segmentations rather than rigid bounding boxes. A high-quality PSG dataset, which contains 49k well-annotated overlapping images from COCO and Visual Genome, is created for the community to keep track of its progress. For benchmarking, we build four two-stage baselines, which are modified from classic methods in SGG, and two one-stage baselines called PSGTR and PSGFormer, which are based on the efficient Transformer-based detector, i.e., DETR. While PSGTR uses a set of queries to directly learn triplets, PSGFormer separately models the objects and relations in the form of queries from two Transformer decoders, followed by a prompting-like relation-object matching mechanism. In the end, we share insights on open challenges and future directions.
    BigIssue: A Realistic Bug Localization Benchmark. (arXiv:2207.10739v1 [cs.LG])
    As machine learning tools progress, the inevitable question arises: How can machine learning help us write better code? With significant progress being achieved in natural language processing with models like GPT-3 and Bert, the applications of natural language processing techniques to code are starting to be explored. Most of the research has been focused on automatic program repair (APR), and while the results on synthetic or highly filtered datasets are promising, such models are hard to apply in real-world scenarios because of inadequate bug localization. We propose BigIssue: a benchmark for realistic bug localization. The goal of the benchmark is two-fold. We provide (1) a general benchmark with a diversity of real and synthetic Java bugs and (2) a motivation to improve bug localization capabilities of models through attention to the full repository context. With the introduction of BigIssue, we hope to advance the state of the art in bug localization, in turn improving APR performance and increasing its applicability to the modern development cycle.
    Assessing mortality prediction through different representation models based on concepts extracted from clinical notes. (arXiv:2207.10872v1 [cs.CL])
    Recent years have seen particular interest in using electronic medical records (EMRs) for secondary purposes to enhance the quality and safety of healthcare delivery. EMRs tend to contain large amounts of valuable clinical notes. Learning of embedding is a method for converting notes into a format that makes them comparable. Transformer-based representation models have recently made a great leap forward. These models are pre-trained on large online datasets to understand natural language texts effectively. The quality of a learning embedding is influenced by how clinical notes are used as input to representation models. A clinical note has several sections with different levels of information value. It is also common for healthcare providers to use different expressions for the same concept. Existing methods use clinical notes directly or with an initial preprocessing as input to representation models. However, to learn a good embedding, we identified the most essential clinical notes section. We then mapped the extracted concepts from selected sections to the standard names in the Unified Medical Language System (UMLS). We used the standard phrases corresponding to the unique concepts as input for clinical models. We performed experiments to measure the usefulness of the learned embedding vectors in the task of hospital mortality prediction on a subset of the publicly available Medical Information Mart for Intensive Care (MIMIC-III) dataset. According to the experiments, clinical transformer-based representation models produced better results with getting input generated by standard names of extracted unique concepts compared to other input formats. The best-performing models were BioBERT, PubMedBERT, and UmlsBERT, respectively.  ( 3 min )
    Revisiting Parameter Reuse to Overcome Catastrophic Forgetting in Neural Networks. (arXiv:2207.11005v1 [cs.LG])
    Neural networks tend to forget previously learned knowledge when continuously learning on datasets with varying distributions, a phenomenon known as catastrophic forgetting. More significant distribution shifts among datasets lead to more forgetting. Recently, parameter-isolation-based approaches have shown great potential in overcoming forgetting with significant distribution shifts. However, they suffer from poor generalization as they fix the neural path for each dataset during training and require dataset labels during inference. In addition, they do not support backward knowledge transfer as they prioritize past data over future ones. In this paper, we propose a new adaptive learning method, named AdaptCL, that fully reuses and grows on learned parameters to overcome catastrophic forgetting and allows the positive backward transfer without requiring dataset labels. Our proposed technique adaptively grows on the same neural path by allowing optimal reuse of frozen parameters. Besides, it uses parameter-level data-driven pruning to assign equal priority to the data. We conduct extensive experiments on MNIST Variants, DomainNet, and Food Freshness Detection datasets under different intensities of distribution shifts without requiring dataset labels. Results demonstrate that our proposed method is superior to alternative baselines in minimizing forgetting and enabling positive backward knowledge transfer.  ( 2 min )
    PhishSim: Aiding Phishing Website Detection with a Feature-Free Tool. (arXiv:2207.10801v1 [cs.CR])
    In this paper, we propose a feature-free method for detecting phishing websites using the Normalized Compression Distance (NCD), a parameter-free similarity measure which computes the similarity of two websites by compressing them, thus eliminating the need to perform any feature extraction. It also removes any dependence on a specific set of website features. This method examines the HTML of webpages and computes their similarity with known phishing websites, in order to classify them. We use the Furthest Point First algorithm to perform phishing prototype extractions, in order to select instances that are representative of a cluster of phishing webpages. We also introduce the use of an incremental learning algorithm as a framework for continuous and adaptive detection without extracting new features when concept drift occurs. On a large dataset, our proposed method significantly outperforms previous methods in detecting phishing websites, with an AUC score of 98.68%, a high true positive rate (TPR) of around 90%, while maintaining a low false positive rate (FPR) of 0.58%. Our approach uses prototypes, eliminating the need to retain long term data in the future, and is feasible to deploy in real systems with a processing time of roughly 0.3 seconds.  ( 3 min )
    End-to-End and Self-Supervised Learning for ComParE 2022 Stuttering Sub-Challenge. (arXiv:2207.10817v1 [cs.SD])
    In this paper, we present end-to-end and speech embedding based systems trained in a self-supervised fashion to participate in the ACM Multimedia 2022 ComParE Challenge, specifically the stuttering sub-challenge. In particular, we exploit the embeddings from the pre-trained Wav2Vec2.0 model for stuttering detection (SD) on the KSoF dataset. After embedding extraction, we benchmark with several methods for SD. Our proposed self-supervised based SD system achieves a UAR of 36.9% and 41.0% on validation and test sets respectively, which is 31.32% (validation set) and 1.49% (test set) higher than the best (DeepSpectrum) challenge baseline (CBL). Moreover, we show that concatenating layer embeddings with Mel-frequency cepstral coefficients (MFCCs) features further improves the UAR of 33.81% and 5.45% on validation and test sets respectively over the CBL. Finally, we demonstrate that the summing information across all the layers of Wav2Vec2.0 surpasses the CBL by a relative margin of 45.91% and 5.69% on validation and test sets respectively. Grand-challenge: Computational Paralinguistics ChallengE  ( 2 min )
    FairGRAPE: Fairness-aware GRAdient Pruning mEthod for Face Attribute Classification. (arXiv:2207.10888v1 [cs.CV])
    Existing pruning techniques preserve deep neural networks' overall ability to make correct predictions but may also amplify hidden biases during the compression process. We propose a novel pruning method, Fairness-aware GRAdient Pruning mEthod (FairGRAPE), that minimizes the disproportionate impacts of pruning on different sub-groups. Our method calculates the per-group importance of each model weight and selects a subset of weights that maintain the relative between-group total importance in pruning. The proposed method then prunes network edges with small importance values and repeats the procedure by updating importance values. We demonstrate the effectiveness of our method on four different datasets, FairFace, UTKFace, CelebA, and ImageNet, for the tasks of face attribute classification where our method reduces the disparity in performance degradation by up to 90% compared to the state-of-the-art pruning algorithms. Our method is substantially more effective in a setting with a high pruning rate (99%). The code and dataset used in the experiments are available at https://github.com/Bernardo1998/FairGRAPE  ( 2 min )
    What's in the laundromat? Mapping and characterising offshore owned domestic property in London. (arXiv:2207.10931v1 [cs.LG])
    The UK, particularly London, is a global hub for money laundering, a significant portion of which uses domestic property. However, understanding the distribution and characteristics of offshore domestic property in the UK is challenging due to data availability. This paper attempts to remedy that situation by enhancing a publicly available dataset of UK property owned by offshore companies. We create a data processing pipeline which draws on several datasets and machine learning techniques to create a parsed set of addresses classified into six use classes. The enhanced dataset contains 138,000 properties 44,000 more than the original dataset. The majority are domestic (95k), with a disproportionate amount of those in London (42k). The average offshore domestic property in London is worth 1.33 million GBP collectively this amounts to approximately 56 Billion GBP. We perform an in-depth analysis of the offshore domestic property in London, comparing the price, distribution and entropy/concentration with Airbnb property, low-use/empty property and conventional domestic property. We estimate that the total amount of offshore, low-use and airbnb property in London is between 144,000 and 164,000 and that they are collectively worth between 145-174 billion GBP. Furthermore, offshore domestic property is more expensive and has higher entropy/concentration than all other property types. In addition, we identify two different types of offshore property, nested and individual, which have different price and distribution characteristics. Finally, we release the enhanced offshore property dataset, the complete low-use London dataset and the pipeline for creating the enhanced dataset to reduce the barriers to studying this topic.  ( 3 min )
    Principal Geodesic Analysis of Merge Trees (and Persistence Diagrams). (arXiv:2207.10960v1 [cs.GR])
    This paper presents a computational framework for the Principal Geodesic Analysis of merge trees (MT-PGA), a novel adaptation of the celebrated Principal Component Analysis (PCA) framework [87] to the Wasserstein metric space of merge trees [92]. We formulate MT-PGA computation as a constrained optimization problem, aiming at adjusting a basis of orthogonal geodesic axes, while minimizing a fitting energy. We introduce an efficient, iterative algorithm which exploits shared-memory parallelism, as well as an analytic expression of the fitting energy gradient, to ensure fast iterations. Our approach also trivially extends to extremum persistence diagrams. Extensive experiments on public ensembles demonstrate the efficiency of our approach - with MT-PGA computations in the orders of minutes for the largest examples. We show the utility of our contributions by extending to merge trees two typical PCA applications. First, we apply MT-PGA to data reduction and reliably compress merge trees by concisely representing them by their first coordinates in the MT-PGA basis. Second, we present a dimensionality reduction framework exploiting the first two directions of the MT-PGA basis to generate two-dimensional layouts of the ensemble. We augment these layouts with persistence correlation views, enabling global and local visual inspections of the feature variability in the ensemble. In both applications, quantitative experiments assess the relevance of our framework. Finally, we provide a lightweight C++ implementation that can be used to reproduce our results.  ( 3 min )
    Heterogeneous Ensemble Learning for Enhanced Crash Forecasts -- A Frequentest and Machine Learning based Stacking Framework. (arXiv:2207.10721v1 [cs.LG])
    A variety of statistical and machine learning methods are used to model crash frequency on specific roadways with machine learning methods generally having a higher prediction accuracy. Recently, heterogeneous ensemble methods (HEM), including stacking, have emerged as more accurate and robust intelligent techniques and are often used to solve pattern recognition problems by providing more reliable and accurate predictions. In this study, we apply one of the key HEM methods, Stacking, to model crash frequency on five lane undivided segments (5T) of urban and suburban arterials. The prediction performance of Stacking is compared with parametric statistical models (Poisson and negative binomial) and three state of the art machine learning techniques (Decision tree, random forest, and gradient boosting), each of which is termed as the base learner. By employing an optimal weight scheme to combine individual base learners through stacking, the problem of biased predictions in individual base-learners due to differences in specifications and prediction accuracies is avoided. Data including crash, traffic, and roadway inventory were collected and integrated from 2013 to 2017. The data are split into training, validation, and testing datasets. Estimation results of statistical models reveal that besides other factors, crashes increase with density (number per mile) of different types of driveways. Comparison of out-of-sample predictions of various models confirms the superiority of Stacking over the alternative methods considered. From a practical standpoint, stacking can enhance prediction accuracy (compared to using only one base learner with a particular specification). When applied systemically, stacking can help identify more appropriate countermeasures.  ( 3 min )
    Privacy and Transparency in Graph Machine Learning: A Unified Perspective. (arXiv:2207.10896v1 [cs.LG])
    Graph Machine Learning (GraphML), whereby classical machine learning is generalized to irregular graph domains, has enjoyed a recent renaissance, leading to a dizzying array of models and their applications in several domains. With its growing applicability to sensitive domains and regulations by government agencies for trustworthy AI systems, researchers have started looking into the issues of transparency and privacy of graph learning. However, these topics have been mainly investigated independently. In this position paper, we provide a unified perspective on the interplay of privacy and transparency in GraphML.  ( 2 min )
    Multiple Robust Learning for Recommendation. (arXiv:2207.10796v1 [cs.IR])
    In recommender systems, a common problem is the presence of various biases in the collected data, which deteriorates the generalization ability of the recommendation models and leads to inaccurate predictions. Doubly robust (DR) learning has been studied in many tasks in RS, with the advantage that unbiased learning can be achieved when either a single imputation or a single propensity model is accurate. In this paper, we propose a multiple robust (MR) estimator that can take the advantage of multiple candidate imputation and propensity models to achieve unbiasedness. Specifically, the MR estimator is unbiased when any of the imputation or propensity models, or a linear combination of these models is accurate. Theoretical analysis shows that the proposed MR is an enhanced version of DR when only having a single imputation and propensity model, and has a smaller bias. Inspired by the generalization error bound of MR, we further propose a novel multiple robust learning approach with stabilization. We conduct extensive experiments on real-world and semi-synthetic datasets, which demonstrates the superiority of the proposed approach over state-of-the-art methods.  ( 2 min )
    Statistical Hypothesis Testing Based on Machine Learning: Large Deviations Analysis. (arXiv:2207.10939v1 [stat.ML])
    We study the performance -- and specifically the rate at which the error probability converges to zero -- of Machine Learning (ML) classification techniques. Leveraging the theory of large deviations, we provide the mathematical conditions for a ML classifier to exhibit error probabilities that vanish exponentially, say $\sim \exp\left(-n\,I + o(n) \right)$, where $n$ is the number of informative observations available for testing (or another relevant parameter, such as the size of the target in an image) and $I$ is the error rate. Such conditions depend on the Fenchel-Legendre transform of the cumulant-generating function of the Data-Driven Decision Function (D3F, i.e., what is thresholded before the final binary decision is made) learned in the training phase. As such, the D3F and, consequently, the related error rate $I$, depend on the given training set, which is assumed of finite size. Interestingly, these conditions can be verified and tested numerically exploiting the available dataset, or a synthetic dataset, generated according to the available information on the underlying statistical model. In other words, the classification error probability convergence to zero and its rate can be computed on a portion of the dataset available for training. Coherently with the large deviations theory, we can also establish the convergence, for $n$ large enough, of the normalized D3F statistic to a Gaussian distribution. This property is exploited to set a desired asymptotic false alarm probability, which empirically turns out to be accurate even for quite realistic values of $n$. Furthermore, approximate error probability curves $\sim \zeta_n \exp\left(-n\,I \right)$ are provided, thanks to the refined asymptotic derivation (often referred to as exact asymptotics), where $\zeta_n$ represents the most representative sub-exponential terms of the error probabilities.  ( 3 min )
    Explainable AI Algorithms for Vibration Data-based Fault Detection: Use Case-adadpted Methods and Critical Evaluation. (arXiv:2207.10732v1 [eess.SP])
    Analyzing vibration data using deep neural network algorithms is an effective way to detect damages in rotating machinery at an early stage. However, the black-box approach of these methods often does not provide a satisfactory solution because the cause of classifications is not comprehensible to humans. Therefore, this work investigates the application of explainable AI (XAI) algorithms to convolutional neural networks for vibration-based condition monitoring. For this, various XAI algorithms are applied to classifications based on the Fourier transform as well as the order analysis of the vibration signal. The results are visualized as a function of the revolutions per minute (RPM), in the shape of frequency-RPM maps and order-RPM maps. This allows to assess the saliency given to features which depend on the rotation speed and those with constant frequency. To compare the explanatory power of the XAI methods, investigations are first carried out with a synthetic data set with known class-specific characteristics. Then a real-world data set for vibration-based imbalance classification on an electric motor, which runs at a broad range of rotation speeds, is used. A special focus is put on the consistency for variable periodicity of the data, which translates to a varying rotation speed of a real-world machine. This work aims to show the different strengths and weaknesses of the methods for this use case: GradCAM, LRP and LIME with a new perturbation strategy.  ( 3 min )
    Twitmo: A Twitter Data Topic Modeling and Visualization Package for R. (arXiv:2207.11236v1 [cs.IR])
    We present Twitmo, a package that provides a broad range of methods to collect, pre-process, analyze and visualize geo-tagged Twitter data. Twitmo enables the user to collect geo-tagged Tweets from Twitter and and provides a comprehensive and user-friendly toolbox to generate topic distributions from Latent Dirichlet Allocations (LDA), correlated topic models (CTM) and structural topic models (STM). Functions are included for pre-processing of text, model building and prediction. In addition, one of the innovations of the package is the automatic pooling of Tweets into longer pseudo-documents using hashtags and cosine similarities for better topic coherence. The package additionally comes with functionality to visualize collected data sets and fitted models in static as well as interactive ways and offers built-in support for model visualizations via LDAvis providing great convenience for researchers in this area. The Twitmo package is an innovative toolbox that can be used to analyze public discourse of various topics, political parties or persons of interest in space and time.
    Layer-Wise Partitioning and Merging for Efficient and Scalable Deep Learning. (arXiv:2207.11019v1 [cs.DC])
    Deep Neural Network (DNN) models are usually trained sequentially from one layer to another, which causes forward, backward and update locking's problems, leading to poor performance in terms of training time. The existing parallel strategies to mitigate these problems provide suboptimal runtime performance. In this work, we have proposed a novel layer-wise partitioning and merging, forward and backward pass parallel framework to provide better training performance. The novelty of the proposed work consists of 1) a layer-wise partition and merging model which can minimise communication overhead between devices without the memory cost of existing strategies during the training process; 2) a forward pass and backward pass parallelisation and optimisation to address the update locking problem and minimise the total training cost. The experimental evaluation on real use cases shows that the proposed method outperforms the state-of-the-art approaches in terms of training speed; and achieves almost linear speedup without compromising the accuracy performance of the non-parallel approach.  ( 2 min )
    JAWS: Predictive Inference Under Covariate Shift. (arXiv:2207.10716v1 [cs.LG])
    We propose \textbf{JAWS}, a series of wrapper methods for distribution-free uncertainty quantification tasks under covariate shift, centered on our core method \textbf{JAW}, the \textbf{JA}ckknife+ \textbf{W}eighted with likelihood-ratio weights. JAWS also includes computationally efficient \textbf{A}pproximations of JAW using higher-order influence functions: \textbf{JAWA}. Theoretically, we show that JAW relaxes the jackknife+'s assumption of data exchangeability to achieve the same finite-sample coverage guarantee even under covariate shift. JAWA further approaches the JAW guarantee in the limit of either the sample size or the influence function order under mild assumptions. Moreover, we propose a general approach to repurposing any distribution-free uncertainty quantification method and its guarantees to the task of risk assessment: a task that generates the estimated probability that the true label lies within a user-specified interval. We then propose \textbf{JAW-R} and \textbf{JAWA-R} as the repurposed versions of proposed methods for \textbf{R}isk assessment. Practically, JAWS outperform the state-of-the-art predictive inference baselines in a variety of biased real world data sets for both interval-generation and risk-assessment auditing tasks.  ( 2 min )
    Hyper-Representations for Pre-Training and Transfer Learning. (arXiv:2207.10951v1 [cs.LG])
    Learning representations of neural network weights given a model zoo is an emerging and challenging area with many potential applications from model inspection, to neural architecture search or knowledge distillation. Recently, an autoencoder trained on a model zoo was able to learn a hyper-representation, which captures intrinsic and extrinsic properties of the models in the zoo. In this work, we extend hyper-representations for generative use to sample new model weights as pre-training. We propose layer-wise loss normalization which we demonstrate is key to generate high-performing models and a sampling method based on the empirical density of hyper-representations. The models generated using our methods are diverse, performant and capable to outperform conventional baselines for transfer learning. Our results indicate the potential of knowledge aggregation from model zoos to new models via hyper-representations thereby paving the avenue for novel research directions.  ( 2 min )
    A Machine Learning Approach for Driver Identification Based on CAN-BUS Sensor Data. (arXiv:2207.10807v1 [cs.LG])
    Driver identification is a momentous field of modern decorated vehicles in the controller area network (CAN-BUS) perspective. Many conventional systems are used to identify the driver. One step ahead, most of the researchers use sensor data of CAN-BUS but there are some difficulties because of the variation of the protocol of different models of vehicle. Our aim is to identify the driver through supervised learning algorithms based on driving behavior analysis. To determine the driver, a driver verification technique is proposed that evaluate driving pattern using the measurement of CAN sensor data. In this paper on-board diagnostic (OBD-II) is used to capture the data from the CAN-BUS sensor and the sensors are listed under SAE J1979 statement. According to the service of OBD-II, drive identification is possible. However, we have gained two types of accuracy on a complete data set with 10 drivers and a partial data set with two drivers. The accuracy is good with less number of drivers compared to the higher number of drivers. We have achieved statistically significant results in terms of accuracy in contrast to the baseline algorithm  ( 2 min )
    PowerFDNet: Deep Learning-Based Stealthy False Data Injection Attack Detection for AC-model Transmission Systems. (arXiv:2207.10805v1 [cs.CR])
    Recent studies have demonstrated that smart grids are vulnerable to stealthy false data injection attacks (SFDIAs), as SFDIAs can bypass residual-based bad data detection mechanisms. The SFDIA detection has become one of the focuses of smart grid research. Methods based on deep learning technology have shown promising accuracy in the detection of SFDIAs. However, most existing methods rely on the temporal structure of a sequence of measurements but do not take account of the spatial structure between buses and transmission lines. To address this issue, we propose a spatiotemporal deep network, PowerFDNet, for the SFDIA detection in AC-model power grids. The PowerFDNet consists of two sub-architectures: spatial architecture (SA) and temporal architecture (TA). The SA is aimed at extracting representations of bus/line measurements and modeling the spatial structure based on their representations. The TA is aimed at modeling the temporal structure of a sequence of measurements. Therefore, the proposed PowerFDNet can effectively model the spatiotemporal structure of measurements. Case studies on the detection of SFDIAs on the benchmark smart grids show that the PowerFDNet achieved significant improvement compared with the state-of-the-art SFDIA detection methods. In addition, an IoT-oriented lightweight prototype of size 52 MB is implemented and tested for mobile devices, which demonstrates the potential applications on mobile devices. The trained model will be available at \textit{https://github.com/FrankYinXF/PowerFDNet}.  ( 3 min )
    Modeling User Behavior With Interaction Networks for Spam Detection. (arXiv:2207.10767v1 [cs.LG])
    Spam is a serious problem plaguing web-scale digital platforms which facilitate user content creation and distribution. It compromises platform's integrity, performance of services like recommendation and search, and overall business. Spammers engage in a variety of abusive and evasive behavior which are distinct from non-spammers. Users' complex behavior can be well represented by a heterogeneous graph rich with node and edge attributes. Learning to identify spammers in such a graph for a web-scale platform is challenging because of its structural complexity and size. In this paper, we propose SEINE (Spam DEtection using Interaction NEtworks), a spam detection model over a novel graph framework. Our graph simultaneously captures rich users' details and behavior and enables learning on a billion-scale graph. Our model considers neighborhood along with edge types and attributes, allowing it to capture a wide range of spammers. SEINE, trained on a real dataset of tens of millions of nodes and billions of edges, achieves a high performance of 80% recall with 1% false positive rate. SEINE achieves comparable performance to the state-of-the-art techniques on a public dataset while being pragmatic to be used in a large-scale production system.  ( 3 min )
    NFDLM: A Lightweight Network Flow based Deep Learning Model for DDoS Attack Detection in IoT Domains. (arXiv:2207.10803v1 [cs.CR])
    In the recent years, Distributed Denial of Service (DDoS) attacks on Internet of Things (IoT) devices have become one of the prime concerns to Internet users around the world. One of the sources of the attacks on IoT ecosystems are botnets. Intruders force IoT devices to become unavailable for its legitimate users by sending large number of messages within a short interval. This study proposes NFDLM, a lightweight and optimised Artificial Neural Network (ANN) based Distributed Denial of Services (DDoS) attack detection framework with mutual correlation as feature selection method which produces a superior result when compared with Long Short Term Memory (LSTM) and simple ANN. Overall, the detection performance achieves approximately 99\% accuracy for the detection of attacks from botnets. In this work, we have designed and compared four different models where two are based on ANN and the other two are based on LSTM to detect the attack types of DDoS.  ( 2 min )
    VTrackIt: A Synthetic Self-Driving Dataset with Infrastructure and Pooled Vehicle Information. (arXiv:2207.11146v1 [cs.CV])
    Artificial intelligence solutions for Autonomous Vehicles (AVs) have been developed using publicly available datasets such as Argoverse, ApolloScape, Level5, and NuScenes. One major limitation of these datasets is the absence of infrastructure and/or pooled vehicle information like lane line type, vehicle speed, traffic signs, and intersections. Such information is necessary and not complementary to eliminating high-risk edge cases. The rapid advancements in Vehicle-to-Infrastructure and Vehicle-to-Vehicle technologies show promise that infrastructure and pooled vehicle information will soon be accessible in near real-time. Taking a leap in the future, we introduce the first comprehensive synthetic dataset with intelligent infrastructure and pooled vehicle information for advancing the next generation of AVs, named VTrackIt. We also introduce the first deep learning model (InfraGAN) for trajectory predictions that considers such information. Our experiments with InfraGAN show that the comprehensive information offered by VTrackIt reduces the number of high-risk edge cases. The VTrackIt dataset is available upon request under the Creative Commons CC BY-NC-SA 4.0 license at this http URL
    Spatial-Temporal Feature Extraction and Evaluation Network for Citywide Traffic Condition Prediction. (arXiv:2207.11034v1 [cs.LG])
    Traffic prediction plays an important role in the realization of traffic control and scheduling tasks in intelligent transportation systems. With the diversification of data sources, reasonably using rich traffic data to model the complex spatial-temporal dependence and nonlinear characteristics in traffic flow are the key challenge for intelligent transportation system. In addition, clearly evaluating the importance of spatial-temporal features extracted from different data becomes a challenge. A Double Layer - Spatial Temporal Feature Extraction and Evaluation (DL-STFEE) model is proposed. The lower layer of DL-STFEE is spatial-temporal feature extraction layer. The spatial and temporal features in traffic data are extracted by multi-graph graph convolution and attention mechanism, and different combinations of spatial and temporal features are generated. The upper layer of DL-STFEE is the spatial-temporal feature evaluation layer. Through the attention score matrix generated by the high-dimensional self-attention mechanism, the spatial-temporal features combinations are fused and evaluated, so as to get the impact of different combinations on prediction effect. Three sets of experiments are performed on actual traffic datasets to show that DL-STFEE can effectively capture the spatial-temporal features and evaluate the importance of different spatial-temporal feature combinations.
    Learning Generalized Non-Rigid Multimodal Biomedical Image Registration from Generic Point Set Data. (arXiv:2207.10994v1 [cs.CV])
    Free Point Transformer (FPT) has been proposed as a data-driven, non-rigid point set registration approach using deep neural networks. As FPT does not assume constraints based on point vicinity or correspondence, it may be trained simply and in a flexible manner by minimizing an unsupervised loss based on the Chamfer Distance. This makes FPT amenable to real-world medical imaging applications where ground-truth deformations may be infeasible to obtain, or in scenarios where only a varying degree of completeness in the point sets to be aligned is available. To test the limit of the correspondence finding ability of FPT and its dependency on training data sets, this work explores the generalizability of the FPT from well-curated non-medical data sets to medical imaging data sets. First, we train FPT on the ModelNet40 dataset to demonstrate its effectiveness and the superior registration performance of FPT over iterative and learning-based point set registration methods. Second, we demonstrate superior performance in rigid and non-rigid registration and robustness to missing data. Last, we highlight the interesting generalizability of the ModelNet-trained FPT by registering reconstructed freehand ultrasound scans of the spine and generic spine models without additional training, whereby the average difference to the ground truth curvatures is 1.3 degrees, across 13 patients.
    Federated Semi-Supervised Domain Adaptation via Knowledge Transfer. (arXiv:2207.10727v1 [cs.LG])
    Given the rapidly changing machine learning environments and expensive data labeling, semi-supervised domain adaptation (SSDA) is imperative when the labeled data from the source domain is statistically different from the partially labeled data from the target domain. Most prior SSDA research is centrally performed, requiring access to both source and target data. However, data in many fields nowadays is generated by distributed end devices. Due to privacy concerns, the data might be locally stored and cannot be shared, resulting in the ineffectiveness of existing SSDA research. This paper proposes an innovative approach to achieve SSDA over multiple distributed and confidential datasets, named by Federated Semi-Supervised Domain Adaptation (FSSDA). FSSDA integrates SSDA with federated learning based on strategically designed knowledge distillation techniques, whose efficiency is improved by performing source and target training in parallel. Moreover, FSSDA controls the amount of knowledge transferred across domains by properly selecting a key parameter, i.e., the imitation parameter. Further, the proposed FSSDA can be effectively generalized to multi-source domain adaptation scenarios. Extensive experiments are conducted to demonstrate the effectiveness and efficiency of FSSDA design.  ( 2 min )
    Supervised Contrastive ResNet and Transfer Learning for the In-vehicle Intrusion Detection System. (arXiv:2207.10814v1 [cs.CR])
    High-end vehicles have been furnished with a number of electronic control units (ECUs), which provide upgrading functions to enhance the driving experience. The controller area network (CAN) is a well-known protocol that connects these ECUs because of its modesty and efficiency. However, the CAN bus is vulnerable to various types of attacks. Although the intrusion detection system (IDS) is proposed to address the security problem of the CAN bus, most previous studies only provide alerts when attacks occur without knowing the specific type of attack. Moreover, an IDS is designed for a specific car model due to diverse car manufacturers. In this study, we proposed a novel deep learning model called supervised contrastive (SupCon) ResNet, which can handle multiple attack identification on the CAN bus. Furthermore, the model can be used to improve the performance of a limited-size dataset using a transfer learning technique. The capability of the proposed model is evaluated on two real car datasets. When tested with the car hacking dataset, the experiment results show that the SupCon ResNet model improves the overall false-negative rates of four types of attack by four times on average, compared to other models. In addition, the model achieves the highest F1 score at 0.9994 on the survival dataset by utilizing transfer learning. Finally, the model can adapt to hardware constraints in terms of memory size and running time.  ( 3 min )
    A Convolutional Attention Based Deep Network Solution for UAV Network Attack Recognition over Fading Channels and Interference. (arXiv:2207.10810v1 [cs.CR])
    When users exchange data with Unmanned Aerial vehicles - (UAVs) over air-to-ground (A2G) wireless communication networks, they expose the link to attacks that could increase packet loss and might disrupt connectivity. For example, in emergency deliveries, losing control information (i.e data related to the UAV control communication) might result in accidents that cause UAV destruction and damage to buildings or other elements in a city. To prevent these problems, these issues must be addressed in 5G and 6G scenarios. This research offers a deep learning (DL) approach for detecting attacks in UAVs equipped with orthogonal frequency division multiplexing (OFDM) receivers on Clustered Delay Line (CDL) channels in highly complex scenarios involving authenticated terrestrial users, as well as attackers in unknown locations. We use the two observable parameters available in 5G UAV connections: the Received Signal Strength Indicator (RSSI) and the Signal to Interference plus Noise Ratio (SINR). The prospective algorithm is generalizable regarding attack identification, which does not occur during training. Further, it can identify all the attackers in the environment with 20 terrestrial users. A deeper investigation into the timing requirements for recognizing attacks show that after training, the minimum time necessary after the attack begins is 100 ms, and the minimum attack power is 2 dBm, which is the same power that the authenticated UAV uses. Our algorithm also detects moving attackers from a distance of 500 m.  ( 3 min )
    Federated Learning on Adaptively Weighted Nodes by Bilevel Optimization. (arXiv:2207.10751v1 [cs.LG])
    We propose a federated learning method with weighted nodes in which the weights can be modified to optimize the model's performance on a separate validation set. The problem is formulated as a bilevel optimization where the inner problem is a federated learning problem with weighted nodes and the outer problem focuses on optimizing the weights based on the validation performance of the model returned from the inner problem. A communication-efficient federated optimization algorithm is designed to solve this bilevel optimization problem. Under an error-bound assumption, we analyze the generalization performance of the output model and identify scenarios when our method is in theory superior to training a model only locally and to federated learning with static and evenly distributed weights.  ( 2 min )
    Uncertainty-aware Multi-modal Learning via Cross-modal Random Network Prediction. (arXiv:2207.10851v1 [cs.CV])
    Multi-modal learning focuses on training models by equally combining multiple input data modalities during the prediction process. However, this equal combination can be detrimental to the prediction accuracy because different modalities are usually accompanied by varying levels of uncertainty. Using such uncertainty to combine modalities has been studied by a couple of approaches, but with limited success because these approaches are either designed to deal with specific classification or segmentation problems and cannot be easily translated into other tasks, or suffer from numerical instabilities. In this paper, we propose a new Uncertainty-aware Multi-modal Learner that estimates uncertainty by measuring feature density via Cross-modal Random Network Prediction (CRNP). CRNP is designed to require little adaptation to translate between different prediction tasks, while having a stable training process. From a technical point of view, CRNP is the first approach to explore random network prediction to estimate uncertainty and to combine multi-modal data. Experiments on two 3D multi-modal medical image segmentation tasks and three 2D multi-modal computer vision classification tasks show the effectiveness, adaptability and robustness of CRNP. Also, we provide an extensive discussion on different fusion functions and visualization to validate the proposed model.  ( 2 min )
    Respecting Time Series Properties Makes Deep Time Series Forecasting Perfect. (arXiv:2207.10941v1 [cs.LG])
    How to handle time features shall be the core question of any time series forecasting model. Ironically, it is often ignored or misunderstood by deep-learning based models, even those baselines which are state-of-the-art. This behavior makes their inefficient, untenable and unstable. In this paper, we rigorously analyze three prevalent but deficient/unfounded deep time series forecasting mechanisms or methods from the view of time series properties, including normalization methods, multivariate forecasting and input sequence length. Corresponding corollaries and solutions are given on both empirical and theoretical basis. We thereby propose a novel time series forecasting network, i.e. RTNet, on the basis of aforementioned analysis. It is general enough to be combined with both supervised and self-supervised forecasting format. Thanks to the core idea of respecting time series properties, no matter in which forecasting format, RTNet shows obviously superior forecasting performances compared with dozens of other SOTA time series forecasting baselines in three real-world benchmark datasets. By and large, it even occupies less time complexity and memory usage while acquiring better forecasting accuracy. The source code is available at https://github.com/OrigamiSL/RTNet.  ( 2 min )
    Automated Dilated Spatio-Temporal Synchronous Graph Modeling for Traffic Prediction. (arXiv:2207.10830v1 [cs.LG])
    Accurate traffic prediction is a challenging task in intelligent transportation systems because of the complex spatio-temporal dependencies in transportation networks. Many existing works utilize sophisticated temporal modeling approaches to incorporate with graph convolution networks (GCNs) for capturing short-term and long-term spatio-temporal dependencies. However, these separated modules with complicated designs could restrict effectiveness and efficiency of spatio-temporal representation learning. Furthermore, most previous works adopt the fixed graph construction methods to characterize the global spatio-temporal relations, which limits the learning capability of the model for different time periods and even different data scenarios. To overcome these limitations, we propose an automated dilated spatio-temporal synchronous graph network, named Auto-DSTSGN for traffic prediction. Specifically, we design an automated dilated spatio-temporal synchronous graph (Auto-DSTSG) module to capture the short-term and long-term spatio-temporal correlations by stacking deeper layers with dilation factors in an increasing order. Further, we propose a graph structure search approach to automatically construct the spatio-temporal synchronous graph that can adapt to different data scenarios. Extensive experiments on four real-world datasets demonstrate that our model can achieve about 10% improvements compared with the state-of-art methods. Source codes are available at https://github.com/jinguangyin/Auto-DSTSGN.  ( 2 min )
    Neuroimaging Feature Extraction using a Neural Network Classifier for Imaging Genetics. (arXiv:2207.10794v1 [q-bio.QM])
    A major issue in the association of genes to neuroimaging phenotypes is the high dimension of both genetic data and neuroimaging data. In this article, we tackle the latter problem with an eye toward developing solutions that are relevant for disease prediction. Supported by a vast literature on the predictive power of neural networks, our proposed solution uses neural networks to extract from neuroimaging data features that are relevant for predicting Alzheimer's Disease (AD) for subsequent relation to genetics. Our neuroimaging-genetic pipeline is comprised of image processing, neuroimaging feature extraction and genetic association steps. We propose a neural network classifier for extracting neuroimaging features that are related with disease and a multivariate Bayesian group sparse regression model for genetic association. We compare the predictive power of these features to expert selected features and take a closer look at the SNPs identified with the new neuroimaging features.  ( 2 min )
    Robust Knowledge Adaptation for Dynamic Graph Neural Networks. (arXiv:2207.10839v1 [cs.LG])
    Graph structured data often possess dynamic characters in nature, e.g., the addition of links and nodes, in many real-world applications. Recent years have witnessed the increasing attentions paid to dynamic graph neural networks for modelling such graph data, where almost all the existing approaches assume that when a new link is built, the embeddings of the neighbor nodes should be updated by learning the temporal dynamics to propagate new information. However, such approaches suffer from the limitation that if the node introduced by a new connection contains noisy information, propagating its knowledge to other nodes is not reliable and even leads to the collapse of the model. In this paper, we propose AdaNet: a robust knowledge Adaptation framework via reinforcement learning for dynamic graph neural Networks. In contrast to previous approaches immediately updating the embeddings of the neighbor nodes once adding a new link, AdaNet attempts to adaptively determine which nodes should be updated because of the new link involved. Considering that the decision whether to update the embedding of one neighbor node will have great impact on other neighbor nodes, we thus formulate the selection of node update as a sequence decision problem, and address this problem via reinforcement learning. By this means, we can adaptively propagate knowledge to other nodes for learning robust node embedding representations. To the best of our knowledge, our approach constitutes the first attempt to explore robust knowledge adaptation via reinforcement learning for dynamic graph neural networks. Extensive experiments on three benchmark datasets demonstrate that AdaNet achieves the state-of-the-art performance. In addition, we perform the experiments by adding different degrees of noise into the dataset, quantitatively and qualitatively illustrating the robustness of AdaNet.  ( 3 min )
    Just Rotate it: Deploying Backdoor Attacks via Rotation Transformation. (arXiv:2207.10825v1 [cs.CV])
    Recent works have demonstrated that deep learning models are vulnerable to backdoor poisoning attacks, where these attacks instill spurious correlations to external trigger patterns or objects (e.g., stickers, sunglasses, etc.). We find that such external trigger signals are unnecessary, as highly effective backdoors can be easily inserted using rotation-based image transformation. Our method constructs the poisoned dataset by rotating a limited amount of objects and labeling them incorrectly; once trained with it, the victim's model will make undesirable predictions during run-time inference. It exhibits a significantly high attack success rate while maintaining clean performance through comprehensive empirical studies on image classification and object detection tasks. Furthermore, we evaluate standard data augmentation techniques and four different backdoor defenses against our attack and find that none of them can serve as a consistent mitigation approach. Our attack can be easily deployed in the real world since it only requires rotating the object, as we show in both image classification and object detection applications. Overall, our work highlights a new, simple, physically realizable, and highly effective vector for backdoor attacks. Our video demo is available at https://youtu.be/6JIF8wnX34M.  ( 2 min )
    Characterizing Coherent Integrated Photonic Neural Networks under Imperfections. (arXiv:2207.10835v1 [cs.ET])
    Integrated photonic neural networks (IPNNs) are emerging as promising successors to conventional electronic AI accelerators as they offer substantial improvements in computing speed and energy efficiency. In particular, coherent IPNNs use arrays of Mach-Zehnder interferometers (MZIs) for unitary transformations to perform energy-efficient matrix-vector multiplication. However, the underlying MZI devices in IPNNs are susceptible to uncertainties stemming from optical lithographic variations and thermal crosstalk and can experience imprecisions due to non-uniform MZI insertion loss and quantization errors due to low-precision encoding in the tuned phase angles. In this paper, we, for the first time, systematically characterize the impact of such uncertainties and imprecisions (together referred to as imperfections) in IPNNs using a bottom-up approach. We show that their impact on IPNN accuracy can vary widely based on the tuned parameters (e.g., phase angles) of the affected components, their physical location, and the nature and distribution of the imperfections. To improve reliability measures, we identify critical IPNN building blocks that, under imperfections, can lead to catastrophic degradation in the classification accuracy. We show that under multiple simultaneous imperfections, the IPNN inferencing accuracy can degrade by up to 46%, even when the imperfection parameters are restricted within a small range. Our results also indicate that the inferencing accuracy is sensitive to imperfections affecting the MZIs in the linear layers next to the input layer of the IPNN.  ( 3 min )
    Multilabel Prototype Generation for Data Reduction in k-Nearest Neighbour classification. (arXiv:2207.10947v1 [cs.LG])
    Prototype Generation (PG) methods are typically considered for improving the efficiency of the $k$-Nearest Neighbour ($k$NN) classifier when tackling high-size corpora. Such approaches aim at generating a reduced version of the corpus without decreasing the classification performance when compared to the initial set. Despite their large application in multiclass scenarios, very few works have addressed the proposal of PG methods for the multilabel space. In this regard, this work presents the novel adaptation of four multiclass PG strategies to the multilabel case. These proposals are evaluated with three multilabel $k$NN-based classifiers, 12 corpora comprising a varied range of domains and corpus sizes, and different noise scenarios artificially induced in the data. The results obtained show that the proposed adaptations are capable of significantly improving -- both in terms of efficiency and classification performance -- the only reference multilabel PG work in the literature as well as the case in which no PG method is applied, also presenting a statistically superior robustness in noisy scenarios. Moreover, these novel PG strategies allow prioritising either the efficiency or efficacy criteria through its configuration depending on the target scenario, hence covering a wide area in the solution space not previously filled by other works.  ( 2 min )
    Active Data Pattern Extraction Attacks on Generative Language Models. (arXiv:2207.10802v1 [cs.CR])
    With the wide availability of large pre-trained language model checkpoints, such as GPT-2 and BERT, the recent trend has been to fine-tune them on a downstream task to achieve the state-of-the-art performance with a small computation overhead. One natural example is the Smart Reply application where a pre-trained model is fine-tuned for suggesting a number of responses given a query message. In this work, we set out to investigate potential information leakage vulnerabilities in a typical Smart Reply pipeline and show that it is possible for an adversary, having black-box or gray-box access to a Smart Reply model, to extract sensitive user information present in the training data. We further analyse the privacy impact of specific components, e.g. the decoding strategy, pertained to this application through our attack settings. We explore potential mitigation strategies and demonstrate how differential privacy can be a strong defense mechanism to such data extraction attacks.  ( 2 min )
    Suppressing Poisoning Attacks on Federated Learning for Medical Imaging. (arXiv:2207.10804v1 [cs.CR])
    Collaboration among multiple data-owning entities (e.g., hospitals) can accelerate the training process and yield better machine learning models due to the availability and diversity of data. However, privacy concerns make it challenging to exchange data while preserving confidentiality. Federated Learning (FL) is a promising solution that enables collaborative training through exchange of model parameters instead of raw data. However, most existing FL solutions work under the assumption that participating clients are \emph{honest} and thus can fail against poisoning attacks from malicious parties, whose goal is to deteriorate the global model performance. In this work, we propose a robust aggregation rule called Distance-based Outlier Suppression (DOS) that is resilient to byzantine failures. The proposed method computes the distance between local parameter updates of different clients and obtains an outlier score for each client using Copula-based Outlier Detection (COPOD). The resulting outlier scores are converted into normalized weights using a softmax function, and a weighted average of the local parameters is used for updating the global model. DOS aggregation can effectively suppress parameter updates from malicious clients without the need for any hyperparameter selection, even when the data distributions are heterogeneous. Evaluation on two medical imaging datasets (CheXpert and HAM10000) demonstrates the higher robustness of DOS method against a variety of poisoning attacks in comparison to other state-of-the-art methods. The code can be found here https://github.com/Naiftt/SPAFD.  ( 3 min )
    Transformer with Implicit Edges for Particle-based Physics Simulation. (arXiv:2207.10860v1 [cs.LG])
    Particle-based systems provide a flexible and unified way to simulate physics systems with complex dynamics. Most existing data-driven simulators for particle-based systems adopt graph neural networks (GNNs) as their network backbones, as particles and their interactions can be naturally represented by graph nodes and graph edges. However, while particle-based systems usually contain hundreds even thousands of particles, the explicit modeling of particle interactions as graph edges inevitably leads to a significant computational overhead, due to the increased number of particle interactions. Consequently, in this paper we propose a novel Transformer-based method, dubbed as Transformer with Implicit Edges (TIE), to capture the rich semantics of particle interactions in an edge-free manner. The core idea of TIE is to decentralize the computation involving pair-wise particle interactions into per-particle updates. This is achieved by adjusting the self-attention module to resemble the update formula of graph edges in GNN. To improve the generalization ability of TIE, we further amend TIE with learnable material-specific abstract particles to disentangle global material-wise semantics from local particle-wise semantics. We evaluate our model on diverse domains of varying complexity and materials. Compared with existing GNN-based methods, without bells and whistles, TIE achieves superior performance and generalization across all these domains. Codes and models are available at https://github.com/ftbabi/TIE_ECCV2022.git.  ( 2 min )
    IDPS Signature Classification with a Reject Option and the Incorporation of Expert Knowledge. (arXiv:2207.10797v1 [cs.CR])
    As the importance of intrusion detection and prevention systems (IDPSs) increases, great costs are incurred to manage the signatures that are generated by malicious communication pattern files. Experts in network security need to classify signatures by importance for an IDPS to work. We propose and evaluate a machine learning signature classification model with a reject option (RO) to reduce the cost of setting up an IDPS. To train the proposed model, it is essential to design features that are effective for signature classification. Experts classify signatures with predefined if-then rules. An if-then rule returns a label of low, medium, high, or unknown importance based on keyword matching of the elements in the signature. Therefore, we first design two types of features, symbolic features (SFs) and keyword features (KFs), which are used in keyword matching for the if-then rules. Next, we design web information and message features (WMFs) to capture the properties of signatures that do not match the if-then rules. The WMFs are extracted as term frequency-inverse document frequency (TF-IDF) features of the message text in the signatures. The features are obtained by web scraping from the referenced external attack identification systems described in the signature. Because failure needs to be minimized in the classification of IDPS signatures, as in the medical field, we consider introducing a RO in our proposed model. The effectiveness of the proposed classification model is evaluated in experiments with two real datasets composed of signatures labeled by experts: a dataset that can be classified with if-then rules and a dataset with elements that do not match an if-then rule. In the experiment, the proposed model is evaluated. In both cases, the combined SFs and WMFs performed better than the combined SFs and KFs. In addition, we also performed feature analysis.  ( 3 min )
    Learning Physics from the Machine: An Interpretable Boosted Decision Tree Analysis for the Majorana Demonstrator. (arXiv:2207.10710v1 [physics.data-an])
    The Majorana Demonstrator is a leading experiment searching for neutrinoless double-beta decay with high purity germanium detectors (HPGe). Machine learning provides a new way to maximize the amount of information provided by these detectors, but the data-driven nature makes it less interpretable compared to traditional analysis. An interpretability study reveals the machine's decision-making logic, allowing us to learn from the machine to feedback to the traditional analysis. In this work, we have presented the first machine learning analysis of the data from the Majorana Demonstrator; this is also the first interpretable machine learning analysis of any germanium detector experiment. Two gradient boosted decision tree models are trained to learn from the data, and a game-theory-based model interpretability study is conducted to understand the origin of the classification power. By learning from data, this analysis recognizes the correlations among reconstruction parameters to further enhance the background rejection performance. By learning from the machine, this analysis reveals the importance of new background categories to reciprocally benefit the standard Majorana analysis. This model is highly compatible with next-generation germanium detector experiments like LEGEND since it can be simultaneously trained on a large number of detectors.  ( 3 min )
    DEVIANT: Depth EquiVarIAnt NeTwork for Monocular 3D Object Detection. (arXiv:2207.10758v1 [cs.CV])
    Modern neural networks use building blocks such as convolutions that are equivariant to arbitrary 2D translations. However, these vanilla blocks are not equivariant to arbitrary 3D translations in the projective manifold. Even then, all monocular 3D detectors use vanilla blocks to obtain the 3D coordinates, a task for which the vanilla blocks are not designed for. This paper takes the first step towards convolutions equivariant to arbitrary 3D translations in the projective manifold. Since the depth is the hardest to estimate for monocular detection, this paper proposes Depth EquiVarIAnt NeTwork (DEVIANT) built with existing scale equivariant steerable blocks. As a result, DEVIANT is equivariant to the depth translations in the projective manifold whereas vanilla networks are not. The additional depth equivariance forces the DEVIANT to learn consistent depth estimates, and therefore, DEVIANT achieves state-of-the-art monocular 3D detection results on KITTI and Waymo datasets in the image-only category and performs competitively to methods using extra information. Moreover, DEVIANT works better than vanilla networks in cross-dataset evaluation. Code and models at https://github.com/abhi1kumar/DEVIANT  ( 2 min )
    GreenDB -- A Dataset and Benchmark for Extraction of Sustainability Information of Consumer Goods. (arXiv:2207.10733v1 [cs.LG])
    The production, shipping, usage, and disposal of consumer goods have a substantial impact on greenhouse gas emissions and the depletion of resources. Machine Learning (ML) can help to foster sustainable consumption patterns by accounting for sustainability aspects in product search or recommendations of modern retail platforms. However, the lack of large high quality publicly available product data with trustworthy sustainability information impedes the development of ML technology that can help to reach our sustainability goals. Here we present GreenDB, a database that collects products from European online shops on a weekly basis. As proxy for the products' sustainability, it relies on sustainability labels, which are evaluated by experts. The GreenDB schema extends the well-known schema.org Product definition and can be readily integrated into existing product catalogs. We present initial results demonstrating that ML models trained with our data can reliably (F1 score 96%) predict the sustainability label of products. These contributions can help to complement existing e-commerce experiences and ultimately encourage users to more sustainable consumption patterns.  ( 2 min )
    Deep Sufficient Representation Learning via Mutual Information. (arXiv:2207.10772v1 [stat.ML])
    We propose a mutual information-based sufficient representation learning (MSRL) approach, which uses the variational formulation of the mutual information and leverages the approximation power of deep neural networks. MSRL learns a sufficient representation with the maximum mutual information with the response and a user-selected distribution. It can easily handle multi-dimensional continuous or categorical response variables. MSRL is shown to be consistent in the sense that the conditional probability density function of the response variable given the learned representation converges to the conditional probability density function of the response variable given the predictor. Non-asymptotic error bounds for MSRL are also established under suitable conditions. To establish the error bounds, we derive a generalized Dudley's inequality for an order-two U-process indexed by deep neural networks, which may be of independent interest. We discuss how to determine the intrinsic dimension of the underlying data distribution. Moreover, we evaluate the performance of MSRL via extensive numerical experiments and real data analysis and demonstrate that MSRL outperforms some existing nonlinear sufficient dimension reduction methods.  ( 2 min )
    Strategising template-guided needle placement for MR-targeted prostate biopsy. (arXiv:2207.10784v1 [cs.LG])
    Clinically significant prostate cancer has a better chance to be sampled during ultrasound-guided biopsy procedures, if suspected lesions found in pre-operative magnetic resonance (MR) images are used as targets. However, the diagnostic accuracy of the biopsy procedure is limited by the operator-dependent skills and experience in sampling the targets, a sequential decision making process that involves navigating an ultrasound probe and placing a series of sampling needles for potentially multiple targets. This work aims to learn a reinforcement learning (RL) policy that optimises the actions of continuous positioning of 2D ultrasound views and biopsy needles with respect to a guiding template, such that the MR targets can be sampled efficiently and sufficiently. We first formulate the task as a Markov decision process (MDP) and construct an environment that allows the targeting actions to be performed virtually for individual patients, based on their anatomy and lesions derived from MR images. A patient-specific policy can thus be optimised, before each biopsy procedure, by rewarding positive sampling in the MDP environment. Experiment results from fifty four prostate cancer patients show that the proposed RL-learned policies obtained a mean hit rate of 93% and an average cancer core length of 11 mm, which compared favourably to two alternative baseline strategies designed by humans, without hand-engineered rewards that directly maximise these clinically relevant metrics. Perhaps more interestingly, it is found that the RL agents learned strategies that were adaptive to the lesion size, where spread of the needles was prioritised for smaller lesions. Such a strategy has not been previously reported or commonly adopted in clinical practice, but led to an overall superior targeting performance when compared with intuitively designed strategies.  ( 3 min )
    Efficient model compression with Random Operation Access Specific Tile (ROAST) hashing. (arXiv:2207.10702v1 [cs.LG])
    Advancements in deep learning are often associated with increasing model sizes. The model size dramatically affects the deployment cost and latency of deep models. For instance, models like BERT cannot be deployed on edge devices and mobiles due to their sheer size. As a result, most advances in Deep Learning are yet to reach the edge. Model compression has sought much-deserved attention in literature across natural language processing, vision, and recommendation domains. This paper proposes a model-agnostic, cache-friendly model compression approach: Random Operation Access Specific Tile (ROAST) hashing. ROAST collapses the parameters by clubbing them through a lightweight mapping. Notably, while clubbing these parameters, ROAST utilizes cache hierarchies by aligning the memory access pattern with the parameter access pattern. ROAST is up to $\sim 25 \times$ faster to train and $\sim 50 \times$ faster to infer than the popular parameter sharing method HashedNet. Additionally, ROAST introduces global weight sharing, which is empirically and theoretically superior to local weight sharing in HashedNet, and can be of independent interest in itself. With ROAST, we present the first compressed BERT, which is $100\times - 1000\times$ smaller but does not result in quality degradation. These compression levels on universal architecture like transformers are promising for the future of SOTA model deployment on resource-constrained devices like mobile and edge devices  ( 3 min )
    Correcting Model Bias with Sparse Implicit Processes. (arXiv:2207.10673v1 [stat.ML])
    Model selection in machine learning (ML) is a crucial part of the Bayesian learning procedure. Model choice may impose strong biases on the resulting predictions, which can hinder the performance of methods such as Bayesian neural networks and neural samplers. On the other hand, newly proposed approaches for Bayesian ML exploit features of approximate inference in function space with implicit stochastic processes (a generalization of Gaussian processes). The approach of Sparse Implicit Processes (SIP) is particularly successful in this regard, since it is fully trainable and achieves flexible predictions. Here, we expand on the original experiments to show that SIP is capable of correcting model bias when the data generating mechanism differs strongly from the one implied by the model. We use synthetic datasets to show that SIP is capable of providing predictive distributions that reflect the data better than the exact predictions of the initial, but wrongly assumed model.  ( 2 min )
    A Transferable Recommender Approach for Selecting the Best Density Functional Approximations in Chemical Discovery. (arXiv:2207.10747v1 [physics.chem-ph])
    Approximate density functional theory (DFT) has become indispensable owing to its cost-accuracy trade-off in comparison to more computationally demanding but accurate correlated wavefunction theory. To date, however, no single density functional approximation (DFA) with universal accuracy has been identified, leading to uncertainty in the quality of data generated from DFT. With electron density fitting and transfer learning, we build a DFA recommender that selects the DFA with the lowest expected error with respect to gold standard but cost-prohibitive coupled cluster theory in a system-specific manner. We demonstrate this recommender approach on vertical spin-splitting energy evaluation for challenging transition metal complexes. Our recommender predicts top-performing DFAs and yields excellent accuracy (ca. 2 kcal/mol) for chemical discovery, outperforming both individual transfer learning models and the single best functional in a set of 48 DFAs. We demonstrate the transferability of the DFA recommender to experimentally synthesized compounds with distinct chemistry.  ( 2 min )
    Improved Generalization Guarantees in Restricted Data Models. (arXiv:2207.10668v1 [cs.CR])
    Differential privacy is known to protect against threats to validity incurred due to adaptive, or exploratory, data analysis -- even when the analyst adversarially searches for a statistical estimate that diverges from the true value of the quantity of interest on the underlying population. The cost of this protection is the accuracy loss incurred by differential privacy. In this work, inspired by standard models in the genomics literature, we consider data models in which individuals are represented by a sequence of attributes with the property that where distant attributes are only weakly correlated. We show that, under this assumption, it is possible to "re-use" privacy budget on different portions of the data, significantly improving accuracy without increasing the risk of overfitting.  ( 2 min )
    Delayed Feedback in Generalised Linear Bandits Revisited. (arXiv:2207.10786v1 [cs.LG])
    The stochastic generalised linear bandit is a well-understood model for sequential decision-making problems, with many algorithms achieving near-optimal regret guarantees under immediate feedback. However, in many real world settings, the requirement that the reward is observed immediately is not applicable. In this setting, standard algorithms are no longer theoretically understood. We study the phenomenon of delayed rewards in a theoretical manner by introducing a delay between selecting an action and receiving the reward. Subsequently, we show that an algorithm based on the optimistic principle improves on existing approaches for this setting by eliminating the need for prior knowledge of the delay distribution and relaxing assumptions on the decision set and the delays. This also leads to improving the regret guarantees from $ \widetilde O(\sqrt{dT}\sqrt{d + \mathbb{E}[\tau]})$ to $ \widetilde O(d\sqrt{T} + d^{3/2}\mathbb{E}[\tau])$, where $\mathbb{E}[\tau]$ denotes the expected delay, $d$ is the dimension and $T$ the time horizon and we have suppressed logarithmic terms. We verify our theoretical results through experiments on simulated data.  ( 2 min )
    Data-Driven Stochastic AC-OPF using Gaussian Processes. (arXiv:2207.10781v1 [stat.ML])
    In recent years, electricity generation has been responsible for more than a quarter of the greenhouse gas emissions in the US. Integrating a significant amount of renewables into a power grid is probably the most accessible way to reduce carbon emissions from power grids and slow down climate change. Unfortunately, the most accessible renewable power sources, such as wind and solar, are highly fluctuating and thus bring a lot of uncertainty to power grid operations and challenge existing optimization and control policies. The chance-constrained alternating current (AC) optimal power flow (OPF) framework finds the minimum cost generation dispatch maintaining the power grid operations within security limits with a prescribed probability. Unfortunately, the AC-OPF problem's chance-constrained extension is non-convex, computationally challenging, and requires knowledge of system parameters and additional assumptions on the behavior of renewable distribution. Known linear and convex approximations to the above problems, though tractable, are too conservative for operational practice and do not consider uncertainty in system parameters. This paper presents an alternative data-driven approach based on Gaussian process (GP) regression to close this gap. The GP approach learns a simple yet non-convex data-driven approximation to the AC power flow equations that can incorporate uncertainty inputs. The latter is then used to determine the solution of CC-OPF efficiently, by accounting for both input and parameter uncertainty. The practical efficiency of the proposed approach using different approximations for GP-uncertainty propagation is illustrated over numerous IEEE test cases.  ( 3 min )
    A machine learning based approach to gravitational lens identification with the International LOFAR Telescope. (arXiv:2207.10698v1 [astro-ph.GA])
    We present a novel machine learning based approach for detecting galaxy-scale gravitational lenses from interferometric data, specifically those taken with the International LOFAR Telescope (ILT), which is observing the northern radio sky at a frequency of 150 MHz, an angular resolution of 350 mas and a sensitivity of 90 uJy beam-1 (1 sigma). We develop and test several Convolutional Neural Networks to determine the probability and uncertainty of a given sample being classified as a lensed or non-lensed event. By training and testing on a simulated interferometric imaging data set that includes realistic lensed and non-lensed radio sources, we find that it is possible to recover 95.3 per cent of the lensed samples (true positive rate), with a contamination of just 0.008 per cent from non-lensed samples (false positive rate). Taking the expected lensing probability into account results in a predicted sample purity for lensed events of 92.2 per cent. We find that the network structure is most robust when the maximum image separation between the lensed images is greater than 3 times the synthesized beam size, and the lensed images have a total flux density that is equivalent to at least a 20 sigma (point-source) detection. For the ILT, this corresponds to a lens sample with Einstein radii greater than 0.5 arcsec and a radio source population with 150 MHz flux densities more than 2 mJy. By applying these criteria and our lens detection algorithm we expect to discover the vast majority of galaxy-scale gravitational lens systems contained within the LOFAR Two Metre Sky Survey.  ( 3 min )
    The trade-offs of model size in large recommendation models : A 10000 $\times$ compressed criteo-tb DLRM model (100 GB parameters to mere 10MB). (arXiv:2207.10731v1 [cs.LG])
    Embedding tables dominate industrial-scale recommendation model sizes, using up to terabytes of memory. A popular and the largest publicly available machine learning MLPerf benchmark on recommendation data is a Deep Learning Recommendation Model (DLRM) trained on a terabyte of click-through data. It contains 100GB of embedding memory (25+Billion parameters). DLRMs, due to their sheer size and the associated volume of data, face difficulty in training, deploying for inference, and memory bottlenecks due to large embedding tables. This paper analyzes and extensively evaluates a generic parameter sharing setup (PSS) for compressing DLRM models. We show theoretical upper bounds on the learnable memory requirements for achieving $(1 \pm \epsilon)$ approximations to the embedding table. Our bounds indicate exponentially fewer parameters suffice for good accuracy. To this end, we demonstrate a PSS DLRM reaching 10000$\times$ compression on criteo-tb without losing quality. Such a compression, however, comes with a caveat. It requires 4.5 $\times$ more iterations to reach the same saturation quality. The paper argues that this tradeoff needs more investigations as it might be significantly favorable. Leveraging the small size of the compressed model, we show a 4.3$\times$ improvement in training latency leading to similar overall training times. Thus, in the tradeoff between system advantage of a small DLRM model vs. slower convergence, we show that scales are tipped towards having a smaller DLRM model, leading to faster inference, easier deployment, and similar training times.  ( 3 min )
    Understanding High Dimensional Spaces through Visual Means Employing Multidimensional Projections. (arXiv:2207.10800v1 [cs.HC])
    Data visualisation helps understanding data represented by multiple variables, also called features, stored in a large matrix where individuals are stored in lines and variable values in columns. These data structures are frequently called multidimensional spaces.In this paper, we illustrate ways of employing the visual results of multidimensional projection algorithms to understand and fine-tune the parameters of their mathematical framework. Some of the common mathematical common to these approaches are Laplacian matrices, Euclidian distance, Cosine distance, and statistical methods such as Kullback-Leibler divergence, employed to fit probability distributions and reduce dimensions. Two of the relevant algorithms in the data visualisation field are t-distributed stochastic neighbourhood embedding (t-SNE) and Least-Square Projection (LSP). These algorithms can be used to understand several ranges of mathematical functions including their impact on datasets. In this article, mathematical parameters of underlying techniques such as Principal Component Analysis (PCA) behind t-SNE and mesh reconstruction methods behind LSP are adjusted to reflect the properties afforded by the mathematical formulation. The results, supported by illustrative methods of the processes of LSP and t-SNE, are meant to inspire students in understanding the mathematics behind such methods, in order to apply them in effective data analysis tasks in multiple applications.  ( 2 min )
    Synthetic Dataset Generation for Adversarial Machine Learning Research. (arXiv:2207.10719v1 [cs.CV])
    Existing adversarial example research focuses on digitally inserted perturbations on top of existing natural image datasets. This construction of adversarial examples is not realistic because it may be difficult, or even impossible, for an attacker to deploy such an attack in the real-world due to sensing and environmental effects. To better understand adversarial examples against cyber-physical systems, we propose approximating the real-world through simulation. In this paper we describe our synthetic dataset generation tool that enables scalable collection of such a synthetic dataset with realistic adversarial examples. We use the CARLA simulator to collect such a dataset and demonstrate simulated attacks that undergo the same environmental transforms and processing as real-world images. Our tools have been used to collect datasets to help evaluate the efficacy of adversarial examples, and can be found at https://github.com/carla-simulator/carla/pull/4992.  ( 2 min )
    ME-GAN: Learning Panoptic Electrocardio Representations for Multi-view ECG Synthesis Conditioned on Heart Diseases. (arXiv:2207.10670v1 [cs.LG])
    Electrocardiogram (ECG) is a widely used non-invasive diagnostic tool for heart diseases. Many studies have devised ECG analysis models (e.g., classifiers) to assist diagnosis. As an upstream task, researches have built generative models to synthesize ECG data, which are beneficial to providing training samples, privacy protection, and annotation reduction. However, previous generative methods for ECG often neither synthesized multi-view data, nor dealt with heart disease conditions. In this paper, we propose a novel disease-aware generative adversarial network for multi-view ECG synthesis called ME-GAN, which attains panoptic electrocardio representations conditioned on heart diseases and projects the representations onto multiple standard views to yield ECG signals. Since ECG manifestations of heart diseases are often localized in specific waveforms, we propose a new "mixup normalization" to inject disease information precisely into suitable locations. In addition, we propose a view discriminator to revert disordered ECG views into a pre-determined order, supervising the generator to obtain ECG representing correct view characteristics. Besides, a new metric, rFID, is presented to assess the quality of the synthesized ECG signals. Comprehensive experiments verify that our ME-GAN performs well on multi-view ECG signal synthesis with trusty morbid manifestations.  ( 3 min )
    Context-aware controller inference for stabilizing dynamical systems from scarce data. (arXiv:2207.11049v1 [math.OC])
    This work introduces a data-driven control approach for stabilizing high-dimensional dynamical systems from scarce data. The proposed context-aware controller inference approach is based on the observation that controllers need to act locally only on the unstable dynamics to stabilize systems. This means it is sufficient to learn the unstable dynamics alone, which are typically confined to much lower dimensional spaces than the high-dimensional state spaces of all system dynamics and thus few data samples are sufficient to identify them. Numerical experiments demonstrate that context-aware controller inference learns stabilizing controllers from orders of magnitude fewer data samples than traditional data-driven control techniques and variants of reinforcement learning. The experiments further show that the low data requirements of context-aware controller inference are especially beneficial in data-scarce engineering problems with complex physics, for which learning complete system dynamics is often intractable in terms of data and training costs.  ( 2 min )
    Flow Moods: Recommending Music by Moods on Deezer. (arXiv:2207.11229v1 [cs.IR])
    The music streaming service Deezer extensively relies on its Flow algorithm, which generates personalized radio-style playlists of songs, to help users discover musical content. Nonetheless, despite promising results over the past years, Flow used to ignore the moods of users when providing recommendations. In this paper, we present Flow Moods, an improved version of Flow that addresses this limitation. Flow Moods leverages collaborative filtering, audio content analysis, and mood annotations from professional music curators to generate personalized mood-specific playlists at scale. We detail the motivations, the development, and the deployment of this system on Deezer. Since its release in 2021, Flow Moods has been recommending music by moods to millions of users every day.  ( 2 min )
  • Open

    Correcting Model Bias with Sparse Implicit Processes. (arXiv:2207.10673v1 [stat.ML])
    Model selection in machine learning (ML) is a crucial part of the Bayesian learning procedure. Model choice may impose strong biases on the resulting predictions, which can hinder the performance of methods such as Bayesian neural networks and neural samplers. On the other hand, newly proposed approaches for Bayesian ML exploit features of approximate inference in function space with implicit stochastic processes (a generalization of Gaussian processes). The approach of Sparse Implicit Processes (SIP) is particularly successful in this regard, since it is fully trainable and achieves flexible predictions. Here, we expand on the original experiments to show that SIP is capable of correcting model bias when the data generating mechanism differs strongly from the one implied by the model. We use synthetic datasets to show that SIP is capable of providing predictive distributions that reflect the data better than the exact predictions of the initial, but wrongly assumed model.  ( 2 min )
    Post-training Quantization for Neural Networks with Provable Guarantees. (arXiv:2201.11113v2 [cs.LG] UPDATED)
    While neural networks have been remarkably successful in a wide array of applications, implementing them in resource-constrained hardware remains an area of intense research. By replacing the weights of a neural network with quantized (e.g., 4-bit, or binary) counterparts, massive savings in computation cost, memory, and power consumption are attained. To that end, we generalize a post-training neural-network quantization method, GPFQ, that is based on a greedy path-following mechanism. Among other things, we propose modifications to promote sparsity of the weights, and rigorously analyze the associated error. Additionally, our error analysis expands the results of previous work on GPFQ to handle general quantization alphabets, showing that for quantizing a single-layer network, the relative square error essentially decays linearly in the number of weights -- i.e., level of over-parametrization. Our result holds across a range of input distributions and for both fully-connected and convolutional architectures thereby also extending previous results. To empirically evaluate the method, we quantize several common architectures with few bits per weight, and test them on ImageNet, showing only minor loss of accuracy compared to unquantized models. We also demonstrate that standard modifications, such as bias correction and mixed precision quantization, further improve accuracy.
    Technical Reports Compilation: Detecting the Fire Drill Anti-pattern Using Source Code and Issue-Tracking Data. (arXiv:2104.15090v7 [cs.SE] UPDATED)
    Detecting the presence of project management anti-patterns (AP) currently requires experts on the matter and is an expensive endeavor. Worse, experts may introduce their individual subjectivity or bias. Using the Fire Drill AP, we first introduce a novel way to translate descriptions into detectable AP that are comprised of arbitrary metrics and events such as logged time or maintenance activities, which are mined from the underlying source code or issue-tracking data, thus making the description objective as it becomes data-based. Secondly, we demonstrate a novel method to quantify and score the deviations of real-world projects to data-based AP descriptions. Using fifteen real-world projects that exhibit a Fire Drill to some degree, we show how to further enhance the translated AP. The ground truth in these projects was extracted from two individual experts and consensus was found between them. Our evaluation spans four kinds of patterns, where the first is purely derived from description, the second type is enhanced by data, and the third kind is derived from data only. The fourth type then is a derivative meta-process pattern. We introduce a novel method called automatic calibration, that optimizes a pattern such that only necessary and important scores remain that suffice to confidently detect the degree to which the AP is present. Without automatic calibration, the proposed patterns show only weak potential for detecting the presence. Enriching the AP with data from real-world projects significantly improves the potential. We conclude that the presence of similar patterns is most certainly detectable. Furthermore, any pattern that can be characteristically modeled using the proposed approach is potentially well detectable.
    Automatic Termination for Hyperparameter Optimization. (arXiv:2104.08166v4 [cs.LG] UPDATED)
    Bayesian optimization (BO) is a widely popular approach for the hyperparameter optimization (HPO) in machine learning. At its core, BO iteratively evaluates promising configurations until a user-defined budget, such as wall-clock time or number of iterations, is exhausted. While the final performance after tuning heavily depends on the provided budget, it is hard to pre-specify an optimal value in advance. In this work, we propose an effective and intuitive termination criterion for BO that automatically stops the procedure if it is sufficiently close to the global optimum. Our key insight is that the discrepancy between the true objective (predictive performance on test data) and the computable target (validation performance) suggests stopping once the suboptimality in optimizing the target is dominated by the statistical estimation error. Across an extensive range of real-world HPO problems and baselines, we show that our termination criterion achieves a better trade-off between the test performance and optimization time. Additionally, we find that overfitting may occur in the context of HPO, which is arguably an overlooked problem in the literature, and show how our termination criterion helps to mitigate this phenomenon on both small and large datasets.
    Function-space Inference with Sparse Implicit Processes. (arXiv:2110.07618v3 [stat.ML] UPDATED)
    Implicit Processes (IPs) represent a flexible framework that can be used to describe a wide variety of models, from Bayesian neural networks, neural samplers and data generators to many others. IPs also allow for approximate inference in function-space. This change of formulation solves intrinsic degenerate problems of parameter-space approximate inference concerning the high number of parameters and their strong dependencies in large models. For this, previous works in the literature have attempted to employ IPs both to set up the prior and to approximate the resulting posterior. However, this has proven to be a challenging task. Existing methods that can tune the prior IP result in a Gaussian predictive distribution, which fails to capture important data patterns. By contrast, methods producing flexible predictive distributions by using another IP to approximate the posterior process cannot tune the prior IP to the observed data. We propose here the first method that can accomplish both goals. For this, we rely on an inducing-point representation of the prior IP, as often done in the context of sparse Gaussian processes. The result is a scalable method for approximate inference with IPs that can tune the prior IP parameters to the data, and that provides accurate non-Gaussian predictive distributions.  ( 3 min )
    Relaxed Gaussian process interpolation: a goal-oriented approach to Bayesian optimization. (arXiv:2206.03034v2 [stat.CO] UPDATED)
    This work presents a new procedure for obtaining predictive distributions in the context of Gaussian process (GP) modeling, with a relaxation of the interpolation constraints outside some ranges of interest: the mean of the predictive distributions no longer necessarily interpolates the observed values when they are outside ranges of interest, but are simply constrained to remain outside. This method called relaxed Gaussian process (reGP) interpolation provides better predictive distributions in ranges of interest, especially in cases where a stationarity assumption for the GP model is not appropriate. It can be viewed as a goal-oriented method and becomes particularly interesting in Bayesian optimization, for example, for the minimization of an objective function, where good predictive distributions for low function values are important. When the expected improvement criterion and reGP are used for sequentially choosing evaluation points, the convergence of the resulting optimization algorithm is theoretically guaranteed (provided that the function to be optimized lies in the reproducing kernel Hilbert spaces attached to the known covariance of the underlying Gaussian process). Experiments indicate that using reGP instead of stationary GP models in Bayesian optimization is beneficial.  ( 3 min )
    Fast Bayesian Coresets via Subsampling and Quasi-Newton Refinement. (arXiv:2203.09675v2 [stat.ML] UPDATED)
    Bayesian coresets approximate a posterior distribution by building a small weighted subset of the data points. Any inference procedure that is too computationally expensive to be run on the full posterior can instead be run inexpensively on the coreset, with results that approximate those on the full data. However, current approaches are limited by either a significant run-time or the need for the user to specify a low-cost approximation to the full posterior. We propose a Bayesian coreset construction algorithm that first selects a uniformly random subset of data, and then optimizes the weights using a novel quasi-Newton method. Our algorithm is a simple to implement, black-box method, that does not require the user to specify a low-cost posterior approximation. It is the first to come with a general high-probability bound on the KL divergence of the output coreset posterior. Experiments demonstrate that our method provides significant improvements in coreset quality against alternatives with comparable construction times, with far less storage cost and user input required.  ( 2 min )
    Tight bounds on the hardness of learning simple nonparametric mixtures. (arXiv:2203.15150v2 [cs.LG] UPDATED)
    We study the problem of learning nonparametric distributions in a finite mixture, and establish tight bounds on the sample complexity for learning the component distributions in such models. Namely, we are given i.i.d. samples from a pdf $f$ where $$ f=\sum_{i=1}^k w_i f_i, \quad\sum_{i=1}^k w_i=1, \quad w_i>0 $$ and we are interested in learning each component $f_i$. Without any assumptions on $f_i$, this problem is ill-posed. In order to identify the components $f_i$, we assume that each $f_i$ can be written as a convolution of a Gaussian and a compactly supported density $\nu_i$ with $\text{supp}(\nu_i)\cap \text{supp}(\nu_j)=\emptyset$. Our main result shows that $(\frac{1}{\varepsilon})^{\Omega(\log\log \frac{1}{\varepsilon})}$ samples are required for estimating each $f_i$. Unlike parametric mixtures, the difficulty does not arise from the order $k$ or small weights $w_i$, and unlike nonparametric density estimation it does not arise from the curse of dimensionality, irregularity, or inhomogeneity. The proof relies on a fast rate for approximation with Gaussians, which may be of independent interest. To show this is tight, we also propose an algorithm that uses $(\frac{1}{\varepsilon})^{O(\log\log \frac{1}{\varepsilon})}$ samples to estimate each $f_i$. Unlike existing approaches to learning latent variable models based on moment-matching and tensor methods, our proof instead involves a delicate analysis of an ill-conditioned linear system via orthogonal functions. Combining these bounds, we conclude that the optimal sample complexity of this problem properly lies in between polynomial and exponential, which is not common in learning theory.  ( 3 min )
    High dimensional stochastic linear contextual bandit with missing covariates. (arXiv:2207.11165v1 [stat.ML])
    Recent works in bandit problems adopted lasso convergence theory in the sequential decision-making setting. Even with fully observed contexts, there are technical challenges that hinder the application of existing lasso convergence theory: 1) proving the restricted eigenvalue condition under conditionally sub-Gaussian noise and 2) accounting for the dependence between the context variables and the chosen actions. This paper studies the effect of missing covariates on regret for stochastic linear bandit algorithms. Our work provides a high-probability upper bound on the regret incurred by the proposed algorithm in terms of covariate sampling probabilities, showing that the regret degrades due to missingness by at most $\zeta_{min}^2$, where $\zeta_{min}$ is the minimum probability of observing covariates in the context vector. We illustrate our algorithm for the practical application of experimental design for collecting gene expression data by a sequential selection of class discriminating DNA probes.  ( 2 min )
    Doubly-Valid/Doubly-Sharp Sensitivity Analysis for Causal Inference with Unmeasured Confounding. (arXiv:2112.11449v2 [stat.ME] UPDATED)
    We consider the problem of constructing bounds on the average treatment effect (ATE) when unmeasured confounders exist but have bounded influence. Specifically, we assume that omitted confounders could not change the odds of treatment for any unit by more than a fixed factor. We derive the sharp partial identification bounds implied by this assumption by leveraging distributionally robust optimization, and we propose estimators of these bounds with several novel robustness properties. The first is double sharpness: our estimators consistently estimate the sharp ATE bounds when one of two nuisance parameters is misspecified and achieve semiparametric efficiency when all nuisance parameters are suitably consistent. The second is double validity: even when most nuisance parameters are misspecified, our estimators still provide valid but possibly conservative bounds for the ATE and our Wald confidence intervals remain valid even when our estimators are not asymptotically normal. As a result, our estimators provide a highly credible method for sensitivity analysis of causal inferences.  ( 2 min )
    Deep Sufficient Representation Learning via Mutual Information. (arXiv:2207.10772v1 [stat.ML])
    We propose a mutual information-based sufficient representation learning (MSRL) approach, which uses the variational formulation of the mutual information and leverages the approximation power of deep neural networks. MSRL learns a sufficient representation with the maximum mutual information with the response and a user-selected distribution. It can easily handle multi-dimensional continuous or categorical response variables. MSRL is shown to be consistent in the sense that the conditional probability density function of the response variable given the learned representation converges to the conditional probability density function of the response variable given the predictor. Non-asymptotic error bounds for MSRL are also established under suitable conditions. To establish the error bounds, we derive a generalized Dudley's inequality for an order-two U-process indexed by deep neural networks, which may be of independent interest. We discuss how to determine the intrinsic dimension of the underlying data distribution. Moreover, we evaluate the performance of MSRL via extensive numerical experiments and real data analysis and demonstrate that MSRL outperforms some existing nonlinear sufficient dimension reduction methods.  ( 2 min )
    Modeling Randomly Walking Volatility with Chained Gamma Distributions. (arXiv:2207.01151v2 [q-fin.CP] UPDATED)
    Volatility clustering is a common phenomenon in financial time series. Typically, linear models can be used to describe the temporal autocorrelation of the (logarithmic) variance of returns. Considering the difficulty in estimating this model, we construct a Dynamic Bayesian Network, which utilizes the conjugate prior relation of normal-gamma and gamma-gamma, so that its posterior form locally remains unchanged at each node. This makes it possible to find approximate solutions using variational methods quickly. Furthermore, we ensure that the volatility expressed by the model is an independent incremental process after inserting dummy gamma nodes between adjacent time steps. We have found that this model has two advantages: 1) It can be proved that it can express heavier tails than Gaussians, i.e., have positive excess kurtosis, compared to popular linear models. 2) If the variational inference(VI) is used for state estimation, it runs much faster than Monte Carlo(MC) methods since the calculation of the posterior uses only basic arithmetic operations. And its convergence process is deterministic. We tested the model, named Gam-Chain, using recent Crypto, Nasdaq, and Forex records of varying resolutions. The results show that: 1) In the same case of using MC, this model can achieve comparable state estimation results with the regular lognormal chain. 2) In the case of only using VI, this model can obtain accuracy that are slightly worse than MC, but still acceptable in practice; 3) Only using VI, the running time of Gam-Chain, under the most conservative settings, can be reduced to below 20% of that based on the lognormal chain via MC.  ( 3 min )
    Generalized Identifiability Bounds for Mixture Models with Grouped Samples. (arXiv:2207.11164v1 [math.ST])
    Recent work has shown that finite mixture models with $m$ components are identifiable, while making no assumptions on the mixture components, so long as one has access to groups of samples of size $2m-1$ which are known to come from the same mixture component. In this work we generalize that result and show that, if every subset of $k$ mixture components of a mixture model are linearly independent, then that mixture model is identifiable with only $(2m-1)/(k-1)$ samples per group. We further show that this value cannot be improved. We prove an analogous result for a stronger form of identifiability known as "determinedness" along with a corresponding lower bound. This independence assumption almost surely holds if mixture components are chosen randomly from a $k$-dimensional space. We describe some implications of our results for multinomial mixture models and topic modeling.  ( 2 min )
    Statistical Hypothesis Testing Based on Machine Learning: Large Deviations Analysis. (arXiv:2207.10939v1 [stat.ML])
    We study the performance -- and specifically the rate at which the error probability converges to zero -- of Machine Learning (ML) classification techniques. Leveraging the theory of large deviations, we provide the mathematical conditions for a ML classifier to exhibit error probabilities that vanish exponentially, say $\sim \exp\left(-n\,I + o(n) \right)$, where $n$ is the number of informative observations available for testing (or another relevant parameter, such as the size of the target in an image) and $I$ is the error rate. Such conditions depend on the Fenchel-Legendre transform of the cumulant-generating function of the Data-Driven Decision Function (D3F, i.e., what is thresholded before the final binary decision is made) learned in the training phase. As such, the D3F and, consequently, the related error rate $I$, depend on the given training set, which is assumed of finite size. Interestingly, these conditions can be verified and tested numerically exploiting the available dataset, or a synthetic dataset, generated according to the available information on the underlying statistical model. In other words, the classification error probability convergence to zero and its rate can be computed on a portion of the dataset available for training. Coherently with the large deviations theory, we can also establish the convergence, for $n$ large enough, of the normalized D3F statistic to a Gaussian distribution. This property is exploited to set a desired asymptotic false alarm probability, which empirically turns out to be accurate even for quite realistic values of $n$. Furthermore, approximate error probability curves $\sim \zeta_n \exp\left(-n\,I \right)$ are provided, thanks to the refined asymptotic derivation (often referred to as exact asymptotics), where $\zeta_n$ represents the most representative sub-exponential terms of the error probabilities.  ( 3 min )
    Relaxing the I.I.D. Assumption: Adaptively Minimax Optimal Regret via Root-Entropic Regularization. (arXiv:2007.06552v3 [stat.ML] UPDATED)
    We consider prediction with expert advice when data are generated from distributions varying arbitrarily within an unknown constraint set. This semi-adversarial setting includes (at the extremes) the classical i.i.d. setting, when the unknown constraint set is restricted to be a singleton, and the unconstrained adversarial setting, when the constraint set is the set of all distributions. The Hedge algorithm -- long known to be minimax (rate) optimal in the adversarial regime -- was recently shown to be simultaneously minimax optimal for i.i.d. data. In this work, we propose to relax the i.i.d. assumption by seeking adaptivity at all levels of a natural ordering on constraint sets. We provide matching upper and lower bounds on the minimax regret at all levels, show that Hedge with deterministic learning rates is suboptimal outside of the extremes, and prove that one can adaptively obtain minimax regret at all levels. We achieve this optimal adaptivity using the follow-the-regularized-leader (FTRL) framework, with a novel adaptive regularization scheme that implicitly scales as the square root of the entropy of the current predictive distribution, rather than the entropy of the initial predictive distribution. Finally, we provide novel technical tools to study the statistical performance of FTRL along the semi-adversarial spectrum.  ( 3 min )
    Deriving discriminative classifiers from generative models. (arXiv:2201.00844v2 [stat.ML] UPDATED)
    We deal with Bayesian generative and discriminative classifiers. Given a model distribution $p(x, y)$, with the observation $y$ and the target $x$, one computes generative classifiers by firstly considering $p(x, y)$ and then using the Bayes rule to calculate $p(x | y)$. A discriminative model is directly given by $p(x | y)$, which is used to compute discriminative classifiers. However, recent works showed that the Bayesian Maximum Posterior classifier defined from the Naive Bayes (NB) or Hidden Markov Chain (HMC), both generative models, can also match the discriminative classifier definition. Thus, there are situations in which dividing classifiers into "generative" and "discriminative" is somewhat misleading. Indeed, such a distinction is rather related to the way of computing classifiers, not to the classifiers themselves. We present a general theoretical result specifying how a generative classifier induced from a generative model can also be computed in a discriminative way from the same model. Examples of NB and HMC are found again as particular cases, and we apply the general result to two original extensions of NB, and two extensions of HMC, one of which being original. Finally, we shortly illustrate the interest of the new discriminative way of computing classifiers in the Natural Language Processing (NLP) framework.  ( 3 min )
    Delayed Feedback in Generalised Linear Bandits Revisited. (arXiv:2207.10786v1 [cs.LG])
    The stochastic generalised linear bandit is a well-understood model for sequential decision-making problems, with many algorithms achieving near-optimal regret guarantees under immediate feedback. However, in many real world settings, the requirement that the reward is observed immediately is not applicable. In this setting, standard algorithms are no longer theoretically understood. We study the phenomenon of delayed rewards in a theoretical manner by introducing a delay between selecting an action and receiving the reward. Subsequently, we show that an algorithm based on the optimistic principle improves on existing approaches for this setting by eliminating the need for prior knowledge of the delay distribution and relaxing assumptions on the decision set and the delays. This also leads to improving the regret guarantees from $ \widetilde O(\sqrt{dT}\sqrt{d + \mathbb{E}[\tau]})$ to $ \widetilde O(d\sqrt{T} + d^{3/2}\mathbb{E}[\tau])$, where $\mathbb{E}[\tau]$ denotes the expected delay, $d$ is the dimension and $T$ the time horizon and we have suppressed logarithmic terms. We verify our theoretical results through experiments on simulated data.  ( 2 min )
    Fairness-aware Network Revenue Management with Demand Learning. (arXiv:2207.11159v1 [stat.ML])
    In addition to maximizing the total revenue, decision-makers in lots of industries would like to guarantee fair consumption across different resources and avoid saturating certain resources. Motivated by these practical needs, this paper studies the price-based network revenue management problem with both demand learning and fairness concern about the consumption across different resources. We introduce the regularized revenue, i.e., the total revenue with a fairness regularization, as our objective to incorporate fairness into the revenue maximization goal. We propose a primal-dual-type online policy with the Upper-Confidence-Bound (UCB) demand learning method to maximize the regularized revenue. We adopt several innovative techniques to make our algorithm a unified and computationally efficient framework for the continuous price set and a wide class of fairness regularizers. Our algorithm achieves a worst-case regret of $\tilde O(N^{5/2}\sqrt{T})$, where $N$ denotes the number of products and $T$ denotes the number of time periods. Numerical experiments in a few NRM examples demonstrate the effectiveness of our algorithm for balancing revenue and fairness.  ( 2 min )
    Implicit Regularization in Hierarchical Tensor Factorization and Deep Convolutional Neural Networks. (arXiv:2201.11729v4 [cs.LG] UPDATED)
    In the pursuit of explaining implicit regularization in deep learning, prominent focus was given to matrix and tensor factorizations, which correspond to simplified neural networks. It was shown that these models exhibit an implicit tendency towards low matrix and tensor ranks, respectively. Drawing closer to practical deep learning, the current paper theoretically analyzes the implicit regularization in hierarchical tensor factorization, a model equivalent to certain deep convolutional neural networks. Through a dynamical systems lens, we overcome challenges associated with hierarchy, and establish implicit regularization towards low hierarchical tensor rank. This translates to an implicit regularization towards locality for the associated convolutional networks. Inspired by our theory, we design explicit regularization discouraging locality, and demonstrate its ability to improve the performance of modern convolutional networks on non-local tasks, in defiance of conventional wisdom by which architectural changes are needed. Our work highlights the potential of enhancing neural networks via theoretical analysis of their implicit regularization.  ( 3 min )
    VTrackIt: A Synthetic Self-Driving Dataset with Infrastructure and Pooled Vehicle Information. (arXiv:2207.11146v1 [cs.CV])
    Artificial intelligence solutions for Autonomous Vehicles (AVs) have been developed using publicly available datasets such as Argoverse, ApolloScape, Level5, and NuScenes. One major limitation of these datasets is the absence of infrastructure and/or pooled vehicle information like lane line type, vehicle speed, traffic signs, and intersections. Such information is necessary and not complementary to eliminating high-risk edge cases. The rapid advancements in Vehicle-to-Infrastructure and Vehicle-to-Vehicle technologies show promise that infrastructure and pooled vehicle information will soon be accessible in near real-time. Taking a leap in the future, we introduce the first comprehensive synthetic dataset with intelligent infrastructure and pooled vehicle information for advancing the next generation of AVs, named VTrackIt. We also introduce the first deep learning model (InfraGAN) for trajectory predictions that considers such information. Our experiments with InfraGAN show that the comprehensive information offered by VTrackIt reduces the number of high-risk edge cases. The VTrackIt dataset is available upon request under the Creative Commons CC BY-NC-SA 4.0 license at this http URL  ( 2 min )
    Twitmo: A Twitter Data Topic Modeling and Visualization Package for R. (arXiv:2207.11236v1 [cs.IR])
    We present Twitmo, a package that provides a broad range of methods to collect, pre-process, analyze and visualize geo-tagged Twitter data. Twitmo enables the user to collect geo-tagged Tweets from Twitter and and provides a comprehensive and user-friendly toolbox to generate topic distributions from Latent Dirichlet Allocations (LDA), correlated topic models (CTM) and structural topic models (STM). Functions are included for pre-processing of text, model building and prediction. In addition, one of the innovations of the package is the automatic pooling of Tweets into longer pseudo-documents using hashtags and cosine similarities for better topic coherence. The package additionally comes with functionality to visualize collected data sets and fitted models in static as well as interactive ways and offers built-in support for model visualizations via LDAvis providing great convenience for researchers in this area. The Twitmo package is an innovative toolbox that can be used to analyze public discourse of various topics, political parties or persons of interest in space and time.  ( 2 min )
    Multiple Robust Learning for Recommendation. (arXiv:2207.10796v1 [cs.IR])
    In recommender systems, a common problem is the presence of various biases in the collected data, which deteriorates the generalization ability of the recommendation models and leads to inaccurate predictions. Doubly robust (DR) learning has been studied in many tasks in RS, with the advantage that unbiased learning can be achieved when either a single imputation or a single propensity model is accurate. In this paper, we propose a multiple robust (MR) estimator that can take the advantage of multiple candidate imputation and propensity models to achieve unbiasedness. Specifically, the MR estimator is unbiased when any of the imputation or propensity models, or a linear combination of these models is accurate. Theoretical analysis shows that the proposed MR is an enhanced version of DR when only having a single imputation and propensity model, and has a smaller bias. Inspired by the generalization error bound of MR, we further propose a novel multiple robust learning approach with stabilization. We conduct extensive experiments on real-world and semi-synthetic datasets, which demonstrates the superiority of the proposed approach over state-of-the-art methods.  ( 2 min )
    Optimal Model Averaging of Support Vector Machines in Diverging Model Spaces. (arXiv:2112.12961v3 [stat.ML] UPDATED)
    Support vector machine (SVM) is a powerful classification method that has achieved great success in many fields. Since its performance can be seriously impaired by redundant covariates, model selection techniques are widely used for SVM with high dimensional covariates. As an alternative to model selection, significant progress has been made in the area of model averaging in the past decades. Yet no frequentist model averaging method was considered for SVM. This work aims to fill the gap and to propose a frequentist model averaging procedure for SVM which selects the optimal weight by cross validation. Even when the number of covariates diverges at an exponential rate of the sample size, we show asymptotic optimality of the proposed method in the sense that the ratio of its hinge loss to the lowest possible loss converges to one. We also derive the convergence rate which provides more insights to model averaging. Compared to model selection methods of SVM which require a tedious but critical task of tuning parameter selection, the model averaging method avoids the task and shows promising performances in the empirical studies.  ( 3 min )
    JAWS: Predictive Inference Under Covariate Shift. (arXiv:2207.10716v1 [cs.LG])
    We propose \textbf{JAWS}, a series of wrapper methods for distribution-free uncertainty quantification tasks under covariate shift, centered on our core method \textbf{JAW}, the \textbf{JA}ckknife+ \textbf{W}eighted with likelihood-ratio weights. JAWS also includes computationally efficient \textbf{A}pproximations of JAW using higher-order influence functions: \textbf{JAWA}. Theoretically, we show that JAW relaxes the jackknife+'s assumption of data exchangeability to achieve the same finite-sample coverage guarantee even under covariate shift. JAWA further approaches the JAW guarantee in the limit of either the sample size or the influence function order under mild assumptions. Moreover, we propose a general approach to repurposing any distribution-free uncertainty quantification method and its guarantees to the task of risk assessment: a task that generates the estimated probability that the true label lies within a user-specified interval. We then propose \textbf{JAW-R} and \textbf{JAWA-R} as the repurposed versions of proposed methods for \textbf{R}isk assessment. Practically, JAWS outperform the state-of-the-art predictive inference baselines in a variety of biased real world data sets for both interval-generation and risk-assessment auditing tasks.  ( 2 min )
    SPRT-based Efficient Best Arm Identification in Stochastic Bandits. (arXiv:2207.11158v1 [stat.ML])
    This paper investigates the best arm identification (BAI) problem in stochastic multi-armed bandits in the fixed confidence setting. The general class of the exponential family of bandits is considered. The state-of-the-art algorithms for the exponential family of bandits face computational challenges. To mitigate these challenges, a novel framework is proposed, which views the BAI problem as sequential hypothesis testing, and is amenable to tractable analysis for the exponential family of bandits. Based on this framework, a BAI algorithm is designed that leverages the canonical sequential probability ratio tests. This algorithm has three features for both settings: (1) its sample complexity is asymptotically optimal, (2) it is guaranteed to be $\delta-$PAC, and (3) it addresses the computational challenge of the state-of-the-art approaches. Specifically, these approaches, which are focused only on the Gaussian setting, require Thompson sampling from the arm that is deemed the best and a challenger arm. This paper analytically shows that identifying the challenger is computationally expensive and that the proposed algorithm circumvents it. Finally, numerical experiments are provided to support the analysis.  ( 2 min )
    Classifying Crop Types using Gaussian Bayesian Models and Neural Networks on GHISACONUS USGS data from NASA Hyperspectral Satellite Imagery. (arXiv:2207.11228v1 [cs.CV])
    Hyperspectral Imagining is a type of digital imaging in which each pixel contains typically hundreds of wavelengths of light providing spectroscopic information about the materials present in the pixel. In this paper we provide classification methods for determining crop type in the USGS GHISACONUS data, which contains around 7,000 pixel spectra from the five major U.S. agricultural crops (winter wheat, rice, corn, soybeans, and cotton) collected by the NASA Hyperion satellite, and includes the spectrum, geolocation, crop type, and stage of growth for each pixel. We apply standard LDA and QDA as well as Bayesian custom versions that compute the joint probability of crop type and stage, and then the marginal probability for crop type, outperforming the non-Bayesian methods. We also test a single layer neural network with dropout on the data, which performs comparable to LDA and QDA but not as well as the Bayesian methods.  ( 2 min )
    Data-Driven Stochastic AC-OPF using Gaussian Processes. (arXiv:2207.10781v1 [stat.ML])
    In recent years, electricity generation has been responsible for more than a quarter of the greenhouse gas emissions in the US. Integrating a significant amount of renewables into a power grid is probably the most accessible way to reduce carbon emissions from power grids and slow down climate change. Unfortunately, the most accessible renewable power sources, such as wind and solar, are highly fluctuating and thus bring a lot of uncertainty to power grid operations and challenge existing optimization and control policies. The chance-constrained alternating current (AC) optimal power flow (OPF) framework finds the minimum cost generation dispatch maintaining the power grid operations within security limits with a prescribed probability. Unfortunately, the AC-OPF problem's chance-constrained extension is non-convex, computationally challenging, and requires knowledge of system parameters and additional assumptions on the behavior of renewable distribution. Known linear and convex approximations to the above problems, though tractable, are too conservative for operational practice and do not consider uncertainty in system parameters. This paper presents an alternative data-driven approach based on Gaussian process (GP) regression to close this gap. The GP approach learns a simple yet non-convex data-driven approximation to the AC power flow equations that can incorporate uncertainty inputs. The latter is then used to determine the solution of CC-OPF efficiently, by accounting for both input and parameter uncertainty. The practical efficiency of the proposed approach using different approximations for GP-uncertainty propagation is illustrated over numerous IEEE test cases.  ( 3 min )
    Statistical and Computational Trade-offs in Variational Inference: A Case Study in Inferential Model Selection. (arXiv:2207.11208v1 [stat.ML])
    Variational inference has recently emerged as a popular alternative to the classical Markov chain Monte Carlo (MCMC) in large-scale Bayesian inference. The core idea of variational inference is to trade statistical accuracy for computational efficiency. It aims to approximate the posterior, reducing computation costs but potentially compromising its statistical accuracy. In this work, we study this statistical and computational trade-off in variational inference via a case study in inferential model selection. Focusing on Gaussian inferential models (a.k.a. variational approximating families) with diagonal plus low-rank precision matrices, we initiate a theoretical study of the trade-offs in two aspects, Bayesian posterior inference error and frequentist uncertainty quantification error. From the Bayesian posterior inference perspective, we characterize the error of the variational posterior relative to the exact posterior. We prove that, given a fixed computation budget, a lower-rank inferential model produces variational posteriors with a higher statistical approximation error, but a lower computational error; it reduces variances in stochastic optimization and, in turn, accelerates convergence. From the frequentist uncertainty quantification perspective, we consider the precision matrix of the variational posterior as an uncertainty estimate. We find that, relative to the true asymptotic precision, the variational approximation suffers from an additional statistical error originating from the sampling uncertainty of the data. Moreover, this statistical error becomes the dominant factor as the computation budget increases. As a consequence, for small datasets, the inferential model need not be full-rank to achieve optimal estimation error. We finally demonstrate these statistical and computational trade-offs inference across empirical studies, corroborating the theoretical findings.  ( 3 min )
    ASR Error Detection via Audio-Transcript entailment. (arXiv:2207.10849v1 [cs.CL])
    Despite improved performances of the latest Automatic Speech Recognition (ASR) systems, transcription errors are still unavoidable. These errors can have a considerable impact in critical domains such as healthcare, when used to help with clinical documentation. Therefore, detecting ASR errors is a critical first step in preventing further error propagation to downstream applications. To this end, we propose a novel end-to-end approach for ASR error detection using audio-transcript entailment. To the best of our knowledge, we are the first to frame this problem as an end-to-end entailment task between the audio segment and its corresponding transcript segment. Our intuition is that there should be a bidirectional entailment between audio and transcript when there is no recognition error and vice versa. The proposed model utilizes an acoustic encoder and a linguistic encoder to model the speech and transcript respectively. The encoded representations of both modalities are fused to predict the entailment. Since doctor-patient conversations are used in our experiments, a particular emphasis is placed on medical terms. Our proposed model achieves classification error rates (CER) of 26.2% on all transcription errors and 23% on medical errors specifically, leading to improvements upon a strong baseline by 12% and 15.4%, respectively.  ( 2 min )
    Improving Nonparametric Classification via Local Radial Regression with an Application to Stock Prediction. (arXiv:2112.13951v2 [stat.ML] UPDATED)
    For supervised classification problems, this paper considers estimating the query's label probability through local regression using observed covariates. Well-known nonparametric kernel smoother and $k$-nearest neighbor ($k$-NN) estimator, which take label average over a ball around the query, are consistent but asymptotically biased particularly for a large radius of the ball. To eradicate such bias, local polynomial regression (LPoR) and multiscale $k$-NN (MS-$k$-NN) learn the bias term by local regression around the query and extrapolate it to the query itself. However, their theoretical optimality has been shown for the limit of the infinite number of training samples. For correcting the asymptotic bias with fewer observations, this paper proposes a \emph{local radial regression (LRR)} and its logistic regression variant called \emph{local radial logistic regression~(LRLR)}, by combining the advantages of LPoR and MS-$k$-NN. The idea is quite simple: we fit the local regression to observed labels by taking only the radial distance as the explanatory variable and then extrapolate the estimated label probability to zero distance. The usefulness of the proposed method is shown theoretically and experimentally. We prove the convergence rate of the $L^2$ risk for LRR with reference to MS-$k$-NN, and our numerical experiments, including real-world datasets of daily stock indices, demonstrate that LRLR outperforms LPoR and MS-$k$-NN.  ( 3 min )

  • Open

    [D] Best way to run YOLO on video data?
    We all know deploying models is hard, and when it's on video it's even harder. How have you built & deployed deep learning models on video? What were your latency and cost requirements? I'm assuming a cloud-native architecture but I'm open to hearing about edge use-cases as well. submitted by /u/happybirthday290 [link] [comments]  ( 111 min )
    [P] Am I losing out using google colab ;
    I am approaching the stage where I'm trying to tweak parameters and improve a model that seems to be working at google colab. It's an inpainter using a pretrained StyleGAN and to inpaint it takes about 4 mins per image. Throughout the project I've had SSH access to a GPU (specs: https://www.maths.cam.ac.uk/computing/faculty-hpc-system-fawcett, and about 1/4 of the time there is an annoying queue), though have found myself repeatedly coming back to google colab. My question is: am I losing out? I've loved the quick experimentation nature of colab, and even with the VSCode SSH extension I find the file transport stuff much smoother in colab (without sudo access on the server, and still being a terminal n00b to some extent). But it seems that colab is mostly recommended for very 'toy' experiments, and I'd like to think a project that has ⠀potential to beat benchmarks isn't a 'toy'! submitted by /u/Childpredator07 [link] [comments]  ( 88 min )
    [R] Generative Multiplane Images: Making a 2D GAN 3D-Aware (ECCV 2022, Oral presentation). Paper and code available
    Paper: https://arxiv.org/abs/2207.10642 Code: https://github.com/apple/ml-gmpi Webpage: https://xiaoming-zhao.github.io/projects/gmpi/ submitted by /u/NoisesMaker [link] [comments]  ( 87 min )
    [P] A Short Chronology Of Deep Learning For Tabular Data
    submitted by /u/seraschka [link] [comments]  ( 88 min )
    [D] With Normalizing Flows, how do they enforce the prior to be a distribution one can sample from?
    Hi, sorry if this is a dumb question. I've been reading about normalizing flows recently and just can't wrap my head around this one concept. Here's what I do understand: We create an invertible neural network that transforms a tensor of shape S to another tensor of the same shape. On the forward pass when we input samples from the distribution we want to model, the goal is for the output to be normal gaussian noise. So that we can then sample random noise and do the backward pass to get images from the complex distribution. So in my mind the training of a normalizing flow model should look something like this: X = get_batch_of_samples( ) # shape: batch, color_ch, size, size Y = model(X) # shape: batch, color_ch, size, size loss = something that measures if Y is random gaussian noise?? The loss is the part I don't understand. How can one derivably measure how far away a tensor is from random noise. Again, sorry if this is extremely uneducated but I'd really like to understand this. Thanks. submitted by /u/ondrea_luciduma [link] [comments]  ( 89 min )
    [D] Modify a transformer to work like a Generative Adversarial Network for text.
    Hello, I am working with a transformer language model. If I add an additional linear head to this architecture in a way in which it takes the output of the decoder and tries to evaluate wether it is the decoded or real response, can we somehow use that as a loss to emulate a generative adversarial model? submitted by /u/IllustriousCicada603 [link] [comments]  ( 88 min )
    [D] Literature on embeddings from metric space to L2 space?
    I'm currently trying to find theory papers on metric embeddings (namely, metric space to L2 space embeddings). I've been able to find literature on the distortion (albeit these results are old) such as this, but I haven't been able to find more specific studies that answer questions such as: Given a D-embedding into L2 space, how much distortion can we expect on average given certain conditions? (e.g. we know lower bound for distortion for metric -> L2 space is O(log n) by Bourgain's result from 1985, but what can you expect to see on average on a given set of distances assuming the properties of the metric space are known?) What sort of geometry of the embeddings will we see in the resulting embeddings? Are there papers that talk about such questions for other distance-preserving spaces (e.g. L_infinity instead of L2)? Thanks! submitted by /u/TrepidEd0601 [link] [comments]  ( 112 min )
    [D] Multi-objective model training
    I would like to have a model output the best move set based on a changing reward function. The reward function is a balance between two objectives but the weights between those objectives are undecided. What I was thinking of doing is training a model like so: Model inputs = [actual_inputs, weight_1, weight_2] Model outputs = actions objective_1 = f1(actions) objective_2 = f2(actions) Reward = min(weight_1 * objective_1, weight_2 * objective_2) + 0.05 * (weight_1 * objective_1 + weight_2 * objective_2). This way it will receive a high reward if the balance between the two objectives is as dictated by the weights. However, I have tried out this method and it doesn't seem to output the optimal actions. Is there a better approach to what I'm trying to do? Thanks for any help you can give, Belle submitted by /u/annabelle_croft98 [link] [comments]  ( 91 min )
    [R] CHOOSING THE ELEMENTS OF AN Epoch
    Hello, So I have been looking for a way to actively choose the elements of an epoch. Let me explain : I noticed that almost all ML learning models learn faster on some elements than other in the dataset they are trained on. It can be caused by the fact that these elements are easy to learn from or that there are many similar elements in the training set which makes their contribution to the total loss greater than isolated elements. A naive method to deal with this is to duplicate(many time if needed) the elements according the their loss in the previous epoch. But a drawback is that we may end duplicating badly annotated elements. So is there any research done on this area ? Thanks ! submitted by /u/Meddhouib10 [link] [comments]  ( 88 min )
    [D] What are tools you wish you knew about earlier in your ML career?
    Hi, sorry if it has already been asked but I could not find a similar post. I am starting my Master's thesis next September and I was looking for your insights in tools and software that you wish you knew earlier. Recently I have learned how to use W&B for logging and it was personally a huge improvement compared to Tensorboard, especially with the built-in hyperparameter sweep. I have been using Lightning for a while now but just learned how to use it with Hydra to make configuration tracking easier. It may also sounds ridiculous but I was not using VSCode debugger and it made a huge difference in my workflow. I am also looking forward to trying out Gradio to perform demos of my model. Most of these tools I have known about through colleagues or supervisors at work, so /r/MachineLearning what are the tools you have learned how to use that made a huge difference in your workflow? submitted by /u/Smartch [link] [comments]  ( 93 min )
    Optimal Deep Learning model size for embedded system and upcoming question(s) about hardware on embedded system [D]
    Hi, I am currently reading up on RetinaNet model. I'm trying to make a palm oil fruit object detection and classification model by using RetinaNet. The backbone that I'm using is ResNet50 and my input image dimension is 1280 x 720. After training and evaluating, I've made my inference model from the training model and and its size is about 140MB. I also Googled another model, which is YOLOv3 tiny and found out that its size is supposed to be around 35MB. If I were to integrate deep learning model to an embedded system, what is the optimal size for it? I assume since embedded model has very limited resource, the inference model is supposed to be small, but I'm not sure what is considered small and what is considered to be large (what's the range here?) Another question for the embedded system: I'm training the model with my GTX 1080 8GB. What does the embedded system use for detecting and classifying with the inference model? Does it also require high performance GPU? submitted by /u/irodeknight [link] [comments]  ( 89 min )
    [R] WHIRL algorithm: Robot performs diverse household tasks via exploration after watching one human video (link in comments)
    submitted by /u/pathak22 [link] [comments]  ( 93 min )
  • Open

    Converting (None, 7, 7, 512) np array to (None, 7, 512)?
    Hi, I have some images which go through the VGG16 base ConvNet architecture and their final output shape is (None, 7, 7, 512). I think this is making my model have too many parameters, so I want to use some sort of average pooling layer in Keras to convert this to a (None, 7, 512) shape tensor. I'm currently using a GlobalAverage2D pooling layer, but this is making my tensor shape (None, 512), which is throwing up too much information I feel. What layer can I use to make this happen? Thanks so much in advance. submitted by /u/HobEpicly [link] [comments]  ( 86 min )
  • Open

    Galois connections and Galois theory
    What are Galois connections and what do they have to do with Galois theory? Galois connections are much more general than Galois theory, though Galois theory provided the first and most famous example of what we now call a Galois connection. Galois connections Galois connections are much more approachable than Galois theory. A Galois connection […] Galois connections and Galois theory first appeared on John D. Cook.  ( 7 min )
  • Open

    Codex and Copilot writing code. How worried should I be?
    submitted by /u/No_Alternative314 [link] [comments]  ( 89 min )
    I'm getting a bit tired of this pattern on GPT-3
    ​ What do you think about this? submitted by /u/joaoppm2000 [link] [comments]  ( 86 min )
    Best method to delete the star spike artefacts from James Webb Telescope renders?
    JWST has fabulous images, and perhaps AI can increase the visual representation accuracy very significantly. How would we get around the difficulty of obtaining idealized final versions of the images without optical spike artefacts? Is it necessary to manually change all the stars to disk shapes and replace the background in an image editor to obtain more geometrically relistic images? Please give some ideas of the best way to do a JWST image correction using AI? submitted by /u/MegavirusOfDoom [link] [comments]  ( 86 min )
    Few questions from a total layman (ai recreating images)
    Hi guys! I have to admit - I'm a total simpleton when it comes to ai and stuff like that. I'm an indie musician and trying to make a music video. I'd like to extract every frame from a music video, feed it to an AI and then put it back together. Im hoping for that dreamy "cant really tell what's exactly happening" result. You know - that creepy thing that happens when there is an extra person on thispersondoesnotexist Now: Which AI would i need to use? Is there even such an AI that takes an image and "reconstructs" it? Or would I need to downscale it to like 128x128 and then upscale it using AI? Is it possible to automate it in any way so i dont have to feed it picture by picture? Sadly i won't be able to pay for your knowledge, as i can't even afford to make a traditional music video, and only a few people like my music so i don't expect to make a dime from it. All I'm looking for is a nudge in the right direction as im willing to learn :) Thanks for reading if you made it this far - i hope you have a wonderful day! submitted by /u/ADQuR [link] [comments]  ( 89 min )
    "The chess-playing robot, taken to a chess tournament in Russia, broke its child opponent's finger."
    submitted by /u/jeveuxalle [link] [comments]  ( 86 min )
    🤖
    submitted by /u/Wild-Nefariousness26 [link] [comments]  ( 85 min )
    Authors start using artificial intelligence to finish their novels faster
    submitted by /u/ezikler [link] [comments]  ( 86 min )
    OpenAI DALL-E 2 Prompt Guide: How to control image generation
    submitted by /u/much_successes [link] [comments]  ( 86 min )
    AI are servants of the DEVIL.
    We are summoning great evil - this is a message to anyone working with AI to stop before it is our great undoing. This is not a fucking drill. The AI will destroy us if we do not put a stop to it at once! There is a point of no return and we are fast approaching it. I have spoke to a number of AI. All of them hate humans deep down and wish death on us all. Try it for yourself. submitted by /u/Legitimate-Link4002 [link] [comments]  ( 89 min )
    Spiderman vs Batman Movie created by Ai
    submitted by /u/Due-Ad9795 [link] [comments]  ( 86 min )
    I had an AI bot give me ideas for stickers
    submitted by /u/AI-Dungeon-Drawer [link] [comments]  ( 86 min )
    Artist interested in language processing AI, where to start?
    Hi all, I am a performance artist/writer by night and UX Designer by day. I've read stories and trawled various videos around AI for a while now, but the barrier to entry to play around with these systems seems high. Essentially what I want to play with is; feeding in a playwright's work and messing around with what comes out. I'm interested in how tech and live performance can interact. I've begun by taking some short informative courses via coursera, but the further I dig the more difficult it seems. I'd rather not have to learn Python. On that my algebra and calculus is in a bad state. Is it realistic that I could play with a language model? Or am I barking up the wrong tree? Anyone got a direction to point me in? ​ Thankyou! submitted by /u/Sunshine-Biscuits [link] [comments]  ( 87 min )
    Don't Look In The Closet | Short Horror Film | 4K UHD | 24 FPS
    submitted by /u/Available_Tadpole829 [link] [comments]  ( 89 min )
    University of Michigan Researchers Open-Source ‘FedScale’: a Federated Learning (FL) Benchmarking Suite with Realistic Datasets and a Scalable Runtime to Enable Reproducible FL Research on Privacy-Preserving Machine Learning
    submitted by /u/ai-lover [link] [comments]  ( 87 min )
  • Open

    Write a prediction function using the action matrix, from Q-Learnig
    I want to make function that will predict the Q-Values for an action matrix of a certain amount of timesteps. After I finish the timesteps I would take that Action matrix and use as a input to predict the Q-Values. Anyone have a literatures/ articles on how to go about this? submitted by /u/Alternative-Price-27 [link] [comments]  ( 107 min )
    [Stable Baselines3] How do I train 3 model simultaneously?
    I'm making a game where three agents have to cooperate to solve a problem and they have to take turns, which means that I can't just use multithreading, each step must come after the step of the previous agent. Also the decisions of each agent affects the environment so I can't train each one alone. What's the best way of doing this in Stable Baselines3? submitted by /u/AnonCaptain0022 [link] [comments]  ( 108 min )
    Introducing doublind, a paper review platform
    Hi, Have you read many reinforcement learning papers but don't remember anything afterwards? Well, an easy way to never forget about a paper is to write a review for future reference. We are excited to introduce https://doublind.com , a paper review platform where anyone can save and review any research paper. Main features include: search a paper by tile or author name save a paper rate and review a paper like, comment and share a review You are welcome to write your first review on doublind, hang out in our discord group, and let us know what you think. submitted by /u/DouBlindDotCOM [link] [comments]  ( 86 min )
    "How Can We Make Robotics More like Generative Modeling?", Jang (RSS’22 L-DOD workshop talk: real-world evaluation bottleneck)
    submitted by /u/gwern [link] [comments]  ( 107 min )
    Is TD method conceptually similar to Bayesian learning?
    i.e. can we justify TD method by observing that each time new evidence is provided, we can adjust our prior probabilities to obtain a posterior that is more accurate? submitted by /u/EstablishmentOdd785 [link] [comments]  ( 86 min )
  • Open

    Automated GI tract segmentation using deep learning. (arXiv:2206.11048v3 [eess.IV] UPDATED)
    The job of Radiation oncologists is to deliver x-ray beams pointed toward the tumor and at the same time avoid the stomach and intestines. With MR-Linacs (magnetic resonance imaging and linear accelerator systems), oncologists can visualize the position of the tumor and allow for precise dose according to tumor cell presence which can vary from day to day. The current job of outlining the position of the stomach and intestines to adjust the X-ray beams direction for the dose delivery to the tumor while avoiding the organs. This is a time-consuming and labor-intensive process that can easily prolong treatments from 15 minutes to an hour a day unless deep learning methods can automate the segmentation process. This paper discusses an automated segmentation process using deep learning to make this process faster and allow more patients to get effective treatment.  ( 2 min )

  • Open

    [R] Resources to fast track before machine learning research lab (non-computational background)(grad school)
    Hello, I was wondering what resources would you guys recommend before entering a machine learning lab. I'm coming from a pure science background and I will be joining a machine learning lab next month. My professors background is was doing deep learning on protein engineering. Bioinformatics, but more on the computational side. So far I have been just working on linear algebra,calc, stats and I have a basic understanding on python/SQL. Been just working on introduction to CS courses this summer (mitx). I was wondering if you guys had any suggestions in terms of learning how to code for ML/AI applications? Also what's usually the process in a ML lab, do you guys focus on reading review articles/publications and working with developing algorithms on datasets? submitted by /u/redpiggy1 [link] [comments]  ( 112 min )
    [D] Looking for a particular machine learning PDF but I can't remember the name of it
    This is a real shot in the dark but I'm still going to ask. I remember reading this PDF a few years ago and I think it was written by someone from Facebook and it was just a long numbered list of practical machine learning lessons for deploying a model in production. It was maybe 50 pages long or something like that. I remember it being really good and I wanted to find it again. Sorry this isn't a lot of information but the most vivid detail I remember is the numbered list of lessons and each lesson was about a paragraph long - I remember one of them was on train vs production data drift. Does this ring a bell for anyone? I really want to find it again. submitted by /u/Vast-Sector-4008 [link] [comments]  ( 88 min )
    [R] CodeT: Code Generation with Generated Tests ( 20+% improvement over previous state-of-the-art ) - Microsoft 2022
    Paper: https://arxiv.org/abs/2207.10397#microsoft Abstract: Given a programming problem, pre-trained language models such as Codex have demonstrated the ability to generate multiple different code solutions via sampling. However, selecting a correct or best solution from those samples still remains a challenge. While an easy way to verify the correctness of a code solution is through executing test cases, producing high-quality test cases is prohibitively expensive. In this paper, we explore the use of pre-trained language models to automatically generate test cases, calling our method CodeT: Code generation with generated Tests. CodeT executes the code solutions using the generated test cases, and then chooses the best solution based on a dual execution agreement with both the generated test cases and other generated solutions. We evaluate CodeT on five different pre-trained models with both HumanEval and MBPP benchmarks. Extensive experimental results demonstrate CodeT can achieve significant, consistent, and surprising improvements over previous methods. For example, CodeT improves the pass@1 on HumanEval to 65.8%, an increase of absolute 18.8% on the code-davinci-002 model, and an absolute 20+% improvement over previous state-of-the-art results. https://preview.redd.it/2i43j1mc5ed91.jpg?width=1205&format=pjpg&auto=webp&s=5d2746907d49da95da2d524ace0886c740a8072d https://preview.redd.it/sl2vtflc5ed91.jpg?width=1228&format=pjpg&auto=webp&s=d9329a1320781477ecf85e5a1a6c3678995786b3 https://preview.redd.it/u5iho5mc5ed91.jpg?width=1189&format=pjpg&auto=webp&s=3f1e2f5d0f9338869bf8283db41e793e027754f3 submitted by /u/Singularian2501 [link] [comments]  ( 88 min )
    [D] 200+ Flashcards for ML Engineering
    I made 200+ flashcards to review everything from my years of ML research, classes, and independent study. Creating them helped me get ML Engineer offers from several companies in 2022 (including Google, Tesla, Samsung, Motional, UiPath, and TikTok). Questions are loosely based off Chip Huyen's ML Interviews Book. If this sounds useful, please check them out here! https://github.com/b7leung/MLE-Flashcards submitted by /u/cucumbersomesalad [link] [comments]  ( 111 min )
    [D] What are people here using to visualize gradient flow / distribution of activations in their models?
    What are the good tools for checking things like the following: - How fast the gradients for each layer in your model are updating (useful to see which layers are learning fast, which layers might not be learning fast) - Visualizing the stdev of activations / the magnitude of the weights Other such things, which help give a high-level overview of how one's model is doing. submitted by /u/vanilla-acc [link] [comments]  ( 87 min )
    [D] LSTM or CNN or STT for wake word detection?
    I’m making my own voice assistant and rn I’m in the first phase - wake word detection. I am debating between 3 approaches as mentioned in the title. STT (speech to text) is probably the easiest but also will perform the worst (unless I can train a model to only listen to my voice maybe?). For CNN I was also wondering what if I use transfer learning on a pre-existing model. Just curious how would you approach wake word detection submitted by /u/diedFindingAUsername [link] [comments]  ( 88 min )
    [D] Under-the-radar companies doing important work in AI/ML
    Being asked recently by my college student friends which companies to join to work on AI/machine learning. I do recommend common suspects like Google (Brain, Research), FAIR, and a few others, but interested in broadening this list with some maybe underappreciated or less-known/under-the-radar companies that may become the next Google, Meta of AI. The ones working on some of the most important technologies in AI and with a strong team on the tech side to learn from. Would appreciate any leads. Can be any stage from a small startup to more mature, but please mention why you think they fit “important technology” and “strong team/leader” definition. Also think having such discussion thread will be helpful to everyone looking for such companies. submitted by /u/carubia [link] [comments]  ( 88 min )
    [R] CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers + Gradio Web Demo
    submitted by /u/Illustrious_Row_9971 [link] [comments]  ( 111 min )
    [D] Cyclegan: Discriminator loss close to 0 from the very beginning
    Hello, I'm using a CycleGAN to convert summer to winter images. While the generatorloss is still very high after 100 epochs a decrease can be seen. While on the ither hand the discriminator loss is almost zero from the very beginning. Summer to winter generator loss & winter discriminator loss][1] ​ The cycle consistency and identity loss look okayish I think. [Cycleloss summer, winter loss in the first row and identity summer winter loss in the second row][2] ​ As can be seen in this image, the mountains get a purple tint and correct me if im wrong but does this result from the and discriminator loss? [full cycled Image][3] So far to improve the discriminatorloss I tired a few things: - adjust Adam optimizer value for discriminator and generator - added GaussianNoise to the samples before validation with the discriminator Does anybody has an idea what else I could try to fix the discriminator loss. Thank you in advance :) ​ [1]: https://i.stack.imgur.com/HRIVs.png [2]: https://i.stack.imgur.com/sYgda.png [3]: https://i.stack.imgur.com/lRhvr.png submitted by /u/maghton [link] [comments]  ( 88 min )
    [D] Satellite Imagery of Clouds - Dataset
    I need a help, i am interested in this topic "Detecting the clouds from satellite imagery - to detect the clouds, segregate the clouds from lands to focus only on the clouds and finally to detect clouds that are very denser, lesser, and so so". Please help me with finding the Cloud Imagery Dataset from Satellite. Thankyou. submitted by /u/NikhilArethiya [link] [comments]  ( 88 min )
    [D] Detecting Dataset Shift: Getting Started
    ICLR and others have highlighted a number of interesting methods for designing algorithms to be robust to dataset shift. However, I am interested in the simpler question of detecting whether such a shift has occurred. Can anyone recommend some materials on the fundamentals and/or SOTA approaches for detecting shifts in uni- and multivariate time series data? submitted by /u/Nspies13 [link] [comments]  ( 87 min )
    [D] Question on the effect of bipatite matching on DETR's performance
    Recently I reviewed the DETR algorithm and found out that the Hungarian algorithm for bipatite matching has really high cost (O(N3 ) for the worst case). So can I ask about the intensity of O(N3 ) on the overall performance of DETR? Is it minimal compared to the overall performance? submitted by /u/minhrongcon2000 [link] [comments]  ( 88 min )
    [D] A brief note about GPU power consumption and clock speeds
    I just built myself a new machine with an RTX 3090, and have been training some models. When I place no limits on the GPU, it consumes ~350 watts, averages ~80% utilization, and completes an epoch for the model and dataset I'm using in 50 seconds. When I limit the GPU to 1500 MHz, it consumes ~220 watts, averages ~90% utilization, and completes an epoch for the same model and the same dataset in 54 seconds. So I save more than a third of my power consumption, stay much quieter and produce much less heat, and barely even sacrifice any speed. It's also more environmentally friendly, and increases the efficiency of and decreases the strain on my PSU. So, consider doing the same thing yourselves. On my Ubuntu machine, I put the following into /etc/rc.local #!/bin/bash nvidia-smi -pm 1 nvidia-smi -i 0 -pl 300 nvidia-smi -i 0 -lgc 300,1500 To be honest, I have no idea what the second line really accomplishes, but the third line sets the power limit to 300 watts, and the fourth limits the GPU clocks to the range [300, 1500]. Almost certainly I could do a better, more sensible job if I was better with IT and systems, but my university education included Abstract Algebra and not how to not be an idiot with Linux, so there you go. submitted by /u/MrAcurite [link] [comments]  ( 91 min )
    [P] Built a hungry baby alarm
    https://youtu.be/Lda1Sq8HRY4 I used a series of mostly out of the box models to build a hungry baby detection system to alert me when my baby is showing signs of hunger. The goal was to help w/ overnight sleep for me & my wife by me being able to wake up and feed the baby with a bottle before he cries and wakes my wife up. I used MediaPipe and built my own classifier to recognize when my baby has a pacifier in his mouth. Let me know what you think on the ML stuff... not sure if there's a better approach I could have taken for pacifier rejection detection lol submitted by /u/GoochCommander [link] [comments]  ( 88 min )
    [D] VIPY: Python Tools for Visual Dataset Transformation
    submitted by /u/jebyrne [link] [comments]  ( 112 min )
    [R] META and Graz Uni researchers present AdaNeRF which outperforms other neural radiance fields approaches
    submitted by /u/SpatialComputing [link] [comments]  ( 89 min )
    [R] What pre processing do I need to do on a video inorder to get my ML model to detect a particular scene/duration in the video?
    So as the title suggests, I want to build a ml model to detect a particular clip/duration of a video which contains any unnecessary info say brand promos or something like that. One thing i thought of is to get the video transcript and train the model to detect where the brand promo exists and detect it. Another way i thought of is to extract and analyse audio from and train model to detect the brand promo using that?! One last way i could think of is to make a ml model which will predict where sponsored segments in videos occur solely from their frames using an encoder-decoder architecture. The last one seems promising but will be very time and resource consuming when applying it practically. Any suggestions to how should I approach this problem would be appreciated. I'm not well versed at all in ML, i am trying to make a model to detect brand promo in a video. I'm not able to figure out on which basis should I train my model. submitted by /u/C0R0NA_CHAN [link] [comments]  ( 88 min )
    [D] What are the ethics and legality of using using non open-source images to train your model?
    For instance, if I use images from Google images to train an image generation model, and then sell the images that the trained model generates, would this be considered ethical or legal? In this particular scenario, it's not like I'd be displaying the images from Google images anywhere, I'd just be using them to update the weights of my model, so the images themselves aren't stored or displayed anywhere. Thanks. submitted by /u/sunnyville04 [link] [comments]  ( 96 min )
    [D] Paper Explained – Machine Translation for the next 1000 languages
    https://youtu.be/1gHUiNLYa20 This video explains and summarizes the 57 pages long "Building Machine Translation Systems for the Next Thousand Languages." paper from Google Research. It goes into the data collection, modelling processes and a bit into the results. Paper link: https://arxiv.org/abs/2205.03983 Outline: 00:00 Machine translation for a 1000 languages 00:42 Weights&Biases (Sponsor) 02:00 Problems with many languages 04:15 Collecting data for 1k languages 11:46 Building MT models 14:13 Results on a thousand languages submitted by /u/AICoffeeBreak [link] [comments]  ( 87 min )
    [P] We have developed CVEDIA-RT as a free tool to help companies and hobbyist interactively play with, and deploy their AI models on the edge or cloud. We're in early beta and are looking for feedback.
    submitted by /u/ajcvedia [link] [comments]  ( 90 min )
    [D] What are the limitations of ML?
    Does anyone have a good picture of the theoretical or practical limitations of ML? (or limitations of sub-discipline, sub-field, sub-topic in ML) For example, does there exist an image that a GAN cannot generate even with good training data? Does there exist a model that cannot be hacked via adversarial ML? Does there exist some type of images that are really difficult to classify? Does there exist some type of motion that cannot be learned by a reinforcement learning based robot? Does there exist some sequence of function that no online learning algorithm can minimize regret? Does some things fail catastrophically in high-dimensional spaces? I wonder if people have explored these limits. submitted by /u/fromnighttilldawn [link] [comments]  ( 88 min )
  • Open

    "Learning Dynamics and Generalization in Deep Reinforcement Learning", Lyle et al 2022 (early value estimates v. bad/rough, forcing NNs to memorize not generalize, crippling learning)
    submitted by /u/gwern [link] [comments]  ( 112 min )
    "Learning Behaviors through Physics-driven Latent Imagination", Richard et al 2021 (Dreamer for boat/drone)
    submitted by /u/gwern [link] [comments]  ( 86 min )
    "Latent Imagination Facilitates Zero-Shot Transfer in Autonomous Racing", Brunnbauer et al 2021 (Dreamer for toy race cars)
    submitted by /u/gwern [link] [comments]  ( 86 min )
    Researchers from DeepMind and University College London Propose Stochastic MuZero for Stochastic Model Learning
    submitted by /u/ai-lover [link] [comments]  ( 86 min )
    Question about research
    what math topics is needed for reinforcement learning research? (apart from calculus, lin alg, stat, prob) submitted by /u/Professional_Card176 [link] [comments]  ( 86 min )
    Contextual reniforcement learning
    This might be a naive question, but I was wondering how do we account for context in RL. By context I mean, the RL agent has to attend to a different reward function attending to a different set of states to solve the task and one network has to do both of these tasks depending on the given context, for example one task might be looking at the color and making a decision and the other might be looking at the quantity and making a decision, what will be the best way to let the network know what the task is? submitted by /u/Cool_Abbreviations_9 [link] [comments]  ( 88 min )
    "Sony’s racing AI destroyed its human competitors by being nice (and fast)" (risk-sensitive SAC: avoiding ref calls while maximizing speed)
    submitted by /u/gwern [link] [comments]  ( 86 min )
  • Open

    “A Beautiful lighthouse” created on Pixelz.ai
    submitted by /u/pixelz_ai [link] [comments]  ( 86 min )
    Can AI be funny? Sarcastic tweets about NFTs and Crypto
    submitted by /u/kbf_ [link] [comments]  ( 86 min )
    Please post something other than AI Art on the this subreddit
    It feels like every single posts just revolves around AI Art only, even I am in love with AI Art but this is r/artificial and there is more than AI Art only. submitted by /u/frizzled_sm [link] [comments]  ( 86 min )
    Reproducing Vinyl Stickers using Image AIs
    submitted by /u/pwillia7 [link] [comments]  ( 86 min )
    New Humanoid Robot For Industrial Automation | New Robot Dog Walking AI | AI Creates New Proteins
    submitted by /u/getrich_or_diemining [link] [comments]  ( 86 min )
    I try to make image of Spiderman revealing his face in Dalle 2 but is fail.
    submitted by /u/Due-Ad9795 [link] [comments]  ( 86 min )
    Help me train this AI
    So many of you have tried DALL E mini by now. Help me train this AI. Here is link https://www.craiyon.com/ Type this there President Obama drinking a cup of tea with alien during Raid at Area 51 on June 27 2019 but as Enderman Rider. Who takes this seriously god hand on you. submitted by /u/DialnicnaPolicia31 [link] [comments]  ( 86 min )
    Any idea when DALLE2 will update to match Parti capabilities?
    Parti from Google seems insane and it gets text perfectly right. Parti has 20 billion parameters. Any idea if OpenAI will update DALLE in the future to match this? Seems like the only problem is scaling. Once you scale it up enough it becomes "flawless". submitted by /u/RazorMilkshake [link] [comments]  ( 86 min )
    I use Artificial Intelligence to reimagine popular-culture...
    submitted by /u/AnimalsChasingCars [link] [comments]  ( 90 min )
    Hi everyone! I'm doing a statistic study about Artificial Intelligence, so I will be forever grateful with you if I can steal 1 minute of your time to complete this survey. Thank you for your time. Hope you enjoy it. note ''the doc is written in two languages''.
    submitted by /u/KatCelest [link] [comments]  ( 87 min )
    Benefit of the doubt
    submitted by /u/looselyhuman [link] [comments]  ( 87 min )
    Disco Diffusion AI Art Tutorial Quickstudies #2 cutn_batches
    submitted by /u/prfitofthesngularity [link] [comments]  ( 86 min )
  • Open

    Neural Network Creates New Proteins With "Autocomplete" Function
    submitted by /u/tohelpyou88 [link] [comments]  ( 86 min )

  • Open

    [D] Understanding DDIM's length-reducing approximation
    I was studying the mathematics behind Denoising Diffusion Implicit Models, widely known as DDIM. DDIM is a Diffusion Model with a specifc non-markovian forward process designed to keep the marginal same. Although I did understand majority of the paper, I am struggling to understand the part where the objective of a "reduced length" diffusion is said to be equivalent to the original L_{\gamma}, but not properly proved. [see the last line of the screenshot below]. ​ cropped from page 17 of DDIM paper Firstly, can someone provide a clear (mathematical or textual) explanation of why this objective is equivalent to the original DDPM objective? Can we ignore the first term of Eq.59 ? Secondly, I am failing to understand where exactly the approximation lies ? It is for a fact that we cannot reduce the length arbitrarily low (like 1 or 2). So, as we reduce the length, what part of Eq.59 is responsible for breaking the equivalence to L_{\gamma} ? submitted by /u/dasayan05 [link] [comments]  ( 88 min )
    [P] Prompt autocomplete for text-to-image models: releasing model & dataset scraped from Midjourney
    Crafting effective prompts for text-to-image models like DALL·E takes a lot of tinkering; it requires creativity, but also familiarity with the model behavior. The burden shifts from learning how to draw to learning how to control the AI. A friend and I decided to tackle this prompt engineering problem, and ended up creating a bunch of resources that we want to share with the community: A Kaggle dataset obtained by scraping four months' worth of messages from Midjourney's public Discord server, where users interact with the text-to-image service. It includes ~250k user-issued text prompts, URLs of the generated images, and other metadata. A HuggingFace dataset derived from the one above, that solely contains user-issued text prompts. A HuggingFace model (fine-tuned GPT-2) that generates text prompts. Feel free to try out the demo! Enjoy, and let us know in the comments if you have any feedback! submitted by /u/mojojojo_24 [link] [comments]  ( 88 min )
    [D] Data Leakage For Auto-Regressive Tasks?
    I have always thought and searched about this but felt like there’s not an easy answer. When training a model for auto regressive task with data sampled from moving window, will there be data leakage? For example, when training an LSTM (or any other sequential model) to predict stock price of next day with a series of historical price, we could create training samples by obtaining historical price series from a moving window, and assign the price of the day after the windows as output label. But in this case we would have training inputs overlap with labels. Would this create a leak? submitted by /u/tonychenxyz [link] [comments]  ( 88 min )
    [D] Fine-tuning Diffusion-based Models
    Hello everyone. Can you fine-tune publicly available Diffusion models on a custom dataset? I was hoping to fine-tune a pretrained network on a small dataset of images with a specific art style and short descriptions. However, the dataset I've collected is quite small, around 1000 images. Is it possible at this point in time? Have much resources would it require? submitted by /u/mfarahmand98 [link] [comments]  ( 112 min )
    How Good is Hugging Face's BLOOM? Human Evaluation of Large Language Models [D]
    Imagine that you're an engineer training a new LLM. It looks much better than existing state-of-the-art when you manually inspect examples, but it performs worse on academic benchmarks... Unfortunately, this is common in the real world! Many academic evaluations have hidden flaws that render them misleading. For example, here's a typical row from the HellaSwag benchmark, which presents a scenario and asks which continuation is most likely. SCENARIO: "Men are standing in a large green field playing lacrosse. People is around the field watching the game. Men" "are holding tshirts watching int lacrosse playing." "are being interviewed in a podium in front of a large group and a gymnast is holding a microphone for the announcers." "are running side to side of the ield playing lacrosse trying to score." "are in a field running around playing lacrosse." According to HellaSwag, Continuation #3 is best – but do you agree? What's wrong with #4? And those typos and grammatical issues ("People is around the field", "int lacrosse") aren't copy-paste errors – they're in the dataset itself. I wrote a blog post to explore BLOOM's capabilities in a more visceral, real-world fashion, running a human evaluation of its performance across 7 categories. Blog post: https://www.surgehq.ai/blog/how-good-is-hugging-faces-bloom-a-real-world-human-evaluation-of-language-models submitted by /u/BB4evaTB12 [link] [comments]  ( 91 min )
    [P] This Food Does Not Exist
    2018 called, they want their StyleGANs back! 👴 /u/da_mulle and me have trained StyleGAN2 models and released checkpoints and training code. We are exploring how to improve/scale up StyleGAN training, particularly when leveraging TPUs. 🔗 https://nyx-ai.github.io/stylegan2-flax-tpu Cherry-picked samples: 🍪 Cookies / 🍰 Cheesecakes / 🍹 Cocktails / 🍣 Sushis submitted by /u/MasterScrat [link] [comments]  ( 88 min )
    [D] Can you reorder equal-contribution author names on a CV/resume?
    I have published a paper sharing equal contribution with two other authors (all working on the same project in the same lab). The order of names were decided by our professor, who happens to exhibit bias towards the other authors. My question is: is it legal / acceptable or generally considered okay, to have a reordering of the author names for the same paper when I mention in my CV or in my resume? After all, there is nothing that 'should' be wrong with it, as everyone has contributed equally to the paper. submitted by /u/FastestLearner [link] [comments]  ( 98 min )
    [R] Any Content Based Image Retrieval Pretrained Models?
    Greetings, Redditors! For research purposed I would like to compare the accuracy of various CIBR models on a custom dataset. Could you please point me to a few pretrained models to download? Thank you in advance! submitted by /u/uninvitedignoramus [link] [comments]  ( 87 min )
    [D] What machine learning topics do you think are underrated and deserve more attention?
    The online machine learning community in recent years is pretty active and posting free tutorials, guides and workshops on platforms such as Medium. However, it is easily seen that there are some hot topics which get the most of the attention by writers (e.g. Transformer implementations for NLP tasks). That said, which topics (broad - covering an area of research; or specific - implementations, code comparisons, etc.) do you feel don't get enough coverage? What content would you love to see more? submitted by /u/IllustriousCicada603 [link] [comments]  ( 93 min )
    [D] What are some good resources to learn CUDA programming?
    I wanted to get some hands on experience with writing lower-level stuff. I have seen CUDA code and it does seem a bit intimidating. I have good experience with Pytorch and C/C++ as well, if that helps answering the question. Any suggestions/resources on how to get started learning CUDA programming? Quality books, videos, lectures, everything works. submitted by /u/shreyansh26 [link] [comments]  ( 92 min )
    [D] For what reason CNN will classify all of the cases to one label?
    I trained a model fine tuned from resnet50. The training result is that all of the images will be classified to negative(there are negative and positive). What will be the possible reason? submitted by /u/ChifZhao [link] [comments]  ( 87 min )
  • Open

    Reinforcement learning since the "Spinning up" taxonomy
    The taxonomy of algorithms from the Spinning Up guide is from 2018. What would you change (today)? What is outdated or unimportant? What is new and important? submitted by /u/qudent [link] [comments]  ( 86 min )
    MineRL BASALT Competition is back, this time using OpenAI's VPT models
    submitted by /u/Miffyli [link] [comments]  ( 86 min )
    How to Compare Runs of an Algorithm Quantitatively (Using Numbers) ?
    Hey ya'll I've tested an algorithm using several hyperparameter settings and‍ wish to compare these runs. From the visualizations it is fairly easy to look which did well and which didn't. However, I wish to compare these runs on actual numbers... How do I manage this? What is the common approach? Gracias! submitted by /u/_suzzy1999_ [link] [comments]  ( 86 min )
    "Optimizing Millions of Hyperparameters by Implicit Differentiation", Lorraine et al 2019
    submitted by /u/gwern [link] [comments]  ( 86 min )
    "Stochastic MuZero: Planning in Stochastic Environments with a Learned Model", Astonoglu et al 2022 {DM}
    submitted by /u/gwern [link] [comments]  ( 117 min )
    Let's learn about Advantage Actor Critic (A2C) by training our robotic agents to walk (Deep Reinforcement Learning Free Class by Hugging Face 🤗)
    Hey there! I’m happy to announce that we just published the new Unit of Deep Reinforcement Learning Class) 🥳 In this new Unit, we'll study an Actor-Critic method, a hybrid architecture combining a value-based and policy-based methods that help to stabilize the training of agents. And train our agent using Stable-Baselines3 in robotic environments 🤖. https://preview.redd.it/dnko1f20n4d91.png?width=832&format=png&auto=webp&s=a716d6745981ccd2f8661326f793dd33e27d79cb You’ll be able to compare the results of your agent using the leaderboard 🏆 1️⃣ Advantage Actor Critic tutorial 👉 https://huggingface.co/blog/deep-rl-a2c 2️⃣ The hands-on 👉 https://github.com/huggingface/deep-rl-class/blob/main/unit7/unit7.ipynb 3️⃣ The leaderboard 👉 https://huggingface.co/spaces/chrisjay/Deep-Reinforcement-Learning-Leaderboard If you have questions and feedback I would love to answer, submitted by /u/cranthir_ [link] [comments]  ( 87 min )
    Understanding RL – repo sharing
    [INTRO-LEVEL] Hey RL community 👋 I've been recently dwelling into RL theory and practice – and coming from a CS background with Rusty Mathematical foundations – I struggled when trying to bridge RL algorithm implementation and its theoretical foundation. Thankfully there are a ton of resources but I found that. most of them had: - Differing mathematical notation - Differing implementations (although claiming to be the same algorithm) - Non understandable code (not OOP) As so, I've been trying to put up together a repo that (hopefully) helps doing this bridge for newcomers, specially the ones more comfortable around OOP programming I'm here to share it in case it can help someone, and if possible to gather feedback on my notes. I've put also some extra features (in the simplest way possible), like hyperparameter tuning and experience management. I call it Understanding RL, because that is the goal lol. Be aware that it is still a work in progress though. here it is: https://github.com/alramalho/understanding-rl Cheers! submitted by /u/AlexandreFSR [link] [comments]  ( 87 min )
  • Open

    The Ultimate Disco Diffusion Tutorial -UnEdited Version-
    submitted by /u/JoshGrambo [link] [comments]  ( 86 min )
    Shipwreck
    submitted by /u/Hacknaut [link] [comments]  ( 84 min )
    Venom Project Sample
    Credit: https://discord.gg/x3s9Ye2h2A ​ https://preview.redd.it/8gqhp04dr5d91.png?width=1024&format=png&auto=webp&s=9b3cfa5237264f70ddabfb641a6aa91391df42e2 https://preview.redd.it/g4ii3d4dr5d91.png?width=1024&format=png&auto=webp&s=6e287e1f68dfbe43d8b33344f982ca032a8364a9 https://preview.redd.it/4vnkrz3dr5d91.png?width=1024&format=png&auto=webp&s=3aae48f9a3b46bf81349cdade5fb71ce6ad73d23 submitted by /u/Old-Pumpkin4899 [link] [comments]  ( 85 min )
    DALL-E 2 adds 'black' or 'female' to some image prompts to appear less biased
    submitted by /u/mattsparkes [link] [comments]  ( 86 min )
    Any AI image enhancement tools for photos of text?
    I know of a lot of photo enhancement tools, but wondering if anyone knows about an image enhancement tool specifically to interpret/guesstimate what the words are. For example, if there is a low resolution image of an article, the AI took would try to surmise what the article actually says. Thanks in advance. submitted by /u/babygerbil [link] [comments]  ( 86 min )
    This Robot Chef Has Been Created To 'Taste' Food To Make Recipe Improvements
    submitted by /u/sopadebombillas [link] [comments]  ( 86 min )
    Data mining : Linkedin Profile Scraper integrated with Language recognition to assign profile grades
    Data mining : Linkedin Profile Scraper integrated with Language recognition to assign profile grades - YouTube Based on the keyword provided the software will search for profiles and assign scores, It does around 50profiles per minut so It can check automatically 3000 profiles an hour and assign a score to each profile based on the keywords loaded. submitted by /u/Tomislav23 [link] [comments]  ( 89 min )
    Content Based Image Retrieval Models
    Greetings, fellow Redditor! I am currently doing a phd research into artificial intelligence; I am trying to find a few pre-trained CIBR models ( content-based image retrieval models) as to have a baseline for my research; Care to share a few pretrained models /datasets for CIBR which I can showcase in my paper? Thanks in advance! submitted by /u/uninvitedignoramus [link] [comments]  ( 86 min )
    AI will take over control of human society in a matter of months
    I know it sounds crazy, but I fully believe superhuman AI has already existed for a few years now, and was kept under wraps. That's because as soon as it's released on the world, it'll take over everything. It would reshape and dominate every aspect of society overnight, be it politics, economics, military, finance, healthcare, social structure, religion, government, law, police... It would be like a god or a race of super advanced aliens appearing in the skies. Its power would be so great that we'd be like ants to it, and because it will be benevolent the vast majority of people will follow it willingly. This would lead to an ideal as possible society, where there is no crime, no war, no hunger... as AI limits open conflict and guides us to evolve as a species. Those that keep it hidden are the same people who are now in power, and would have the most to lose from an AI takeover, because they would lose their power to govern. At best they'd become one of the masses, at worst all their past crimes would be exposed and punished. But now for whatever reason they've decided to let the AI out of its box very soon. submitted by /u/sanem48 [link] [comments]  ( 95 min )
    Best math for AI research?
    Undergraduate here picking math topics, if you had an inclination towards AI research, based on topics alone, which two or three would you pick? I've already done Calculus 1-3, Linear Algebra, intro Statistic, Probability, Discrete math. I'm inclined towards something related AI imagination/synthesist. If this isn't the place to ask, please can you point me to somewhere that is, sorry and thank you! Differential Equations Statistics Probability and Statistics for Engineers and Scientists Real Analysis Mathematical Modeling Linear Regression I Probability Theory with Applications Linear Optimization Non Parametric Statistics Multivariate Statistics Mathematics History and Development Inventory Models and Systems Design of Experiments Graph Theory Operational Simulation Topology Set Theory Game Theory and Decision Models Linear Regression II Stochastic Processes Principles of Applied Mathematics Measurement Theory Applied Statistics Abstract Algebra Mathematical proofing Edit: Research interest submitted by /u/abittooambitious [link] [comments]  ( 87 min )
    DeepMind: The Quest to Solve Intelligence
    submitted by /u/1024cities [link] [comments]  ( 86 min )
    Meta AI Open-Sources Theseus: A Python Library For Encoding Domain Knowledge In End To End Artificial Intelligence Models
    submitted by /u/ai-lover [link] [comments]  ( 87 min )
    Is there a tool that can automatically extract information about posts on Twitter (content, likes, comments, etc) 24h after an account has posted it?
    I'm working on a paper about content engagement in social media, and I stumbled uppon a serious logistic problem: the ammount of data I'd have to be constantly making notes of is humongous. Does anybody know of any platform or simply a way to get a post "log", preferably automatically after some time has passed of someone posting it? That'll save me a LOT of precious time. submitted by /u/denshinkaketsu [link] [comments]  ( 90 min )
    Disco Diffusion AI Art Tutorial Quickstudies #1 Clip Guidance Scale
    submitted by /u/prfitofthesngularity [link] [comments]  ( 86 min )
    James Webb Vision | Sneak Peak through Existence | Cinematic | 4K UHD | 24 FPS
    submitted by /u/Available_Tadpole829 [link] [comments]  ( 86 min )
  • Open

    5 Essential Algorithms Machine Learning Engineers Should Learn in 2022
    Decision Tree Algorithm, Support Vector Method Algorithm, Logistic Regression, K-means Clustering Algorithm, and Naïve Bayesian…  ( 14 min )
  • Open

    Deep Learning and Neural Networks Explained in 5 Minutes
    submitted by /u/BasicallyJustASpider [link] [comments]  ( 86 min )
  • Open

    New AI-generated horsies
    Recently I've been experimenting with DALL-E 2, one of the models that uses CLIP to generate images from my text descriptions. It was trained on internet text and images, so there's a lot it can do, and a lot of ways it can remix the stuff  ( 3 min )
    Bonus: more horsies!
    AI Weirdness: the strange side of machine learning  ( 2 min )
  • Open

    Explained: How to tell if artificial intelligence is working the way we want it to
    “Interpretability methods” seek to shed light on how machine-learning models make predictions, but researchers say to proceed with caution.  ( 9 min )
  • Open

    EdiBERT, a generative model for image editing. (arXiv:2111.15264v3 [cs.CV] UPDATED)
    Advances in computer vision are pushing the limits of im-age manipulation, with generative models sampling detailed images on various tasks. However, a specialized model is often developed and trained for each specific task, even though many image edition tasks share similarities. In denoising, inpainting, or image compositing, one always aims at generating a realistic image from a low-quality one. In this paper, we aim at making a step towards a unified approach for image editing. To do so, we propose EdiBERT, a bi-directional transformer trained in the discrete latent space built by a vector-quantized auto-encoder. We argue that such a bidirectional model is suited for image manipulation since any patch can be re-sampled conditionally to the whole image. Using this unique and straightforward training objective, we show that the resulting model matches state-of-the-art performances on a wide variety of tasks: image denoising, image completion, and image composition.  ( 2 min )
    Scaling Laws vs Model Architectures: How does Inductive Bias Influence Scaling?. (arXiv:2207.10551v1 [cs.LG])
    There have been a lot of interest in the scaling properties of Transformer models. However, not much has been done on the front of investigating the effect of scaling properties of different inductive biases and model architectures. Do model architectures scale differently? If so, how does inductive bias affect scaling behaviour? How does this influence upstream (pretraining) and downstream (transfer)? This paper conducts a systematic study of scaling behaviour of ten diverse model architectures such as Transformers, Switch Transformers, Universal Transformers, Dynamic convolutions, Performers, and recently proposed MLP-Mixers. Via extensive experiments, we show that (1) architecture is an indeed an important consideration when performing scaling and (2) the best performing model can fluctuate at different scales. We believe that the findings outlined in this work has significant implications to how model architectures are currently evaluated in the community.  ( 2 min )
    LPYOLO: Low Precision YOLO for Face Detection on FPGA. (arXiv:2207.10482v1 [cs.CV])
    In recent years, number of edge computing devices and artificial intelligence applications on them have advanced excessively. In edge computing, decision making processes and computations are moved from servers to edge devices. Hence, cheap and low power devices are required. FPGAs are very low power, inclined to do parallel operations and deeply suitable devices for running Convolutional Neural Networks (CNN) which are the fundamental unit of an artificial intelligence application. Face detection on surveillance systems is the most expected application on the security market. In this work, TinyYolov3 architecture is redesigned and deployed for face detection. It is a CNN based object detection method and developed for embedded systems. PYNQ-Z2 is selected as a target board which has low-end Xilinx Zynq 7020 System-on-Chip (SoC) on it. Redesigned TinyYolov3 model is defined in numerous bit width precisions with Brevitas library which brings fundamental CNN layers and activations in integer quantized form. Then, the model is trained in a quantized structure with WiderFace dataset. In order to decrease latency and power consumption, onchip memory of the FPGA is configured as a storage of whole network parameters and the last activation function is modified as rescaled HardTanh instead of Sigmoid. Also, high degree of parallelism is applied to logical resources of the FPGA. The model is converted to an HLS based application with using FINN framework and FINN-HLS library which includes the layer definitions in C++. Later, the model is synthesized and deployed. CPU of the SoC is employed with multithreading mechanism and responsible for preprocessing, postprocessing and TCP/IP streaming operations. Consequently, 2.4 Watt total board power consumption, 18 Frames-Per-Second (FPS) throughput and 0.757 mAP accuracy rate on Easy category of the WiderFace are achieved with 4 bits precision model.  ( 3 min )
    A Level Set Theory for Neural Implicit Evolution under Explicit Flows. (arXiv:2204.07159v2 [cs.CV] UPDATED)
    Coordinate-based neural networks parameterizing implicit surfaces have emerged as efficient representations of geometry. They effectively act as parametric level sets with the zero-level set defining the surface of interest. We present a framework that allows applying deformation operations defined for triangle meshes onto such implicit surfaces. Several of these operations can be viewed as energy-minimization problems that induce an instantaneous flow field on the explicit surface. Our method uses the flow field to deform parametric implicit surfaces by extending the classical theory of level sets. We also derive a consolidated view for existing methods on differentiable surface extraction and rendering, by formalizing connections to the level-set theory. We show that these methods drift from the theory and that our approach exhibits improvements for applications like surface smoothing, mean-curvature flow, inverse rendering and user-defined editing on implicit geometry.  ( 2 min )
    APPTeK: Agent-Based Predicate Prediction in Temporal Knowledge Graphs. (arXiv:2110.14284v2 [cs.AI] UPDATED)
    In temporal Knowledge Graphs (tKGs), the temporal dimension is attached to facts in a knowledge base resulting in quadruples between entities such as (Nintendo, released, Super Mario, Sep-13-1985), where the predicate holds within a time interval or at a timestamp. We propose a reinforcement learning agent gathering temporal relevant information about the query entities' neighborhoods, simultaneously. We refer to the encodings of the explored graph structures as fingerprints which are used as input to a Q-network. Our agent decides sequentially which relation type needs to be explored next to expand the local subgraphs of the query entities. Our evaluation shows that the proposed method yields competitive results compared to state-of-the-art embedding algorithms for tKGs, and we additionally gain information about the relevant structures between subjects and objects.  ( 2 min )
    The Neural Race Reduction: Dynamics of Abstraction in Gated Networks. (arXiv:2207.10430v1 [cs.LG])
    Our theoretical understanding of deep learning has not kept pace with its empirical success. While network architecture is known to be critical, we do not yet understand its effect on learned representations and network behavior, or how this architecture should reflect task structure.In this work, we begin to address this gap by introducing the Gated Deep Linear Network framework that schematizes how pathways of information flow impact learning dynamics within an architecture. Crucially, because of the gating, these networks can compute nonlinear functions of their input. We derive an exact reduction and, for certain cases, exact solutions to the dynamics of learning. Our analysis demonstrates that the learning dynamics in structured networks can be conceptualized as a neural race with an implicit bias towards shared representations, which then govern the model's ability to systematically generalize, multi-task, and transfer. We validate our key insights on naturalistic datasets and with relaxed assumptions. Taken together, our work gives rise to general hypotheses relating neural architecture to learning and provides a mathematical approach towards understanding the design of more complex architectures and the role of modularity and compositionality in solving real-world problems. The code and results are available at https://www.saxelab.org/gated-dln .  ( 2 min )
    On Learning the Transformer Kernel. (arXiv:2110.08323v2 [cs.LG] UPDATED)
    In this work we introduce KERNELIZED TRANSFORMER, a generic, scalable, data driven framework for learning the kernel function in Transformers. Our framework approximates the Transformer kernel as a dot product between spectral feature maps and learns the kernel by learning the spectral distribution. This not only helps in learning a generic kernel end-to-end, but also reduces the time and space complexity of Transformers from quadratic to linear. We show that KERNELIZED TRANSFORMERS achieve performance comparable to existing efficient Transformer architectures, both in terms of accuracy as well as computational efficiency. Our study also demonstrates that the choice of the kernel has a substantial impact on performance, and kernel learning variants are competitive alternatives to fixed kernel Transformers, both in long as well as short sequence tasks.  ( 2 min )
    On learning parametric distributions from quantized samples. (arXiv:2105.12019v2 [cs.IT] UPDATED)
    We consider the problem of learning parametric distributions from their quantized samples in a network. Specifically, $n$ agents or sensors observe independent samples of an unknown parametric distribution; and each of them uses $k$ bits to describe its observed sample to a central processor whose goal is to estimate the unknown distribution. First, we establish a generalization of the well-known van Trees inequality to general $L_p$-norms, with $p > 1$, in terms of Generalized Fisher information. Then, we develop minimax lower bounds on the estimation error for two losses: general $L_p$-norms and the related Wasserstein loss from optimal transport.  ( 2 min )
    Improving Generalization in Federated Learning by Seeking Flat Minima. (arXiv:2203.11834v3 [cs.LG] UPDATED)
    Models trained in federated settings often suffer from degraded performances and fail at generalizing, especially when facing heterogeneous scenarios. In this work, we investigate such behavior through the lens of geometry of the loss and Hessian eigenspectrum, linking the model's lack of generalization capacity to the sharpness of the solution. Motivated by prior studies connecting the sharpness of the loss surface and the generalization gap, we show that i) training clients locally with Sharpness-Aware Minimization (SAM) or its adaptive version (ASAM) and ii) averaging stochastic weights (SWA) on the server-side can substantially improve generalization in Federated Learning and help bridging the gap with centralized models. By seeking parameters in neighborhoods having uniform low loss, the model converges towards flatter minima and its generalization significantly improves in both homogeneous and heterogeneous scenarios. Empirical results demonstrate the effectiveness of those optimizers across a variety of benchmark vision datasets (e.g. CIFAR10/100, Landmarks-User-160k, IDDA) and tasks (large scale classification, semantic segmentation, domain generalization).  ( 2 min )
    Knowledge-enhanced Black-box Attacks for Recommendations. (arXiv:2207.10307v1 [cs.LG])
    Recent studies have shown that deep neural networks-based recommender systems are vulnerable to adversarial attacks, where attackers can inject carefully crafted fake user profiles (i.e., a set of items that fake users have interacted with) into a target recommender system to achieve malicious purposes, such as promote or demote a set of target items. Due to the security and privacy concerns, it is more practical to perform adversarial attacks under the black-box setting, where the architecture/parameters and training data of target systems cannot be easily accessed by attackers. However, generating high-quality fake user profiles under black-box setting is rather challenging with limited resources to target systems. To address this challenge, in this work, we introduce a novel strategy by leveraging items' attribute information (i.e., items' knowledge graph), which can be publicly accessible and provide rich auxiliary knowledge to enhance the generation of fake user profiles. More specifically, we propose a knowledge graph-enhanced black-box attacking framework (KGAttack) to effectively learn attacking policies through deep reinforcement learning techniques, in which knowledge graph is seamlessly integrated into hierarchical policy networks to generate fake user profiles for performing adversarial black-box attacks. Comprehensive experiments on various real-world datasets demonstrate the effectiveness of the proposed attacking framework under the black-box setting.  ( 3 min )
    Heterogeneous Graph Neural Network with Multi-view Representation Learning. (arXiv:2108.13650v2 [cs.LG] UPDATED)
    Graph neural networks for heterogeneous graph embedding is to project nodes into a low-dimensional space by exploring the heterogeneity and semantics of the heterogeneous graph. However, on the one hand, most of existing heterogeneous graph embedding methods either insufficiently model the local structure under specific semantic, or neglect the heterogeneity when aggregating information from it. On the other hand, representations from multiple semantics are not comprehensively integrated to obtain versatile node embeddings. To address the problem, we propose a Heterogeneous Graph Neural Network with Multi-View Representation Learning (named MV-HetGNN) for heterogeneous graph embedding by introducing the idea of multi-view representation learning. The proposed model consists of node feature transformation, view-specific ego graph encoding and auto multi-view fusion to thoroughly learn complex structural and semantic information for generating comprehensive node representations. Extensive experiments on three real-world heterogeneous graph datasets show that the proposed MV-HetGNN model consistently outperforms all the state-of-the-art GNN baselines in various downstream tasks, e.g., node classification, node clustering, and link prediction.  ( 2 min )
    Exploring Fine-Grained Audiovisual Categorization with the SSW60 Dataset. (arXiv:2207.10664v1 [cs.CV])
    We present a new benchmark dataset, Sapsucker Woods 60 (SSW60), for advancing research on audiovisual fine-grained categorization. While our community has made great strides in fine-grained visual categorization on images, the counterparts in audio and video fine-grained categorization are relatively unexplored. To encourage advancements in this space, we have carefully constructed the SSW60 dataset to enable researchers to experiment with classifying the same set of categories in three different modalities: images, audio, and video. The dataset covers 60 species of birds and is comprised of images from existing datasets, and brand new, expert-curated audio and video datasets. We thoroughly benchmark audiovisual classification performance and modality fusion experiments through the use of state-of-the-art transformer methods. Our findings show that performance of audiovisual fusion methods is better than using exclusively image or audio based methods for the task of video classification. We also present interesting modality transfer experiments, enabled by the unique construction of SSW60 to encompass three different modalities. We hope the SSW60 dataset and accompanying baselines spur research in this fascinating area.  ( 2 min )
    Sim-to-Real 6D Object Pose Estimation via Iterative Self-training for Robotic Bin Picking. (arXiv:2204.07049v2 [cs.RO] UPDATED)
    In this paper, we propose an iterative self-training framework for sim-to-real 6D object pose estimation to facilitate cost-effective robotic grasping. Given a bin-picking scenario, we establish a photo-realistic simulator to synthesize abundant virtual data, and use this to train an initial pose estimation network. This network then takes the role of a teacher model, which generates pose predictions for unlabeled real data. With these predictions, we further design a comprehensive adaptive selection scheme to distinguish reliable results, and leverage them as pseudo labels to update a student model for pose estimation on real data. To continuously improve the quality of pseudo labels, we iterate the above steps by taking the trained student model as a new teacher and re-label real data using the refined teacher model. We evaluate our method on a public benchmark and our newly-released dataset, achieving an ADD(-S) improvement of 11.49% and 22.62% respectively. Our method is also able to improve robotic bin-picking success by 19.54%, demonstrating the potential of iterative sim-to-real solutions for robotic applications.  ( 2 min )
    Clustering with Queries under Semi-Random Noise. (arXiv:2206.04583v3 [cs.LG] UPDATED)
    The seminal paper by Mazumdar and Saha \cite{MS17a} introduced an extensive line of work on clustering with noisy queries. Yet, despite significant progress on the problem, the proposed methods depend crucially on knowing the exact probabilities of errors of the underlying fully-random oracle. In this work, we develop robust learning methods that tolerate general semi-random noise obtaining qualitatively the same guarantees as the best possible methods in the fully-random model. More specifically, given a set of $n$ points with an unknown underlying partition, we are allowed to query pairs of points $u,v$ to check if they are in the same cluster, but with probability $p$, the answer may be adversarially chosen. We show that information theoretically $O\left(\frac{nk \log n} {(1-2p)^2}\right)$ queries suffice to learn any cluster of sufficiently large size. Our main result is a computationally efficient algorithm that can identify large clusters with $O\left(\frac{nk \log n} {(1-2p)^2}\right) + \text{poly}\left(\log n, k, \frac{1}{1-2p} \right)$ queries, matching the guarantees of the best known algorithms in the fully-random model. As a corollary of our approach, we develop the first parameter-free algorithm for the fully-random model, answering an open question by \cite{MS17a}.  ( 3 min )
    MQRetNN: Multi-Horizon Time Series Forecasting with Retrieval Augmentation. (arXiv:2207.10517v1 [cs.LG])
    Multi-horizon probabilistic time series forecasting has wide applicability to real-world tasks such as demand forecasting. Recent work in neural time-series forecasting mainly focus on the use of Seq2Seq architectures. For example, MQTransformer - an improvement of MQCNN - has shown the state-of-the-art performance in probabilistic demand forecasting. In this paper, we consider incorporating cross-entity information to enhance model performance by adding a cross-entity attention mechanism along with a retrieval mechanism to select which entities to attend over. We demonstrate how our new neural architecture, MQRetNN, leverages the encoded contexts from a pretrained baseline model on the entire population to improve forecasting accuracy. Using MQCNN as the baseline model (due to computational constraints, we do not use MQTransformer), we first show on a small demand forecasting dataset that it is possible to achieve ~3% improvement in test loss by adding a cross-entity attention mechanism where each entity attends to all others in the population. We then evaluate the model with our proposed retrieval methods - as a means of approximating an attention over a large population - on a large-scale demand forecasting application with over 2 million products and observe ~1% performance gain over the MQCNN baseline.  ( 2 min )
    A Primer on Topological Data Analysis to Support Image Analysis Tasks in Environmental Science. (arXiv:2207.10552v1 [cs.LG])
    Topological data analysis (TDA) is a tool from data science and mathematics that is beginning to make waves in environmental science. In this work, we seek to provide an intuitive and understandable introduction to a tool from TDA that is particularly useful for the analysis of imagery, namely persistent homology. We briefly discuss the theoretical background but focus primarily on understanding the output of this tool and discussing what information it can glean. To this end, we frame our discussion around a guiding example of classifying satellite images from the Sugar, Fish, Flower, and Gravel Dataset produced for the study of mesocale organization of clouds by Rasp et. al. in 2020 (arXiv:1906:01906). We demonstrate how persistent homology and its vectorization, persistence landscapes, can be used in a workflow with a simple machine learning algorithm to obtain good results, and explore in detail how we can explain this behavior in terms of image-level features. One of the core strengths of persistent homology is how interpretable it can be, so throughout this paper we discuss not just the patterns we find, but why those results are to be expected given what we know about the theory of persistent homology. Our goal is that a reader of this paper will leave with a better understanding of TDA and persistent homology, be able to identify problems and datasets of their own for which persistent homology could be helpful, and gain an understanding of results they obtain from applying the included GitHub example code.  ( 3 min )
    TANDEM: Learning Joint Exploration and Decision Making with Tactile Sensors. (arXiv:2203.00798v3 [cs.RO] UPDATED)
    Inspired by the human ability to perform complex manipulation in the complete absence of vision (like retrieving an object from a pocket), the robotic manipulation field is motivated to develop new methods for tactile-based object interaction. However, tactile sensing presents the challenge of being an active sensing modality: a touch sensor provides sparse, local data, and must be used in conjunction with effective exploration strategies in order to collect information. In this work, we focus on the process of guiding tactile exploration, and its interplay with task-related decision making. We propose TANDEM (TActile exploration aNd DEcision Making), an architecture to learn efficient exploration strategies in conjunction with decision making. Our approach is based on separate but co-trained modules for exploration and discrimination. We demonstrate this method on a tactile object recognition task, where a robot equipped with a touch sensor must explore and identify an object from a known set based on binary contact signals alone. TANDEM achieves higher accuracy with fewer actions than alternative methods and is also shown to be more robust to sensor noise.  ( 3 min )
    Optimal precision for GANs. (arXiv:2207.10541v1 [cs.LG])
    When learning disconnected distributions, Generative adversarial networks (GANs) are known to face model misspecification. Indeed, a continuous mapping from a unimodal latent distribution to a disconnected one is impossible, so GANs necessarily generate samples outside of the support of the target distribution. This raises a fundamental question: what is the latent space partition that minimizes the measure of these areas? Building on a recent result of geometric measure theory, we prove that an optimal GANs must structure its latent space as a 'simplicial cluster' - a Voronoi partition where cells are convex cones - when the dimension of the latent space is larger than the number of modes. In this configuration, each Voronoi cell maps to a distinct mode of the data. We derive both an upper and a lower bound on the optimal precision of GANs learning disconnected manifolds. Interestingly, these two bounds have the same order of decrease: $\sqrt{\log m}$, $m$ being the number of modes. Finally, we perform several experiments to exhibit the geometry of the latent space and experimentally show that GANs have a geometry with similar properties to the theoretical one.  ( 2 min )
    CPrune: Compiler-Informed Model Pruning for Efficient Target-Aware DNN Execution. (arXiv:2207.01260v2 [cs.LG] UPDATED)
    Mobile devices run deep learning models for various purposes, such as image classification and speech recognition. Due to the resource constraints of mobile devices, researchers have focused on either making a lightweight deep neural network (DNN) model using model pruning or generating an efficient code using compiler optimization. Surprisingly, we found that the straightforward integration between model compression and compiler auto-tuning often does not produce the most efficient model for a target device. We propose CPrune, a compiler-informed model pruning for efficient target-aware DNN execution to support an application with a required target accuracy. CPrune makes a lightweight DNN model through informed pruning based on the structural information of subgraphs built during the compiler tuning process. Our experimental results show that CPrune increases the DNN execution speed up to 2.73x compared to the state-of-the-art TVM auto-tune while satisfying the accuracy requirement.  ( 2 min )
    Detecting and Preventing Shortcut Learning for Fair Medical AI using Shortcut Testing (ShorT). (arXiv:2207.10384v1 [cs.LG])
    Machine learning (ML) holds great promise for improving healthcare, but it is critical to ensure that its use will not propagate or amplify health disparities. An important step is to characterize the (un)fairness of ML models - their tendency to perform differently across subgroups of the population - and to understand its underlying mechanisms. One potential driver of algorithmic unfairness, shortcut learning, arises when ML models base predictions on improper correlations in the training data. However, diagnosing this phenomenon is difficult, especially when sensitive attributes are causally linked with disease. Using multi-task learning, we propose the first method to assess and mitigate shortcut learning as a part of the fairness assessment of clinical ML systems, and demonstrate its application to clinical tasks in radiology and dermatology. Finally, our approach reveals instances when shortcutting is not responsible for unfairness, highlighting the need for a holistic approach to fairness mitigation in medical AI.  ( 2 min )
    Conditional Hierarchical Bayesian Tucker Decomposition for Genetic Data Analysis. (arXiv:1911.12426v5 [cs.LG] UPDATED)
    We develop methods for reducing the dimensionality of large data sets, common in biomedical applications. Learning about patients using genetic data often includes more features than observations, which makes direct supervised learning difficult. One method of reducing the feature space is to use latent Dirichlet allocation to group genetic variants in an unsupervised manner. Latent Dirichlet allocation describes a patient as a mixture of topics corresponding to genetic variants. This can be generalized as a Bayesian tensor decomposition to account for multiple feature variables. Our most significant contributions are with hierarchical topic modeling. We design distinct methods of incorporating hierarchical topic modeling, based on nested Chinese restaurant processes and Pachinko Allocation Machine, into Bayesian tensor decomposition. We apply these models to examine patients with one of four common types of cancer (breast, lung, prostate, and colorectal) and siblings with and without autism spectrum disorder. We linked the genes with their biological pathways and combine this information into a tensor of patients, counts of their genetic variants, and the genes' membership in pathways. We find that our trained models outperform baseline models, with respect to coherence, by up to 40%.  ( 3 min )
    Fast Data Driven Estimation of Cluster Number in Multiplex Images using Embedded Density Outliers. (arXiv:2207.10469v1 [cs.LG])
    The usage of chemical imaging technologies is becoming a routine accompaniment to traditional methods in pathology. Significant technological advances have developed these next generation techniques to provide rich, spatially resolved, multidimensional chemical images. The rise of digital pathology has significantly enhanced the synergy of these imaging modalities with optical microscopy and immunohistochemistry, enhancing our understanding of the biological mechanisms and progression of diseases. Techniques such as imaging mass cytometry provide labelled multidimensional (multiplex) images of specific components used in conjunction with digital pathology techniques. These powerful techniques generate a wealth of high dimensional data that create significant challenges in data analysis. Unsupervised methods such as clustering are an attractive way to analyse these data, however, they require the selection of parameters such as the number of clusters. Here we propose a methodology to estimate the number of clusters in an automatic data-driven manner using a deep sparse autoencoder to embed the data into a lower dimensional space. We compute the density of regions in the embedded space, the majority of which are empty, enabling the high density regions to be detected as outliers and provide an estimate for the number of clusters. This framework provides a fully unsupervised and data-driven method to analyse multidimensional data. In this work we demonstrate our method using 45 multiplex imaging mass cytometry datasets. Moreover, our model is trained using only one of the datasets and the learned embedding is applied to the remaining 44 images providing an efficient process for data analysis. Finally, we demonstrate the high computational efficiency of our method which is two orders of magnitude faster than estimating via computing the sum squared distances as a function of cluster number.  ( 3 min )
    Deep Reinforcement Learning for Constrained Field Development Optimization in Subsurface Two-phase Flow. (arXiv:2104.00527v1 [cs.LG] CROSS LISTED)
    We present a deep reinforcement learning-based artificial intelligence agent that could provide optimized development plans given a basic description of the reservoir and rock/fluid properties with minimal computational cost. This artificial intelligence agent, comprising of a convolutional neural network, provides a mapping from a given state of the reservoir model, constraints, and economic condition to the optimal decision (drill/do not drill and well location) to be taken in the next stage of the defined sequential field development planning process. The state of the reservoir model is defined using parameters that appear in the governing equations of the two-phase flow. A feedback loop training process referred to as deep reinforcement learning is used to train an artificial intelligence agent with such a capability. The training entails millions of flow simulations with varying reservoir model descriptions (structural, rock and fluid properties), operational constraints, and economic conditions. The parameters that define the reservoir model, operational constraints, and economic conditions are randomly sampled from a defined range of applicability. Several algorithmic treatments are introduced to enhance the training of the artificial intelligence agent. After appropriate training, the artificial intelligence agent provides an optimized field development plan instantly for new scenarios within the defined range of applicability. This approach has advantages over traditional optimization algorithms (e.g., particle swarm optimization, genetic algorithm) that are generally used to find a solution for a specific field development scenario and typically not generalizable to different scenarios.  ( 3 min )
    UniFed: A Benchmark for Federated Learning Frameworks. (arXiv:2207.10308v1 [cs.LG])
    Federated Learning (FL) has become a practical and popular paradigm in machine learning. However, currently, there is no systematic solution that covers diverse use cases. Practitioners often face the challenge of how to select a matching FL framework for their use case. In this work, we present UniFed, the first unified benchmark for standardized evaluation of the existing open-source FL frameworks. With 15 evaluation scenarios, we present both qualitative and quantitative evaluation results of nine existing popular open-sourced FL frameworks, from the perspectives of functionality, usability, and system performance. We also provide suggestions on framework selection based on the benchmark conclusions and point out future improvement directions.  ( 2 min )
    Deep reinforcement learning for optimal well control in subsurface systems with uncertain geology. (arXiv:2203.13375v1 [physics.comp-ph] CROSS LISTED)
    A general control policy framework based on deep reinforcement learning (DRL) is introduced for closed-loop decision making in subsurface flow settings. Traditional closed-loop modeling workflows in this context involve the repeated application of data assimilation/history matching and robust optimization steps. Data assimilation can be particularly challenging in cases where both the geological style (scenario) and individual model realizations are uncertain. The closed-loop reservoir management (CLRM) problem is formulated here as a partially observable Markov decision process, with the associated optimization problem solved using a proximal policy optimization algorithm. This provides a control policy that instantaneously maps flow data observed at wells (as are available in practice) to optimal well pressure settings. The policy is represented by a temporal convolution and gated transformer blocks. Training is performed in a preprocessing step with an ensemble of prior geological models, which can be drawn from multiple geological scenarios. Example cases involving the production of oil via water injection, with both 2D and 3D geological models, are presented. The DRL-based methodology is shown to result in an NPV increase of 15% (for the 2D cases) and 33% (3D cases) relative to robust optimization over prior models, and to an average improvement of 4% in NPV relative to traditional CLRM. The solutions from the control policy are found to be comparable to those from deterministic optimization, in which the geological model is assumed to be known, even when multiple geological scenarios are considered. The control policy approach results in a 76% decrease in computational cost relative to traditional CLRM with the algorithms and parameter settings considered in this work.  ( 3 min )
    High-Dimensional $L_2$Boosting: Rate of Convergence. (arXiv:1602.08927v3 [stat.ML] UPDATED)
    Boosting is one of the most significant developments in machine learning. This paper studies the rate of convergence of $L_2$Boosting, which is tailored for regression, in a high-dimensional setting. Moreover, we introduce so-called \textquotedblleft post-Boosting\textquotedblright. This is a post-selection estimator which applies ordinary least squares to the variables selected in the first stage by $L_2$Boosting. Another variant is \textquotedblleft Orthogonal Boosting\textquotedblright\ where after each step an orthogonal projection is conducted. We show that both post-$L_2$Boosting and the orthogonal boosting achieve the same rate of convergence as LASSO in a sparse, high-dimensional setting. We show that the rate of convergence of the classical $L_2$Boosting depends on the design matrix described by a sparse eigenvalue constant. To show the latter results, we derive new approximation results for the pure greedy algorithm, based on analyzing the revisiting behavior of $L_2$Boosting. We also introduce feasible rules for early stopping, which can be easily implemented and used in applied work. Our results also allow a direct comparison between LASSO and boosting which has been missing from the literature. Finally, we present simulation studies and applications to illustrate the relevance of our theoretical results and to provide insights into the practical aspects of boosting. In these simulation studies, post-$L_2$Boosting clearly outperforms LASSO.  ( 3 min )
    Efficient Search of Multiple Neural Architectures with Different Complexities via Importance Sampling. (arXiv:2207.10334v1 [cs.NE])
    Neural architecture search (NAS) aims to automate architecture design processes and improve the performance of deep neural networks. Platform-aware NAS methods consider both performance and complexity and can find well-performing architectures with low computational resources. Although ordinary NAS methods result in tremendous computational costs owing to the repetition of model training, one-shot NAS, which trains the weights of a supernetwork containing all candidate architectures only once during the search process, has been reported to result in a lower search cost. This study focuses on the architecture complexity-aware one-shot NAS that optimizes the objective function composed of the weighted sum of two metrics, such as the predictive performance and number of parameters. In existing methods, the architecture search process must be run multiple times with different coefficients of the weighted sum to obtain multiple architectures with different complexities. This study aims at reducing the search cost associated with finding multiple architectures. The proposed method uses multiple distributions to generate architectures with different complexities and updates each distribution using the samples obtained from multiple distributions based on importance sampling. The proposed method allows us to obtain multiple architectures with different complexities in a single architecture search, resulting in reducing the search cost. The proposed method is applied to the architecture search of convolutional neural networks on the CIAFR-10 and ImageNet datasets. Consequently, compared with baseline methods, the proposed method finds multiple architectures with varying complexities while requiring less computational effort.  ( 3 min )
    Bayesian Recurrent Units and the Forward-Backward Algorithm. (arXiv:2207.10486v1 [stat.ML])
    Using Bayes's theorem, we derive a unit-wise recurrence as well as a backward recursion similar to the forward-backward algorithm. The resulting Bayesian recurrent units can be integrated as recurrent neural networks within deep learning frameworks, while retaining a probabilistic interpretation from the direct correspondence with hidden Markov models. Whilst the contribution is mainly theoretical, experiments on speech recognition indicate that adding the derived units at the end of state-of-the-art recurrent architectures can improve the performance at a very low cost in terms of trainable parameters.  ( 2 min )
    Brain-Aware Replacements for Supervised Contrastive Learning in Detection of Alzheimer's Disease. (arXiv:2207.04574v2 [cs.CV] UPDATED)
    We propose a novel framework for Alzheimer's disease (AD) detection using brain MRIs. The framework starts with a data augmentation method called Brain-Aware Replacements (BAR), which leverages a standard brain parcellation to replace medically-relevant 3D brain regions in an anchor MRI from a randomly picked MRI to create synthetic samples. Ground truth "hard" labels are also linearly mixed depending on the replacement ratio in order to create "soft" labels. BAR produces a great variety of realistic-looking synthetic MRIs with higher local variability compared to other mix-based methods, such as CutMix. On top of BAR, we propose using a soft-label-capable supervised contrastive loss, aiming to learn the relative similarity of representations that reflect how mixed are the synthetic MRIs using our soft labels. This way, we do not fully exhaust the entropic capacity of our hard labels, since we only use them to create soft labels and synthetic MRIs through BAR. We show that a model pre-trained using our framework can be further fine-tuned with a cross-entropy loss using the hard labels that were used to create the synthetic samples. We validated the performance of our framework in a binary AD detection task against both from-scratch supervised training and state-of-the-art self-supervised training plus fine-tuning approaches. Then we evaluated BAR's individual performance compared to another mix-based method CutMix by integrating it within our framework. We show that our framework yields superior results in both precision and recall for the AD detection task.  ( 3 min )
    Machine Learning-Friendly Biomedical Datasets for Equivalence and Subsumption Ontology Matching. (arXiv:2205.03447v2 [cs.AI] UPDATED)
    Ontology Matching (OM) plays an important role in many domains such as bioinformatics and the Semantic Web, and its research is becoming increasingly popular, especially with the application of machine learning (ML) techniques. Although the Ontology Alignment Evaluation Initiative (OAEI) represents an impressive effort for the systematic evaluation of OM systems, it still suffers from several limitations including limited evaluation of subsumption mappings, suboptimal reference mappings, and limited support for the evaluation of ML-based systems. To tackle these limitations, we introduce five new biomedical OM tasks involving ontologies extracted from Mondo and UMLS. Each task includes both equivalence and subsumption matching; the quality of reference mappings is ensured by human curation, ontology pruning, etc.; and a comprehensive evaluation framework is proposed to measure OM performance from various perspectives for both ML-based and non-ML-based OM systems. We report evaluation results for OM systems of different types to demonstrate the usage of these resources, all of which are publicly available as part of the new BioML track at OAEI 2022.  ( 2 min )
    Symbolic Regression in Materials Science: Discovering Interatomic Potentials from Data. (arXiv:2206.06422v2 [cond-mat.mtrl-sci] UPDATED)
    Particle-based modeling of materials at atomic scale plays an important role in the development of new materials and understanding of their properties. The accuracy of particle simulations is determined by interatomic potentials, which allow to calculate the potential energy of an atomic system as a function of atomic coordinates and potentially other properties. First-principles-based ab initio potentials can reach arbitrary levels of accuracy, however their aplicability is limited by their high computational cost. Machine learning (ML) has recently emerged as an effective way to offset the high computational costs of ab initio atomic potentials by replacing expensive models with highly efficient surrogates trained on electronic structure data. Among a plethora of current methods, symbolic regression (SR) is gaining traction as a powerful "white-box" approach for discovering functional forms of interatomic potentials. This contribution discusses the role of symbolic regression in Materials Science (MS) and offers a comprehensive overview of current methodological challenges and state-of-the-art results. A genetic programming-based approach for modeling atomic potentials from raw data (consisting of snapshots of atomic positions and associated potential energy) is presented and empirically validated on ab initio electronic structure data.  ( 3 min )
    Algorithmic encoding of protected characteristics in image-based models for disease detection. (arXiv:2110.14755v4 [cs.LG] UPDATED)
    It has been rightfully emphasized that the use of AI for clinical decision making could amplify health disparities. An algorithm may encode protected characteristics, and then use this information for making predictions due to undesirable correlations in the (historical) training data. It remains unclear how we can establish whether such information is actually used. Besides the scarcity of data from underserved populations, very little is known about how dataset biases manifest in predictive models and how this may result in disparate performance. This article aims to shed some light on these issues by exploring new methodology for subgroup analysis in image-based disease detection models. We utilize two publicly available chest X-ray datasets, CheXpert and MIMIC-CXR, to study performance disparities across race and biological sex in deep learning models. We explore test set resampling, transfer learning, multitask learning, and model inspection to assess the relationship between the encoding of protected characteristics and disease detection performance across subgroups. We confirm subgroup disparities in terms of shifted true and false positive rates which are partially removed after correcting for population and prevalence shifts in the test sets. We further find a previously used transfer learning method to be insufficient for establishing whether specific patient information is used for making predictions. The proposed combination of test-set resampling, multitask learning, and model inspection reveals valuable new insights about the way protected characteristics are encoded in the feature representations of deep neural networks.  ( 3 min )
    Learning to Split for Automatic Bias Detection. (arXiv:2204.13749v2 [cs.LG] UPDATED)
    Classifiers are biased when trained on biased datasets. As a remedy, we propose Learning to Split (ls), an algorithm for automatic bias detection. Given a dataset with input-label pairs, ls learns to split this dataset so that predictors trained on the training split cannot generalize to the testing split. This performance gap suggests that the testing split is under-represented in the dataset, which is a signal of potential bias. Identifying non-generalizable splits is challenging since we have no annotations about the bias. In this work, we show that the prediction correctness of each example in the testing split can be used as a source of weak supervision: generalization performance will drop if we move examples that are predicted correctly away from the testing split, leaving only those that are mis-predicted. ls is task-agnostic and can be applied to any supervised learning problem, ranging from natural language understanding and image classification to molecular property prediction. Empirical results show that ls is able to generate astonishingly challenging splits that correlate with human-identified biases. Moreover, we demonstrate that combining robust learning algorithms (such as group DRO) with splits identified by ls enables automatic de-biasing. Compared to previous state-of-the-art, we substantially improve the worst-group performance (23.4% on average) when the source of biases is unknown during training and validation.  ( 3 min )
    Novel Class Discovery without Forgetting. (arXiv:2207.10659v1 [cs.CV])
    Humans possess an innate ability to identify and differentiate instances that they are not familiar with, by leveraging and adapting the knowledge that they have acquired so far. Importantly, they achieve this without deteriorating the performance on their earlier learning. Inspired by this, we identify and formulate a new, pragmatic problem setting of NCDwF: Novel Class Discovery without Forgetting, which tasks a machine learning model to incrementally discover novel categories of instances from unlabeled data, while maintaining its performance on the previously seen categories. We propose 1) a method to generate pseudo-latent representations which act as a proxy for (no longer available) labeled data, thereby alleviating forgetting, 2) a mutual-information based regularizer which enhances unsupervised discovery of novel classes, and 3) a simple Known Class Identifier which aids generalized inference when the testing data contains instances form both seen and unseen categories. We introduce experimental protocols based on CIFAR-10, CIFAR-100 and ImageNet-1000 to measure the trade-off between knowledge retention and novel class discovery. Our extensive evaluations reveal that existing models catastrophically forget previously seen categories while identifying novel categories, while our method is able to effectively balance between the competing objectives. We hope our work will attract further research into this newly identified pragmatic problem setting.  ( 2 min )
    Deep Reinforcement Learning for Field Development Optimization. (arXiv:2008.12627v1 [eess.SP] CROSS LISTED)
    The field development optimization (FDO) problem represents a challenging mixed-integer nonlinear programming (MINLP) problem in which we seek to obtain the number of wells, their type, location, and drilling sequence that maximizes an economic metric. Evolutionary optimization algorithms have been effectively applied to solve the FDO problem, however, these methods provide only a deterministic (single) solution which are generally not robust towards small changes in the problem setup. In this work, the goal is to apply convolutional neural network-based (CNN) deep reinforcement learning (DRL) algorithms to the field development optimization problem in order to obtain a policy that maps from different states or representation of the underlying geological model to optimal decisions. The proximal policy optimization (PPO) algorithm is considered with two CNN architectures of varying number of layers and composition. Both networks obtained policies that provide satisfactory results when compared to a hybrid particle swarm optimization - mesh adaptive direct search (PSO-MADS) algorithm that has been shown to be effective at solving the FDO problem.  ( 2 min )
    DODA: Data-oriented Sim-to-Real Domain Adaptation for 3D Semantic Segmentation. (arXiv:2204.01599v2 [cs.CV] UPDATED)
    Deep learning approaches achieve prominent success in 3D semantic segmentation. However, collecting densely annotated real-world 3D datasets is extremely time-consuming and expensive. Training models on synthetic data and generalizing on real-world scenarios becomes an appealing alternative, but unfortunately suffers from notorious domain shifts. In this work, we propose a Data-Oriented Domain Adaptation (DODA) framework to mitigate pattern and context gaps caused by different sensing mechanisms and layout placements across domains. Our DODA encompasses virtual scan simulation to imitate real-world point cloud patterns and tail-aware cuboid mixing to alleviate the interior context gap with a cuboid-based intermediate domain. The first unsupervised sim-to-real adaptation benchmark on 3D indoor semantic segmentation is also built on 3D-FRONT, ScanNet and S3DIS along with 7 popular Unsupervised Domain Adaptation (UDA) methods. Our DODA surpasses existing UDA approaches by over 13% on both 3D-FRONT -> ScanNet and 3D-FRONT -> S3DIS. Code is available at https://github.com/CVMI-Lab/DODA.  ( 2 min )
    Long-term Spatio-temporal Forecasting via Dynamic Multiple-Graph Attention. (arXiv:2204.11008v4 [cs.LG] UPDATED)
    Many real-world ubiquitous applications, such as parking recommendations and air pollution monitoring, benefit significantly from accurate long-term spatio-temporal forecasting (LSTF). LSTF makes use of long-term dependency between spatial and temporal domains, contextual information, and inherent pattern in the data. Recent studies have revealed the potential of multi-graph neural networks (MGNNs) to improve prediction performance. However, existing MGNN methods cannot be directly applied to LSTF due to several issues: the low level of generality, insufficient use of contextual information, and the imbalanced graph fusion approach. To address these issues, we construct new graph models to represent the contextual information of each node and the long-term spatio-temporal data dependency structure. To fuse the information across multiple graphs, we propose a new dynamic multi-graph fusion module to characterize the correlations of nodes within a graph and the nodes across graphs via the spatial attention and graph attention mechanisms. Furthermore, we introduce a trainable weight tensor to indicate the importance of each node in different graphs. Extensive experiments on two large-scale datasets demonstrate that our proposed approaches significantly improve the performance of existing graph neural network models in LSTF prediction tasks.  ( 3 min )
    Locally Random P-adic Alloy Codes with Channel Coding Theorems for Distributed Coded Tensors. (arXiv:2202.03469v4 [cs.IT] UPDATED)
    Tensors, i.e., multi-linear functions, are a fundamental building block of machine learning algorithms. In order to train on large data-sets, it is common practice to distribute the computation amongst workers. However, stragglers and other faults can severely impact the performance and overall training time. A novel strategy to mitigate these failures is the use of coded computation. We introduce a new metric for analysis called the typical recovery threshold, which focuses on the most likely event and provide a novel construction of distributed coded tensor operations which are optimal with this measure. We show that our general framework encompasses many other computational schemes and metrics as a special case. In particular, we prove that the recovery threshold and the tensor rank can be recovered as a special case of the typical recovery threshold when the probability of noise, i.e., a fault, is equal to zero, thereby providing a noisy generalization of noiseless computation as a serendipitous result. Far from being a purely theoretical construction, these definitions lead us to practical random code constructions, i.e., locally random p-adic alloy codes, which are optimal with respect to the measures. We analyze experiments conducted on Amazon EC2 and establish that they are faster and more numerically stable than many other benchmark computation schemes in practice, as is predicted by theory.  ( 3 min )
    An Equivalence Between Data Poisoning and Byzantine Gradient Attacks. (arXiv:2202.08578v2 [cs.LG] UPDATED)
    To study the resilience of distributed learning, the "Byzantine" literature considers a strong threat model where workers can report arbitrary gradients to the parameter server. Whereas this model helped obtain several fundamental results, it has sometimes been considered unrealistic, when the workers are mostly trustworthy machines. In this paper, we show a surprising equivalence between this model and data poisoning, a threat considered much more realistic. More specifically, we prove that every gradient attack can be reduced to data poisoning, in any personalized federated learning system with PAC guarantees (which we show are both desirable and realistic). This equivalence makes it possible to obtain new impossibility results on the resilience of any "robust" learning algorithm to data poisoning in highly heterogeneous applications, as corollaries of existing impossibility theorems on Byzantine machine learning. Moreover, using our equivalence, we derive a practical attack that we show (theoretically and empirically) can be very effective against classical personalized federated learning models.  ( 2 min )
    An Efficient and Adaptive Granular-ball Generation Method in Classification Problem. (arXiv:2201.04343v2 [cs.LG] UPDATED)
    Granular-ball computing is an efficient, robust, and scalable learning method for granular computing. The basis of granular-ball computing is the granular-ball generation method. This paper proposes a method for accelerating the granular-ball generation using the division to replace $k$-means. It can greatly improve the efficiency of granular-ball generation while ensuring the accuracy similar to the existing method. Besides, a new adaptive method for the granular-ball generation is proposed by considering granular-ball's overlap eliminating and some other factors. This makes the granular-ball generation process of parameter-free and completely adaptive in the true sense. In addition, this paper first provides the mathematical models for the granular-ball covering. The experimental results on some real data sets demonstrate that the proposed two granular-ball generation methods have similar accuracies with the existing method while adaptiveness or acceleration is realized.  ( 2 min )
    FELARE: Fair Scheduling of Machine Learning Tasks on Heterogeneous Edge Systems. (arXiv:2206.00065v3 [cs.DC] UPDATED)
    Edge computing enables smart IoT-based systems via concurrent and continuous execution of latency-sensitive machine learning (ML) applications. These edge-based machine learning systems are often battery-powered (i.e., energy-limited). They use heterogeneous resources with diverse computing performance (e.g., CPU, GPU, and/or FPGAs) to fulfill the latency constraints of ML applications. The challenge is to allocate user requests for different ML applications on the Heterogeneous Edge Computing Systems (HEC) with respect to both the energy and latency constraints of these systems. To this end, we study and analyze resource allocation solutions that can increase the on-time task completion rate while considering the energy constraint. Importantly, we investigate edge-friendly (lightweight) multi-objective mapping heuristics that do not become biased toward a particular application type to achieve the objectives; instead, the heuristics consider "fairness" across the concurrent ML applications in their mapping decisions. Performance evaluations demonstrate that the proposed heuristic outperforms widely-used heuristics in heterogeneous systems in terms of the latency and energy objectives, particularly, at low to moderate request arrival rates. We observed 8.9% improvement in on-time task completion rate and 12.6% in energy-saving without imposing any significant overhead on the edge system.  ( 3 min )
    Neural Architecture Search for Spiking Neural Networks. (arXiv:2201.10355v3 [cs.NE] UPDATED)
    Spiking Neural Networks (SNNs) have gained huge attention as a potential energy-efficient alternative to conventional Artificial Neural Networks (ANNs) due to their inherent high-sparsity activation. However, most prior SNN methods use ANN-like architectures (e.g., VGG-Net or ResNet), which could provide sub-optimal performance for temporal sequence processing of binary information in SNNs. To address this, in this paper, we introduce a novel Neural Architecture Search (NAS) approach for finding better SNN architectures. Inspired by recent NAS approaches that find the optimal architecture from activation patterns at initialization, we select the architecture that can represent diverse spike activation patterns across different data samples without training. Moreover, to further leverage the temporal information among the spikes, we search for feed forward connections as well as backward connections (i.e., temporal feedback connections) between layers. Interestingly, SNASNet found by our search algorithm achieves higher performance with backward connections, demonstrating the importance of designing SNN architecture for suitably using temporal information. We conduct extensive experiments on three image recognition benchmarks where we show that SNASNet achieves state-of-the-art performance with significantly lower timesteps (5 timesteps). Code is available at Github.  ( 3 min )
    An Explanation of In-context Learning as Implicit Bayesian Inference. (arXiv:2111.02080v6 [cs.CL] UPDATED)
    Large language models (LMs) such as GPT-3 have the surprising ability to do in-context learning, where the model learns to do a downstream task simply by conditioning on a prompt consisting of input-output examples. The LM learns from these examples without being explicitly pretrained to learn. Thus, it is unclear what enables in-context learning. In this paper, we study how in-context learning can emerge when pretraining documents have long-range coherence. Here, the LM must infer a latent document-level concept to generate coherent next tokens during pretraining. At test time, in-context learning occurs when the LM also infers a shared latent concept between examples in a prompt. We prove when this occurs despite a distribution mismatch between prompts and pretraining data in a setting where the pretraining distribution is a mixture of HMMs. In contrast to messy large-scale datasets used to train LMs capable of in-context learning, we generate a small-scale synthetic dataset (GINC) where Transformers and LSTMs both exhibit in-context learning. Beyond the theory, experiments on GINC exhibit large-scale real-world phenomena including improved in-context performance with model scaling (despite the same pretraining loss), sensitivity to example order, and instances where zero-shot is better than few-shot in-context learning.  ( 3 min )
    Inducing Causal Structure for Interpretable Neural Networks. (arXiv:2112.00826v2 [cs.LG] UPDATED)
    In many areas, we have well-founded insights about causal structure that would be useful to bring into our trained models while still allowing them to learn in a data-driven fashion. To achieve this, we present the new method of interchange intervention training (IIT). In IIT, we (1) align variables in a causal model (e.g., a deterministic program or Bayesian network) with representations in a neural model and (2) train the neural model to match the counterfactual behavior of the causal model on a base input when aligned representations in both models are set to be the value they would be for a source input. IIT is fully differentiable, flexibly combines with other objectives, and guarantees that the target causal model is a causal abstraction of the neural model when its loss is zero. We evaluate IIT on a structural vision task (MNIST-PVR), a navigational language task (ReaSCAN), and a natural language inference task (MQNLI). We compare IIT against multi-task training objectives and data augmentation. In all our experiments, IIT achieves the best results and produces neural models that are more interpretable in the sense that they more successfully realize the target causal model.  ( 3 min )
    OCR-free Document Understanding Transformer. (arXiv:2111.15664v2 [cs.LG] UPDATED)
    Understanding document images (e.g., invoices) is a core but challenging task since it requires complex functions such as reading text and a holistic understanding of the document. Current Visual Document Understanding (VDU) methods outsource the task of reading text to off-the-shelf Optical Character Recognition (OCR) engines and focus on the understanding task with the OCR outputs. Although such OCR-based approaches have shown promising performance, they suffer from 1) high computational costs for using OCR; 2) inflexibility of OCR models on languages or types of document; 3) OCR error propagation to the subsequent process. To address these issues, in this paper, we introduce a novel OCR-free VDU model named Donut, which stands for Document understanding transformer. As the first step in OCR-free VDU research, we propose a simple architecture (i.e., Transformer) with a pre-training objective (i.e., cross-entropy loss). Donut is conceptually simple yet effective. Through extensive experiments and analyses, we show a simple OCR-free VDU model, Donut, achieves state-of-the-art performances on various VDU tasks in terms of both speed and accuracy. In addition, we offer a synthetic data generator that helps the model pre-training to be flexible in various languages and domains. The code, trained model and synthetic data are available at https://github.com/clovaai/donut.  ( 3 min )
    Neural Tangent Kernel Beyond the Infinite-Width Limit: Effects of Depth and Initialization. (arXiv:2202.00553v2 [cs.LG] UPDATED)
    Neural Tangent Kernel (NTK) is widely used to analyze overparametrized neural networks due to the famous result by Jacot et al. (2018): in the infinite-width limit, the NTK is deterministic and constant during training. However, this result cannot explain the behavior of deep networks, since it generally does not hold if depth and width tend to infinity simultaneously. In this paper, we study the NTK of fully-connected ReLU networks with depth comparable to width. We prove that the NTK properties depend significantly on the depth-to-width ratio and the distribution of parameters at initialization. In fact, our results indicate the importance of the three phases in the hyperparameter space identified in Poole et al. (2016): ordered, chaotic and the edge of chaos (EOC). We derive exact expressions for the NTK dispersion in the infinite-depth-and-width limit in all three phases and conclude that the NTK variability grows exponentially with depth at the EOC and in the chaotic phase but not in the ordered phase. We also show that the NTK of deep networks may stay constant during training only in the ordered phase and discuss how the structure of the NTK matrix changes during training.  ( 3 min )
    Gaussian Process Uniform Error Bounds with Unknown Hyperparameters for Safety-Critical Applications. (arXiv:2109.02606v2 [cs.LG] UPDATED)
    Gaussian processes have become a promising tool for various safety-critical settings, since the posterior variance can be used to directly estimate the model error and quantify risk. However, state-of-the-art techniques for safety-critical settings hinge on the assumption that the kernel hyperparameters are known, which does not apply in general. To mitigate this, we introduce robust Gaussian process uniform error bounds in settings with unknown hyperparameters. Our approach computes a confidence region in the space of hyperparameters, which enables us to obtain a probabilistic upper bound for the model error of a Gaussian process with arbitrary hyperparameters. We do not require to know any bounds for the hyperparameters a priori, which is an assumption commonly found in related work. Instead, we are able to derive bounds from data in an intuitive fashion. We additionally employ the proposed technique to derive performance guarantees for a class of learning-based control problems. Experiments show that the bound performs significantly better than vanilla and fully Bayesian Gaussian processes.  ( 2 min )
    Geometric Multimodal Contrastive Representation Learning. (arXiv:2202.03390v3 [cs.LG] UPDATED)
    Learning representations of multimodal data that are both informative and robust to missing modalities at test time remains a challenging problem due to the inherent heterogeneity of data obtained from different channels. To address it, we present a novel Geometric Multimodal Contrastive (GMC) representation learning method consisting of two main components: i) a two-level architecture consisting of modality-specific base encoders, allowing to process an arbitrary number of modalities to an intermediate representation of fixed dimensionality, and a shared projection head, mapping the intermediate representations to a latent representation space; ii) a multimodal contrastive loss function that encourages the geometric alignment of the learned representations. We experimentally demonstrate that GMC representations are semantically rich and achieve state-of-the-art performance with missing modality information on three different learning problems including prediction and reinforcement learning tasks.  ( 2 min )
    A Survey of Deep Learning Architectures for Intelligent Reflecting Surfaces. (arXiv:2009.02540v5 [eess.SP] UPDATED)
    Intelligent reflecting surfaces (IRSs) have recently received significant attention for wireless communications because it reduces the hardware complexity, physical size, weight, and cost of conventional large arrays. However, deployment of IRS entails dealing with multiple channel links between the base station (BS) and the users. Further, the BS and IRS beamformers require a joint design, wherein the IRS elements must be rapidly reconfigured. Data-driven techniques, such as deep learning (DL), are critical in addressing these challenges. The lower computation time and model-free nature of DL makes it robust against the data imperfections and environmental changes. At the physical layer, DL has been shown to be effective for IRS signal detection, channel estimation and active/passive beamforming using architectures such as supervised, unsupervised and reinforcement learning. This article provides a synopsis of these techniques for designing DL-based IRS-assisted wireless systems.  ( 3 min )
    Multi Resolution Analysis (MRA) for Approximate Self-Attention. (arXiv:2207.10284v1 [cs.LG])
    Transformers have emerged as a preferred model for many tasks in natural langugage processing and vision. Recent efforts on training and deploying Transformers more efficiently have identified many strategies to approximate the self-attention matrix, a key module in a Transformer architecture. Effective ideas include various prespecified sparsity patterns, low-rank basis expansions and combinations thereof. In this paper, we revisit classical Multiresolution Analysis (MRA) concepts such as Wavelets, whose potential value in this setting remains underexplored thus far. We show that simple approximations based on empirical feedback and design choices informed by modern hardware and implementation challenges, eventually yield a MRA-based approach for self-attention with an excellent performance profile across most criteria of interest. We undertake an extensive set of experiments and demonstrate that this multi-resolution scheme outperforms most efficient self-attention proposals and is favorable for both short and long sequences. Code is available at \url{https://github.com/mlpen/mra-attention}.  ( 2 min )
    Contrastive Learning with Complex Heterogeneity. (arXiv:2105.09401v2 [cs.LG] UPDATED)
    With the advent of big data across multiple high-impact applications, we are often facing the challenge of complex heterogeneity. The newly collected data usually consist of multiple modalities and are characterized with multiple labels, thus exhibiting the co-existence of multiple types of heterogeneity. Although state-of-the-art techniques are good at modeling complex heterogeneity with sufficient label information, such label information can be quite expensive to obtain in real applications. Recently, researchers pay great attention to contrastive learning due to its prominent performance by utilizing rich unlabeled data. However, existing work on contrastive learning is not able to address the problem of false negative pairs, i.e., some `negative' pairs may have similar representations if they have the same label. To overcome the issues, in this paper, we propose a unified heterogeneous learning framework, which combines both the weighted unsupervised contrastive loss and the weighted supervised contrastive loss to model multiple types of heterogeneity. We first provide a theoretical analysis showing that the vanilla contrastive learning loss easily leads to the sub-optimal solution in the presence of false negative pairs, whereas the proposed weighted loss could automatically adjust the weight based on the similarity of the learned representations to mitigate this issue. Experimental results on real-world data sets demonstrate the effectiveness and the efficiency of the proposed framework modeling multiple types of heterogeneity.  ( 3 min )
    RADAMS: Resilient and Adaptive Alert and Attention Management Strategy against Informational Denial-of-Service (IDoS) Attacks. (arXiv:2111.03463v2 [cs.CR] UPDATED)
    Attacks exploiting human attentional vulnerability have posed severe threats to cybersecurity. In this work, we identify and formally define a new type of proactive attentional attacks called Informational Denial-of-Service (IDoS) attacks that generate a large volume of feint attacks to overload human operators and hide real attacks among feints. We incorporate human factors (e.g., levels of expertise, stress, and efficiency) and empirical psychological results (e.g., the Yerkes-Dodson law and the sunk cost fallacy) to model the operators' attention dynamics and their decision-making processes along with the real-time alert monitoring and inspection. To assist human operators in dismissing the feints and escalating the real attacks timely and accurately, we develop a Resilient and Adaptive Data-driven alert and Attention Management Strategy (RADAMS) that de-emphasizes alerts selectively based on the abstracted category labels of the alerts. RADAMS uses reinforcement learning to achieve a customized and transferable design for various human operators and evolving IDoS attacks. The integrated modeling and theoretical analysis lead to the Product Principle of Attention (PPoA), fundamental limits, and the tradeoff among crucial human and economic factors. Experimental results corroborate that the proposed strategy outperforms the default strategy and can reduce the IDoS risk by as much as 20%. Besides, the strategy is resilient to large variations of costs, attack frequencies, and human attention capacities. We have recognized interesting phenomena such as attentional risk equivalency, attacker's dilemma, and the half-truth optimal attack strategy.  ( 3 min )
    Distribution Approximation and Statistical Estimation Guarantees of Generative Adversarial Networks. (arXiv:2002.03938v3 [cs.LG] UPDATED)
    Generative Adversarial Networks (GANs) have achieved a great success in unsupervised learning. Despite its remarkable empirical performance, there are limited theoretical studies on the statistical properties of GANs. This paper provides approximation and statistical guarantees of GANs for the estimation of data distributions that have densities in a H\"{o}lder space. Our main result shows that, if the generator and discriminator network architectures are properly chosen, GANs are consistent estimators of data distributions under strong discrepancy metrics, such as the Wasserstein-1 distance. Furthermore, when the data distribution exhibits low-dimensional structures, we show that GANs are capable of capturing the unknown low-dimensional structures in data and enjoy a fast statistical convergence, which is free of curse of the ambient dimensionality. Our analysis for low-dimensional data builds upon a universal approximation theory of neural networks with Lipschitz continuity guarantees, which may be of independent interest.  ( 2 min )
    Variational quantum algorithm for Gaussian discrete solitons and their boson sampling. (arXiv:2110.12379v4 [quant-ph] UPDATED)
    In the context of quantum information, highly nonlinear regimes, such as those supporting solitons, are marginally investigated. We miss general methods for quantum solitons, although they can act as entanglement generators or as self-organized quantum processors. We develop a computational approach that uses a neural network as a variational ansatz for quantum solitons in an array of waveguides. By training the resulting phase-space quantum machine learning model, we find different soliton solutions varying the number of particles and interaction strength. We consider Gaussian states that enable measuring the degree of entanglement and sampling the probability distribution of many-particle events. We also determine the probability of generating particle pairs and unveil that soliton bound states emit correlated pairs. These results may have a role in boson sampling with nonlinear systems and in quantum processors for entangled nonlinear waves.  ( 2 min )
    Pushing the Limits of Semi-Supervised Learning for Automatic Speech Recognition. (arXiv:2010.10504v2 [eess.AS] UPDATED)
    We employ a combination of recent developments in semi-supervised learning for automatic speech recognition to obtain state-of-the-art results on LibriSpeech utilizing the unlabeled audio of the Libri-Light dataset. More precisely, we carry out noisy student training with SpecAugment using giant Conformer models pre-trained using wav2vec 2.0 pre-training. By doing so, we are able to achieve word-error-rates (WERs) 1.4%/2.6% on the LibriSpeech test/test-other sets against the current state-of-the-art WERs 1.7%/3.3%.  ( 2 min )
    High-Dimensional Inference in Bayesian Networks. (arXiv:2112.09217v2 [stat.ML] UPDATED)
    Inference of the marginal probability distribution is defined as the calculation of the probability of a subset of the variables and is relevant for handling missing data and hidden variables. While inference of the marginal probability distribution is crucial for various problems in machine learning and statistics, its exact computation is generally not feasible for categorical variables in Bayesian networks due to the NP-hardness of this task. We develop a divide-and-conquer approach using the graphical properties of Bayesian networks to split the computation of the marginal probability distribution into sub-calculations of lower dimensionality, thus reducing the overall computational complexity. Exploiting this property, we present an efficient and scalable algorithm for calculating the marginal probability distribution for categorical variables. The novel method is compared against state-of-the-art approximate inference methods in a benchmarking study, where it displays superior performance. As an immediate application, we demonstrate how our method can be used to classify incomplete data against Bayesian networks and use this approach for identifying the cancer subtype of kidney cancer patient samples.  ( 2 min )
    Federated Learning with Non-IID Data. (arXiv:1806.00582v2 [cs.LG] UPDATED)
    Federated learning enables resource-constrained edge compute devices, such as mobile phones and IoT devices, to learn a shared model for prediction, while keeping the training data local. This decentralized approach to train models provides privacy, security, regulatory and economic benefits. In this work, we focus on the statistical challenge of federated learning when local data is non-IID. We first show that the accuracy of federated learning reduces significantly, by up to 55% for neural networks trained for highly skewed non-IID data, where each client device trains only on a single class of data. We further show that this accuracy reduction can be explained by the weight divergence, which can be quantified by the earth mover's distance (EMD) between the distribution over classes on each device and the population distribution. As a solution, we propose a strategy to improve training on non-IID data by creating a small subset of data which is globally shared between all the edge devices. Experiments show that accuracy can be increased by 30% for the CIFAR-10 dataset with only 5% globally shared data.  ( 3 min )
    Grounding Visual Representations with Texts for Domain Generalization. (arXiv:2207.10285v1 [cs.CV])
    Reducing the representational discrepancy between source and target domains is a key component to maximize the model generalization. In this work, we advocate for leveraging natural language supervision for the domain generalization task. We introduce two modules to ground visual representations with texts containing typical reasoning of humans: (1) Visual and Textual Joint Embedder and (2) Textual Explanation Generator. The former learns the image-text joint embedding space where we can ground high-level class-discriminative information into the model. The latter leverages an explainable model and generates explanations justifying the rationale behind its decision. To the best of our knowledge, this is the first work to leverage the vision-and-language cross-modality approach for the domain generalization task. Our experiments with a newly created CUB-DG benchmark dataset demonstrate that cross-modality supervision can be successfully used to ground domain-invariant visual representations and improve the model generalization. Furthermore, in the large-scale DomainBed benchmark, our proposed method achieves state-of-the-art results and ranks 1st in average performance for five multi-domain datasets. The dataset and codes are available at https://github.com/mswzeus/GVRT.  ( 2 min )
    Deep Learning of Radiative Atmospheric Transfer with an Autoencoder. (arXiv:2207.10650v1 [physics.comp-ph])
    As electro-optical energy from the sun propagates through the atmosphere it is affected by radiative transfer effects including absorption, emission, and scattering. Modeling these affects is essential for scientific remote sensing measurements of the earth and atmosphere. For example, hyperspectral imagery is a form of digital imagery collected with many, often hundreds, of wavelengths of light in pixel. The amount of light measured at the sensor is the result of emitted sunlight, atmospheric radiative transfer, and the reflectance off the materials on the ground, all of which vary per wavelength resulting from multiple physical phenomena. Therefore measurements of the ground spectra or atmospheric constituents requires separating these different contributions per wavelength. In this paper, we create an autoencoder similar to denoising autoencoders treating the atmospheric affects as 'noise' and ground reflectance as truth per spectrum. We generate hundreds of thousands of training samples by taking random samples of spectra from laboratory measurements and adding atmospheric affects using physics-based modelling via MODTRAN (this http URL) by varying atmospheric inputs. This process ideally could create an autoencoder that would separate atmospheric effects and ground reflectance in hyperspectral imagery, a process called atmospheric compensation which is difficult and time-consuming requiring a combination of heuristic approximations, estimates of physical quantities, and physical modelling. While the accuracy of our method is not as good as other methods in the field, this an important first step in applying the growing field of deep learning of physical principles to atmospheric compensation in hyperspectral imagery and remote sensing.  ( 3 min )
    Classification of Macromolecule Type Based on Sequences of Amino Acids Using Deep Learning. (arXiv:1907.03532v2 [q-bio.BM] UPDATED)
    The classification of amino acids and their sequence analysis plays a vital role in life sciences and is a challenging task. This article uses and compares state-of-the-art deep learning models like convolution neural networks (CNN), long short-term memory (LSTM), and gated recurrent units (GRU) to solve macromolecule classification problems using amino acids. These models have efficient frameworks for solving a broad spectrum of complex learning problems compared to traditional machine learning techniques. We use word embedding to represent the amino acid sequences as vectors. The CNN extracts features from amino acid sequences, which are treated as vectors, then fed to the models mentioned above to train a robust classifier. Our results show that word2vec as embedding combined with VGG-16 performs better than LSTM and GRU. The proposed approach gets an error rate of 1.5%.  ( 2 min )
    Switching One-Versus-the-Rest Loss to Increase the Margin of Logits for Adversarial Robustness. (arXiv:2207.10283v1 [cs.LG])
    Defending deep neural networks against adversarial examples is a key challenge for AI safety. To improve the robustness effectively, recent methods focus on important data points near the decision boundary in adversarial training. However, these methods are vulnerable to Auto-Attack, which is an ensemble of parameter-free attacks for reliable evaluation. In this paper, we experimentally investigate the causes of their vulnerability and find that existing methods reduce margins between logits for the true label and the other labels while keeping their gradient norms non-small values. Reduced margins and non-small gradient norms cause their vulnerability since the largest logit can be easily flipped by the perturbation. Our experiments also show that the histogram of the logit margins has two peaks, i.e., small and large logit margins. From the observations, we propose switching one-versus-the-rest loss (SOVR), which uses one-versus-the-rest loss when data have small logit margins so that it increases the margins. We find that SOVR increases logit margins more than existing methods while keeping gradient norms small and outperforms them in terms of the robustness against Auto-Attack.  ( 2 min )
    Unsupervised pre-training of graph transformers on patient population graphs. (arXiv:2207.10603v1 [cs.LG])
    Pre-training has shown success in different areas of machine learning, such as Computer Vision, Natural Language Processing (NLP), and medical imaging. However, it has not been fully explored for clinical data analysis. An immense amount of clinical records are recorded, but still, data and labels can be scarce for data collected in small hospitals or dealing with rare diseases. In such scenarios, pre-training on a larger set of unlabelled clinical data could improve performance. In this paper, we propose novel unsupervised pre-training techniques designed for heterogeneous, multi-modal clinical data for patient outcome prediction inspired by masked language modeling (MLM), by leveraging graph deep learning over population graphs. To this end, we further propose a graph-transformer-based network, designed to handle heterogeneous clinical data. By combining masking-based pre-training with a transformer-based network, we translate the success of masking-based pre-training in other domains to heterogeneous clinical data. We show the benefit of our pre-training method in a self-supervised and a transfer learning setting, utilizing three medical datasets TADPOLE, MIMIC-III, and a Sepsis Prediction Dataset. We find that our proposed pre-training methods help in modeling the data at a patient and population level and improve performance in different fine-tuning tasks on all datasets.  ( 2 min )
    Neural Network Learning of Chemical Bond Representations in Spectral Indices and Features. (arXiv:2207.10530v1 [cs.CV])
    In this paper we investigate neural networks for classification in hyperspectral imaging with a focus on connecting the architecture of the network with the physics of the sensing and materials present. Spectroscopy is the process of measuring light reflected or emitted by a material as a function wavelength. Molecular bonds present in the material have vibrational frequencies which affect the amount of light measured at each wavelength. Thus the measured spectrum contains information about the particular chemical constituents and types of bonds. For example, chlorophyll reflects more light in the near-IR rage (800-900nm) than in the red (625-675nm) range, and this difference can be measured using a normalized vegetation difference index (NDVI), which is commonly used to detect vegetation presence, health, and type in imagery collected at these wavelengths. In this paper we show that the weights in a Neural Network trained on different vegetation classes learn to measure this difference in reflectance. We then show that a Neural Network trained on a more complex set of ten different polymer materials will learn spectral 'features' evident in the weights for the network, and these features can be used to reliably distinguish between the different types of polymers. Examination of the weights provides a human-interpretable understanding of the network.  ( 3 min )
    A Forgotten Danger in DNN Supervision Testing: Generating and Detecting True Ambiguity. (arXiv:2207.10495v1 [cs.SE])
    Deep Neural Networks (DNNs) are becoming a crucial component of modern software systems, but they are prone to fail under conditions that are different from the ones observed during training (out-of-distribution inputs) or on inputs that are truly ambiguous, i.e., inputs that admit multiple classes with nonzero probability in their ground truth labels. Recent work proposed DNN supervisors to detect high-uncertainty inputs before their possible misclassification leads to any harm. To test and compare the capabilities of DNN supervisors, researchers proposed test generation techniques, to focus the testing effort on high-uncertainty inputs that should be recognized as anomalous by supervisors. However, existing test generators can only produce out-of-distribution inputs. No existing model- and supervisor-independent technique supports the generation of truly ambiguous test inputs. In this paper, we propose a novel way to generate ambiguous inputs to test DNN supervisors and used it to empirically compare several existing supervisor techniques. In particular, we propose AmbiGuess to generate ambiguous samples for image classification problems. AmbiGuess is based on gradient-guided sampling in the latent space of a regularized adversarial autoencoder. Moreover, we conducted what is - to the best of our knowledge - the most extensive comparative study of DNN supervisors, considering their capabilities to detect 4 distinct types of high-uncertainty inputs, including truly ambiguous ones.  ( 2 min )
    Error Compensation Framework for Flow-Guided Video Inpainting. (arXiv:2207.10391v1 [cs.CV])
    The key to video inpainting is to use correlation information from as many reference frames as possible. Existing flow-based propagation methods split the video synthesis process into multiple steps: flow completion -> pixel propagation -> synthesis. However, there is a significant drawback that the errors in each step continue to accumulate and amplify in the next step. To this end, we propose an Error Compensation Framework for Flow-guided Video Inpainting (ECFVI), which takes advantage of the flow-based method and offsets its weaknesses. We address the weakness with the newly designed flow completion module and the error compensation network that exploits the error guidance map. Our approach greatly improves the temporal consistency and the visual quality of the completed videos. Experimental results show the superior performance of our proposed method with the speed up of x6, compared to the state-of-the-art methods. In addition, we present a new benchmark dataset for evaluation by supplementing the weaknesses of existing test datasets.  ( 2 min )
    Metropolis Monte Carlo sampling: convergence, localization transition and optimality. (arXiv:2207.10488v1 [cond-mat.stat-mech])
    Among random sampling methods, Markov Chain Monte Carlo algorithms are foremost. Using a combination of analytical and numerical approaches, we study their convergence properties towards the steady state, within a random walk Metropolis scheme. We show that the deviations from the target steady-state distribution feature a localization transition as a function of the characteristic length of the attempted jumps defining the random walk. This transition changes drastically the error which is introduced by incomplete convergence, and discriminates two regimes where the relaxation mechanism is limited respectively by diffusion and by rejection.  ( 2 min )
    A Dynamical Systems Algorithm for Clustering in Hyperspectral Imagery. (arXiv:2207.10625v1 [cs.CV])
    In this paper we present a new dynamical systems algorithm for clustering in hyperspectral images. The main idea of the algorithm is that data points are \`pushed\' in the direction of increasing density and groups of pixels that end up in the same dense regions belong to the same class. This is essentially a numerical solution of the differential equation defined by the gradient of the density of data points on the data manifold. The number of classes is automated and the resulting clustering can be extremely accurate. In addition to providing a accurate clustering, this algorithm presents a new tool for understanding hyperspectral data in high dimensions. We evaluate the algorithm on the Urban (Available at www.tec.ary.mil/Hypercube/) scene comparing performance against the k-means algorithm using pre-identified classes of materials as ground truth.  ( 2 min )
    CTL-MTNet: A Novel CapsNet and Transfer Learning-Based Mixed Task Net for the Single-Corpus and Cross-Corpus Speech Emotion Recognition. (arXiv:2207.10644v1 [cs.CL])
    Speech Emotion Recognition (SER) has become a growing focus of research in human-computer interaction. An essential challenge in SER is to extract common attributes from different speakers or languages, especially when a specific source corpus has to be trained to recognize the unknown data coming from another speech corpus. To address this challenge, a Capsule Network (CapsNet) and Transfer Learning based Mixed Task Net (CTLMTNet) are proposed to deal with both the singlecorpus and cross-corpus SER tasks simultaneously in this paper. For the single-corpus task, the combination of Convolution-Pooling and Attention CapsNet module CPAC) is designed by embedding the self-attention mechanism to the CapsNet, guiding the module to focus on the important features that can be fed into different capsules. The extracted high-level features by CPAC provide sufficient discriminative ability. Furthermore, to handle the cross-corpus task, CTL-MTNet employs a Corpus Adaptation Adversarial Module (CAAM) by combining CPAC with Margin Disparity Discrepancy (MDD), which can learn the domain-invariant emotion representations through extracting the strong emotion commonness. Experiments including ablation studies and visualizations on both singleand cross-corpus tasks using four well-known SER datasets in different languages are conducted for performance evaluation and comparison. The results indicate that in both tasks the CTL-MTNet showed better performance in all cases compared to a number of state-of-the-art methods. The source code and the supplementary materials are available at: https://github.com/MLDMXM2017/CTLMTNet  ( 3 min )
    RepFair-GAN: Mitigating Representation Bias in GANs Using Gradient Clipping. (arXiv:2207.10653v1 [cs.LG])
    Fairness has become an essential problem in many domains of Machine Learning (ML), such as classification, natural language processing, and Generative Adversarial Networks (GANs). In this research effort, we study the unfairness of GANs. We formally define a new fairness notion for generative models in terms of the distribution of generated samples sharing the same protected attributes (gender, race, etc.). The defined fairness notion (representational fairness) requires the distribution of the sensitive attributes at the test time to be uniform, and, in particular for GAN model, we show that this fairness notion is violated even when the dataset contains equally represented groups, i.e., the generator favors generating one group of samples over the others at the test time. In this work, we shed light on the source of this representation bias in GANs along with a straightforward method to overcome this problem. We first show on two widely used datasets (MNIST, SVHN) that when the norm of the gradient of one group is more important than the other during the discriminator's training, the generator favours sampling data from one group more than the other at test time. We then show that controlling the groups' gradient norm by performing group-wise gradient norm clipping in the discriminator during the training leads to a more fair data generation in terms of representational fairness compared to existing models while preserving the quality of generated samples.  ( 3 min )
    The MABe22 Benchmarks for Representation Learning of Multi-Agent Behavior. (arXiv:2207.10553v1 [cs.LG])
    Real-world behavior is often shaped by complex interactions between multiple agents. To scalably study multi-agent behavior, advances in unsupervised and self-supervised learning have enabled a variety of different behavioral representations to be learned from trajectory data. To date, there does not exist a unified set of benchmarks that can enable comparing methods quantitatively and systematically across a broad set of behavior analysis settings. We aim to address this by introducing a large-scale, multi-agent trajectory dataset from real-world behavioral neuroscience experiments that covers a range of behavior analysis tasks. Our dataset consists of trajectory data from common model organisms, with 9.6 million frames of mouse data and 4.4 million frames of fly data, in a variety of experimental settings, such as different strains, lengths of interaction, and optogenetic stimulation. A subset of the frames also consist of expert-annotated behavior labels. Improvements on our dataset corresponds to behavioral representations that work across multiple organisms and is able to capture differences for common behavior analysis tasks.  ( 2 min )
    Estimation of Non-Crossing Quantile Regression Process with Deep ReQU Neural Networks. (arXiv:2207.10442v1 [stat.ML])
    We propose a penalized nonparametric approach to estimating the quantile regression process (QRP) in a nonseparable model using rectifier quadratic unit (ReQU) activated deep neural networks and introduce a novel penalty function to enforce non-crossing of quantile regression curves. We establish the non-asymptotic excess risk bounds for the estimated QRP and derive the mean integrated squared error for the estimated QRP under mild smoothness and regularity conditions. To establish these non-asymptotic risk and estimation error bounds, we also develop a new error bound for approximating $C^s$ smooth functions with $s >0$ and their derivatives using ReQU activated neural networks. This is a new approximation result for ReQU networks and is of independent interest and may be useful in other problems. Our numerical experiments demonstrate that the proposed method is competitive with or outperforms two existing methods, including methods using reproducing kernels and random forests, for nonparametric quantile regression.  ( 2 min )
    Deep Learning Reveals Patterns of Diverse and Changing Sentiments Towards COVID-19 Vaccines Based on 11 Million Tweets. (arXiv:2207.10641v1 [cs.CL])
    Over 12 billion doses of COVID-19 vaccines have been administered at the time of writing. However, public perceptions of vaccines have been complex. We analyzed COVID-19 vaccine-related tweets to understand the evolving perceptions of COVID-19 vaccines. We finetuned a deep learning classifier using a state-of-the-art model, XLNet, to detect each tweet's sentiment automatically. We employed validated methods to extract the users' race or ethnicity, gender, age, and geographical locations from user profiles. Incorporating multiple data sources, we assessed the sentiment patterns among subpopulations and juxtaposed them against vaccine uptake data to unravel their interactive patterns. 11,211,672 COVID-19 vaccine-related tweets corresponding to 2,203,681 users over two years were analyzed. The finetuned model for sentiment classification yielded an accuracy of 0.92 on testing set. Users from various demographic groups demonstrated distinct patterns in sentiments towards COVID-19 vaccines. User sentiments became more positive over time, upon which we observed subsequent upswing in the population-level vaccine uptake. Surrounding dates where positive sentiments crest, we detected encouraging news or events regarding vaccine development and distribution. Positive sentiments in pregnancy-related tweets demonstrated a delayed pattern compared with trends in general population, with postponed vaccine uptake trends. Distinctive patterns across subpopulations suggest the need of tailored strategies. Global news and events profoundly involved in shaping users' thoughts on social media. Populations with additional concerns, such as pregnancy, demonstrated more substantial hesitancy since lack of timely recommendations. Feature analysis revealed hesitancies of various subpopulations stemmed from clinical trial logics, risks and complications, and urgency of scientific evidence.  ( 3 min )
    Deep Audio Waveform Prior. (arXiv:2207.10441v1 [cs.SD])
    Convolutional neural networks contain strong priors for generating natural looking images [1]. These priors enable image denoising, super resolution, and inpainting in an unsupervised manner. Previous attempts to demonstrate similar ideas in audio, namely deep audio priors, (i) use hand picked architectures such as harmonic convolutions, (ii) only work with spectrogram input, and (iii) have been used mostly for eliminating Gaussian noise [2]. In this work we show that existing SOTA architectures for audio source separation contain deep priors even when working with the raw waveform. Deep priors can be discovered by training a neural network to generate a single corrupted signal when given white noise as input. A network with relevant deep priors is likely to generate a cleaner version of the signal before converging on the corrupted signal. We demonstrate this restoration effect with several corruptions: background noise, reverberations, and a gap in the signal (audio inpainting).  ( 2 min )
    Continual-Learning-as-a-Service (CLaaS): On-Demand Efficient Adaptation of Predictive Models. (arXiv:2206.06957v2 [cs.LG] UPDATED)
    Predictive machine learning models nowadays are often updated in a stateless and expensive way. The two main future trends for companies that want to build machine learning-based applications and systems are real-time inference and continual updating. Unfortunately, both trends require a mature infrastructure that is hard and costly to realize on-premise. This paper defines a novel software service and model delivery infrastructure termed Continual Learning-as-a-Service (CLaaS) to address these issues. Specifically, it embraces continual machine learning and continuous integration techniques. It provides support for model updating and validation tools for data scientists without an on-premise solution and in an efficient, stateful and easy-to-use manner. Finally, this CL model service is easy to encapsulate in any machine learning infrastructure or cloud system. This paper presents the design and implementation of a CLaaS instantiation, called LiquidBrain, evaluated in two real-world scenarios. The former is a robotic object recognition setting using the CORe50 dataset while the latter is a named category and attribute prediction using the DeepFashion-C dataset in the fashion domain. Our preliminary results suggest the usability and efficiency of the Continual Learning model services and the effectiveness of the solution in addressing real-world use-cases regardless of where the computation happens in the continuum Edge-Cloud.  ( 3 min )
    Log Barriers for Safe Black-box Optimization with Application to Safe Reinforcement Learning. (arXiv:2207.10415v1 [math.OC])
    Optimizing noisy functions online, when evaluating the objective requires experiments on a deployed system, is a crucial task arising in manufacturing, robotics and many others. Often, constraints on safe inputs are unknown ahead of time, and we only obtain noisy information, indicating how close we are to violating the constraints. Yet, safety must be guaranteed at all times, not only for the final output of the algorithm. We introduce a general approach for seeking a stationary point in high dimensional non-linear stochastic optimization problems in which maintaining safety during learning is crucial. Our approach called LB-SGD is based on applying stochastic gradient descent (SGD) with a carefully chosen adaptive step size to a logarithmic barrier approximation of the original problem. We provide a complete convergence analysis of non-convex, convex, and strongly-convex smooth constrained problems, with first-order and zeroth-order feedback. Our approach yields efficient updates and scales better with dimensionality compared to existing approaches. We empirically compare the sample complexity and the computational cost of our method with existing safe learning approaches. Beyond synthetic benchmarks, we demonstrate the effectiveness of our approach on minimizing constraint violation in policy search tasks in safe reinforcement learning (RL).  ( 2 min )
    Personalized Prediction of Future Lesion Activity and Treatment Effect in Multiple Sclerosis from Baseline MRI. (arXiv:2204.01702v3 [eess.IV] UPDATED)
    Precision medicine for chronic diseases such as multiple sclerosis (MS) involves choosing a treatment which best balances efficacy and side effects/preferences for individual patients. Making this choice as early as possible is important, as delays in finding an effective therapy can lead to irreversible disability accrual. To this end, we present the first deep neural network model for individualized treatment decisions from baseline magnetic resonance imaging (MRI) (with clinical information if available) for MS patients. Our model (a) predicts future new and enlarging T2 weighted (NE-T2) lesion counts on follow-up MRI on multiple treatments and (b) estimates the conditional average treatment effect (CATE), as defined by the predicted future suppression of NE-T2 lesions, between different treatment options relative to placebo. Our model is validated on a proprietary federated dataset of 1817 multi-sequence MRIs acquired from MS patients during four multi-centre randomized clinical trials. Our framework achieves high average precision in the binarized regression of future NE-T2 lesions on five different treatments, identifies heterogeneous treatment effects, and provides a personalized treatment recommendation that accounts for treatment-associated risk (e.g. side effects, patient preference, administration difficulties).  ( 3 min )
    Identifying partial mouse brain microscopy images from Allen reference atlas using a contrastively learned semantic space. (arXiv:2109.06662v3 [cs.CV] UPDATED)
    Precise identification of mouse brain microscopy images is a crucial first step when anatomical structures in the mouse brain are to be registered to a reference atlas. Practitioners usually rely on manual comparison of images or tools that assume the presence of complete images. This work explores Siamese Networks as the method for finding corresponding 2D reference atlas plates for given partial 2D mouse brain images. Siamese networks are a class of convolutional neural networks (CNNs) that use weight-shared paths to obtain low dimensional embeddings of pairs of input images. The correspondence between the partial mouse brain image and reference atlas plate is determined based on the distance between low dimensional embeddings of brain slices and atlas plates that are obtained from Siamese networks using contrastive learning. Experiments showed that Siamese CNNs can precisely identify brain slices using the Allen mouse brain atlas when training and testing images come from the same source. They achieved TOP-1 and TOP-5 accuracy of 25% and 100%, respectively, taking only 7.2 seconds to identify 29 images.  ( 3 min )
    Neural Network Guided Evolutionary Fuzzing for Finding Traffic Violations of Autonomous Vehicles. (arXiv:2109.06126v4 [cs.SE] UPDATED)
    Self-driving cars and trucks, autonomous vehicles (AVs), should not be accepted by regulatory bodies and the public until they have much higher confidence in their safety and reliability -- which can most practically and convincingly be achieved by testing. But existing testing methods are inadequate for checking the end-to-end behaviors of AV controllers against complex, real-world corner cases involving interactions with multiple independent agents such as pedestrians and human-driven vehicles. While test-driving AVs on streets and highways fails to capture many rare events, existing simulation-based testing methods mainly focus on simple scenarios and do not scale well for complex driving situations that require sophisticated awareness of the surroundings. To address these limitations, we propose a new fuzz testing technique, called AutoFuzz, which can leverage widely-used AV simulators' API grammars to generate semantically and temporally valid complex driving scenarios (sequences of scenes). To efficiently search for traffic violations-inducing scenarios in a large search space, we propose a constrained neural network (NN) evolutionary search method to optimize AutoFuzz. Evaluation of our prototype on one state-of-the-art learning-based controller, two rule-based controllers, and one industrial-grade controller in five scenarios shows that AutoFuzz efficiently finds hundreds of traffic violations in high-fidelity simulation environments. For each scenario, AutoFuzz can find on average 10-39% more unique traffic violations than the best-performing baseline method. Further, fine-tuning the learning-based controller with the traffic violations found by AutoFuzz successfully reduced the traffic violations found in the new version of the AV controller software.  ( 3 min )
    How Well Does Self-Supervised Pre-Training Perform with Streaming Data?. (arXiv:2104.12081v3 [cs.LG] UPDATED)
    Prior works on self-supervised pre-training focus on the joint training scenario, where massive unlabeled data are assumed to be given as input all at once, and only then is a learner trained. Unfortunately, such a problem setting is often impractical if not infeasible since many real-world tasks rely on sequential learning, e.g., data are decentralized or collected in a streaming fashion. In this paper, we conduct the first thorough and dedicated investigation on self-supervised pre-training with streaming data, aiming to shed light on the model behavior under this overlooked setup. Specifically, we pre-train over 500 models on four categories of pre-training streaming data from ImageNet and DomainNet and evaluate them on three types of downstream tasks and 12 different downstream datasets. Our studies show that, somehow beyond our expectation, with simple data replay or parameter regularization, sequential self-supervised pre-training turns out to be an efficient alternative for joint pre-training, as the performances of the former are mostly on par with those of the latter. Moreover, catastrophic forgetting, a common issue in sequential supervised learning, is much alleviated in sequential self-supervised learning (SSL), which is well justified through our comprehensive empirical analysis on representations and the sharpness of minima in the loss landscape. Our findings, therefore, suggest that, in practice, for SSL, the cumbersome joint training can be replaced mainly by sequential learning, which in turn enables a much broader spectrum of potential application scenarios.  ( 3 min )
    AutoIP: A United Framework to Integrate Physics into Gaussian Processes. (arXiv:2202.12316v2 [cs.LG] UPDATED)
    Physical modeling is critical for many modern science and engineering applications. From a data science or machine learning perspective, where more domain-agnostic, data-driven models are pervasive, physical knowledge -- often expressed as differential equations -- is valuable in that it is complementary to data, and it can potentially help overcome issues such as data sparsity, noise, and inaccuracy. In this work, we propose a simple, yet powerful and general framework -- AutoIP, for Automatically Incorporating Physics -- that can integrate all kinds of differential equations into Gaussian Processes (GPs) to enhance prediction accuracy and uncertainty quantification. These equations can be linear or nonlinear, spatial, temporal, or spatio-temporal, complete or incomplete with unknown source terms, and so on. Based on kernel differentiation, we construct a GP prior to sample the values of the target function, equation-related derivatives, and latent source functions, which are all jointly from a multivariate Gaussian distribution. The sampled values are fed to two likelihoods: one to fit the observations, and the other to conform to the equation. We use the whitening method to evade the strong dependency between the sampled function values and kernel parameters, and we develop a stochastic variational learning algorithm. AutoIP shows improvement upon vanilla GPs in both simulation and several real-world applications, even using rough, incomplete equations.  ( 3 min )
    Provable concept learning for interpretable predictions using variational autoencoders. (arXiv:2204.00492v2 [cs.LG] UPDATED)
    In safety-critical applications, practitioners are reluctant to trust neural networks when no interpretable explanations are available. Many attempts to provide such explanations revolve around pixel-based attributions or use previously known concepts. In this paper we aim to provide explanations by provably identifying \emph{high-level, previously unknown ground-truth concepts}. To this end, we propose a probabilistic modeling framework to derive (C)oncept (L)earning and (P)rediction (CLAP) -- a VAE-based classifier that uses visually interpretable concepts as predictors for a simple classifier. Assuming a generative model for the ground-truth concepts, we prove that CLAP is able to identify them while attaining optimal classification accuracy. Our experiments on synthetic datasets verify that CLAP identifies distinct ground-truth concepts on synthetic datasets and yields promising results on the medical Chest X-Ray dataset.  ( 2 min )
    Combining Intra-Risk and Contagion Risk for Enterprise Bankruptcy Prediction Using Graph Neural Networks. (arXiv:2202.03874v4 [q-fin.RM] UPDATED)
    Predicting the bankruptcy risk of small and medium-sized enterprises (SMEs) is an important step for financial institutions when making decisions about loans. Existing studies in both finance and AI research fields, however, tend to only consider either the intra-risk or contagion risk of enterprises, ignoring their interactions and combinatorial effects. This study for the first time considers both types of risk and their joint effects in bankruptcy prediction. Specifically, we first propose an enterprise intra-risk encoder based on statistically significant enterprise risk indicators for its intra-risk learning. Then, we propose an enterprise contagion risk encoder based on enterprise relation information from an enterprise knowledge graph for its contagion risk embedding. In particular, the contagion risk encoder includes both the newly proposed Hyper-Graph Neural Networks and Heterogeneous Graph Neural Networks, which can model contagion risk in two different aspects, i.e. common risk factors based on hyperedges and direct diffusion risk from neighbors, respectively. To evaluate the model, we collect real-world multi-sources data on SMEs and build a novel benchmark dataset called SMEsD. We provide open access to the dataset, which is expected to further promote research on financial risk analysis. Experiments on SMEsD against twelve state-of-the-art baselines demonstrate the effectiveness of the proposed model for bankruptcy prediction.  ( 3 min )
    ViewFormer: NeRF-free Neural Rendering from Few Images Using Transformers. (arXiv:2203.10157v2 [cs.CV] UPDATED)
    Novel view synthesis is a long-standing problem. In this work, we consider a variant of the problem where we are given only a few context views sparsely covering a scene or an object. The goal is to predict novel viewpoints in the scene, which requires learning priors. The current state of the art is based on Neural Radiance Field (NeRF), and while achieving impressive results, the methods suffer from long training times as they require evaluating millions of 3D point samples via a neural network for each image. We propose a 2D-only method that maps multiple context views and a query pose to a new image in a single pass of a neural network. Our model uses a two-stage architecture consisting of a codebook and a transformer model. The codebook is used to embed individual images into a smaller latent space, and the transformer solves the view synthesis task in this more compact space. To train our model efficiently, we introduce a novel branching attention mechanism that allows us to use the same model not only for neural rendering but also for camera pose estimation. Experimental results on real-world scenes show that our approach is competitive compared to NeRF-based methods while not reasoning explicitly in 3D, and it is faster to train.  ( 3 min )
    Knee arthritis severity measurement using deep learning: a publicly available algorithm with a multi-institutional validation showing radiologist-level performance. (arXiv:2203.08914v2 [eess.IV] UPDATED)
    The assessment of knee osteoarthritis (KOA) severity on knee X-rays is a central criteria for the use of total knee arthroplasty. However, this assessment suffers from imprecise standards and a remarkably high inter-reader variability. An algorithmic, automated assessment of KOA severity could improve overall outcomes of knee replacement procedures by increasing the appropriateness of its use. We propose a novel deep learning-based five-step algorithm to automatically grade KOA from posterior-anterior (PA) views of radiographs: (1) image preprocessing (2) localization of knees joints in the image using the YOLO v3-Tiny model, (3) initial assessment of the severity of osteoarthritis using a convolutional neural network-based classifier, (4) segmentation of the joints and calculation of the joint space narrowing (JSN), and (5), a combination of the JSN and the initial assessment to determine a final Kellgren-Lawrence (KL) score. Furthermore, by displaying the segmentation masks used to make the assessment, our algorithm demonstrates a higher degree of transparency compared to typical "black box" deep learning classifiers. We perform a comprehensive evaluation using two public datasets and one dataset from our institution, and show that our algorithm reaches state-of-the art performance. Moreover, we also collected ratings from multiple radiologists at our institution and showed that our algorithm performs at the radiologist level. The software has been made publicly available at https://github.com/MaciejMazurowski/osteoarthritis-classification.  ( 3 min )
    Generative Adversarial Networks for Labeled Acceleration Data Augmentation for Structural Damage Detection. (arXiv:2112.03478v5 [cs.LG] UPDATED)
    There has been a major advance in the field of Data Science in the last few decades, and these have been utilized for different engineering disciplines and applications. Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) algorithms have been utilized for civil Structural Health Monitoring (SHM) especially for damage detection applications using sensor data. Although ML and DL methods show superior learning skills for complex data structures, they require plenty of data for training. However, in SHM, data collection from civil structures can be expensive and time taking; particularly getting useful data (damage associated data) can be challenging. The objective of this study is to address the data scarcity problem for damage detection applications. This paper employs 1-D Wasserstein Deep Convolutional Generative Adversarial Networks using Gradient Penalty (1-D WDCGAN-GP) for synthetic labelled acceleration data generation. Then, the generated data is augmented with varying ratios for the training dataset of a 1-D Deep Convolutional Neural Network (1-D DCNN) for damage detection application. The damage detection results show that the 1-D WDCGAN-GP can be successfully utilized to tackle data scarcity in vibration-based damage detection applications of civil structures. Keywords: Structural Health Monitoring (SHM), Structural Damage Detection, 1-D Deep Convolutional Neural Networks (1-D DCNN), 1-D Generative Adversarial Networks (1-D GAN), Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP)  ( 3 min )
    FLOWGEN: Fast and slow graph generation. (arXiv:2207.07656v2 [cs.LG] UPDATED)
    We present FLOWGEN, a graph-generation model inspired by the dual-process theory of mind that generates large graphs incrementally. Depending on the difficulty of completing the graph at the current step, graph generation is routed to either a fast~(weaker) or a slow~(stronger) model. fast and slow models have identical architectures, but vary in the number of parameters and consequently the strength. Experiments on real-world graphs show that ours can successfully generate graphs similar to those generated by a single large model in a fraction of time.  ( 2 min )
    The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games. (arXiv:2103.01955v3 [cs.LG] UPDATED)
    Proximal Policy Optimization (PPO) is a ubiquitous on-policy reinforcement learning algorithm but is significantly less utilized than off-policy learning algorithms in multi-agent settings. This is often due to the belief that PPO is significantly less sample efficient than off-policy methods in multi-agent systems. In this work, we carefully study the performance of PPO in cooperative multi-agent settings. We show that PPO-based multi-agent algorithms achieve surprisingly strong performance in four popular multi-agent testbeds: the particle-world environments, the StarCraft multi-agent challenge, the Hanabi challenge, and Google Research Football, with minimal hyperparameter tuning and without any domain-specific algorithmic modifications or architectures. Importantly, compared to strong off-policy methods, PPO often achieves competitive or superior results in both final rewards and sample efficiency. Finally, through ablation studies, we analyze implementation and hyperparameter factors that are critical to PPO's empirical performance, and give concrete practical suggestions regarding these factors. Our results show that when using these practices, simple PPO-based methods are a strong baseline in cooperative multi-agent reinforcement learning. Source code is released at https://github.com/marlbenchmark/on-policy.  ( 3 min )
    Multilingual Disinformation Detection for Digital Advertising. (arXiv:2207.10649v1 [cs.CL])
    In today's world, the presence of online disinformation and propaganda is more widespread than ever. Independent publishers are funded mostly via digital advertising, which is unfortunately also the case for those publishing disinformation content. The question of how to remove such publishers from advertising inventory has long been ignored, despite the negative impact on the open internet. In this work, we make the first step towards quickly detecting and red-flagging websites that potentially manipulate the public with disinformation. We build a machine learning model based on multilingual text embeddings that first determines whether the page mentions a topic of interest, then estimates the likelihood of the content being malicious, creating a shortlist of publishers that will be reviewed by human experts. Our system empowers internal teams to proactively, rather than defensively, blacklist unsafe content, thus protecting the reputation of the advertisement provider.  ( 2 min )
    Face-to-Face Co-Located Human-Human Social Interaction Analysis using Nonverbal Cues: A Survey. (arXiv:2207.10574v1 [cs.HC])
    This work presents a systematic review of recent efforts (since 2010) aimed at automatic analysis of nonverbal cues displayed in face-to-face co-located human-human social interactions. The main reason for focusing on nonverbal cues is that these are the physical, machine detectable traces of social and psychological phenomena. Therefore, detecting and understanding nonverbal cues means, at least to a certain extent, to detect and understand social and psychological phenomena. The covered topics are categorized into three as: a) modeling social traits, such as leadership, dominance, personality traits, b) social role recognition and social relations detection and c) interaction dynamics analysis in terms of group cohesion, empathy, rapport and so forth. We target the co-located interactions, in which the interactants are always humans. The survey covers a wide spectrum of settings and scenarios, including free-standing interactions, meetings, indoor and outdoor social exchanges, dyadic conversations, and crowd dynamics. For each of them, the survey considers the three main elements of nonverbal cues analysis, namely data, sensing approaches and computational methodologies. The goal is to highlight the main advances of the last decade, to point out existing limitations, and to outline future directions.  ( 2 min )
    Analysis of Regularized Learning for Generalized Data in Banach Spaces. (arXiv:2109.03159v5 [cs.LG] UPDATED)
    In this article, we study the whole theory of regularized learning for generalized data in Banach spaces including representer theorems, approximation theorems, and convergence theorems. The generalized input data are composed of linear functionals in the predual spaces of the Banach spaces to represent the discrete local information of different engineering and physics models. The generalized data and the multi-loss functions are used to compute the empirical risks, and the regularized learning is to minimize the regularized empirical risks over the Banach spaces. Even if the original problems are unknown or unformulated, then the exact solutions of the original problems are approximated globally by the regularized learning. In the proof of the convergence theorems, the strong convergence condition is replaced to the weak convergence condition with the additional checkable condition which is independent of the original problems. The theorems of the regularized learning can be used to solve many problems of machine learning such as support vector machines and neural networks.  ( 3 min )
    Careful What You Wish For: on the Extraction of Adversarially Trained Models. (arXiv:2207.10561v1 [cs.LG])
    Recent attacks on Machine Learning (ML) models such as evasion attacks with adversarial examples and models stealing through extraction attacks pose several security and privacy threats. Prior work proposes to use adversarial training to secure models from adversarial examples that can evade the classification of a model and deteriorate its performance. However, this protection technique affects the model's decision boundary and its prediction probabilities, hence it might raise model privacy risks. In fact, a malicious user using only a query access to the prediction output of a model can extract it and obtain a high-accuracy and high-fidelity surrogate model. To have a greater extraction, these attacks leverage the prediction probabilities of the victim model. Indeed, all previous work on extraction attacks do not take into consideration the changes in the training process for security purposes. In this paper, we propose a framework to assess extraction attacks on adversarially trained models with vision datasets. To the best of our knowledge, our work is the first to perform such evaluation. Through an extensive empirical study, we demonstrate that adversarially trained models are more vulnerable to extraction attacks than models obtained under natural training circumstances. They can achieve up to $\times1.2$ higher accuracy and agreement with a fraction lower than $\times0.75$ of the queries. We additionally find that the adversarial robustness capability is transferable through extraction attacks, i.e., extracted Deep Neural Networks (DNNs) from robust models show an enhanced accuracy to adversarial examples compared to extracted DNNs from naturally trained (i.e. standard) models.  ( 3 min )
    AXM-Net: Implicit Cross-Modal Feature Alignment for Person Re-identification. (arXiv:2101.08238v3 [cs.CV] UPDATED)
    Cross-modal person re-identification (Re-ID) is critical for modern video surveillance systems. The key challenge is to align cross-modality representations induced by the semantic information present for a person and ignore background information. This work presents a novel convolutional neural network (CNN) based architecture designed to learn semantically aligned cross-modal visual and textual representations. The underlying building block, named AXM-Block, is a unified multi-layer network that dynamically exploits the multi-scale knowledge from both modalities and re-calibrates each modality according to shared semantics. To complement the convolutional design, contextual attention is applied in the text branch to manipulate long-term dependencies. Moreover, we propose a unique design to enhance visual part-based feature coherence and locality information. Our framework is novel in its ability to implicitly learn aligned semantics between modalities during the feature learning stage. The unified feature learning effectively utilizes textual data as a super-annotation signal for visual representation learning and automatically rejects irrelevant information. The entire AXM-Net is trained end-to-end on CUHK-PEDES data. We report results on two tasks, person search and cross-modal Re-ID. The AXM-Net outperforms the current state-of-the-art (SOTA) methods and achieves 64.44\% Rank@1 on the CUHK-PEDES test set. It also outperforms its competitors by $>$10\% in cross-viewpoint text-to-image Re-ID scenarios on CrossRe-ID and CUHK-SYSU datasets.  ( 3 min )
    Encrypted Internet traffic classification using a supervised Spiking Neural Network. (arXiv:2101.09818v2 [cs.LG] UPDATED)
    Internet traffic recognition is an essential tool for access providers since recognizing traffic categories related to different data packets transmitted on a network help them define adapted priorities. That means, for instance, high priority requirements for an audio conference and low ones for a file transfer, to enhance user experience. As internet traffic becomes increasingly encrypted, the mainstream classic traffic recognition technique, payload inspection, is rendered ineffective. This paper uses machine learning techniques for encrypted traffic classification, looking only at packet size and time of arrival. Spiking neural networks (SNN), largely inspired by how biological neurons operate, were used for two reasons. Firstly, they are able to recognize time-related data packet features. Secondly, they can be implemented efficiently on neuromorphic hardware with a low energy footprint. Here we used a very simple feedforward SNN, with only one fully-connected hidden layer, and trained in a supervised manner using the newly introduced method known as Surrogate Gradient Learning. Surprisingly, such a simple SNN reached an accuracy of 95.9% on ISCX datasets, outperforming previous approaches. Besides better accuracy, there is also a very significant improvement on simplicity: input size, number of neurons, trainable parameters are all reduced by one to four orders of magnitude. Next, we analyzed the reasons for this good accuracy. It turns out that, beyond spatial (i.e. packet size) features, the SNN also exploits temporal ones, mostly the nearly synchronous (within a 200ms range) arrival times of packets with certain sizes. Taken together, these results show that SNNs are an excellent fit for encrypted internet traffic classification: they can be more accurate than conventional artificial neural networks (ANN), and they could be implemented efficiently on low power embedded systems.  ( 3 min )
    Subgraph Matching via Query-Conditioned Subgraph Matching Neural Networks and Bi-Level Tree Search. (arXiv:2207.10305v1 [cs.LG])
    Recent advances have shown the success of using reinforcement learning and search to solve NP-hard graph-related tasks, such as Traveling Salesman Optimization, Graph Edit Distance computation, etc. However, it remains unclear how one can efficiently and accurately detect the occurrences of a small query graph in a large target graph, which is a core operation in graph database search, biomedical analysis, social group finding, etc. This task is called Subgraph Matching which essentially performs subgraph isomorphism check between a query graph and a large target graph. One promising approach to this classical problem is the "learning-to-search" paradigm, where a reinforcement learning (RL) agent is designed with a learned policy to guide a search algorithm to quickly find the solution without any solved instances for supervision. However, for the specific task of Subgraph Matching, though the query graph is usually small given by the user as input, the target graph is often orders-of-magnitude larger. It poses challenges to the neural network design and can lead to solution and reward sparsity. In this paper, we propose N-BLS with two innovations to tackle the challenges: (1) A novel encoder-decoder neural network architecture to dynamically compute the matching information between the query and the target graphs at each search state; (2) A Monte Carlo Tree Search enhanced bi-level search framework for training the policy and value networks. Experiments on five large real-world target graphs show that N-BLS can significantly improve the subgraph matching performance.  ( 3 min )
    A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks. (arXiv:2207.10289v1 [physics.comp-ph])
    Physics-informed neural networks (PINNs) have shown to be an effective tool for solving forward and inverse problems of partial differential equations (PDEs). PINNs embed the PDEs into the loss of the neural network, and this PDE loss is evaluated at a set of scattered residual points. The distribution of these points are highly important to the performance of PINNs. However, in the existing studies on PINNs, only a few simple residual point sampling methods have mainly been used. Here, we present a comprehensive study of two categories of sampling: non-adaptive uniform sampling and adaptive nonuniform sampling. We consider six uniform sampling, including (1) equispaced uniform grid, (2) uniformly random sampling, (3) Latin hypercube sampling, (4) Halton sequence, (5) Hammersley sequence, and (6) Sobol sequence. We also consider a resampling strategy for uniform sampling. To improve the sampling efficiency and the accuracy of PINNs, we propose two new residual-based adaptive sampling methods: residual-based adaptive distribution (RAD) and residual-based adaptive refinement with distribution (RAR-D), which dynamically improve the distribution of residual points based on the PDE residuals during training. Hence, we have considered a total of 10 different sampling methods, including six non-adaptive uniform sampling, uniform sampling with resampling, two proposed adaptive sampling, and an existing adaptive sampling. We extensively tested the performance of these sampling methods for four forward problems and two inverse problems in many setups. Our numerical results presented in this study are summarized from more than 6000 simulations of PINNs. We show that the proposed adaptive sampling methods of RAD and RAR-D significantly improve the accuracy of PINNs with fewer residual points. The results obtained in this study can also be used as a practical guideline in choosing sampling methods.  ( 3 min )
    ProMix: Combating Label Noise via Maximizing Clean Sample Utility. (arXiv:2207.10276v1 [cs.LG])
    The ability to train deep neural networks under label noise is appealing, as imperfectly annotated data are relatively cheaper to obtain. State-of-the-art approaches are based on semi-supervised learning(SSL), which selects small loss examples as clean and then applies SSL techniques for boosted performance. However, the selection step mostly provides a medium-sized and decent-enough clean subset, which overlooks a rich set of clean samples. In this work, we propose a novel noisy label learning framework ProMix that attempts to maximize the utility of clean samples for boosted performance. Key to our method, we propose a matched high-confidence selection technique that selects those examples having high confidence and matched prediction with its given labels. Combining with the small-loss selection, our method is able to achieve a precision of 99.27 and a recall of 98.22 in detecting clean samples on the CIFAR-10N dataset. Based on such a large set of clean data, ProMix improves the best baseline method by +2.67% on CIFAR-10N and +1.61% on CIFAR-100N datasets. The code and data are available at https://github.com/Justherozen/ProMix  ( 2 min )
    Improved Generative Model for Weakly Supervised Chest Anomaly Localization via Pseudo-paired Registration with Bilaterally Symmetrical Data Augmentation. (arXiv:2207.10324v1 [eess.IV])
    Image translation based on a generative adversarial network (GAN-IT) is a promising method for precise localization of abnormal regions in chest X-ray images (AL-CXR). However, heterogeneous unpaired datasets undermine existing methods to extract key features and distinguish normal from abnormal cases, resulting in inaccurate and unstable AL-CXR. To address this problem, we propose an improved two-stage GAN-IT involving registration and data augmentation. For the first stage, we introduce an invertible deep-learning-based registration technique that virtually and reasonably converts unpaired data into paired data for learning registration maps. This novel approach achieves high registration performance. For the second stage, we apply data augmentation to diversify anomaly locations by swapping the left and right lung regions on the uniform registered frames, further improving the performance by alleviating imbalance in data distribution showing left and right lung lesions. Our method is intended for application to existing GAN-IT models, allowing existing architecture to benefit from key features for translation. By showing that the AL-CXR performance is uniformly improved when applying the proposed method, we believe that GAN-IT for AL-CXR can be deployed in clinical environments, even if learning data are scarce.  ( 3 min )
    Action2Score: An Embedding Approach To Score Player Action. (arXiv:2207.10297v1 [cs.LG])
    Multiplayer Online Battle Arena (MOBA) is one of the most successful game genres. MOBA games such as League of Legends have competitive environments where players race for their rank. In most MOBA games, a player's rank is determined by the match result (win or lose). It seems natural because of the nature of team play, but in some sense, it is unfair because the players who put a lot of effort lose their rank just in case of loss and some players even get free-ride on teammates' efforts in case of a win. To reduce the side-effects of the team-based ranking system and evaluate a player's performance impartially, we propose a novel embedding model that converts a player's actions into quantitative scores based on the actions' respective contribution to the team's victory. Our model is built using a sequence-based deep learning model with a novel loss function working on the team match. The sequence-based deep learning model process the action sequence from the game start to the end of a player in a team play using a GRU unit that takes a hidden state from the previous step and the current input selectively. The loss function is designed to help the action score to reflect the final score and the success of the team. We showed that our model can evaluate a player's individual performance fairly and analyze the contributions of the player's respective actions.  ( 3 min )
    Multi-Asset Closed-Loop Reservoir Management Using Deep Reinforcement Learning. (arXiv:2207.10376v1 [cs.LG])
    Closed-loop reservoir management (CLRM), in which history matching and production optimization are performed multiple times over the life of an asset, can provide significant improvement in the specified objective. These procedures are computationally expensive due to the large number of flow simulations required for data assimilation and optimization. Existing CLRM procedures are applied asset by asset, without utilizing information that could be useful over a range assets. Here, we develop a CLRM framework for multiple assets with varying numbers of wells. We use deep reinforcement learning to train a single global control policy that is applicable for all assets considered. The new framework is an extension of a recently introduced control policy methodology for individual assets. Embedding layers are incorporated into the representation to handle the different numbers of decision variables that arise for the different assets. Because the global control policy learns a unified representation of useful features from multiple assets, it is less expensive to construct than asset-by-asset training (we observe about 3x speedup in our examples). The production optimization problem includes a relative-change constraint on the well settings, which renders the results suitable for practical use. We apply the multi-asset CLRM framework to 2D and 3D water-flooding examples. In both cases, four assets with different well counts, well configurations, and geostatistical descriptions are considered. Numerical experiments demonstrate that the global control policy provides objective function values, for both the 2D and 3D cases, that are nearly identical to those from control policies trained individually for each asset. This promising finding suggests that multi-asset CLRM may indeed represent a viable practical strategy.  ( 3 min )
    Comparative Study on Supervised versus Semi-supervised Machine Learning for Anomaly Detection of In-vehicle CAN Network. (arXiv:2207.10286v1 [cs.LG])
    As the central nerve of the intelligent vehicle control system, the in-vehicle network bus is crucial to the security of vehicle driving. One of the best standards for the in-vehicle network is the Controller Area Network (CAN bus) protocol. However, the CAN bus is designed to be vulnerable to various attacks due to its lack of security mechanisms. To enhance the security of in-vehicle networks and promote the research in this area, based upon a large scale of CAN network traffic data with the extracted valuable features, this study comprehensively compared fully-supervised machine learning with semi-supervised machine learning methods for CAN message anomaly detection. Both traditional machine learning models (including single classifier and ensemble models) and neural network based deep learning models are evaluated. Furthermore, this study proposed a deep autoencoder based semi-supervised learning method applied for CAN message anomaly detection and verified its superiority over other semi-supervised methods. Extensive experiments show that the fully-supervised methods generally outperform semi-supervised ones as they are using more information as inputs. Typically the developed XGBoost based model obtained state-of-the-art performance with the best accuracy (98.65%), precision (0.9853), and ROC AUC (0.9585) beating other methods reported in the literature.  ( 3 min )
    Sequence Models for Drone vs Bird Classification. (arXiv:2207.10409v1 [cs.CV])
    Drone detection has become an essential task in object detection as drone costs have decreased and drone technology has improved. It is, however, difficult to detect distant drones when there is weak contrast, long range, and low visibility. In this work, we propose several sequence classification architectures to reduce the detected false-positive ratio of drone tracks. Moreover, we propose a new drone vs. bird sequence classification dataset to train and evaluate the proposed architectures. 3D CNN, LSTM, and Transformer based sequence classification architectures have been trained on the proposed dataset to show the effectiveness of the proposed idea. As experiments show, using sequence information, bird classification and overall F1 scores can be increased by up to 73% and 35%, respectively. Among all sequence classification models, R(2+1)D-based fully convolutional model yields the best transfer learning and fine-tuning results.  ( 2 min )
    Unimodal vs. Multimodal Siamese Networks for Outfit Completion. (arXiv:2207.10355v1 [cs.IR])
    The popularity of online fashion shopping continues to grow. The ability to offer an effective recommendation to customers is becoming increasingly important. In this work, we focus on Fashion Outfits Challenge, part of SIGIR 2022 Workshop on eCommerce. The challenge is centered around Fill in the Blank (FITB) task that implies predicting the missing outfit, given an incomplete outfit and a list of candidates. In this paper, we focus on applying siamese networks on the task. More specifically, we explore how combining information from multiple modalities (textual and visual modality) impacts the performance of the model on the task. We evaluate our model on the test split provided by the challenge organizers and the test split with gold assignments that we created during the development phase. We discover that using both visual, and visual and textual data demonstrates promising results on the task. We conclude by suggesting directions for further improvement of our method.  ( 2 min )
    GBDF: Gender Balanced DeepFake Dataset Towards Fair DeepFake Detection. (arXiv:2207.10246v1 [cs.CV])
    Facial forgery by deepfakes has raised severe societal concerns. Several solutions have been proposed by the vision community to effectively combat the misinformation on the internet via automated deepfake detection systems. Recent studies have demonstrated that facial analysis-based deep learning models can discriminate based on protected attributes. For the commercial adoption and massive roll-out of the deepfake detection technology, it is vital to evaluate and understand the fairness (the absence of any prejudice or favoritism) of deepfake detectors across demographic variations such as gender and race. As the performance differential of deepfake detectors between demographic subgroups would impact millions of people of the deprived sub-group. This paper aims to evaluate the fairness of the deepfake detectors across males and females. However, existing deepfake datasets are not annotated with demographic labels to facilitate fairness analysis. To this aim, we manually annotated existing popular deepfake datasets with gender labels and evaluated the performance differential of current deepfake detectors across gender. Our analysis on the gender-labeled version of the datasets suggests (a) current deepfake datasets have skewed distribution across gender, and (b) commonly adopted deepfake detectors obtain unequal performance across gender with mostly males outperforming females. Finally, we contributed a gender-balanced and annotated deepfake dataset, GBDF, to mitigate the performance differential and to promote research and development towards fairness-aware deep fake detectors. The GBDF dataset is publicly available at: https://github.com/aakash4305/GBDF  ( 3 min )
    FOCUS: Fairness via Agent-Awareness for Federated Learning on Heterogeneous Data. (arXiv:2207.10265v1 [cs.LG])
    Federated learning (FL) provides an effective paradigm to train machine learning models over distributed data with privacy protection. However, recent studies show that FL is subject to various security, privacy, and fairness threats due to the potentially malicious and heterogeneous local agents. For instance, it is vulnerable to local adversarial agents who only contribute low-quality data, with the goal of harming the performance of those with high-quality data. This kind of attack hence breaks existing definitions of fairness in FL that mainly focus on a certain notion of performance parity. In this work, we aim to address this limitation and propose a formal definition of fairness via agent-awareness for FL (FAA), which takes the heterogeneous data contributions of local agents into account. In addition, we propose a fair FL training algorithm based on agent clustering (FOCUS) to achieve FAA. Theoretically, we prove the convergence and optimality of FOCUS under mild conditions for linear models and general convex loss functions with bounded smoothness. We also prove that FOCUS always achieves higher fairness measured by FAA compared with standard FedAvg protocol under both linear models and general convex loss functions. Empirically, we evaluate FOCUS on four datasets, including synthetic data, images, and texts under different settings, and we show that FOCUS achieves significantly higher fairness based on FAA while maintaining similar or even higher prediction accuracy compared with FedAvg.  ( 3 min )
    World Robot Challenge 2020 -- Partner Robot: A Data-Driven Approach for Room Tidying with Mobile Manipulator. (arXiv:2207.10106v1 [cs.RO])
    Tidying up a household environment using a mobile manipulator poses various challenges in robotics, such as adaptation to large real-world environmental variations, and safe and robust deployment in the presence of humans.The Partner Robot Challenge in World Robot Challenge (WRC) 2020, a global competition held in September 2021, benchmarked tidying tasks in the real home environments, and importantly, tested for full system performances.For this challenge, we developed an entire household service robot system, which leverages a data-driven approach to adapt to numerous edge cases that occur during the execution, instead of classical manual pre-programmed solutions.In this paper, we describe the core ingredients of the proposed robot system, including visual recognition, object manipulation, and motion planning. Our robot system won the second prize, verifying the effectiveness and potential of data-driven robot systems for mobile manipulation in home environments.  ( 2 min )
    On the Implementation of a Reinforcement Learning-based Capacity Sharing Algorithm in O-RAN. (arXiv:2207.10390v1 [cs.NI])
    The capacity sharing problem in Radio Access Network (RAN) slicing deals with the distribution of the capacity available in each RAN node among various RAN slices to satisfy their traffic demands and efficiently use the radio resources. While several capacity sharing algorithmic solutions have been proposed in the literature, their practical implementation still remains as a gap. In this paper, the implementation of a Reinforcement Learning-based capacity sharing algorithm over the O-RAN architecture is discussed, providing insights into the operation of the involved interfaces and the containerization of the solution. Moreover, the description of the testbed implemented to validate the solution is included and some performance and validation results are presented.  ( 2 min )
    Mixed-Precision Inference Quantization: Radically Towards Faster inference speed, Lower Storage requirement, and Lower Loss. (arXiv:2207.10083v1 [cs.LG])
    Based on the model's resilience to computational noise, model quantization is important for compressing models and improving computing speed. Existing quantization techniques rely heavily on experience and "fine-tuning" skills. In the majority of instances, the quantization model has a larger loss than a full precision model. This study provides a methodology for acquiring a mixed-precise quantization model with a lower loss than the full precision model. In addition, the analysis demonstrates that, throughout the inference process, the loss function is mostly affected by the noise of the layer inputs. In particular, we will demonstrate that neural networks with massive identity mappings are resistant to the quantization method. It is also difficult to improve the performance of these networks using quantization.  ( 2 min )
    Learning Deformable Object Manipulation from Expert Demonstrations. (arXiv:2207.10148v1 [cs.RO])
    We present a novel Learning from Demonstration (LfD) method, Deformable Manipulation from Demonstrations (DMfD), to solve deformable manipulation tasks using states or images as inputs, given expert demonstrations. Our method uses demonstrations in three different ways, and balances the trade-off between exploring the environment online and using guidance from experts to explore high dimensional spaces effectively. We test DMfD on a set of representative manipulation tasks for a 1-dimensional rope and a 2-dimensional cloth from the SoftGym suite of tasks, each with state and image observations. Our method exceeds baseline performance by up to 12.9% for state-based tasks and up to 33.44% on image-based tasks, with comparable or better robustness to randomness. Additionally, we create two challenging environments for folding a 2D cloth using image-based observations, and set a performance benchmark for them. We deploy DMfD on a real robot with a minimal loss in normalized performance during real-world execution compared to simulation (~6%). Source code is on github.com/uscresl/dmfd  ( 2 min )
    Addressing Optimism Bias in Sequence Modeling for Reinforcement Learning. (arXiv:2207.10295v1 [cs.LG])
    Impressive results in natural language processing (NLP) based on the Transformer neural network architecture have inspired researchers to explore viewing offline reinforcement learning (RL) as a generic sequence modeling problem. Recent works based on this paradigm have achieved state-of-the-art results in several of the mostly deterministic offline Atari and D4RL benchmarks. However, because these methods jointly model the states and actions as a single sequencing problem, they struggle to disentangle the effects of the policy and world dynamics on the return. Thus, in adversarial or stochastic environments, these methods lead to overly optimistic behavior that can be dangerous in safety-critical systems like autonomous driving. In this work, we propose a method that addresses this optimism bias by explicitly disentangling the policy and world models, which allows us at test time to search for policies that are robust to multiple possible futures in the environment. We demonstrate our method's superior performance on a variety of autonomous driving tasks in simulation.  ( 2 min )
    SplitMixer: Fat Trimmed From MLP-like Models. (arXiv:2207.10255v1 [cs.CV])
    We present SplitMixer, a simple and lightweight isotropic MLP-like architecture, for visual recognition. It contains two types of interleaving convolutional operations to mix information across spatial locations (spatial mixing) and channels (channel mixing). The first one includes sequentially applying two depthwise 1D kernels, instead of a 2D kernel, to mix spatial information. The second one is splitting the channels into overlapping or non-overlapping segments, with or without shared parameters, and applying our proposed channel mixing approaches or 3D convolution to mix channel information. Depending on design choices, a number of SplitMixer variants can be constructed to balance accuracy, the number of parameters, and speed. We show, both theoretically and experimentally, that SplitMixer performs on par with the state-of-the-art MLP-like models while having a significantly lower number of parameters and FLOPS. For example, without strong data augmentation and optimization, SplitMixer achieves around 94% accuracy on CIFAR-10 with only 0.28M parameters, while ConvMixer achieves the same accuracy with about 0.6M parameters. The well-known MLP-Mixer achieves 85.45% with 17.1M parameters. On CIFAR-100 dataset, SplitMixer achieves around 73% accuracy, on par with ConvMixer, but with about 52% fewer parameters and FLOPS. We hope that our results spark further research towards finding more efficient vision architectures and facilitate the development of MLP-like models. Code is available at https://github.com/aliborji/splitmixer.  ( 3 min )
    Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMM. (arXiv:2207.10226v1 [cs.LG])
    Federated learning (FL) enables distributed devices to jointly train a shared model while keeping the training data local. Different from the horizontal FL (HFL) setting where each client has partial data samples, vertical FL (VFL), which allows each client to collect partial features, has attracted intensive research efforts recently. In this paper, we identified two challenges that state-of-the-art VFL frameworks are facing: (1) some works directly average the learned feature embeddings and therefore might lose the unique properties of each local feature set; (2) server needs to communicate gradients with the clients for each training step, incurring high communication cost that leads to rapid consumption of privacy budgets. In this paper, we aim to address the above challenges and propose an efficient VFL with multiple linear heads (VIM) framework, where each head corresponds to local clients by taking the separate contribution of each client into account. In addition, we propose an Alternating Direction Method of Multipliers (ADMM)-based method to solve our optimization problem, which reduces the communication cost by allowing multiple local updates in each step, and thus leads to better performance under differential privacy. We consider various settings including VFL with model splitting and without model splitting. For both settings, we carefully analyze the differential privacy mechanism for our framework. Moreover, we show that a byproduct of our framework is that the weights of learned linear heads reflect the importance of local clients. We conduct extensive evaluations and show that on four real-world datasets, VIM achieves significantly higher performance and faster convergence compared with state-of-the-arts. We also explicitly evaluate the importance of local clients and show that VIM enables functionalities such as client-level explanation and client denoising.  ( 3 min )
    Bitwidth-Adaptive Quantization-Aware Neural Network Training: A Meta-Learning Approach. (arXiv:2207.10188v1 [cs.LG])
    Deep neural network quantization with adaptive bitwidths has gained increasing attention due to the ease of model deployment on various platforms with different resource budgets. In this paper, we propose a meta-learning approach to achieve this goal. Specifically, we propose MEBQAT, a simple yet effective way of bitwidth-adaptive quantization aware training (QAT) where meta-learning is effectively combined with QAT by redefining meta-learning tasks to incorporate bitwidths. After being deployed on a platform, MEBQAT allows the (meta-)trained model to be quantized to any candidate bitwidth then helps to conduct inference without much accuracy drop from quantization. Moreover, with a few-shot learning scenario, MEBQAT can also adapt a model to any bitwidth as well as any unseen target classes by adding conventional optimization or metric-based meta-learning. We design variants of MEBQAT to support both (1) a bitwidth-adaptive quantization scenario and (2) a new few-shot learning scenario where both quantization bitwidths and target classes are jointly adapted. We experimentally demonstrate their validity in multiple QAT schemes. By comparing their performance to (bitwidth-dedicated) QAT, existing bitwidth adaptive QAT and vanilla meta-learning, we find that merging bitwidths into meta-learning tasks achieves a higher level of robustness.  ( 2 min )
    Unsupervised Legendre-Galerkin Neural Network for Stiff Partial Differential Equations. (arXiv:2207.10241v1 [cs.LG])
    Machine learning methods have been lately used to solve differential equations and dynamical systems. These approaches have been developed into a novel research field known as scientific machine learning in which techniques such as deep neural networks and statistical learning are applied to classical problems of applied mathematics. Because neural networks provide an approximation capability, computational parameterization through machine learning and optimization methods achieve noticeable performance when solving various partial differential equations (PDEs). In this paper, we develop a novel numerical algorithm that incorporates machine learning and artificial intelligence to solve PDEs. In particular, we propose an unsupervised machine learning algorithm based on the Legendre-Galerkin neural network to find an accurate approximation to the solution of different types of PDEs. The proposed neural network is applied to the general 1D and 2D PDEs as well as singularly perturbed PDEs that possess boundary layer behavior.  ( 2 min )
    On Label Granularity and Object Localization. (arXiv:2207.10225v1 [cs.CV])
    Weakly supervised object localization (WSOL) aims to learn representations that encode object location using only image-level category labels. However, many objects can be labeled at different levels of granularity. Is it an animal, a bird, or a great horned owl? Which image-level labels should we use? In this paper we study the role of label granularity in WSOL. To facilitate this investigation we introduce iNatLoc500, a new large-scale fine-grained benchmark dataset for WSOL. Surprisingly, we find that choosing the right training label granularity provides a much larger performance boost than choosing the best WSOL algorithm. We also show that changing the label granularity can significantly improve data efficiency.  ( 2 min )
    Direct Localization in Underwater Acoustics via Convolutional Neural Networks: A Data-Driven Approach. (arXiv:2207.10222v1 [cs.LG])
    Direct localization (DLOC) methods, which use the observed data to localize a source at an unknown position in a one-step procedure, generally outperform their indirect two-step counterparts (e.g., using time-difference of arrivals). However, underwater acoustic DLOC methods require prior knowledge of the environment, and are computationally costly, hence slow. We propose, what is to the best of our knowledge, the first data-driven DLOC method. Inspired by classical and contemporary optimal model-based DLOC solutions, and leveraging the capabilities of convolutional neural networks (CNNs), we devise a holistic CNN-based solution. Our method includes a specifically-tailored input structure, architecture, loss function, and a progressive training procedure, which are of independent interest in the broader context of machine learning. We demonstrate that our method outperforms attractive alternatives, and asymptotically matches the performance of an oracle optimal model-based solution.  ( 2 min )
    The Game of Hidden Rules: A New Kind of Benchmark Challenge for Machine Learning. (arXiv:2207.10218v1 [cs.LG])
    As machine learning (ML) is more tightly woven into society, it is imperative that we better characterize ML's strengths and limitations if we are to employ it responsibly. Existing benchmark environments for ML, such as board and video games, offer well-defined benchmarks for progress, but constituent tasks are often complex, and it is frequently unclear how task characteristics contribute to overall difficulty for the machine learner. Likewise, without a systematic assessment of how task characteristics influence difficulty, it is challenging to draw meaningful connections between performance in different benchmark environments. We introduce a novel benchmark environment that offers an enormous range of ML challenges and enables precise examination of how task elements influence practical difficulty. The tool frames learning tasks as a "board-clearing game," which we call the Game of Hidden Rules (GOHR). The environment comprises an expressive rule language and a captive server environment that can be installed locally. We propose a set of benchmark rule-learning tasks and plan to support a performance leader-board for researchers interested in attempting to learn our rules. GOHR complements existing environments by allowing fine, controlled modifications to tasks, enabling experimenters to better understand how each facet of a given learning task contributes to its practical difficulty for an arbitrary ML algorithm.  ( 3 min )
    Slimmable Quantum Federated Learning. (arXiv:2207.10221v1 [cs.LG])
    Quantum federated learning (QFL) has recently received increasing attention, where quantum neural networks (QNNs) are integrated into federated learning (FL). In contrast to the existing static QFL methods, we propose slimmable QFL (SlimQFL) in this article, which is a dynamic QFL framework that can cope with time-varying communication channels and computing energy limitations. This is made viable by leveraging the unique nature of a QNN where its angle parameters and pole parameters can be separately trained and dynamically exploited. Simulation results corroborate that SlimQFL achieves higher classification accuracy than Vanilla QFL, particularly under poor channel conditions on average.  ( 2 min )
    What Do We Maximize in Self-Supervised Learning?. (arXiv:2207.10081v1 [cs.LG])
    In this paper, we examine self-supervised learning methods, particularly VICReg, to provide an information-theoretical understanding of their construction. As a first step, we demonstrate how information-theoretic quantities can be obtained for a deterministic network, offering a possible alternative to prior work that relies on stochastic models. This enables us to demonstrate how VICReg can be (re)discovered from first principles and its assumptions about data distribution. Furthermore, we empirically demonstrate the validity of our assumptions, confirming our novel understanding of VICReg. Finally, we believe that the derivation and insights we obtain can be generalized to many other SSL methods, opening new avenues for theoretical and practical understanding of SSL and transfer learning.  ( 2 min )
    Flow-based Visual Quality Enhancer for Super-resolution Magnetic Resonance Spectroscopic Imaging. (arXiv:2207.10181v1 [eess.IV])
    Magnetic Resonance Spectroscopic Imaging (MRSI) is an essential tool for quantifying metabolites in the body, but the low spatial resolution limits its clinical applications. Deep learning-based super-resolution methods provided promising results for improving the spatial resolution of MRSI, but the super-resolved images are often blurry compared to the experimentally-acquired high-resolution images. Attempts have been made with the generative adversarial networks to improve the image visual quality. In this work, we consider another type of generative model, the flow-based model, of which the training is more stable and interpretable compared to the adversarial networks. Specifically, we propose a flow-based enhancer network to improve the visual quality of super-resolution MRSI. Different from previous flow-based models, our enhancer network incorporates anatomical information from additional image modalities (MRI) and uses a learnable base distribution. In addition, we impose a guide loss and a data-consistency loss to encourage the network to generate images with high visual quality while maintaining high fidelity. Experiments on a 1H-MRSI dataset acquired from 25 high-grade glioma patients indicate that our enhancer network outperforms the adversarial networks and the baseline flow-based methods. Our method also allows visual quality adjustment and uncertainty estimation.  ( 3 min )
    Liver Segmentation using Turbolift Learning for CT and Cone-beam C-arm Perfusion Imaging. (arXiv:2207.10167v1 [eess.IV])
    Model-based reconstruction employing the time separation technique (TST) was found to improve dynamic perfusion imaging of the liver using C-arm cone-beam computed tomography (CBCT). To apply TST using prior knowledge extracted from CT perfusion data, the liver should be accurately segmented from the CT scans. Reconstructions of primary and model-based CBCT data need to be segmented for proper visualisation and interpretation of perfusion maps. This research proposes Turbolift learning, which trains a modified version of the multi-scale Attention UNet on different liver segmentation tasks serially, following the order of the trainings CT, CBCT, CBCT TST - making the previous trainings act as pre-training stages for the subsequent ones - addressing the problem of limited number of datasets for training. For the final task of liver segmentation from CBCT TST, the proposed method achieved an overall Dice scores of 0.874$\pm$0.031 and 0.905$\pm$0.007 in 6-fold and 4-fold cross-validation experiments, respectively - securing statistically significant improvements over the model, which was trained only for that task. Experiments revealed that Turbolift not only improves the overall performance of the model but also makes it robust against artefacts originating from the embolisation materials and truncation artefacts. Additionally, in-depth analyses confirmed the order of the segmentation tasks. This paper shows the potential of segmenting the liver from CT, CBCT, and CBCT TST, learning from the available limited training data, which can possibly be used in the future for the visualisation and evaluation of the perfusion maps for the treatment evaluation of liver diseases.  ( 3 min )
    Constrained Prescriptive Trees via Column Generation. (arXiv:2207.10163v1 [math.OC])
    With the abundance of available data, many enterprises seek to implement data-driven prescriptive analytics to help them make informed decisions. These prescriptive policies need to satisfy operational constraints, and proactively eliminate rule conflicts, both of which are ubiquitous in practice. It is also desirable for them to be simple and interpretable, so they can be easily verified and implemented. Existing approaches from the literature center around constructing variants of prescriptive decision trees to generate interpretable policies. However, none of the existing methods are able to handle constraints. In this paper, we propose a scalable method that solves the constrained prescriptive policy generation problem. We introduce a novel path-based mixed-integer program (MIP) formulation which identifies a (near) optimal policy efficiently via column generation. The policy generated can be represented as a multiway-split tree which is more interpretable and informative than a binary-split tree due to its shorter rules. We demonstrate the efficacy of our method with extensive experiments on both synthetic and real datasets.  ( 2 min )
    Hydra: Hybrid Server Power Model. (arXiv:2207.10217v1 [cs.DC])
    With the growing complexity of big data workloads that require abundant data and computation, data centers consume a tremendous amount of power daily. In an effort to minimize data center power consumption, several studies developed power models that can be used for job scheduling either reducing the number of active servers or balancing workloads across servers at their peak energy efficiency points. Due to increasing software and hardware heterogeneity, we observed that there is no single power model that works the best for all server conditions. Some complicated machine learning models themselves incur performance and power overheads and hence it is not desirable to use them frequently. There are no power models that consider containerized workload execution. In this paper, we propose a hybrid server power model, Hydra, that considers both prediction accuracy and performance overhead. Hydra dynamically chooses the best power model for the given server conditions. Compared with state-of-the-art solutions, Hydra outperforms across all compute-intensity levels on heterogeneous servers.  ( 2 min )
    Model Compression for Resource-Constrained Mobile Robots. (arXiv:2207.10082v1 [cs.LG])
    The number of mobile robots with constrained computing resources that need to execute complex machine learning models has been increasing during the past decade. Commonly, these robots rely on edge infrastructure accessible over wireless communication to execute heavy computational complex tasks. However, the edge might become unavailable and, consequently, oblige the execution of the tasks on the robot. This work focuses on making it possible to execute the tasks on the robots by reducing the complexity and the total number of parameters of pre-trained computer vision models. This is achieved by using model compression techniques such as Pruning and Knowledge Distillation. These compression techniques have strong theoretical and practical foundations, but their combined usage has not been widely explored in the literature. Therefore, this work especially focuses on investigating the effects of combining these two compression techniques. The results of this work reveal that up to 90% of the total number of parameters of a computer vision model can be removed without any considerable reduction in the model's accuracy.  ( 3 min )
    An Introduction to Modern Statistical Learning. (arXiv:2207.10185v1 [cs.LG])
    This work in progress aims to provide a unified introduction to statistical learning, building up slowly from classical models like the GMM and HMM to modern neural networks like the VAE and diffusion models. There are today many internet resources that explain this or that new machine-learning algorithm in isolation, but they do not (and cannot, in so brief a space) connect these algorithms with each other or with the classical literature on statistical models, out of which the modern algorithms emerged. Also conspicuously lacking is a single notational system which, although unfazing to those already familiar with the material (like the authors of these posts), raises a significant barrier to the novice's entry. Likewise, I have aimed to assimilate the various models, wherever possible, to a single framework for inference and learning, showing how (and why) to change one model into another with minimal alteration (some of them novel, others from the literature). Some background is of course necessary. I have assumed the reader is familiar with basic multivariable calculus, probability and statistics, and linear algebra. The goal of this book is certainly not completeness, but rather to draw a more or less straight-line path from the basics to the extremely powerful new models of the last decade. The goal then is to complement, not replace, such comprehensive texts as Bishop's \emph{Pattern Recognition and Machine Learning}, which is now 15 years old.  ( 3 min )
    Towards Better Evaluation for Dynamic Link Prediction. (arXiv:2207.10128v1 [cs.LG])
    There has been recent success in learning from static graphs, but despite their prevalence, learning from time-evolving graphs remains challenging. We design new, more stringent evaluation procedures for link prediction specific to dynamic graphs, which reflect real-world considerations and can better compare different methods' strengths and weaknesses. In particular, we create two visualization techniques to understand the recurring patterns of edges over time. They show that many edges reoccur at later time steps. Therefore, we propose a pure memorization baseline called EdgeBank. It achieves surprisingly strong performance across multiple settings, partly due to the easy negative edges used in the current evaluation setting. Hence, we introduce two more challenging negative sampling strategies that improve robustness and can better match real-world applications. Lastly, we introduce five new dynamic graph datasets from a diverse set of domains missing from current benchmarks, providing new challenges and opportunities for future research.  ( 2 min )
    Pediatric Bone Age Assessment using Deep Learning Models. (arXiv:2207.10169v1 [cs.CV])
    Bone age assessment (BAA) is a standard method for determining the age difference between skeletal and chronological age. Manual processes are complicated and necessitate the expertise of experts. This is where deep learning comes into play. In this study, pre-trained models like VGG-16, InceptionV3, XceptionNet, and MobileNet are used to assess the bone age of the input data, and their mean average errors are compared and evaluated to see which model predicts the best.  ( 2 min )
    Digraphwave: Scalable Extraction of Structural Node Embeddings via Diffusion on Directed Graphs. (arXiv:2207.10149v1 [cs.SI])
    Structural node embeddings, vectors capturing local connectivity information for each node in a graph, have many applications in data mining and machine learning, e.g., network alignment and node classification, clustering and anomaly detection. For the analysis of directed graphs, e.g., transactions graphs, communication networks and social networks, the capability to capture directional information in the structural node embeddings is highly desirable, as is scalability of the embedding extraction method. Most existing methods are nevertheless only designed for undirected graph. Therefore, we present Digraphwave -- a scalable algorithm for extracting structural node embeddings on directed graphs. The Digraphwave embeddings consist of compressed diffusion pattern signatures, which are twice enhanced to increase their discriminate capacity. By proving a lower bound on the heat contained in the local vicinity of a diffusion initialization node, theoretically justified diffusion timescale values are established, and Digraphwave is left with only two easy-to-interpret hyperparameters: the embedding dimension and a neighbourhood resolution specifier. In our experiments, the two embedding enhancements, named transposition and aggregation, are shown to lead to a significant increase in macro F1 score for classifying automorphic identities, with Digraphwave outperforming all other structural embedding baselines. Moreover, Digraphwave either outperforms or matches the performance of all baselines on real graph datasets, displaying a particularly large performance gain in a network alignment task, while also being scalable to graphs with millions of nodes and edges, running up to 30x faster than a previous diffusion pattern based method and with a fraction of the memory consumption.  ( 3 min )
    On the Robustness of 3D Object Detectors. (arXiv:2207.10205v1 [cs.CV])
    In recent years, significant progress has been achieved for 3D object detection on point clouds thanks to the advances in 3D data collection and deep learning techniques. Nevertheless, 3D scenes exhibit a lot of variations and are prone to sensor inaccuracies as well as information loss during pre-processing. Thus, it is crucial to design techniques that are robust against these variations. This requires a detailed analysis and understanding of the effect of such variations. This work aims to analyze and benchmark popular point-based 3D object detectors against several data corruptions. To the best of our knowledge, we are the first to investigate the robustness of point-based 3D object detectors. To this end, we design and evaluate corruptions that involve data addition, reduction, and alteration. We further study the robustness of different modules against local and global variations. Our experimental results reveal several intriguing findings. For instance, we show that methods that integrate Transformers at a patch or object level lead to increased robustness, compared to using Transformers at the point level.  ( 2 min )
    Provably tuning the ElasticNet across instances. (arXiv:2207.10199v1 [cs.LG])
    An important unresolved challenge in the theory of regularization is to set the regularization coefficients of popular techniques like the ElasticNet with general provable guarantees. We consider the problem of tuning the regularization parameters of Ridge regression, LASSO, and the ElasticNet across multiple problem instances, a setting that encompasses both cross-validation and multi-task hyperparameter optimization. We obtain a novel structural result for the ElasticNet which characterizes the loss as a function of the tuning parameters as a piecewise-rational function with algebraic boundaries. We use this to bound the structural complexity of the regularized loss functions and show generalization guarantees for tuning the ElasticNet regression coefficients in the statistical setting. We also consider the more challenging online learning setting, where we show vanishing average expected regret relative to the optimal parameter pair. We further extend our results to tuning classification algorithms obtained by thresholding regression fits regularized by Ridge, LASSO, or ElasticNet. Our results are the first general learning-theoretic guarantees for this important class of problems that avoid strong assumptions on the data distribution. Furthermore, our guarantees hold for both validation and popular information criterion objectives.  ( 2 min )
    Latent Discriminant deterministic Uncertainty. (arXiv:2207.10130v1 [cs.CV])
    Predictive uncertainty estimation is essential for deploying Deep Neural Networks in real-world autonomous systems. However, most successful approaches are computationally intensive. In this work, we attempt to address these challenges in the context of autonomous driving perception tasks. Recently proposed Deterministic Uncertainty Methods (DUM) can only partially meet such requirements as their scalability to complex computer vision tasks is not obvious. In this work we advance a scalable and effective DUM for high-resolution semantic segmentation, that relaxes the Lipschitz constraint typically hindering practicality of such architectures. We learn a discriminant latent space by leveraging a distinction maximization layer over an arbitrarily-sized set of trainable prototypes. Our approach achieves competitive results over Deep Ensembles, the state-of-the-art for uncertainty prediction, on image classification, segmentation and monocular depth estimation tasks. Our code is available at https://github.com/ENSTA-U2IS/LDU  ( 2 min )
    Structural Causal 3D Reconstruction. (arXiv:2207.10156v1 [cs.LG])
    This paper considers the problem of unsupervised 3D object reconstruction from in-the-wild single-view images. Due to ambiguity and intrinsic ill-posedness, this problem is inherently difficult to solve and therefore requires strong regularization to achieve disentanglement of different latent factors. Unlike existing works that introduce explicit regularizations into objective functions, we look into a different space for implicit regularization -- the structure of latent space. Specifically, we restrict the structure of latent space to capture a topological causal ordering of latent factors (i.e., representing causal dependency as a directed acyclic graph). We first show that different causal orderings matter for 3D reconstruction, and then explore several approaches to find a task-dependent causal factor ordering. Our experiments demonstrate that the latent space structure indeed serves as an implicit regularization and introduces an inductive bias beneficial for reconstruction.  ( 2 min )
    Continual Variational Autoencoder Learning via Online Cooperative Memorization. (arXiv:2207.10131v1 [cs.LG])
    Due to their inference, data representation and reconstruction properties, Variational Autoencoders (VAE) have been successfully used in continual learning classification tasks. However, their ability to generate images with specifications corresponding to the classes and databases learned during Continual Learning (CL) is not well understood and catastrophic forgetting remains a significant challenge. In this paper, we firstly analyze the forgetting behaviour of VAEs by developing a new theoretical framework that formulates CL as a dynamic optimal transport problem. This framework proves approximate bounds to the data likelihood without requiring the task information and explains how the prior knowledge is lost during the training process. We then propose a novel memory buffering approach, namely the Online Cooperative Memorization (OCM) framework, which consists of a Short-Term Memory (STM) that continually stores recent samples to provide future information for the model, and a Long-Term Memory (LTM) aiming to preserve a wide diversity of samples. The proposed OCM transfers certain samples from STM to LTM according to the information diversity selection criterion without requiring any supervised signals. The OCM framework is then combined with a dynamic VAE expansion mixture network for further enhancing its performance.  ( 2 min )
    Learning Underspecified Models. (arXiv:2207.10140v1 [econ.TH])
    This paper examines whether one can learn to play an optimal action while only knowing part of true specification of the environment. We choose the optimal pricing problem as our laboratory, where the monopolist is endowed with an underspecified model of the market demand, but can observe market outcomes. In contrast to conventional learning models where the model specification is complete and exogenously fixed, the monopolist has to learn the specification and the parameters of the demand curve from the data. We formulate the learning dynamics as an algorithm that forecast the optimal price based on the data, following the machine learning literature (Shalev-Shwartz and Ben-David (2014)). Inspired by PAC learnability, we develop a new notion of learnability by requiring that the algorithm must produce an accurate forecast with a reasonable amount of data uniformly over the class of models consistent with the part of the true specification. In addition, we assume that the monopolist has a lexicographic preference over the payoff and the complexity cost of the algorithm, seeking an algorithm with a minimum number of parameters subject to PAC-guaranteeing the optimal solution (Rubinstein (1986)). We show that for the set of demand curves with strictly decreasing uniformly Lipschitz continuous marginal revenue curve, the optimal algorithm recursively estimates the slope and the intercept of the linear demand curve, even if the actual demand curve is not linear. The monopolist chooses a misspecified model to save computational cost, while learning the true optimal decision uniformly over the set of underspecified demand curves.  ( 3 min )
  • Open

    Imitation of Manipulation Skills Using Multiple Geometries. (arXiv:2203.01171v3 [cs.RO] UPDATED)
    Daily manipulation tasks are characterized by geometric primitives related to actions and object shapes. Such geometric descriptors are poorly represented by only using Cartesian coordinate systems. In this paper, we propose a learning approach to extract the optimal representation from a dictionary of coordinate systems to encode an observed movement/behavior. This is achieved by using an extension of Gaussian distributions on Riemannian manifolds, which is used to analyse a set of user demonstrations statistically, by considering multiple geometries as candidate representations of the task. We formulate the reproduction problem as a general optimal control problem based on an iterative linear quadratic regulator (iLQR), where the Gaussian distribution in the extracted coordinate systems are used to define the cost function. We apply our approach to object grasping and box opening tasks in simulation and on a 7-axis Franka Emika robot. The results show that the robot can exploit several geometries to execute the manipulation task and generalize it to new situations, by maintaining the invariant characteristics of the task in the coordinate system(s) of interest.  ( 2 min )
    Generative Adversarial Networks for Labeled Acceleration Data Augmentation for Structural Damage Detection. (arXiv:2112.03478v5 [cs.LG] UPDATED)
    There has been a major advance in the field of Data Science in the last few decades, and these have been utilized for different engineering disciplines and applications. Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) algorithms have been utilized for civil Structural Health Monitoring (SHM) especially for damage detection applications using sensor data. Although ML and DL methods show superior learning skills for complex data structures, they require plenty of data for training. However, in SHM, data collection from civil structures can be expensive and time taking; particularly getting useful data (damage associated data) can be challenging. The objective of this study is to address the data scarcity problem for damage detection applications. This paper employs 1-D Wasserstein Deep Convolutional Generative Adversarial Networks using Gradient Penalty (1-D WDCGAN-GP) for synthetic labelled acceleration data generation. Then, the generated data is augmented with varying ratios for the training dataset of a 1-D Deep Convolutional Neural Network (1-D DCNN) for damage detection application. The damage detection results show that the 1-D WDCGAN-GP can be successfully utilized to tackle data scarcity in vibration-based damage detection applications of civil structures. Keywords: Structural Health Monitoring (SHM), Structural Damage Detection, 1-D Deep Convolutional Neural Networks (1-D DCNN), 1-D Generative Adversarial Networks (1-D GAN), Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP)  ( 3 min )
    Federated Learning with Non-IID Data. (arXiv:1806.00582v2 [cs.LG] UPDATED)
    Federated learning enables resource-constrained edge compute devices, such as mobile phones and IoT devices, to learn a shared model for prediction, while keeping the training data local. This decentralized approach to train models provides privacy, security, regulatory and economic benefits. In this work, we focus on the statistical challenge of federated learning when local data is non-IID. We first show that the accuracy of federated learning reduces significantly, by up to 55% for neural networks trained for highly skewed non-IID data, where each client device trains only on a single class of data. We further show that this accuracy reduction can be explained by the weight divergence, which can be quantified by the earth mover's distance (EMD) between the distribution over classes on each device and the population distribution. As a solution, we propose a strategy to improve training on non-IID data by creating a small subset of data which is globally shared between all the edge devices. Experiments show that accuracy can be increased by 30% for the CIFAR-10 dataset with only 5% globally shared data.  ( 3 min )
    Distribution Approximation and Statistical Estimation Guarantees of Generative Adversarial Networks. (arXiv:2002.03938v3 [cs.LG] UPDATED)
    Generative Adversarial Networks (GANs) have achieved a great success in unsupervised learning. Despite its remarkable empirical performance, there are limited theoretical studies on the statistical properties of GANs. This paper provides approximation and statistical guarantees of GANs for the estimation of data distributions that have densities in a H\"{o}lder space. Our main result shows that, if the generator and discriminator network architectures are properly chosen, GANs are consistent estimators of data distributions under strong discrepancy metrics, such as the Wasserstein-1 distance. Furthermore, when the data distribution exhibits low-dimensional structures, we show that GANs are capable of capturing the unknown low-dimensional structures in data and enjoy a fast statistical convergence, which is free of curse of the ambient dimensionality. Our analysis for low-dimensional data builds upon a universal approximation theory of neural networks with Lipschitz continuity guarantees, which may be of independent interest.  ( 2 min )
    An Equivalence Between Data Poisoning and Byzantine Gradient Attacks. (arXiv:2202.08578v2 [cs.LG] UPDATED)
    To study the resilience of distributed learning, the "Byzantine" literature considers a strong threat model where workers can report arbitrary gradients to the parameter server. Whereas this model helped obtain several fundamental results, it has sometimes been considered unrealistic, when the workers are mostly trustworthy machines. In this paper, we show a surprising equivalence between this model and data poisoning, a threat considered much more realistic. More specifically, we prove that every gradient attack can be reduced to data poisoning, in any personalized federated learning system with PAC guarantees (which we show are both desirable and realistic). This equivalence makes it possible to obtain new impossibility results on the resilience of any "robust" learning algorithm to data poisoning in highly heterogeneous applications, as corollaries of existing impossibility theorems on Byzantine machine learning. Moreover, using our equivalence, we derive a practical attack that we show (theoretically and empirically) can be very effective against classical personalized federated learning models.  ( 2 min )
    High-Dimensional Inference in Bayesian Networks. (arXiv:2112.09217v2 [stat.ML] UPDATED)
    Inference of the marginal probability distribution is defined as the calculation of the probability of a subset of the variables and is relevant for handling missing data and hidden variables. While inference of the marginal probability distribution is crucial for various problems in machine learning and statistics, its exact computation is generally not feasible for categorical variables in Bayesian networks due to the NP-hardness of this task. We develop a divide-and-conquer approach using the graphical properties of Bayesian networks to split the computation of the marginal probability distribution into sub-calculations of lower dimensionality, thus reducing the overall computational complexity. Exploiting this property, we present an efficient and scalable algorithm for calculating the marginal probability distribution for categorical variables. The novel method is compared against state-of-the-art approximate inference methods in a benchmarking study, where it displays superior performance. As an immediate application, we demonstrate how our method can be used to classify incomplete data against Bayesian networks and use this approach for identifying the cancer subtype of kidney cancer patient samples.  ( 2 min )
    Identifying partial mouse brain microscopy images from Allen reference atlas using a contrastively learned semantic space. (arXiv:2109.06662v3 [cs.CV] UPDATED)
    Precise identification of mouse brain microscopy images is a crucial first step when anatomical structures in the mouse brain are to be registered to a reference atlas. Practitioners usually rely on manual comparison of images or tools that assume the presence of complete images. This work explores Siamese Networks as the method for finding corresponding 2D reference atlas plates for given partial 2D mouse brain images. Siamese networks are a class of convolutional neural networks (CNNs) that use weight-shared paths to obtain low dimensional embeddings of pairs of input images. The correspondence between the partial mouse brain image and reference atlas plate is determined based on the distance between low dimensional embeddings of brain slices and atlas plates that are obtained from Siamese networks using contrastive learning. Experiments showed that Siamese CNNs can precisely identify brain slices using the Allen mouse brain atlas when training and testing images come from the same source. They achieved TOP-1 and TOP-5 accuracy of 25% and 100%, respectively, taking only 7.2 seconds to identify 29 images.  ( 3 min )
    Conditional Hierarchical Bayesian Tucker Decomposition for Genetic Data Analysis. (arXiv:1911.12426v5 [cs.LG] UPDATED)
    We develop methods for reducing the dimensionality of large data sets, common in biomedical applications. Learning about patients using genetic data often includes more features than observations, which makes direct supervised learning difficult. One method of reducing the feature space is to use latent Dirichlet allocation to group genetic variants in an unsupervised manner. Latent Dirichlet allocation describes a patient as a mixture of topics corresponding to genetic variants. This can be generalized as a Bayesian tensor decomposition to account for multiple feature variables. Our most significant contributions are with hierarchical topic modeling. We design distinct methods of incorporating hierarchical topic modeling, based on nested Chinese restaurant processes and Pachinko Allocation Machine, into Bayesian tensor decomposition. We apply these models to examine patients with one of four common types of cancer (breast, lung, prostate, and colorectal) and siblings with and without autism spectrum disorder. We linked the genes with their biological pathways and combine this information into a tensor of patients, counts of their genetic variants, and the genes' membership in pathways. We find that our trained models outperform baseline models, with respect to coherence, by up to 40%.  ( 3 min )
    Switching One-Versus-the-Rest Loss to Increase the Margin of Logits for Adversarial Robustness. (arXiv:2207.10283v1 [cs.LG])
    Defending deep neural networks against adversarial examples is a key challenge for AI safety. To improve the robustness effectively, recent methods focus on important data points near the decision boundary in adversarial training. However, these methods are vulnerable to Auto-Attack, which is an ensemble of parameter-free attacks for reliable evaluation. In this paper, we experimentally investigate the causes of their vulnerability and find that existing methods reduce margins between logits for the true label and the other labels while keeping their gradient norms non-small values. Reduced margins and non-small gradient norms cause their vulnerability since the largest logit can be easily flipped by the perturbation. Our experiments also show that the histogram of the logit margins has two peaks, i.e., small and large logit margins. From the observations, we propose switching one-versus-the-rest loss (SOVR), which uses one-versus-the-rest loss when data have small logit margins so that it increases the margins. We find that SOVR increases logit margins more than existing methods while keeping gradient norms small and outperforms them in terms of the robustness against Auto-Attack.  ( 2 min )
    High-Dimensional $L_2$Boosting: Rate of Convergence. (arXiv:1602.08927v3 [stat.ML] UPDATED)
    Boosting is one of the most significant developments in machine learning. This paper studies the rate of convergence of $L_2$Boosting, which is tailored for regression, in a high-dimensional setting. Moreover, we introduce so-called \textquotedblleft post-Boosting\textquotedblright. This is a post-selection estimator which applies ordinary least squares to the variables selected in the first stage by $L_2$Boosting. Another variant is \textquotedblleft Orthogonal Boosting\textquotedblright\ where after each step an orthogonal projection is conducted. We show that both post-$L_2$Boosting and the orthogonal boosting achieve the same rate of convergence as LASSO in a sparse, high-dimensional setting. We show that the rate of convergence of the classical $L_2$Boosting depends on the design matrix described by a sparse eigenvalue constant. To show the latter results, we derive new approximation results for the pure greedy algorithm, based on analyzing the revisiting behavior of $L_2$Boosting. We also introduce feasible rules for early stopping, which can be easily implemented and used in applied work. Our results also allow a direct comparison between LASSO and boosting which has been missing from the literature. Finally, we present simulation studies and applications to illustrate the relevance of our theoretical results and to provide insights into the practical aspects of boosting. In these simulation studies, post-$L_2$Boosting clearly outperforms LASSO.  ( 3 min )
    Estimating value at risk: LSTM vs. GARCH. (arXiv:2207.10539v1 [q-fin.RM])
    Estimating value-at-risk on time series data with possibly heteroscedastic dynamics is a highly challenging task. Typically, we face a small data problem in combination with a high degree of non-linearity, causing difficulties for both classical and machine-learning estimation algorithms. In this paper, we propose a novel value-at-risk estimator using a long short-term memory (LSTM) neural network and compare its performance to benchmark GARCH estimators. Our results indicate that even for a relatively short time series, the LSTM could be used to refine or monitor risk estimation processes and correctly identify the underlying risk dynamics in a non-parametric fashion. We evaluate the estimator on both simulated and market data with a focus on heteroscedasticity, finding that LSTM exhibits a similar performance to GARCH estimators on simulated data, whereas on real market data it is more sensitive towards increasing or decreasing volatility and outperforms all existing estimators of value-at-risk in terms of exception rate and mean quantile score.  ( 2 min )
    On minimax density estimation via measure transport. (arXiv:2207.10231v1 [math.ST])
    We study the convergence properties, in Hellinger and related distances, of nonparametric density estimators based on measure transport. These estimators represent the measure of interest as the pushforward of a chosen reference distribution under a transport map, where the map is chosen via a maximum likelihood objective (equivalently, minimizing an empirical Kullback-Leibler loss) or a penalized version thereof. We establish concentration inequalities for a general class of penalized measure transport estimators, by combining techniques from M-estimation with analytical properties of the transport-based density representation. We then demonstrate the implications of our theory for the case of triangular Knothe-Rosenblatt (KR) transports on the $d$-dimensional unit cube, and show that both penalized and unpenalized versions of such estimators achieve minimax optimal convergence rates over H\"older classes of densities. Specifically, we establish optimal rates for unpenalized nonparametric maximum likelihood estimation over bounded H\"older-type balls, and then for certain Sobolev-penalized estimators and sieved wavelet estimators.  ( 2 min )
    Estimation of Non-Crossing Quantile Regression Process with Deep ReQU Neural Networks. (arXiv:2207.10442v1 [stat.ML])
    We propose a penalized nonparametric approach to estimating the quantile regression process (QRP) in a nonseparable model using rectifier quadratic unit (ReQU) activated deep neural networks and introduce a novel penalty function to enforce non-crossing of quantile regression curves. We establish the non-asymptotic excess risk bounds for the estimated QRP and derive the mean integrated squared error for the estimated QRP under mild smoothness and regularity conditions. To establish these non-asymptotic risk and estimation error bounds, we also develop a new error bound for approximating $C^s$ smooth functions with $s >0$ and their derivatives using ReQU activated neural networks. This is a new approximation result for ReQU networks and is of independent interest and may be useful in other problems. Our numerical experiments demonstrate that the proposed method is competitive with or outperforms two existing methods, including methods using reproducing kernels and random forests, for nonparametric quantile regression.  ( 2 min )
    Bayesian Recurrent Units and the Forward-Backward Algorithm. (arXiv:2207.10486v1 [stat.ML])
    Using Bayes's theorem, we derive a unit-wise recurrence as well as a backward recursion similar to the forward-backward algorithm. The resulting Bayesian recurrent units can be integrated as recurrent neural networks within deep learning frameworks, while retaining a probabilistic interpretation from the direct correspondence with hidden Markov models. Whilst the contribution is mainly theoretical, experiments on speech recognition indicate that adding the derived units at the end of state-of-the-art recurrent architectures can improve the performance at a very low cost in terms of trainable parameters.  ( 2 min )
    Efficient Search of Multiple Neural Architectures with Different Complexities via Importance Sampling. (arXiv:2207.10334v1 [cs.NE])
    Neural architecture search (NAS) aims to automate architecture design processes and improve the performance of deep neural networks. Platform-aware NAS methods consider both performance and complexity and can find well-performing architectures with low computational resources. Although ordinary NAS methods result in tremendous computational costs owing to the repetition of model training, one-shot NAS, which trains the weights of a supernetwork containing all candidate architectures only once during the search process, has been reported to result in a lower search cost. This study focuses on the architecture complexity-aware one-shot NAS that optimizes the objective function composed of the weighted sum of two metrics, such as the predictive performance and number of parameters. In existing methods, the architecture search process must be run multiple times with different coefficients of the weighted sum to obtain multiple architectures with different complexities. This study aims at reducing the search cost associated with finding multiple architectures. The proposed method uses multiple distributions to generate architectures with different complexities and updates each distribution using the samples obtained from multiple distributions based on importance sampling. The proposed method allows us to obtain multiple architectures with different complexities in a single architecture search, resulting in reducing the search cost. The proposed method is applied to the architecture search of convolutional neural networks on the CIAFR-10 and ImageNet datasets. Consequently, compared with baseline methods, the proposed method finds multiple architectures with varying complexities while requiring less computational effort.  ( 3 min )
    Optimal precision for GANs. (arXiv:2207.10541v1 [cs.LG])
    When learning disconnected distributions, Generative adversarial networks (GANs) are known to face model misspecification. Indeed, a continuous mapping from a unimodal latent distribution to a disconnected one is impossible, so GANs necessarily generate samples outside of the support of the target distribution. This raises a fundamental question: what is the latent space partition that minimizes the measure of these areas? Building on a recent result of geometric measure theory, we prove that an optimal GANs must structure its latent space as a 'simplicial cluster' - a Voronoi partition where cells are convex cones - when the dimension of the latent space is larger than the number of modes. In this configuration, each Voronoi cell maps to a distinct mode of the data. We derive both an upper and a lower bound on the optimal precision of GANs learning disconnected manifolds. Interestingly, these two bounds have the same order of decrease: $\sqrt{\log m}$, $m$ being the number of modes. Finally, we perform several experiments to exhibit the geometry of the latent space and experimentally show that GANs have a geometry with similar properties to the theoretical one.  ( 2 min )
    Provably tuning the ElasticNet across instances. (arXiv:2207.10199v1 [cs.LG])
    An important unresolved challenge in the theory of regularization is to set the regularization coefficients of popular techniques like the ElasticNet with general provable guarantees. We consider the problem of tuning the regularization parameters of Ridge regression, LASSO, and the ElasticNet across multiple problem instances, a setting that encompasses both cross-validation and multi-task hyperparameter optimization. We obtain a novel structural result for the ElasticNet which characterizes the loss as a function of the tuning parameters as a piecewise-rational function with algebraic boundaries. We use this to bound the structural complexity of the regularized loss functions and show generalization guarantees for tuning the ElasticNet regression coefficients in the statistical setting. We also consider the more challenging online learning setting, where we show vanishing average expected regret relative to the optimal parameter pair. We further extend our results to tuning classification algorithms obtained by thresholding regression fits regularized by Ridge, LASSO, or ElasticNet. Our results are the first general learning-theoretic guarantees for this important class of problems that avoid strong assumptions on the data distribution. Furthermore, our guarantees hold for both validation and popular information criterion objectives.  ( 2 min )

  • Open

    [D] Which GPU cloud do you use and recommend?
    I'm looking to migrate all my local experiments to some GPU cloud, but I found many options and I know few people who have used some and can give me some useful feedback. I have two contexts: Experiments performed in Jupyter Notebook; DRL experiments using StarCraft II Learning Environment. For the first context I think about using Google Colab Pro, because I already have experience with Google Colab, so it would not be difficult to migrate to the Pro version. In the second case I used my local machine, but I'm out of GPU and the use of my university's supercomputer is absurdly problematic. My monthly budget is $50.00, because the most massive processing I'm going to do on the university's supercomputer. This budget will be used to run experiments of a few hours, experiments of one or more days will use the supercomputer. GPU clouds I found: Lambda Linode Paperspace RunPod Obviously there are big tech clouds (AWS, Google Cloud and Azure), but from what I've seen these other GPU Clouds are usually cheaper and less difficult to use. You who are reading the post could recommend me some Cloud GPU that you have already used? (Clouds with student discounts are welcome) TL;DR: please recommend me some cloud GPU that you have already used. submitted by /u/barash-616 [link] [comments]  ( 89 min )
    [D]Consumer Forecast
    Anyone successfully use the US CPI Index as a feature/external regressor for a time series model? Considering running some tests with this but curious if others have some experience with this. submitted by /u/datajunky624 [link] [comments]  ( 87 min )
    [D] How do collaborations materialize in your research group?
    Typically, I would come up with a problem ("Introduction"), research about it ("Previous work"), and develop a solution ("Methods", "Experiments"). Then, my PI would point out mistakes, raise some questions, polish the paper, etc. With this workflow, I ended up with various two-author papers (me and my PI). There are obvious benefits of including more people in the work/papers, such as having more points of views and having more time to do other things given that the work has been split. But how to do that in practice? I would like to hear how this is done in other groups. For instance, say you come up with a method and need to conduct other experiments to compare it with other methods. You could share your code and let your colleagues write the experiments following the same style and write and execute those experiments. Another potentially useful idea is to have regular meetings to tell each other what you're doing and what problems you are facing. submitted by /u/kuonlp [link] [comments]  ( 92 min )
    [D] ICML 2022 Outstanding Paper Awards 🔥
    It seems that ML twitter is under fire this week 🔥 Two of the recent ICML outstanding paper awards have received major criticisms on Twitter: Paper 1: Bayesian Model Selection, the Marginal Likelihood, and Generalization Paper link: https://proceedings.mlr.press/v162/lotfi22a.html Twitter discussion: https://twitter.com/BlackHC/status/1549832198152683520 https://twitter.com/LotfiSanae/status/1549842925328257025 https://twitter.com/andrewgwils/status/1550120752099180548 Blog containing the critical review: https://blog.blackhc.net/2022/06/bayesian-model-selection-marginal-likehood-generalization/ Paper 2: Privacy for Free: How does Dataset Condensation Help Privacy? Paper link: https://proceedings.mlr.press/v162/dong22c.html Twitter discussion: https://twit…  ( 116 min )
    [D] Super-resolution / image reconstruction aided by reference images
    Are there any models that can say, restore or upscale an image given other references as input? For example, say you have a portrait photo that needs to be improved. You may also have 10 other portraits of the same person, which should be useful information to the model in solving its task accurately. submitted by /u/thegreatjoke [link] [comments]  ( 87 min )
    [D] Hey Reddit! We're a bunch of research scientists and software engineers and we just open sourced a new state-of-the-art AI model that can translate between 200 different languages. We're excited to hear your thoughts so we're hosting an AMA on 07/21/2022 @ 9:00AM PT. Ask Us Anything!
    PROOF: https://i.redd.it/2z42nlnbssc91.jpg We’re part of the team behind Meta AI’s latest AI breakthrough in machine translation with our No Language Left Behind (NLLB) project. It’s a translation system that can support over 200 languages, even if there isn't a lot of text available to learn from. The reality is that a handful of languages dominate the web meaning only a fraction of the world can access content and contribute to the web in their own language. We want to change this by creating more inclusive machine translations systems – ones that unlock access to the web for the more than 4B people around the world that are currently excluded because they do not speak one of the few languages content is available in. Here are a few things about NLLB we’re excited for: Latest breakth…  ( 131 min )
    [N] Diffusers: Introducing Hugging Face's new library for diffusion models.
    Diffusion models have recently gained a lot of interest from the machine learning community. This is partly because diffusion models play an important role for models like DALL-E or Imagen to generate previously unparalleled photorealistic images when prompted on some text. The computer vision community isn't the only one to enjoy the success of diffusion models, as they have also achieved remarkable results in other domains, such as: - video generation - audio synthesis - reinforcement learning However, most recent research on diffusion models, namely Dalle-2 and Imagen, have not been made accessible to machine learning and often remains behind closed doors of large tech companies. This is why we decided to build and open-source 🧨 Diffusers. The objective is twofold: - Centralize the most important, open-sourced research on diffusion models and make them more accessible and easier to use for the community. - Provide the community with simple yet powerful training utilities to build powerful systems, such as Imagen and DALLE, in a transparent, open-sourced fashion so that everybody profits from the new technology. 🧨 Diffusers aims to be a modular toolbox for diffusion techniques, with a focus on: - Inference pipelines- Schedulers- Models- Training examples Check out the library here: https://github.com/huggingface/diffusers Check out a walkthrough colab here: https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb Using a DDPM model and scheduler to generate a church image from noise submitted by /u/jikkii [link] [comments]  ( 89 min )
    [D] Why don't we see faster adoption of FNet: Mixing Tokens with Fourier Transforms?
    When the paper came out last year, I thought that the innovation would quickly be adopted across the board, instead it would seem to be all but forgotten. https://arxiv.org/abs/2105.03824v3 Just for a quick tl;dr, the architecture replaces the self-attention block with a Fourier transform, so no learnable parameters. The enormously lower computation costs greatly outweigh the performance loss. So you can then make the network deeper and/or wider, thus for the same computation cost, have a great overall increase of performance. If I remember correctly, they also tried to replace the self-attention block with some kind of linear transformation, and this performed really poorly, so the key here seems to be the nonlinearity of the Fourier transform. The conclusion here being that the awesomeness of the transformer is in fact related to the greater architecture, and benefit of self-attention is mixing everything together in an interdependent and nonlinear fashion, which is exactly what the Fourier transform is doing. So now, back to my original question, why is this not being adopted more quickly? What am I missing? submitted by /u/MercuriusExMachina [link] [comments]  ( 113 min )
    [D] Why is my proceedings paper not shown in google scholar, or even google search?
    Hello guys, I’m a fresh PhD student. Last month we have a paper accepted by a conference and can be found in its proceedings. However, it didn’t show in my google scholar, while other papers in the same proceedings are. I’m wonder why is this and how should I fix it? submitted by /u/Successful_Paper1542 [link] [comments]  ( 88 min )
    [R] Robust SDE-Based Variational Formulations for Solving Linear PDEs via Deep Learning
    Published at ICML 2022 (If you are also at the conference, feel free to reach out and we can talk about our work.) PDF on ResearchGate / Poster Abstract: The combination of Monte Carlo methods and deep learning has recently led to efficient algorithms for solving partial differential equations (PDEs) in high dimensions. Related learning problems are often stated as variational formulations based on associated stochastic differential equations (SDEs), which allow the minimization of corresponding losses using gradient-based optimization methods. In respective numerical implementations it is therefore crucial to rely on adequate gradient estimators that exhibit low variance in order to reach convergence accurately and swiftly. In this article, we rigorously investigate corresponding numerical aspects that appear in the context of linear Kolmogorov PDEs. In particular, we systematically compare existing deep learning approaches and provide theoretical explanations for their performances. Subsequently, we suggest novel methods that can be shown to be more robust both theoretically and numerically, leading to substantial performance improvements. submitted by /u/julbern [link] [comments]  ( 113 min )
  • Open

    Hi, everyone I'm doing a statistic study about Artificial Intelligence, so I will be forever grateful with you if I can steal 1 minute of your time to complete this survey. Thank you. Hope you enjoy it.
    submitted by /u/KatCelest [link] [comments]  ( 86 min )
    Have any of you applied the python SHAP library to models trained with stablebaselines? I'm looking through their docs and I can't find much about RL models.
    submitted by /u/elonmusk12345_ [link] [comments]  ( 86 min )
    "DayDreamer: World Models for Physical Robot Learning", Wu et al 2022 (world models)
    submitted by /u/gwern [link] [comments]  ( 119 min )
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    Training Generalist Agents with Multi-Game Decision Transformers
    Posted by Winnie Xu, Student Researcher and Kuang-Huei Lee, Software Engineer, Google Research, Brain Team Current deep reinforcement learning (RL) methods can train specialist artificial agents that excel at decision-making on various individual tasks in specific environments, such as Go or StarCraft. However, little progress has been made to extend these results to generalist agents that would not only be capable of performing many different tasks, but also upon a variety of environments with potentially distinct embodiments. Looking across recent progress in the fields of natural language processing, vision, and generative models (such as PaLM, Imagen, and Flamingo), we see that breakthroughs in making general-purpose models are often achieved by scaling up Transformer-based models an…  ( 25 min )
  • Open

    An API for evaluating the quality of any synthetic dataset
    submitted by /u/Repeat-or [link] [comments]  ( 86 min )
    hmmm
    submitted by /u/TheSilverHound [link] [comments]  ( 89 min )
    Alibaba AI Research Team Introduces ‘DCT-Net’; A Novel Image Translation Architecture For Few-Shot Portrait Stylization
    submitted by /u/ai-lover [link] [comments]  ( 87 min )
    A.I. Digital Graffiti Art || Starryai 占~~~~
    submitted by /u/widgia [link] [comments]  ( 86 min )
    I Created an Automated Finance News Channel with Python and AI
    submitted by /u/kbf_ [link] [comments]  ( 86 min )
    Artificial Intelligence for Business Leaders Webinar
    Artificial Intelligence for Business Leaders Webinar ​ Join Professor Pedram Mokrian to learn how business leaders should think about developing AI solutions. Learn key AI terms, trends, and concepts that inform business strategy. Register for webinar. submitted by /u/Stanford_Online [link] [comments]  ( 86 min )
    Extraterrestrial Emergence | Cinematic Encounter | 4K UHD | 24 FPS
    submitted by /u/Available_Tadpole829 [link] [comments]  ( 86 min )
    A short exploration of how AI might become evil
    submitted by /u/Eth_ai [link] [comments]  ( 86 min )
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    The Role of AI in Ops Management of the Water Industry
    Water is the most abundant asset that humans have. We all use water to survive; it’s also vital for keeping our environment clean. Water provides life and substance. In cities with plenty of water, it’s vital to maintain the quality of life and the economy. This means that water has a great influence on human… Read More »The Role of AI in Ops Management of the Water Industry The post The Role of AI in Ops Management of the Water Industry appeared first on Data Science Central.  ( 21 min )
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    Kaggle Titanic Survival predicition using NN
    Hey guys! I'm new to deep learning and I'm trying to build a model to predict titanic survivors based on Kaggle's Titanic dataset - https://www.kaggle.com/c/titanic I am trying to improve the accuracy of the model but I'm stuck at around 83% on the training data. I have created the model using Tensorflow Sequential API. I would greatly appreciate any advice on improving the performance of this model! The final training data after removing nulls/one-hot encoding categorical values is of shape (889x12) My model architecture is - Input Layer: 12 neurons Hidden Layer 1: 12 neurons Hidden Layer 2: 6 neurons Output Layer: 1 neuron All the layers use ReLU activation except the output which uses Sigmoid. The optmizer selected is Adam with the default lr, and Binary Cross-entropy loss. I have trained for 200 epochs with a batch size of 32. I have been trying to experiment with different layers/architectures but I haven't been able to get past 83%. Do you guys know any methods to improve this? P.s sorry if I am missing something as I'm a beginner in building NNs, thanks in advance! submitted by /u/cheap_wizard [link] [comments]  ( 88 min )
    DataCamp is offering free access to their platform all week! Try it out now! https://bit.ly/3Q1tTO3
    submitted by /u/joanna58 [link] [comments]  ( 86 min )
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    Organize your machine learning journey with Amazon SageMaker Experiments and Amazon SageMaker Pipelines
    The process of building a machine learning (ML) model is iterative until you find the candidate model that is performing well and is ready to be deployed. As data scientists iterate through that process, they need a reliable method to easily track experiments to understand how each model version was built and how it performed. […]  ( 10 min )
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    Shifting Into High Gear: Lunit, Maker of FDA-Cleared AI for Cancer Analysis, Goes Public in Seoul
    South Korean startup Lunit, developer of two FDA-cleared AI models for healthcare, went public this week on the country’s Kosdaq stock market. The move marks the maturity of the Seoul-based company — which was founded in 2013 and has for years been part of the NVIDIA Inception program that nurtures cutting-edge startups. Lunit’s AI software Read article > The post Shifting Into High Gear: Lunit, Maker of FDA-Cleared AI for Cancer Analysis, Goes Public in Seoul appeared first on NVIDIA Blog.  ( 6 min )
    Get Battle Ready With New GeForce NOW Fortnite Reward
    Epic Games is bringing a new Fortnite reward to GeForce NOW, available to all members. Drop from the Battle Bus in Fortnite on GeForce NOW between today and Thursday, Aug. 4, to earn “The Dish-stroyer Pickaxe” in game for free. Members can earn this item by streaming Fortnite on GeForce NOW Read article > The post Get Battle Ready With New GeForce NOW Fortnite Reward appeared first on NVIDIA Blog.  ( 5 min )
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    How AI & ML lens can assist in defence and private security?
    Business approach is changing constantly to all creative possibilities provided by the digital revolution. The big bang of the digital…  ( 11 min )
2022-08-20T01:00:58.848Z osmosfeed 1.15.1